University of Wollongong Thesis Collections

University of Wollongong Thesis Collection

University of Wollongong Year

Financial integration of the MENA

emerging stock markets

Hazem Ali MarashdehUniversity of Wollongong

Marashdeh, Hazem Ali, Financial integration of the MENA emerging stock markets, PhDthesis, School of Economic and Information Systems, University of Wollongong, 2006.http://ro.uow.edu.au/theses/543

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http://ro.uow.edu.au/theses/543

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Financial Integration of the MENA Emerging Stock

Markets

A thesis submitted in fulfillment of the requirements for the award of the degree

Doctor of Philosophy

from

University of Wollongong

by

Hazem Ali Marashdeh

BEc (Economics and Accounting), Jordan

MEc (Economics), Jordan

School of Economic and Information System

2006

ii

Certification

I, Hazem Marashdeh, declare that this dissertation, submitted in fulfillment of the

requirement for the award of Doctor of Philosophy in the faculty of commerce,

University of Wollongong, is wholly my own work unless otherwise referenced or

acknowledged. The document has not been submitted for qualifications at any other

academic institution.

Hazem Marashdeh

December 2005

iii

Dedication

To the dearest friend

my father

Ali Marashdeh

iv

Acknowledgments

I would like to express my sincere gratitude and appreciation to my supervisor

Associate Professor Edger Wilson for his supervision, guidance and encouragement

throughout the study. His profound knowledge and experience provided me with the

opportunity to broaden my knowledge and to make a significant progress. Also I would

like to thank Associate Professor Amnon Livermore for his supervision through most of

the study. His insight into economic theory and creative comments were of a great

assistance. And I would like to thank Dr. Abbas Valadkhani for his supervision in the

final stages of the study. His exceptional editing skills and invaluable suggestions

always inspired me to strive for quality work.

I would like to thank my wife Diana Bakir for her love, care and extraordinary

support throughout my long study period. Dear Diana, you will always be in my heart.

I am very grateful to my parents, for their blessing, encouragement and support

throughout the duration of this study. Also I would like to thank my dearest brother,

Mones and my sisters, Arwa, Rabab, Aram, Demh, Layla, Lama and Farah for their

encouragement and support. Also, I would like to thank my father and mother-in-law

for their support.

Also, I would like to express my sincere thanks to my colleagues at the faculty

of commerce, Akhsyim Afandi, Mosayeb Bahlavani, Min Shrestha, Maen Al-hawari

and Reetu Verma. I have greatly benefited from their comments throughout my study

period. Finally, I would also like to thank Dr. Aktham Maghyereh for his help at the

early stage of this study.

v

Abstract

The main objective of this study is to examine the financial integration among

four emerging stock markets in the Middle East and North Africa (MENA) region,

namely, Egypt, Turkey, Jordan and Morocco. Their interrelationships with three

developed markets, the US, UK and Germany, are also examined. The motivation

behind this study is that, although a lot of research has been focused on stock market

integration, the emphasis has been mostly on developed markets. Stock market

integration in the MENA region has not been investigated deeply enough despite the

region being of a global economic and political importance.

To attain this objective, the study conducts recent econometric techniques on the

monthly time series of stock market price indices. It starts with testing for a unit root in

the presence of structural change at an unknown time of the break, using the

Innovational Outlier (IO) model. To empirically examine the financial integration, the

study utilizes the newly proposed autoregressive distributed lag (ARDL) approach to

cointegration. The ARDL approach has been recognized as more preferable in

estimating the long-run equilibrium relationship than other cointegration approaches in

small samples with mixed order process. Finally, the study explores the short and long-

run dynamic relationships among these markets using Granger-causality within a

correctly specified vector error correction model (VECM).

The empirical results indicate that all variables show evidence of non-

stationarity, even in the presence of structural change. The endogenously determined

times of the breaks for all markets coincide with observed real events which affected

each market. This result is consistence with the efficient market hypothesis as the non-

stationarity random walk is associated with the weak form of the efficient market

hypothesis. Consequently, this result emphasises that the stock markets in the MENA

region are efficient.

The cointegration test results show that there are long-run equilibrium

relationships among all stock markets in the MENA region. This indicates that stock

markets in the MENA region move together in the long-run. So, at the regional level all

markets are integrated. At the same time no long-run equilibrium relationship is found

between MENA markets and developed markets. This means that the MENA stock

markets are segmented from developed markets. However, Egypt was the exceptional

vi

case; the study found that the stock market of Egypt has long-run equilibrium

relationship with the US and UK markets.

The implications of these findings are analysed at two levels, the regional and

international. At the regional level, the existence if cointegration among the MENA

markets implies the existence of the law of one price (LOOP). This means that the

potential of regional investors for obtaining abnormal profits through portfolio

diversification is limited in the long-run. The reason for this is that as the MENA stock

markets are cointegrated, abnormal profits will be arbitraged away in the long-run.

However, despite no arbitrage opportunities in the long-run, investors can still achieve

arbitrage profits through portfolio diversification in the short-run.

At the international level, the results show that stock markets in Turkey, Jordan

and Morocco are not integrated with developed markets. This means that there is no

long-run impact from developed stock markets towards these markets. However, a long-

run relationship is found between Egypt and both US and UK when Egypt is a

dependent variable. Based on these results, there are opportunities for international

investors to obtain long-run gains through international portfolio diversification in stock

markets of Turkey, Jordan and Morocco. Also at the same time, investors from these

three countries have the opportunities to obtain long-run gains through investing in

developed markets. The existence of long-run relationships between Egypt and both US

and UK implies that the potential for investors from the Egyptian stock market to obtain

abnormal profit through portfolio diversification in the US and UK is limited in the

long-run. However, there are opportunities for achieving abnormal profit by investing in

Germany as it is not cointegrated with the MENA markets. In the short-run, arbitrage

opportunities and possible profits may also be achieved from diversification as the

LOOP may not hold.

In addition to these findings, an important contribution is made by this study. It

contradicted Granger’s (1986) theory on the relationship between the existence of

cointegration and market efficiency. Granger (1986) asserted that the existence of

cointegration between two stock prices implies the ability to predict each price

movement, which indicates market inefficiency. Also, this study does not fully agree

with another stream of studies, such as Wallace (1992), Baffes (1994), Engle (1996),

Ahlgren and Antell (2002) and Masih and Masih (2002) in which they asserted that

cointegration does not necessarily imply market inefficiency or efficiency. However,

what this study tries to bring out is that if cointegration exists between two stock

vii

markets then these markets are efficient in the long-run because the existence of

cointegrated vector implies the (LOOP). Therefore, little or no arbitrage opportunities or

possible benefit can be achieved from the diversification of a portfolio across markets.

However, with the short-run error correction model (ECM), there could exist arbitrage

opportunities and possible benefits from diversification. That is, the LOOP may not

hold in the short run.

The results of Granger-causality test based on the vector error correction model

(VECM) reveal the existence of short-run causal relationships among the MENA

markets. This means that these markets influence each other. Also, the results show that

developed markets influenced stock markets in the MENA region. In the short-run,

there is unidirectional Granger-causality running from stock prices in Turkey, Morocco,

the US and UK to Egypt. Also, there is unidirectional Granger-causality running from

Germany and the US towards Turkey. In addition, The UK and Turkey are found to

Granger-cause the stock prices in Jordan. Finally, there is a unidirectional Granger-

causality from Germany to Morocco.

Finally, despite the empirical results show that there is a possibility of an

increase in the portfolio equity flow to the MENA stock markets, the statistics of

portfolio equity flow show little portfolio inflow to the region from developed countries

over the period of study. Some of the reasons behind this situation are that most of these

markets are still from some perspective underdeveloped, vulnerable to macroeconomic

shocks and political instability in the region. Based on this, the study suggests that huge

efforts should be carried on to improve the institutional reforms in these markets and

increase the degree of openness for foreign capital. Also increasing the markets

capitalization and adopting new technology are very crucial factors for attracting equity

portfolio to the region.

viii

Table of Contents

Certification ii Dedication iii Acknowledgment iv Abstract v Table of Contents viii List of Tables xi List of Figures xii Abbreviations xiii Publication from the research xiv Chapter 1. Introduction

1.1 Background of the Study 1

1.2 Objective of the Study 4

1.3 Data and Methodology 7

1.3.1 Data Sources 7

1.3.2 Method of the Study 8

1.4 Structure of the Study 9

Chapter 2. The Early Theoretical Models Relating to Stock Market Integration

2.1 Introduction 12

2.2 The Notion of Stock Markets Integration 13

2.3 The Early Theoretical Studies Relating to Stock Markets Integration 18

2.4 Asset Pricing Model for Testing Stock Market Integration 22

2.5 Arbitrage Pricing Theory (APT) for Testing Stock Markets Integration

30

2.6 Alternative Approaches for Testing Stock Markets Integration 36

2.7 Conclusion 38

Chapter 3. The Recent Techniques Relating to Stock Market Integration: The Cointegration Approach

3.1 Introduction 40

3.2 Cointegration Approach for Testing Stock Market Integration 41

3.3 The Asian Financial Crisis and Stock Market Integration 54

3.4 Efficient Market Hypothesis 60

3.4.1 Cointegration and Stock Market Efficiency 61

3.4.2 More Evidences on Stock Markets Efficiency 66

3.4.3 A new Approach for the Relationship between Cointegration and Efficiency

67

3.5 The Integration of the Emerging Stock Markets in the MENA Region 69

ix

3.6 Conclusion 77

Chapter 4. Features and Characteristics of the Emerging Stock Markets in the MENA Region

4.1 Introduction 78

4.2 General Economic Features of the MENA Region 80

4.3 An Overview of the Emerging Stock Markets in the MENA Region 87

4.3.1 Stock Market Liberalization of the Emerging Stock markets in MENA Region

88

4.3.2 The Stock Market in Egypt 92

4.3.3 The Stock Market in Turkey 97

4.3.4 The Stock Market in Jordan 102

4.3.5 The Stock Market in Morocco 108

4.4 Conclusion 113

Chapter 5. Structural Changes and Efficiency in the MENA Stock Markets

5.1 Introduction 115

5.2 Data and descriptive statistics 116

5.3 The Conventional Augmented Dickey-Fuller (ADF) and Phillips– Perron (PP) Unit Root Tests

143

5.4 The Development of Testing for Structural Change 148

5.4.1 Procedures for Selecting the Order of the Lag 156

5.4.2 Procedures for Determining the Time of the Break 157

5.5 Testing for Structural Changes in MENA Stock Markets 158

5.6 The Random Walk Behavior and the Efficiency of the MENA Stock Markets

170

5.7 Conclusion 171

Chapter 6. Stock Market Integration in the MENA Region: Cointegration and Causality Tests

6.1 Introduction 173

6.2 The Autoregressive Distributed Lag (ARDL) Approach to Cointegration

174

6.3 Model Specification 179

6.4 Interpretation of the Results 183

6.4.1 Stock Market of Egypt 185

6.4.2 Stock Market of Turkey 188

6.4.3 Stock Market of Jordan 190

6.4.4 Stock Market of Morocco 192

x

6.5 Implications of the Empirical Results 195

6.6 Granger Causality 204

6.7 Conclusion 211

Chapter 7. Summary and Conclusions

7.1 Introduction 213

7.2 Summary of the Study 214

7.3 Implications of the Study 222

7.4 Contribution of the Study 224

7.5 Suggestions for Future Research 226

Appendices

Appendix A. Conventional Unit Root Tests 228

Appendix B. Cointegration and Causality Tests 238

Appendix C. Diagnostic Tests 243

Bibliography 247

xi

List of Tables

2.1 A summary for the Results of the Main Previous Studies 35 3.1 Summary of Selective empirical Studies on Stock Market Integration 57 4.1 Economic Overview for MENA Countries 86 4.2 Openness of Stock Markets in MENA Region 89 4.3 Portfolio Equity Net Flows to Stock Markets in MENA Region 90 4.4 Egypt Stock Market Indicators 95 4.5 Istanbul Stock Exchange Indicators 100 4.6 Amman Stock Exchange Indicators 106 4.7 Casablanca Stock Exchange Indicators 111 5.1 Descriptive Statistics for Monthly Stock Returns in (Local Currency) 136 5.2 Descriptive Statistics for Monthly Stock Returns in ($US) 138 5.3 Correlation Coefficients for Monthly Stock Indices in (Local Currency) 140 5.4 Correlation Coefficients for Monthly Rate of Returns in (Local Currency) 140 5.5 Correlation Coefficients for Monthly Stock Indices in ($US) 142 5.6 Correlation Coefficients for Monthly Rate of Return in ($US) 142 5.7 Estimated Results of ADF and PP Unit Root Tests (Local Currency) 145 5.8 Estimated Results of ADF and (PP) Unit Root Tests ($US) 145 5.9 Estimated Results of ADF and PP Unit Root Tests (Local Currency) 146 5.10 Estimated Results of ADF and PP Unit Root Tests ($US) 146 5.11 Empirical Results, Perron’s (1997) Model (IO2), (Local Currency) 163 5.12 Empirical Results, Perron’s (1997) Model (IO1), (Local Currency) 163 5.13 Empirical Results, Perron and Vogelsang (1992) (IO), (Local Currency) 165 5.14 Empirical Results, Perron’s (1997) Model (IO2), ($US) 168 5.15 Empirical Results, Perron’s (1997) Model (IO1), ($US) 168 5.16 Empirical Results, Perron and Vogelsang (1992) (IO) Model, ($US) 169 6.1 F-Statistics for Testing the Existence of a long-Run Relationship 184 6.2 Long-Run Coefficients Estimated Based on ARDL (1,0,0,0,1,1,0) Model

Selected Based on SBC. Dependent Variable: Egypt (lnE) 185

6.3 Error Correction Model (ECM) Results for the Selected ARDL (1,0,0,0,1,1,0) Selected Based on SBC. Dependent Variable: ∆lnE

187

6.4 Long-Run Coefficients Estimated Based on ARDL (1,0,0,0,0,0,1) Model Selected Based on SBC. Dependent Variable: Turkey (lnT)

188

6.5 Error Correction Model (ECM) Results for the Selected ARDL (1,0,0,0,0,0,1) Selected Based on SBC. Dependent Variable: ∆lnT

190

6.6 Long-Run Coefficients Estimated Based on ARDL (1,0,1,0,0,2,0) Model Selected Based on SBC. Dependent Variable: Jordan (lnJ)

191

6.7 Error Correction Model (ECM) Results for the Selected ARDL (1,0,1,0,0,2,0) Selected Based on SBC. Dependent Variable: ∆lnJ

192

6.8 Long-Run Coefficients Estimated Based on ARDL (1,0,0,0,0,0,0) Model Selected Based on SBC. Dependent Variable: Morocco (lnM)

193

6.9 Error Correction Model (ECM) Results for the Selected ARDL (1,0,0,0,0,0,0) Selected Based on SBC. Dependent Variable: ∆lnM

194

6.10 The long-Run Impacts on Stock Markets in the MENA Region 195 6.11 Net Inward Portfolio Equity Flows to developing Countries, 1995-2003 200 6.12 Granger Causality Results Based on Vector-Error Correction Model 208 6.13 Recent American Aids to Egypt 209

xii

List of Figures

4.1 Market Capitalization in Egypt Stock Exchange 1994-2004, $US Million 96 4.2 Trading Value in Egypt Stock Exchange 1994-2004, $US Million 96 4.3 Market Capitalization in Istanbul Stock Exchange 1994-2004, $US Million 101 4.4 Trading Value in Istanbul Stock Exchange 1994-2004, $US Million 101 4.5 Market Capitalization in Amman Stock Exchange 1994-2004, $US Million 107 4.6 Trading Value in Amman Stock Exchange 1994-2004, $US Million 107 4.7 Market Capitalization in Casablanca Stock Exchange 1994-2004, $US Million 112 4.8 Trading Value in Casablanca Stock Exchange 1994-2004, $US Million 112 5.1 Stock Price Indices in MENA region (Local Currency) 118 5.2 Stock Price Indices in MENA region ($US) 118 5.3 Stock Price Indices in All Countries ($US) 120 5.4 Monthly Stock Price Index in Egypt (Local Currency) 121 5.5 Monthly Stock Price Index in Egypt ($US) 121 5.6 Monthly Stock Price Index in Turkey (Local Currency) 122 5.7 Monthly Stock Price Index in Turkey ($US) 122 5.8 Monthly Stock Price Index in Jordan (Local Currency) 123 5.9 Monthly Stock Price Index in Jordan ($US) 123 5.10 Monthly Stock Price Index in Morocco (Local Currency) 124 5.11 Monthly Stock Price Index in Morocco ($US) 124 5.12 Monthly Stock Price Index in United Kingdom (Local Currency) 125 5.13 Monthly Stock Price Index in United Kingdom ($US) 125 5.14 Monthly Stock Price Index in Germany (Local Currency) 126 5.15 Monthly Stock Price Index in Germany ($US) 126 5.16 Monthly Stock Price Index in the United States 127 5.17 Monthly Rate of Return in Egypt (Local Currency) 128 5.18 Monthly Rate of Return in Egypt ($US) 128 5.19 Monthly Rate of Return in Turkey (Local Currency) 129 5.20 Monthly Rate of Return in Turkey ($US) 129 5.21 Monthly Rate of Return in Jordan (Local Currency) 130 5.22 Monthly Rate of Return in Jordan ($US) 130 5.23 Monthly Rate of Return in Morocco (Local Currency) 131 5.24 Monthly Rate of Return in Morocco ($US) 131 5.25 Monthly Rate of Return in United Kingdom (Local Currency) 132 5.26 Monthly Rate of Return in United Kingdom ($US) 132 5.27 Monthly Rate of Return in Germany (Local Currency) 133 5.28 Monthly Rate of Return in Germany ($US) 133 5.29 Monthly Rate of Return in United States (Local Currency) 134 5.30 Plots of the series and Estimated Timing of Structural Breaks 162

xiii

Abbreviations

ADF Augmented Dickey Fuller ADR American Depositary Receipts AFM Amman Financial Market APT Arbitrage Pricing Theory ARDL Autoregressive Distributed Lag ARVAR Augmented Restricted Vector Autoregression ASE Amman Stock Exchange AUVAR Augmented Unrestricted Vector Autoregression CAPM Capital Asset Pricing Model CASE Cairo and Alexandria Stock Exchange CMA Capital Market Authority CRDW Cointegration Regression Durbin Watson CSE Casablanca Stock Exchange ECM Error Correction Model ECT Error Correction Term EMH Efficient Market Hypothesis GARCH Generalized Autoregressive Conditional Heteroscedasticity GCC Gulf Cooperation Council GDP Gross Domestic Product GDR Global Depositary Receipts GNP Gross National Product HSBC Hong Kong and Shanghai Banking Corporation ICAPM International Asset Pricing Model IMF International Monetary Fund IPO International Public Offering IRF Impulse Response Function ISE Istanbul Stock Exchange JD Jordanian Dinar JJ Johansen-Juselius JSC Jordan Securities Commission LOOP Law of One Price MENA Middle East and North Africa OECD Organization for Economic Cooperation and Development OLS Ordinary Least Square PP Phillips and Perron SDC Securities Depository Centre UVAR Unrestricted Vector Autoregression VAR Vector Autoregressive Model VDC Variance Decomposition VECM Vector Error Correction Model WTO World Trade Organization

xiv

Publication from the Research

1 Marashdeh, H., 2005, “Testing For Structural Changes in MENA Equity Markets”, 46th NZAE Conference, New Zealand. This article has been considered as “a quality assured paper”. Available on line at: http://www.nzae.org.nz/conferences/2005/QA29-Hazem_Marashdeh.pdf

2 Marashdeh, H. and E. J. Wilson, 2005, “Structural Changes in the Middle East Stock Markets: The Case of Israel and Arab Countries”, University of Wollongong, Working Paper, 05-22. Available on line at:

http://www.uow.edu.au/commerce/econ/wpapers.html

3 Marashdeh, H., 2005, “Stock market integration in the MENA region: An application of the ARDL bound testing approach”, University of Wollongong, Working Paper, 05-27. Available on line at: http://www.uow.edu.au/commerce/econ/wpapers.html

4 Marashdeh, H, 2005, “Cointegration and efficiency: An empirical investigation of the Middle East stock markets”. A presentation delivered at Workshop of: “Mathematics in Finance”. Sponsored by School of Applied Mathematics and Statistics, University of Wollongong, 25th November 2005.

5 Marashdeh, H, 2005, “Interdependence of the MENA emerging stock markets: A Cointegration Approach”, Accepted to the 4th INFINITI Conference on International Finance, University of Dublin, Trinity College, Monday 12-Tuesday 13 June 2006.

6 Marashdeh, H. and Ali Saleh, 2006, “Re-visiting trade and Budget deficit in Lebanon: Critique”, University of Wollongong, Working Paper, 06-07. Available on line at:

http://www.uow.edu.au/commerce/econ/wpapers.html

1

Chapter One

Introduction

1.1 Background of the Study The analysis of the degree of international stock market integration has attracted a great

deal of interest in recent time. The term “stock market integration” refers to an area of

research in financial economics that covers many aspects of interrelationships between

stock markets. Market integration could be considered as a situation where there are no

impediments, such as legal restrictions, transaction costs, taxes and tariffs against the

trade in foreign assets or the mobility of portfolio equity flows. In the case of stock

market integration, all assets with the same level of risk have the potential to attract the

same return across all different markets.

The issues of financial integration of emerging stock markets have received a

great deal of interest of practitioners and academic researchers. Over the last two

decades a significant volume of research has been concerned with the integration of the

world’s major stock markets. The emerging stock markets in some developing countries

have achieved considerable improvements over the last two decades. Several factors

have played vital roles in these improvements, such as the conduct of sound

macroeconomic policies, stock markets reforms, privatization and financial

liberalization. One of the main reasons for this study focusing on emerging markets is

because there is an increase in funds flowing from developed markets toward

developing markets, and therefore these markets are becoming increasingly important in

terms of portfolio management (Hawawini, 1994).

2

It is important to define in more detail what constitutes definitions of stock

market. A stock market is considered as an emerging market if it has started a transition

process, growing in size and increasing in complexity. According to the International

Financial Corporation of the World Bank Emerging Markets Database, a stock market is

classified “emerging” if it is located in a low or middle income economy, and if its

invest able market capitalization is low relative to its per capita gross domestic product

(GDP).

Stock market integration has become a catchphrase in modern financial theory

and several arguments emphasize the desirability of market integration. One such

argument is based on the competitive auction-model. This model asserts that where

there are no barriers to capital movement, stock market integration leads to a more

efficient allocation of the world’s resources, and capital will seek higher returns to

investment, moving from stock markets where capital is relatively abundant to another

where capital is relatively scarce. These characteristics of stock markets enable the

competitive auction-model to function more effectively in order to equilibrate markets.

Another argument is that in case of integration among stock markets, the systematic risk

(market risk) becomes unsystematic risk (firm risk), and this kind of risk can be

diversified or eliminated by including the security as part of diversifiable portfolio.

For stock market integration to be effective requires sound macroeconomic

conditions and a sound domestic financial sector. Integration of stock markets causes all

risk factors to be traded at the same price. For example, if the stock markets in the

Middle East and North Africa (MENA) region are fully integrated then the business

cycle risk or the inflation risk would have the same price in all MENA markets. In other

words, stock market integration means that the law of one price is fully consistent

throughout all traded assets. In general it is believed that as markets become more

3

integrated, the cost of capital decreases, because the removal of investment barriers

allows for imported risk sharing between domestic and foreign agents.

This thesis examines the integration stock markets in the MENA region. In

particular, the study focuses on four emerging markets in this region, namely Egypt,

Turkey, Jordan and Morocco. These markets are considered to be relatively active

compared to other stock markets in the region. The study also, examines the integration

between these markets and the markets of the representative developed economies of

the US, UK and Germany. The MENA region is considered as one of the richest in the

world in terms of natural resource, qualified endowments and trained labour force, and

per capita GDP. The four countries that are considered in this study, have adopted

several sound macroeconomic policies over the last two decades which have contributed

to higher economic growth and to overcome macroeconomic imbalances in their

economies. These policies include financial liberalization, trade liberalization, openness

to foreign direct investment, implementation of sound economic management and

privatization programs. Moreover, these polices are considered as indispensable in order

for these countries to face the growing challenges that resulted from the recent changes

in the global economy. In addition to the economic importance of the MENA region, the

political developments have huge consequences on the international political stage

In order to examine the degree of financial integration of stock markets in the

MENA region, and between these markets and developed markets, this study starts with

testing for the presence of structural change. The tests for a unit root in the time series

of stock prices are unlike the conventional stationarity tests, which are found to be

biased towards the non-rejection of the null hypothesis of a unit root in the presence of

structural change. That is, the conventional unit root tests lack power in correctly

rejected the null hypothesis in the presence of structural changes. This study employs

4

the Innovational Outlier (IO) model proposed by Perron (1997) which models the break

as a gradual change in the trend function with an endogenously determined break date.

This procedure has not been applied to studying stock markets in the MENA region.

The second stage of the study in the financial integration among stock markets

in the MENA region (and between these markets and developed markets) utilizes the

newly proposed autoregressive distributed lag (ARDL) estimation approach. This

procedure is recognized as the preferred method in estimating the long-run cointegrating

relationship. This study also explores the short-run and the long-run dynamic

relationships for the stock markets in the MENA region as well as conducting Granger-

causality tests which are augmented with an error correction term. Finally, the study

considers the relationship between the existence of cointegration among stock markets

and the efficient market hypothesis (EMH).

1.2 Objective of the Study The main objective of this study is to examine the financial integration among the stock

markets of Egypt, Turkey, Jordan and Morocco, and between these markets and the

developed stock markets, of the US, UK and Germany. To attain this purpose, the thesis

aims to achieve the following:

1. To present an inclusive revision of the notion of stock market integration from

different points of view. Also, the study will present several arguments for the

desirability and benefits of international stock market integration. Moreover, the

study will review the early theoretical models relating to stock market

integration. In particular, the study analyzes the literature relating to stock

market integration using different models, such as the capital asset pricing

5

models (CAPM) and the arbitrage pricing theory (APT). Also the study will

explore some other alternative approaches for testing stock market integration

such as an innovative econometric methodology.

2. To critically review and analyze the recent techniques relating to the analysis of

stock market integration, such as cointegration techniques, generalized

autoregressive conditional heteroscedasticity (GARCH) model, Granger-

causality and vector autoregressive model (VAR). These techniques have been

applied to test for stock market integration in different regions in the world. The

cointegration approach has been widely recognized as the most suitable

approach for testing the co-movements between stock markets. This co-

movement indicates the existence of cointegration between them, which in fact

implies the existence of integration internationally between stock markets.

However, there will be a concentration on the studies that focus on the emerging

stock markets in the MENA region.

3. To outline the main features of the emerging stock markets in the MENA region.

By doing this, the study will concentrate on the main financial indicators in these

markets. These indicators include market capitalization, trading value, turnover

ratio and number of listed companies. Also the study will shed light on the

economies of these countries and on the macroeconomic polices adopted.

4. To test the unit root hypothesis in the presence of structural change at an

unknown time of the break, using Perron’s (1997) and Perron and Vogelsang

(1992) Innovational Outlier (IO) model. According to Perron (1989) most

economic time series are characterized by stochastic, rather than deterministic,

non-stationarity. Perron (1989) argued that macroeconomic time series may be

stationary if one allows for structural changes in the trend function of the

6

regression. When there are structural breaks present, the Dickey–Fuller statistics

are biased towards the non-rejection of unit root hypothesis. Moreover, if

structural changes exist in the data generating process which are not allowed for

in the specification of an econometric model. The results could be biased

towards the erroneous non-rejection of the non-stationarity hypothesis (Perron,

1997). However, according to the reviewed literature on the MENA region, this

issue has not been addressed in any known study. This study provides a new

contribution to the literature since there has not been any empirical study that

has used this technique when studying the stock markets in the MENA region.

5. To empirically estimate the long-run relationship among stock markets in the

MENA region, and between these markets and developed markets. This study

utilizes the newly proposed autoregressive distributed lag (ARDL) approach.

The ARDL procedure has been recognized as more preferable in estimating the

long-run cointegrating relationship than other cointegration approaches. One of

the ARDL features is that it is unlike conventional cointegration approaches

which concentrate on cases where the underlying variables are integrated of

order one. The ARDL approach is applicable irrespective of whether the

underlying regressors are all )0(I , all )1(I or mixed processes. Also, the ARDL

estimation procedure is more robust and performs well for small sample sizes

than other cointegration approaches. Using of the ARDL procedure in this thesis

is considered a significant contribution to the literature of stock market

integration in the MENA region since it has not been used before.

6. To construct standard Granger causality tests which are correctly specified to

include the lagged vector error correction term when variables are cointegrated

(VECM). This test helps explore the short and long-run dynamic relationships

7

among the MENA stock markets, and between them and developed markets.

The estimated long-run causal relationship among stock markets is based on the

error correction term. Granger (1988) asserts that neglecting the error correction

term when testing for causality among cointegrated variables leads to serious

biases due to filtering out low-frequency (long-run) information.

7. To explore the efficient market hypothesis and the relationship between the

existence of cointegration among stock markets and the efficiency of these

markets. Recently, there have been many contradictory views on this issue in the

last decade. Some have argued that the existence of cointegration between stock

markets implies that these markets are inefficient. Others have argued that the

existence of cointegration between markets implies nothing about efficiency or

inefficiency. This study will try to present different point of view in related to

this issue.

8. Finally, to shed more light on the consequences and implications of finding

integration among these markets. This study considers the effect of integration

on portfolio diversification and the portfolio equity net flows between stock

markets in the MENA region.

1.3 Data and Methodology 1.3.1 Data Sources This study uses monthly stock price indices for four stock markets in the MENA region,

namely, Egypt, Turkey, Jordan and Morocco and for three developed stock markets,

namely US, UK and Germany. The study covers the period December 1994 to June

8

2004. Different sources for the data have been used. The study depends on the following

sources:

1. Morgan Stanley Capital International (MSCI), (www.msci.com).

2. Standard & Poor’s, Emerging Stock Markets Factbook, 2002.

3. Standard & Poor’s, Global Stock Markets Factbook, 2005.

4. International Financial Statistics Yearbook (IFS), various issues.

5. The World Bank, Global Development Finance, CD ROM, 2004.

6. World Development Indicators database, April 2005.

7. Arab Monetary Fund, (www.amf.org.ae).

1.3.2 Method of the Study This study reviews different financial and economic approaches that have been

developed to measure stock market integration. By reviewing these approaches, the

study covers a range of important literature on this issue. A thorough discussion of the

early theoretical models such as capital asset pricing model (CAPM), and a review the

literature related to Arbitrage Pricing Theory (APT) are conducted. Moreover, this study

reviews the recent econometric techniques used for measuring stock market integration.

These techniques include different approaches to cointegration analysis, such as Engle-

Granger, Johansen-Juselius, autoregressive conditional hetroskedasticity (ARCH),

generalized autoregressive conditional hetroskedasticity (GARCH) model, vector

autoregressive (VAR) and the autoregressive distributed lag (ARDL) approaches.

In order to measure the integration of stock markets in the MENA region, the

analysis implements advanced techniques which test the unit root hypothesis in the

presence of structural change at an unknown time of the break. For this purpose, the

9

study uses Perron’s (1997) and Perron and Vogelsang (1992) Innovational Outlier (IO)

model.

The newly proposed autoregressive distributed lag (ARDL) procedure to

cointegration is used as more of the recent studies have indicated that the ARDL

approach is preferred in estimating the long-run cointegration relationships. The ARDL

procedure is applicable irrespective of whether the underlying regressors are

purely )0(I , purely )1(I or mutually cointegrated. Also, it is more robust and performs

well for small sample sizes – such as this study - than other cointegration techniques. In

addition to the ARDL model, the Granger-causality is used. This test is augmented with

a lagged error correction term when variables are cointegrated. The conducting of this

test helps to explore the short-run and long-run dynamic relationships among MENA

stock markets and between MENA markets and developed markets.

1.4 Structure of the Study This thesis proceeds as follows. Chapter 2 reviews most of the literature related to stock

markets integration in both developed and emerging stock markets. The chapter starts

with a revision of the notion of stock market integration. After that, it discusses the

early theoretical studies related to stock market integration, such as capital asset pricing

model (CAPM), arbitrage pricing theory (APT), and some other alternative approaches

for testing integration.

Chapter 3 presents an inclusive discussion of the literature that has used

cointegration approaches to examine stock market integration. It also presents an

analysis of the concept of efficient market hypothesis and how it functions in the case of

10

cointegration among stock markets. Finally, the chapter reviews relevant research on

stock market integration in the MENA region.

Chapter 4 presents a review of the general features of the four countries Egypt,

Turkey, Jordan and Morocco in the MENA region. At the same time it discusses the

main macroeconomic indicators for these countries. Following this general review, the

chapter presents a discussion for each stock market in terms of the main indicators for

each market such as market capitalization, turnover ratio, trading value and number of

listed companies.

Chapter 5 presents a descriptive analysis for the stock price indices in the

MENA region and some developed markets. It investigates some important statistical

characteristics of these markets. And empirically estimates the conventional Augmented

Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests. As a significant

contribution by this study, this chapter analyzes the development of testing for structural

change, was firstly proposed by Perron (1989). Following this analysis, the study tests

the unit root hypothesis in the presence of endogenously determined structural change in

these stock markets.

Chapter 6 empirically estimates the integration among stock markets in the

MENA region, and between these markets and some representative developed markets

The study uses the recently developed autoregressive distributed lag (ARDL) model for

estimation. Moreover, the chapter estimates the Granger-causality among stock markets.

Based on the results of these approaches, the policy implications for the findings are

presented.

Chapter 7 summaries the major conclusions derived throughout the thesis. The

chapter presents a summary of the previous chapters and discusses the policy

11

implications of the major results. Finally, suggestions for future work are provided at

the end of the chapter.

12

Chapter Two

The Early Theoretical Models Relating to Stock Market

Integration

2.1 Introduction Over the last three decades a significant amount of research has focused on the issue of

stock market integration, and how to measure this integration. The term “stock markets

integration” refers to an area of research in financial economics that covers many aspects of

interrelationship across stock markets. Various schools of thoughts have been developed to

measure the integration of stock market. Some have used the correlation of the local market

return with the world return as a measure of integration; others have concentrated on the

investment restriction as indicators of integration.

A revolution in the field of portfolio theory started with the invented of assets

pricing models, such as Capital Asset Pricing Models (CAPM) and International Capital

Asset Pricing Models (ICAPM). According to these models, national markets are

considered to be integrated if securities with the same risk characteristics are priced the

same, even if they are traded on different markets. An alternative model to the CAPM is

what is called Arbitrage Pricing Theory (APT), which assumes that stock prices can be

influenced by not only the market risk, but also by several sources of systematic risk in the

economy.

13

The main objectives of this chapter are to consider and analyze the literature

relating to stock market integration that use different versions of asset pricing models, such

as (CAPM), and also review the literature relating to arbitrage pricing theory (APT). This

chapter consists of seven sections. Section 1 provides an introduction. Section 2 presents an

inclusive revision of the notion of stock market integration. Section 3 discusses the early

theoretical studies relating to stock market integration. Section 4 presents a thorough

discussion of the asset pricing models used for testing stock markets integration. Section 5

reviews the arbitrage pricing theory (APT) and the literature that uses this model for testing

stock market integration. Section 6 presents some alternative approaches for testing stock

market integration. Finally, the last section offers some concluding remarks.

2.2 The Notion of Stock Markets Integration Stock market integration could be considered as a status where there are no impediments,

such as legal restrictions, transaction costs, taxes, tariffs, and all types of controls against

the trading in foreign assets or on the mobility of portfolio equity flow. In the case of stock

market integration, all assets with the same level of risk grasp the same return across all

different markets.

Various schools of thought have been developed for measuring stock market

integration. In general, the common factor for most of these schools is the law of one price

(LOOP). That is, when transaction costs and taxes are not taken into account, identical

securities should carry the same price across all stock markets where such securities are

traded. In other words, if two or more markets are integrated then the identical securities

should be priced identically within both markets (Oxelheim, 2001). The existence of stock

14

market integration implies that stocks in all markets are exposed to the same risk factors

and the risk premia on each factor is the same in all markets.

Stulz (1981a) defined stock markets as being integrated “if assets with perfectly

correlated returns have the same price, regardless of the location in which they trade”. A

fully integrated market is defined as a situation where investors earn the same risk-adjusted

expected return on similar financial instruments in different national markets (Jorion and

Schwartz, 1986, p.603) which means no arbitrage profit to be achieved. In other words, if

the risk of an identical financial instrument is traded on the same price in different markets,

then it will be an indication of integration between these markets. However, a stock market

is considered to be more integrated, if there are stronger domestic returns depend on

contemporaneous world market shocks. This definition emphasizes not only the openness

of stock markets but also measures directly the extent to which shocks are transmitted

across stock markets. The transmission of a shock requires both the removal of barriers and

the capital itself flows across markets in order to take advantage of potential market

opportunities (Fratzscher, 2002). It is believed that, when a stock market is more fully

integrated, both the given market and the country’s economy will not be isolated from

external influence any more.

The rapid development of telecommunications and computer technology and the

widespread of using the Internet have been considered as important tools for making stock

markets practically and institutionally more integrated. These tools enable investors, agents,

traders, and all participants in stock markets to have the access to information, and

consequently the ability to manage their portfolios more efficiently.

15

Other important tools pertain to the removal, or at least the diminishing, of

government impediments that includes legal restrictions, transaction taxes, and all types of

controls against either the movement of capital or exchange and interest rates. All of these

steps have been known as “the liberalization process”. However, these improvements that

started in developed countries in 1970s and 1980s, continued during the 1990s in most

developing countries, and many countries are still adopting improvements (Akdogan,

1995).

Several arguments are presented for the desirability of international stock market

integration. According to Akdogan (1995, pp. 62-65) these arguments are:

1- Based on the competitive auction-model, and in case of no barriers to capital

movement, stock market integration leads to a more efficient allocation of the

world’s resources, and capital will seek higher returns to investment, moving from

capital market where capital is relatively abundant to another where capital is

relatively scarce. These characteristics of capital markets enable the competitive

auction-model to function more effectively to economically equilibrate the market.

2- In case of integration among all stock markets, the systematic risk (market risk)

becomes an unsystematic risk (firm-specific risk), and this kind of risk can be

diversified or eliminated away by including the security as part of diversifiable

portfolio.

3- It has been argued that corporate financial strategies depend on whether

international stock markets are integrated or not. In case of integrated market, all

firms can raise their capital with lower costs than firms do in a segmented market.

Also capital budgeting decisions for firms normally depends on their exposure to

international capital that is the marginal cost of capital of a firm that uses

16

international sources is lower than the marginal cost of capital of firm that uses only

domestic sources.

However, there are some developments that help this desirability of international

stock market integration. Narayan et al. (2004) mentioned some of these developments; the

removal of barriers across national borders that leads to increase the flow of capital, the

reduction in transaction costs and a commensurable increase in the flow of information, and

the important implication of stock markets integration for portfolio theory, which advocates

that investors diversify their assets across different stock markets provided that returns to

stocks in these markets are less than perfectly correlated with domestic market.

Financial market integration has several direct and indirect potential benefits;

Rangvid (2001) mentions some of the direct benefits as follows:

1- It allows for international capital flows to emerging markets, enabling these to

increase investment ratios thereby promoting real growth. In general, once funds

move freely between stock markets, this will give more momentum to these

markets.

2- It enables agents in the financial markets to price assets with identical risk patterns

in the same premises.

3- It allows for better risk sharing among agents who are trying diversifying their

consumption risk, as caused by differences in time patterns of returns to real capital

investment.

4- It enables agents to diversify risk in financial markets, and thus undertake projects

with higher expected returns for the same degree of risk

5- It lowers the cost of capital and smoothen the growth of investment.

17

Due to the indirect benefits, it has been widely accepted in the literature of this field

that integration plays a vital role in improving resource allocation through the financial

markets, thereby increasing safety of financial operations and also thereby strengthening

the domestic financial markets through such financial sector reforms. It is also believed that

it has an indirect positive effect on other economic sectors through its positive effect on

economic growth in general. Monetary policy, which is considered one of the main

economic policies, is affected greatly by the integration of stock markets, and this

integration itself has strong implications in international finance (Rutledge and Karim,

2004).

Also, it is widely accepted that economic and financial policies have a great

influence on the status of stock markets. Bekaert and Harvey (1995) state that, “whether a

market is integrated with the world capital markets or segmented is greatly influenced by

the economic and financial policies followed by its government or other regulatory

institutions”. By following this argument, many empirical studies have examined the

relationship between the degree of economic integration and the degree of stock market

integration.

However, the efficiency of assets allocation is considered as the main economic

reason that determines the integration of different stock markets. The flow of capital

between different countries depends on the difference between marginal productivities; that

is capital flows from those where the marginal productivity is low to those where marginal

productivity is high. According to this capital transfer, an equalization of marginal

productivity of capital of the different countries will be achieved, and welfare gains in all

the countries engaged in the process will be created.

18

2.3 The Early Theoretical Studies Relating to Stock Markets Integration

One of the earliest studies that designate the importance of diversification of risk was the

seminal work by Markowitz (1952). He revolutionizes the field of portfolio theory. He uses

a mean-variance efficient portfolio framework, a portfolio that has the highest expected

return at the given level of risk. His theory has been depicted graphically as the efficient

frontier model; but sometimes it is referred to as the efficient set. Markowitz (1952)

explains that diversification benefit could be achieved when additionally not highly

correlated securities are added into a portfolio. The model shows a simple geometric graph

of the trade-off between risk and return, the frontier itself is a composition of many

portfolios; more specifically the efficient portfolios are a subset of minimum variance

portfolios offering the highest return for each level of risk (Frino, et, al. 2001, pp. 139-141).

Going beyond Markowitz, Tobin (1958) argues that agents would diversify their

saving between a risk free asset and a single portfolio of risky assets. By combining a risk-

free asset with risky assets, it is possible to construct portfolios whose risk-return profiles

are superior to those of portfolios on efficient frontier. By doing this, what is called the

capital market line has been constructed as a tangent line to the efficient frontier that passes

through the risk-free rate.

Based on Markowitz’s work, and in the context of domestic market, Sharpe (1964),

Lintner (1965) and Mosin (1966) independently develop one of the most famous financial

equilibrium models, the capital asset pricing model, which is referred to as the CAPM. In

their model they assume that markets are segmented. The development of this model played

19

a very important role in establishing the foundation of the modern portfolio theory. CAPM

is “an equilibrium economic model for valuing stocks by relating risk and expected return”

(www.investorword.com). It provides a precise prediction on this relationship. The model

is graphically represented by the capital market line and is implied by the following

relationship:

[ ]fmifi rrrr −Ε+=Ε )()( β (2.1)

Where )( irΕ and )( mrΕ denote the expected return on security i and the market portfolio,

fr is the return on risk- free security1, and iβ (beta) measures the sensitivity of security i

to the market risk factor- the slope of the line-, and it is quantified by:

)var(),cov(

m

im

rrr

=β (2.2)

where ),cov( im rr is the covariance of returns of the i th asset with the market, )var( mr is the

total risk of the i th asset. This total risk can be partitioned into two parts by using ordinary

least squares as follow:

)var()var()var( 2 eRR mii += β (2.3)

where )var(2mi Rβ is the market risk (systematic risk) or the undiversifiable risk, which is

the portion of an asset’s risk that cannot be eliminated via diversification. This risk

indicates how including a particular asset in a diversified portfolio will contribute to the

riskness of the portfolio, in other words this sort of risk relates to general market

movements. )var(e is the firm- specific risk or (unsystematic risk) that can be diversified or

eliminated away (cancel out) by including the security as part of diversifiable portfolio.

1 In many empirical investigations, the U.S Treasury Bills are used as a proxy riskless rate of interest. See: Errunza, Losq and Padmanabhan (1992) and Ragunathan (1997).

20

According to Akdogan (1995) and Bekaert & Harvey (2003), the CAPM is based on

the following assumptions:

1- Investors care only about mean and variance factors, in other words they function as

mean and variance optimizers, their investment decisions based on expected return

and the variances of security return.

2- Investors are risk averse.

3- Assets returns are multivariate normally distributed.

4- Investors can borrow and lend at the same riskless rate.

5- There exists a free-risk asset.

6- Perfectly competitive markets and all information are reflected fully in prices.

7- There are no transactions costs and taxes.

8- Capital markets are in equilibrium.

9- All investors have “one-period” time horizon.

Arising from various versions of asset pricing models including CAPM, early

studies investigate issues of diversification and financial integration. According to these

models, national markets are considered to be integrated if securities with the same risk

characteristics are priced the same, even if they are traded on different markets.

Back to the CAPM, which firstly has been developed in the context of domestic

market, the idea of diversification is considered as the core concept of this model. Relying

on that, in the case of implementation of CAPM in a single country, there will be a unitary

price of risk, and the price of all assets reflects the level of systematic risk they possess, and

so the assets are considered to be integrated but in the same country. In this case, the

21

CAPM assumes markets are completely segmented. This approach has been used by Sharpe

(1964), Lintner (1965) and Black (1972).

An alternative model to the CAPM has been developed by Ross (1976). This model

is called the arbitrage pricing theory (APT). The APT is unlike CAPM, which involves

some unrealistic assumptions and that it has only one source of systematic risk (market

return). The APT model depends on no arbitrage conditions like CAPM. It assumes that

stock prices can be influenced not only by the market risk, but also by several sources of

systematic risk in the economy. These sources can be thought of as factors, in addition to

the market, like inflation, aggregate output, industry effect, interest rate…etc. (Mishkin,

1998). The model has the following general form:

i

n

iiii uFBBR ++=Ε ∑

=10)( (2.4)

where )( iRΕ : expected return of asset i

0B : a constant

nFF ...1 : values of factors from 1 to n

iB : sensitivity of asset return to particular factor

iu : residual term

Basically, most of the early theoretical research on market integration has been built

according to these two models, the CAPM and the APT.

22

2.4 Asset Pricing Model for Testing Stock Market Integration As mentioned earlier, the capital asset pricing model (CAPM) has been developed

assuming the case of a completely segmented market. On the other hand, asset pricing

models in the international context generally assume markets to be perfectly integrated.

These include studies like Solink (1974), Stulz (1981), Adler & Dumas (1983) and Dumas

& Solink (1995) who use a world CAPM with currency risk (exchange risk). Solink (1983),

and Cho, Eun, & Senbet (1986) who use world arbitrage pricing theory. Wheatley (1988)

uses a consumption-based asset pricing model. Harvey (1991) uses world CAPM. Within

this context multiple risk factor models studied by Ferson and Harvey (1994, 1997). The

international factor latent models were studied by Bekaert & Hordick (1992), Campbell &

Hamao (1992) and Harvey, Solink, & Zhou (1994). All of these approaches will be studies

in full details later in this chapter.

In reality, it is hard to find both cases - either perfectly segmented or perfectly

integrated markets –, in fact there are different kinds of barriers between capital markets.

These barriers take the form of capital control, transaction cost and taxes. These barriers

restrain the investors from diversifying their portfolios in different markets. They have been

considered as extra costs that international investors face. These costs play an important

role in determining where the investments will take place, either in local markets or abroad.

That is if these costs outweigh the benefits of diversification, then it is more likely that

these investment will take place in the local market or vice versa.

Black (1974) discusses these issues deeply. He develops an international capital

asset pricing model in which there are explicit barriers to international investment in the

23

form of a tax on holdings of assets in one country by residents of another country. His

theoretical model took the following form:

[ ]mmiii RRRR τβτ −−Ε=−−Ε )()( (2.5)

which after rearranging we obtain:

[ ]mmiii RRRR τβτ −−Ε++=Ε )()( (2.6)

where R is the short-term interest rate in country C

iτ : the tax rate on security i for investors in country C

)( mRΕ : the expected return of the market portfolio specific to country C

mτ : the tax rate on market portfolio for investors in country C

iβ : defining by the )var(),cov( mmi RRR , which is the systematic risk of

security i.

It is clear that equation 2.6 is a modified form of equation 2.1, which represents the

capital asset pricing model. So, if the taxes on international investments are

zero )0( == mi ττ then equation 2.6 will be the same as equation 2.1 that is CAPM. Black

also suggests a uniform tax rate across countries. By estimating this tax rate in his model, it

becomes much easier to estimate the strength of barriers to international investment in risky

assets.

Stehle (1977) asserts on the importance of the world market factor as a determinant

of assets returns. He uses the stock indices data for the US and ten other countries during

the period from December 1958 to December 1975. A CAPM framework is applied to test

the extent to which risk can be diversified in a segmented market and not in an international

market and vice versa (Ragunathan, 1997, p. 5). The results of Stehle’s estimations are

24

inconclusive, and the poor statistical results he got could indicate that these models may not

be well-specified.

In a similar work to Black (1974), Stulz (1981a) constructs a model of international

asset pricing in which a cost is associated with holding risky foreign securities. As in Black

(1974), Stulz (1981a) uses the proportional taxes to model barriers to international

investment. However, the investor in his study would pay taxes on absolute value rather

than the net value of her/his holding of foreign securities. For simplicity, Stulz assumes that

only domestic investors face the taxes on their foreign holdings. Stulz concludes that in

each country, all investors hold the same portfolio of risky assets. The key feature of Stulz

model is that there are some nontraded foreign assets. Based on that, for investors who face

barriers to international investment, the world market portfolio will be inefficient, and the

world market will not be in equilibrium.

Based on the international asset pricing model, Jorion and Schwartz (1986),

empirically examine the integration versus segmentation between Canadian stock market

and global North America market, the global North America markets have been represented

by US stock market. The study uses monthly rates of return from January 1963 to

December 1982. The results indicate that International CAPM was not a good description

of the pricing of Canadian securities. The study rejects the joint hypothesis of integration

between the Canadian stock market and the North American stock market, and shows

strong evidence of segmentation in the pricing stocks. Depending on these results, the study

indicates that legal barriers play an important role in causing this segmentation.

In the same direction of continuing the constructing of international asset pricing

models, Errunza and Losq (1985) postulate a mildly segmented market structure. In this

setting, mild segmentation occurs because investors from one country cannot invest in

25

stocks from another country, while investors from the other country do not face such

restrictions, and this leads to what is known as “market imperfection”. Also securities from

country 1 are eligible, and the securities from country 2 are ineligible for country 1.

Errunza and Losq examine the inability of some investors to trade in a particular class of

securities. They use the data from nine less developed countries and a random sample from

the US during the period 1976-1980. However, although their results accept the case of

mild segmentation, they are still inconclusive. They attribute this weakness to the kind of

restrictions imposed in the real world (Teng, 1998, p. 6). Errunza, Losq and Padmanabhan

(1992) use a maximum likelihood procedure to test the same model as in Errunza and Losq

(1985), their results are again inconclusive and similar to the previous one, which indicates

that the world capital markets are neither integrated nor segmented (Ragunathan, 1997,

p.5). However the use of the US Treasury bill rate as a proxy for risk rate is considered as a

shortcoming of their study, because this kind of rate is not used in emerging markets.

Buckberg (1995) uses a conditional international capital asset pricing model

(ICAPM) in an attempt to investigate whether emerging stock markets are part of global

financial market or not, and to what extent emerging markets behave like industrial markets

in relation to the world portfolio. According to Buckberg (1995, p. 57):

“Conditional refers to the use of conditioning information – some

information set 1−tZ - to calculate expected moments and to test properly

the ICAPM as a relation between expected returns and ex ante risk.”

Buckberg (1995) uses monthly data over the period 1977-1991 for twenty emerging

markets. The results indicate that six markets out of ten rejected ICAPM during the period

1977-1984. However, eighteen of the twenty markets are integrated with world markets

26

during the period 1984-1991. The study concludes that the main reason for this integration

to occur is the large capital inflows from industrial economies to these emerging markets

during 1980’s.

In a different methodology to the previous asset pricing studies, Bekaert and Harvey

(1995) develop an innovative econometric methodology, by employing a conditional

regime-switching model. Using this methodology allows for the degree of market

integration to change through time and so is the model allows for switching between

segmentation and integration by attaching probabilities to the respective asset pricing

models (Bekaert and Harvey, 1995). The model is considered as an innovative idea, since it

uses dynamic partial segmented / partial integrated instead of static segmented / integrated

paradigm. The model is conditional, that is the predetermined information is allowed to

affect the expected returns, covariance, variance, and the integration measure (Bekaert,

1995, p. 405). The model has the following general form:

[ ] [ ] [ ]tittititwtitttitit rrrrE ,11,1,,,111,,1 var)1(,cov −−−−−−− −+= λφλφ (2.7)

where tir , is the return on asset i at time t , twr , is the return on a value-weighted world

portfolio at time t , 1, −tiφ is the econometrician’s time-varying assessment of the likelihood

that the market is integrated and 1−tλ is the conditionally expected world price of

covariance risk for time t . The study uses the data of 12 emerging markets and 21

developed markets, and that is over the period 1975-1992. The study finds that some

emerging markets exhibit time-varying integration, others appear to be more integrated than

expected, and others are more segmented even though there is a free access to their capital

markets. The study suggests that it is not the case that world capital markets have become

27

more integrated. Despite a number of developing countries who have removed or relaxed

the restrictions on foreign stock ownership in the 1990’s, only 4 out of 12 countries have

higher integration measure in the same period. Another interesting result is that emerging

markets are characterized by high volatility, the standard deviations range from 18% in

Jordan to 53% in Taiwan, while developed markets characterized by relatively low

volatility comparing to emerging market, and the standard deviation range from 15% to

42%. Finally, the study indicates the existence of interrelationship between economic

growth and the status of capital market and the stage financial market development. This

interrelationship implies that economic growth is fundamentally linked to financial

integration.

In a recent study, Carrieri, Erronza and Hogan (2002) continue discussing the issue

of time-varying market integration for 8 emerging markets over the period 1976- 2000. The

GARCH-M methodology has been used to estimate the Errunza and Losq (1985) model

and empirically measure the time variation in market integration. They conclude that not

only the local risk is the most relevant factor in explaining the time variation of emerging

market return across the 8 countries, but also global risk is conditionally priced for some

markets. However, the study does not relate market integration to expected return. The

countries in the sample show wide range of integration alternatives from Argentina being

the most segmented to Mexico the most integrated. The study also shows that using the

correlations of market wide index return, as a measure of market integration is not a proper

measurement. In general the study shows that the degree of integration is higher in the

1990s. Finally, the study infers evidence that financial market development,

macroeconomic development and financial liberalization policies are very essential factors

for financial market integration.

28

Frank de Long and Frans A. de Roon (2002) use a similar model to the CAPM.

Their argument based on the notion that time variation in the integration level is important

for estimating the effects of liberalization on the cost of capital in emerging markets. In the

case of partially segmented market, the study considers the fraction of assets in an economy

that cannot be traded by foreign investors as a measure of market segmentation and has to

be held by domestic investors only which is called non-investable assets, whereas the asset

that can be traded freely refer to as investable assets. The effect of market segmentation or

integration on expected return for freely tradable (investable assets) and nontraded assets

(non-investable assets) has been estimated using a set of 30 emerging markets, which are

grouped into four regions Latin America, Asia and the Far East, Europe and the MENA

region, over the period 1988-2000. The study finds that the effect of the level of

segmentation or integration on the expected return in emerging markets exists in both kinds

of assets, although the results are weaker for the non investable assets than for the

investable assets. The expected returns in emerging markets are affected by the level of

segmentation not only in the country itself, but also by the level of segmentation in other

countries in the same region. The study also finds a significant time-variation in the betas

relative to the world portfolio, an annual increase of 0.09 in beta has been found due to the

decreased segmentation of the emerging markets.

Another asset pricing model that has been used in the literature to test the

integration of stock markets is the consumption-based asset pricing model. The basic idea

of this model asserts that the simple relation between consumption and assets return

captures the implication of complex dynamic international multi factor asset pricing model

(Campbell and Cochrane, 2000, p. 2863). Wheatly (1988), uses this model to test

international stock market integration. The model predicts that there is an asset pricing line

29

for each country that relates representative individuals expected real return on each asset to

the covariance of this return with growth in the individual’s real consumption. In this study,

the model predicts that each country has an asset pricing line (APL); this line relates the

expected real return to the covariance of this return with the growth in the individual’s real

consumption. He also suggests that markets are internationally integrated if assets of equal

risk, either located in one country or more, yield the same expected return. The joint

hypothesis will be rejected if securities lie at a significant distance from the (APL). The

study uses monthly data for U.S stock market and seventeen international markets and that

is from January 1960 to December 1985. The results do not reject the hypothesis, which

means the existence of stock markets integration.

Recently, CAPM has become more susceptible to criticism depending on the fact

that all investors are supposed to hold portfolio that contains both riskless portfolio and

market portfolio, which means that all investors hold the same combination of market

portfolio that contains all risky assets. In practice, however, it is still difficult to construct a

proxy that contains all assets (Agarwal, 2000).

30

2.5 Arbitrage Pricing Theory (APT) for Testing Stock Markets Integration

An alternative asset pricing model has been developed by the literature is the Arbitrage

Pricing theory (APT). A significant amount of literature has been using APT for testing

stock market integration. This model assumes that stock’s prices can be influenced by

several sources of systematic risk in the economy, other than the market risk. These sources

can be thought of as factors, in addition to the market.

In this context, Cho, Eun, and Senbet (1986) use the Arbitrage Pricing Theory

(APT) in an international setting to test stock market integration in eleven different

countries during the period (January 1973 - December 1983). They use the “Inter-battery

factor analysis” to estimate the common factor loading of two different groups of assets by

examining the inter-group sample covariance matrix only rather that the entire sample

covariance matrix. By doing so, they can alleviate the problem of variation of factor

structure, especially within countries. They also use Chow test method to measure the

validity of APT. The results of the cross-sectional test reject the joint hypothesis that the

international capital markets are integrated and that APT is valid internationally. The study

could not determine whether rejection of the joint hypothesis reflects segmentation of

capital markets or failure in the international APT.

Gultekin, Gultekin, and Penati (1989) use multi factor asset pricing model to test

stock markets integration or segmentation in U.S. and Japan. They hypothesize that market

segmentation could be either the result of government imposed barriers, or the individuals

attitudes and irrationality. The study uses weekly data during the period from January 1,

1977 to December 31, 1984. This period is divided into two sample periods, January 1977

31

to December 1980, and January 1981 to December 1984. These two samples are closed to

the year 1980, in which a big change in the capital control regime in Japan occurred, and

the enactment of the foreign exchange and foreign trade control law are completely

liberalized. The results indicate that the hypothesis of perfect integration has been rejected

before 1980 (before liberalization) and not rejected after 1980 (after liberalization). They

conclude that governments (public policies) could be a source of segmentation between

international stock markets. As a consequence of using weekly data, some economic

variables are not included, whereas stock market indices in the form of percentage change

are used, and this could be the a weakness of the study.

In a more comprehensive study, Korajczyk and Viallet (1989) investigate the

domestic and international version of the capital asset pricing model (CAPM) and the

arbitrage pricing theory (APT). They use the data on a large number of assets traded in four

countries, namely US, England, Japan and France, which is more than the countries in

Gultekin et al (1989) study, during the period January 1969 through December 1983. These

four markets represented nearly 65% of the world stock markets capitalization at the end of

19831. The study focuses on the following main issues. Firstly it investigates whether the

APT has greater explanatory power than CAPM domestically as well as internationally.

Secondly it investigates, whether international versions of the asset pricing models

outperforms or underperforms single economy versions. Finally, it investigates the

influence of changes in the regulation of international financial markets on the deviations of

returns from the predicted asset pricing relations. The findings of the study are as follows:

1 Market capitalization represents the aggregate value of a company or a stock. It is obtained by multiplying the number of shares outstanding by their current price per share. (http://wwwinvestorwords.com/2969/market_capitalization.html.)

32

First, the multi factor models tend to outperform single-index models in both domestic and

international forms. Second, the performance of the models is affected by changes in

capital deregulation. This result is similar to Gultekin et al. (1989) in that it shows that

governments (public policies) are a source of segmentation between international stock

markets.

In a similar case study to Cho et al (1986), Mittoo, Usha R. (1992) reexamines the

integration of the Canadian and the US markets using a different method during the period

1976-1986. In his study, Mittoo uses both the CAPM and APT frameworks. For testing

integration and segmentation by CAPM, they use the following model:

IittID

IDiFtIt

Ii

IDi

IDIi

IFtit eVRRRR ++−++−=− −

−−−)(20 )()1( βββγβγ (2.8)

where itR and ItR are the monthly returns on asset i and the integrated market index in

month t respectively, FtR is the yield on the three month Canadian treasury bill in month

t , Iiβ is the systematic risk of asset i relative to the integrated index, and tIDV )( − is the

residual in month t obtained by the projection of the Canadian market index on the

integrated market index.

When applying the APT framework, the study uses five economic factors1:

1- industrial production,

2- the differences in short term interest rate,

3- the risk premium,

4- the term structure.

1 These factors and others can be found in details in Chen, Roll, and Ross (1986), they discuss the motivation of these factors and others in details

33

5- the return on the market,

According to the study, when using APT framework, the asset returns follow a

multi–factor model:

∑=

++=S

kitktikit

Jit uER

1

δβ (2.9)

where itR and itE are the actual and expected returns on asset i respectively in period t ,

ktδ is the k th risk factor, ikβ is the sensitivity of asset i to the k th factor, and itu is a

normally distributed error term with mean zero. By assuming no arbitrage opportunities the

expected returns on asset i becomes:

∑=

+=s

kikkFtit RE

1βλ (2.10)

where FtR is the risk free rate, and kλ is the risk premium associated with the k th factor.

By substituting (2.9) into (2.8), we get:

∑=

++=s

kitikkFtit eRR

1

βλ (2.11)

where ∑=

+=s

kitktikit ue

1δβ is an error term.

The evidence in both models suggests a move from segmentation during the period 1976-

1981, to integration during the period 1982-1986. Part of this result is similar to Jorion and

Schwartz in the 1968-1982 period. However more literature has investigated the

relationship between macroeconomic variables and stock markets return using APT1. The

results of these studies suggest the following macroeconomic variables that have effect on

1 See: Chen et al. (1986), Beenstock and Chan (1988) and Priestley (1996).

34

stock return: interest rate, expected inflation, unexpected inflation, industrial production,

input cost, money supply and exchange rate.

35

Table 2.1 A summary for the Results of the Main Previous Studies

The Author Asset Pricing Model Data & Period of Study Results

Stehle (1977) CAPM Monthly data 1958-1975 Inconclusive

Jorion and Schwartz

(1986)

CAPM Monthly data 1963-1982 Reject integration

Errunza and Losq

(1985)

CAPM Monthly data 1976-1980 Not rejected mild

segmentation

Errunza et al (1992) CAPM Monthly data 1975-1987 Not rejected mild

segmentation

Buckberg (1995) ICAPM Monthly data 1977-1991 Not rejected integration

Bekaert and Harvey

(1995)

CAPM: Conditional

regime-switching

model

Monthly data 1975-1992 Not rejected integration

Carrieri et al (2002) CAPM Monthly data 1976-2000 Not rejected integration

Wheatly (1988) Consumption CAPM Monthly data 1960-1985 Not rejected integration

Cho et al. (1986) APT Monthly data 1973-1983 Reject integration

Gultekin et al (1989) APT Weekly data 1977-1984 Reject integration in

sub period and not

rejected it in another

Korajczyk and Viallet

(1989)

CAPM, APT Monthly data 1969-1983 Reject and not rejected

integration

Mittoo (1992) CAPM, APT Monthly data 1976-1986 Reject and not rejected

integration

36

2.6 Alternative Approaches for Testing Stock Markets Integration

Different approaches have been used to test stock markets integration. Several studies have

used simple correlation and multiple regression models to test the market integration. The

correlation coefficients are measured either against stock prices or against standard

deviations of stock prices across stock markets, see: Akdogan (1995), Bhooca-com, A. &

Stansell, S.R. (1990) and Marashdeh (1994). However, it becomes clear that relying on

correlation analysis for testing integration or other phenomena is quite questionable. Many

studies have pointed to the problems of using this technique. One of them is that it does not

eliminate the spurious relationships. Some studies also show that “the conventional cross-

correlation coefficients are biased upwards during a period of increased volatility” (Wilson

et. al. p. 8, 2002).

Other studies have concentrated on the capital flow and its effect on stock markets

integration that is if there is capital flows between stock markets, it is an indication of

existence of integration (Erronza, et al. 1992). Some other intuitive techniques have been

adopted on the basis of monitoring volatility interaction for measuring the integration

(Darbar and Deb 1997). Some others have used coherence analysis; (see: Sewell Stansell

and Lee 1996).

Akdogan (1996) suggests different approach to measure the financial markets

integration; this approach depends on an international risk decomposition model. The study

introduces a quantifiable measure of market integration that can be used to rank countries

by their level of integration; first it starts with the single-index return generating, which is

given by:

37

iwiii RR εβα ++= (2.12)

where: iβ is the i th country vis-à-vis the global benchmark index wR , and iε is the random

error term. The variance of the i th portfolio is decomposed into the following two

components:

)var()var()var( 2iwii RR εβ += (2.13)

Dividing both sides by )var( iR yields,

1=+ ii qp (2.14)

)var()var(2

i

wii R

Rp β= (2.15)

where iP is the fraction of systematic risk in country i vis-à-vis the global benchmark

portfolio, which measures the contribution of this market to the global market risk, in other

words iP measures the integration of market i with the world market, so a market with

smaller fraction of systematic risk is more segmented from the world market, and market

with larger fraction of systematic risk is more integrated with the world market. A sample

of 26 countries has been used in this study for the period (1972-1989). The result suggests

that some small and medium-sized European markets along with most emerging markets

exhibit segmentation.

Barari (2003) extends the work of Akdogan (1996, 1997), and uses an international

risk decomposition model. He extends equation (2.12) to measure iR against two

benchmark portfolios, a regional index and a global index. The two-index, return-

generating process of the i th country portfolio is given by:

iRUR gigririi εββα +++= (2.16)

38

The study empirically estimates the global and regional integration scores for a sample of

six Latin American countries, namely Argentina, Brazil, Chile, Colombia, Mexico, and

Venezuela, during the period 1988-2001. The results show that there was a move toward

regional integration but not global integration for most of the countries in the sample.

2.7 Conclusion

After reviewing the notion of stock markets integration, this chapter presents a

thorough discussion of the early theoretical models relating to stock markets integration;

such as asset pricing models and arbitrage pricing theory. According to the asset pricing

models, stock markets are considered to be integrated if securities with the same risk are

prices the same. So in the case of implementing CAPM, there will be a unitary price risk,

and the price of all assets reflects the level of systematic risk they possess, and so the assets

are considered to be integrated. Also, the chapter reviews another asset pricing model that

is the consumption-based asset pricing model, this model shows that the simple relation

between consumption and assets return captures the implication of complex dynamic

international multi factor asset pricing model.

Regarding the Arbitrage Pricing Theory (APT), this model assumes that stock

prices can be influenced by not only the market risk, but also by several sources of

systematic risk in the economy. In a contrast to the assets pricing models, some studies use

what is called a conditional regime-switching model, this model allows for switching

between segmentation and integration by attaching probabilities to the respective asset

pricing models.

39

Finally, the chapter reviewed some alternative approaches for testing stock market

integration. These approaches include the correlation coefficients approach, the capital flow

between stock markets; monitoring volatility interaction for measuring integration, and the

international risk decomposition model.

40

Chapter Three

The Recent Techniques Relating to Stock Market

Integration: the Cointegration Approach

3.1 Introduction There is a growing theoretical and empirical literature dealing with stock market

integration. Most of recent studies rely on recent econometrics techniques, such as

cointegration approaches, generalized autoregressive conditional heteroscedasticity

(GARCH) model. Granger-causality, vector autoregressive (VAR) model and variance

decomposition to measure the stock market integration among national stock markets.

As stock price indices trend to move together over a long period of time, this co-

movement indicates the existence of cointegration among stock price indices. This linkage

and co-movement among stock markets indices have been studies by several studies. At the

same time, more literature concerning finance has concentrated on the efficiency of stock

markets, and the relation between the existence of long-run equilibrium relationship among

stock markets and the efficiency of these markets. However, based on this literature,

controversial results have been provided depending on how the efficiency itself is

interpreted.

The main objectives of this chapter are to review the literature relating to using

different cointegration approaches for testing stock market integration. Also to review the

literature relating to Efficient Market Hypothesis (EMH), and the effect of the existence of

41

cointegration on the stock market efficiency. More concentration will be on the emerging

stock markets in the MENA (Middle East and North Africa) region. This chapter is divided

into six sections. Section 2 analyzes the use of cointegration for testing stock market

integration. Section 3 sheds some light on the Asian financial crises and stock market

integration. Section 4 discusses and analyzes the relationship between the Efficient Market

Hypothesis and stock market integration. Section 5 investigates the stock market integration

in MENA region. Finally, the last section offers some concluding remarks.

3.2 Cointegration Approach for Testing Stock Market

Integration In the last two sections, it has been realized that a huge volume of earlier research on

financial market integration has been done using capital asset pricing models or arbitrage

pricing theory. Most of recent studies have been using modern econometric techniques such

as Granger-causality analysis, generalized autoregressive conditional hetroskedasticity

(GARH) model, vector autoregressive model (VAR) and different approaches of

cointegration analysis, such as Johansen- Juselius approach, and the ARDL approach. In

this section a review of some of the literature that has been used the cointegration and

modern econometrics techniques will be presented.

One of the idiosyncrasies of stock prices is that over long period they trend to move

together and follow a common upward trend (Saini, et al. 2002). Many studies try to

calculate the number of common stochastic trends. If stock markets are integrated, it is

expected for the indices in these markets to display common trends. But since these indices

are nonstationary, then using cointegration method becomes necessary (Dickinson, 2000).

42

The existence of co-movements between the securities prices indicates stock market

integration. This co-movement, which indicates the existence of cointegration between two

stock markets, implies that one of the markets will help predicting the other market’s

returns, since a valid error correcting representation will exist. Also, they share at least one

common stochastic trend and they will tend to drift together over time.

The linkage and long-run co-movements among international stock markets either

developed or emerging have been studied by several researchers. Before delving into the

details of these studies, an important question should be raised here, and that is why shocks

in one market should affect other markets? Janakiramanan and Lamba (1998) mentioned

four factors that cause these influences:

1- Dominant economic power, actions taken by an influential economic power

country, like US, will have worldwide repercussions.

2- Macroeconomic variables in different countries play important role in determining

the cointegration among stock markets in these countries. Kasa (1992) finds that

when a comovement exists among stock markets, also a comovement among

macroeconomic variables in these courtiers exists.

3- Common investor groups, when two countries share geographical proximity and

have similar groups of investors in their markets, these markets are more likely to

influence each other. However, it is worth mentioning here that having common

economic and geographic ties do not necessarily lead the national stock markets to

follow the same stochastic trend (Chan, et al., 1997, p. 809).

4- Multiple stock listing, when a stock is listed in two countries at the same time, this

means that any shocks in one market will be transmitted to the other.

43

5- Indirect influences, when stock market (A) reacts directly to a shock in another

stock market, say (B), and when the same stock market (A) reacts indirectly to a

shock happened in another stock market, say (C), which already has been affected

by the initial shock from stock market (B).

In fact not only these factors that causes the comovements between stock markets,

the major global events, especially political conflicts that happen in some parts of the

world, such as the first and the second Gulf wars, and the September 11 attacks, have

caused a downward trend in different national stock markets at the same time.

To review the literature that uses modern econometric techniques, particularly the

use of cointegration, this present study starts with Kasa (1992) who tries to give evidence

related to the number of common trends in the stock markets of five major industrialized

countries namely US, Canada, Germany, England and Japan. More specifically the study

tries to count how many common stochastic trends exist in these countries. What are the

sources of these trends? Do they reflect the economic integration of international financial

markets, or are they cointegrated for other reasons? The study uses monthly and quarterly

data from January 1974 to August 1990. It uses Johansen’s maximum likelihood approach

(1990) to test for Cointegration. VAR(k) model is the starting point of this approach. It

takes the following form:

tktktt XAXAX εµ ++++= −− ...11 (3.1)

with some rearrangement Johansen rewrite the equation (3.1) as follows:

tktktktt XXXX εµ +Π+∆Γ++∆Γ+=∆ −+−−− 1111 ... (3.2)

where

tX : a vector of nonstationary variables.

44

),...( ttt AAI −−−−=Γ .1,...,1 −= ki

)...1( kt AA −−−−=Π

The long–run information in the tX process is summarized by the matrix Π , and the rank

of the matrix determines the number of cointegration vectors. So if matrix Π has rank r

then we conclude that there are r cointegrating relationships among elements of tX or

equivalently rn − common stochastic trends. The results indicate that the price indices for

the five stock markets are all cointegrated a round a single common stochastic trend. In the

long run, the total return indices are closely linked, which indicates the existence of

cointegration relationship. Kasa finds that Japanese market has the most important trend,

and Canada has the least important trend. The study takes the analysis a little bit further by

comparing the cointegration structure of stock prices with cointegration structure of their

dividend payments. So, the existence of stochastic trends among stock markets indices can

be explained by the common stochastic trends among dividends. Finally, the study

indicates that as these markets are perfectly correlated over long horizons, the gain from

international diversification have probably been overstated in the literature. However, Kasa

implies that, as national stock markets can deviate from the trend they show for several

years, then the benefits of diversification may still be achievable over the short-run. Kasa

indicates another two important results. Firstly, he finds a single common stochastic trend

when using capital international’s dividends data. Secondly, when using GNP data to proxy

for dividends, he finds a single common stochastic trend, which means that macroeconomic

variables play an important role in the long-run comovements of stock prices.

These results have been supported later by Janakiramanan and Lamba (1998) who

have speculated about the factors that cause national stock market to move together over

45

the long period, especially the “common investor groups” factor. In general, the findings of

Kasa (1992) show clear and strong link between financial integration and cointegration

among stock prices.

Regarding the effect of some macroeconomic variables on the stock market

movements, many studies investigate this issue, Campble and Ammer (1993) use vector

autoregression (VAR) to estimate the dynamic responses of the system when including

stock dividends, short-term interest rate and inflation as additional explanatory variables.

The results of their study indicate that the real long-term interest rate has a positive, but

minimal impact on the variance of excess stock returns, while the big impact is due to the

changes in forecasts of future dividends1.

In a similar approach to Kasa (1992), Corhay et al. (1993) use both the static

regression and the VAR-based maximum likelihood framework to estimate the degree of

stock market integration in five major European stock markets: France, Germany, Italy,

Netherlands and the United Kingdom, from 1st March 1975 to 30th September 1991. Using

weekly data during this period, the study finds evidence of cointegration between the stock

price series of these countries, except for Italian stock prices that do not seem to influence

this long run relationship.

In an attempt to analyze the benefit of international equity diversification for

Australian investors, Allen and Macdonald (1995) use a cointegration framework that

covers the period 1970-92. The study uses monthly data for 16 stock markets and uses the

standard Engle-Granger two-step ordinary least squares procedure. The study finds

1 Many researchers have focused on the relationship between integration of stock markets and the real economic integration, see Ammer and Mei (1996) who show that the stock index comovements is a good indicator of real economic integration. Another study by Dickinson (2000) considers stock index movement as a function of increasing real integration.

46

evidence of cointegration over the sample period between Australia and Canada, Australia

and UK, and Australia and Hong Kong. The results imply that Australians investors can

have a potential long-run portfolio diversification gains in other countries where no

evidence of cointegration have been found. The result of using Johansen maximum

likelihood procedure suggests that Australian market is cointegrated with Germany and

Switzerland markets, so the two techniques lead to different conclusions.

Masih and Masih (1997) examine the pattern of dynamic linkages among national

stock prices of four Asian countries, namely Taiwan, South Korea, Singapore and Hong

Kong, and four developed markets, Japan, US, UK and Germany. The authors use different

econometrics techniques including unit root testing, multivariate cointegration, and vector

error correction modeling (VECM). Forecast error variance decomposition (VDC) and

impulse response functions (IRFs). In particular the study employs a multivariate dynamic

framework, which allows for both short-run and long–run relationship to manifest over

time. The concept of cointegration is used to test whether the four Asian markets shared

any degree of long-run integration with developed markets. By using monthly data during

the period from January 1982 to June 1994, the study reaches to the following three

conclusions:

1- A cointegration between the Asian markets and the developed markets. This result-

according to the study- is important and valuable for assisting financial analysis.

2- The Granger–causal chain implied by the dynamic analysis (based on VECMs and

VDCs) suggests that the Hong Kong market predominantly led the other markets.

Hong Kong market was the initial receptor of exogenous shocks to its equilibrium

relationship and the other markets have to bear the burden of short-run adjustment.

47

The study finds short–run linkages ran from Korea to Taiwan, Taiwan to Singapore

and Singapore to Korea.

3- The US market plays an important role in dominating the region (four Asian

countries) by explaining the greatest percentage of variances in these Asian markets,

without being explained by any other market.

Another interesting result is that the Singaporean stock market does not significantly

influence the long-run relationship in both Japan and US vectors.

In a similar approach to Masih and Masih (1997), Phylaktis (1999) tries to see

whether there is an increase in the degree of capital market integration between Taiwan,

South Korea, Singapore and Hong Kong with the US and Japan, and to examine with which

of these two countries (US and Japan) the degree of capital market integration is grater. The

sample is divided into two sub-periods in order to examine the effect of deregulation. The

first period ended in December 1980 which represents the period of regulation, while the

second sub-period starts on January 1981, and ended in October 1993, which represents the

liberalization period. The study examines the capital market integration by looking at the

equalization of real interest rates using the cointegration methodology. The study uses

different indicators for capital market integration other than the one customarily used in the

literature. These indicators relate to the speed of adjustment of interest rate to re-establish

long-run equilibrium following a shock in one of the rates using innovation accounting

analysis. Finally the study explores the short-run dynamic through multivariate Granger-

causality test in order to find whether it is the real interest rate of US or of Japan that

derives the interest rate in the Pacific Basin countries. According to the study, the

multivariate approach is superior to the bivariate approach. The results indicate the

existence of integration and real interest rate parity in the Pacific Basin region and that the

48

degree of integration has increased in post-liberalization period in Singapore, Taiwan and

Korea. Another important result is that there is grater capital market integration with Japan

than with USA in the second sub-period, which shows the domination of Japan’s influence

over the region.

Chouldhry (1997) empirically investigates the long-run relationship between six

Latin American stock indices (Argentina, Brazil, Chile, Colombia, Mexico and Venezuela)

and United State. The study uses the log weekly stock indices from January 1989 to

December 1993. All of these markets are considered as emerging markets. The study is

looking for a possible multivariate long-run stationary relationship between these six Latin

Americans emerging stock markets themselves, and then between these markets and United

States stock price index. Empirical investigations are conducted by means of unit root tests,

cointegration tests, and error-correction models. To test cointegration, the study uses the

Johansen maximum likelihood approach, which is a multivariate cointegration test that can

identify the number of cointegration vectors:

∑=

−− +Π+∆Γ+=∆N

ittitit XXCX

11 η (3.3)

where tX is a vector of nonstationary variables.

Moreover the study uses error-correction model in order to capture the short-run dynamic

adjustment of cointegrated variables (Engle and Granger 1987). The model has the

following form:

ttti uXLACX εθ ++∆+=∆ −1)( (3.4)

where C is a vector of constant terms.

)(LA is a matrix of finite order lag polynomials.

49

The results find common stochastic trends between the different indices with and without

the United States index. This means that there is a long-run stationary relationship between

the six Latin American stock markets indices, and also between the indices of these markets

and the United States index. According to the study, this is due to the globalization of the

emerging markets in the late 1980’s and early 1990’s.

The relationship between stock markets integration and diversification has been

analyzed by many researchers1. The core idea of this relationship is that if stock markets

tend to move together in the long run, then the benefit of diversification portfolios a cross

these markets will be eliminated.

Garrett and Spyrou (1997) examine whether the finding of common trends is

sufficient to justify statements such as the long-run benefit to diversification that is

eradicated. More specifically, the study examines this issue in relation to emerging markets,

and how it will affect an investor in the UK and the US. The study uses monthly data over

the period from January 1976 to December 1994 in Argentina, Brazil, Chile and Mexico;

and over the period from January 1985 to December 1994 in India, Malaysia, the

Philippines, South Korea, Taiwan and Thailand. By implementing Johansen and Juselius

(1990) approach, the results suggest that both groups of countries (Latin American and

Asia-Pacific) are driven by one common stochastic trend for each region. The study still

argues that the composition of the common trend is limited to few markets that actually

react to it. So, this evidence suggests that emerging stock markets – in these two regions-

offer benefit in terms of diversification, even in the long run.

1 See Kasa (1992), and Corhay, Rad and Urbain (1993) and their references.

50

Janakiramanan and Lamba (1998) analyze the dynamic relationship between daily

returns of eight Pacific-Basin countries (Australia, Hong Kong, Japan, New Zealand,

Singapore, Indonesia, Malaysia, and Thailand) and the US over the period 1988-1996. The

study uses vector autoregression (VAR), which takes the following form:

)()()()(1

tektRkACtRp

k+−+= ∑

=

(3.5)

In order to analyze the dynamic relationship in the system, the (VAR) model is transformed

to its moving average form:

)()()(0

ktukCtRk

−=∑∞

=

(3.6)

So, by estimating equation (3.6), the study can trace the dynamic responses to shocks in the

system. The results exhibit a US influence on all other markets except for Indonesia, and

when the study excluded the US market from the VAR system it finds linkages between

these markets, which in turn and according to the study, are traced to the indirect influence

of the US market. Also the study finds a significant mutual influence between the markets

that are geographically and economically close to each other.

Rangvid (2001) uses a recursive common stochastic trends analysis to examine

whether European stock markets have become more integrated during the period 1960-

1999. Particularly, the study tests the hypothesis of increased convergence by calculating

the number of significant cointegration vectors at different times and investigates whether

this number increases as the sample period is extended. To test this hypothesis, the share

price indices for UK, France and Germany are used. The results indicate that the price

indices for the three countries share common stochastic trends over the whole sample, and

the recursive approach points towards an increase in the number of cointegration vectors as

51

the sample period extended. This increase in cointegration vectors implies that these

markets were being increasingly integrated during the last two decades, especially at the

early 1980s.

Nevertheless, this result has been criticized by Pascual (2003), who is concerned

about the validity of this result. He argues that the increasing values of the trace statistics-

that are already interpreted by Rangvid (2001) as an increase in cointegration- may be a

reflection of the higher power of the Johansen test as the sample size increase from 20 to

156 observations. Pascual (2003) presents an alternative approach to test the increasing of

stock market integration. The study estimates the time-path followed by the coefficient of

the error correction term (ECT). He studies the same countries as Rangvid (2001) during

the period 1964 to 1999.

Pascual (2003, p. 198) argues that:

“The error correction term (ECT) reflects deviations from the long-

run cointegration relationship, therefore the coefficient of the ECT

represents the speed adjustment to deviations from the long-run

equilibrium. Higher values of those coefficients can be interpreted as a

higher degree of stock market integration”.

However, the study comes to results different from Rangvid’s (2001). The cointegration

test shows an increasing financial integration in the case of France, but none in the case of

UK and Germany.

In a recent study by Tahai et al (2004), they investigates the financial cointegration

of the G7 stock markets. They employ monthly stock indices of the G7 during the period

from March 1978 to December 1997. To determine the integration indices, an )2(I VAR

model with two lags and trend restricted to lie in the cointegration space is estimated for the

52

seven stock markets. This model was developed by Johansen (1992), and takes the

following formula1:

∑−

=−−− +∆Ψ+Γ∆−Π=∆

2

1

211

2k

ititittt xxxx υ (3.7)

where tx (log of price index) is a ρ -dimensional vector of I(1) time series,

Π : a matrix which is used to determine the cointegration rank ( p) of the VAR,

tυ : an error having a nonsingular matrix,

∑−

=

Γ−=Γ1

1

k

iiI , ∑

−

=

Γ−=Ψ1k

iji and 2,...,1 −= ki .

This model is defined as a subclass of the VAR with parameters that satisfy the following

two reduced ranked conditions

βα ′=Π , where ,α β are rp × matrices of rank pr <

The results show comovements of stock returns of market indices of the G7. But

related to the diversification issue, the study- as many others- indicates that the investors

with long holding periods in perfectly cointegrated markets, gains from international

diversification may have been overstated in the literature.

Narayan et al. (2004) examine the dynamic linkages among the four stock markets

in South Asia, namely India, Pakistan, Sri Lanka and Bangladesh. The study covers the

period from 2 January 1995 to 23 November 2001. The autoregressive distributive lag

(ARDL) approach to cointegration is used to test for the existence of long-run relationships.

This approach involves two stages. At the first stage the existence of the long-run relation

between the four stock price indices is tested by computing the F-statistics. At the second

stage the coefficients of the long run relations are estimated (Pesaran and Pesaran, 1997).

1 For more information about Johansen’s model, see Appendix B, sections 4 and 5.

53

The study reaches three main findings. First, there is a long-run relationship between the

stock prices of the four countries when stock prices in Pakistan are considered as the

dependent variable. Second, in the long-run, stock prices in Bangladesh, India and Sri

Lanka Granger-cause stock price in Pakistan. In the short run there is unidirectional

Granger-causality from stock prices in Pakistan to India, stock prices in Sri Lanka to India

and from stock prices in Pakistan to India. Third, from Granger causality analysis, it is

found that Bangladesh is the most exogenous market. The ARDL approach used by the

study is a recent and advanced approach and it is preferred than other methods like Engle

and Granger (1987) and Johansen (1988). It also becomes one of the recent techniques for

cointegration for many reasons like being more robust for small sample sizes, and being

applied irrespective of whether the regressors are I(0) or I(1).

54

3.3 The Asian Financial Crisis and Stock Market Integration The Asian financial crisis in 1997 and 1998 is considered as the first emerging stock market

crisis with global impact. It started in some South Asian countries in July 1997 when the

Thai Baht was devaluated, followed by the crash of Hong Kong in the late of October 1997,

then the Korean crisis in November 1997, then the panic spread among many countries in

the region (China, India, Japan, Singapore, Hong Kong and Taiwan). Numerous studies

have discussed this crisis from different point of views. There is a consensus on some of the

main causative factors that created this crisis. Some of these factors were that the financial

system in these countries had relied upon an old technology, the capital markets were

poorly developed, the banks extended credit excessively, so the capital was misallocated

and went on without strong credit controls (Sheng and Tu, 2000, p. 346). The global impact

of the Asian financial crisis in 1997 and 1998 has been investigated by many researchers.

Some of those researchers concentrated on the issue of the relationship between this crisis

and stock market integration in different parts of the world. Others concentrated on the

cointegration among some Asian capital markets before, during, and after the crisis. Details

will be shown below.

Sheng and Tu (2000) investigate the linkage among some national stock markets

(12 Asian-Pacific countries) before and during the Asian financial crisis. They use

Johansen (1988) multivariate cointegration, the error correction model (ECM) and Granger

causality. In order to investigate the linkages among the markets, the data are divided into

two groups before the crisis of July 1, 1997 - June 30, 1998. When Thailand was excluded

from the cointegration testing due to its stationary properties, one cointegration vector

appeared during the period of the crisis but not before, and when it was included two

55

cointegration vectors appeared. This indicates that Thailand played an important role in the

crisis. When applying the Granger causality test, the result show that the US market causes

some Asian countries during the crisis, and only three markets feedback the US, namely

Hong Kong, South Korea and China.

Wang et al. (2003) investigate the effect of the financial crisis on the African

markets. The researchers examine both the global and regional integration of African stock

markets. They use a recent and well-developed technique of generalized impulse response

analysis (Persaran and Shin, 1998) to estimate dynamic causal linkage across the largest

five African stock markets (South Africa, Egypt, Morocco, Nigeria and Zimbabwe) and the

US market. The sample period consists of daily stock index closing prices from January

1996 to May 2002. The study starts with the VAR model of order (k):

∑=

− ++=k

ittitit eXYAY

1

ψ ),...,1( Tt = (3.8)

Impulsive response function is used for short-run dynamic linkages among the stock

markets. One weakness of this technique is its sensitivity to the ordering of variables when

the residual covariance matrix is non-diagonal. To overcome this problem, the general

impulse response analysis technique is used, this approach is invariant to the ordering of the

variables in the VAR model and it takes the following form:

∑∞

=−=∆

0iitit eCY ),...,2,1( Tt = (3.9)

where iC is the moving average coefficient, and it can be obtained from iA in the previous

equation. The study makes the following conclusions: Firstly, the interdependence among

the African markets was limited in the short as well as in the long-run, and the regional

integration between these markets was weakened after the 1997-1998 crisis, which is

56

opposite to Asian stock markets that demonstrate more positive regional integration after

the crisis. Secondly, the US has a significant influence on South Africa stock markets, but

not on the other African stock markets. Finally, the degree of integration among emerging

markets is showing a tendency for a change over time, especially around the periods

marked by financial crisis.

In a recent study by Yang et al. (2003), the same econometric techniques that used

by Wang et al. (2003) to estimate the long-run relationship and short-run dynamic casual

linkage across ten Asian emerging stock markets, in addition to the US and Japan. The

study uses daily index closing prices during three periods:

1- Pre-crisis (2 January 1995 – 31 December 1996)

2- During crisis (1 July 1997 – 30 June 1998)

3- Post- crisis (July 1998 – 15 May 2001)

The results show that these markets are more integrated after the crisis than before the

crisis. It also shows that the US has substantially influenced the Asian markets during the

three periods, but was not influenced by these markets. The degree of integration among

countries tends to be changed over time, especially around the periods marked by financial

crisis. However, Table 3.1 presents a summary for the results of the previous studies.

57

Table 3.1 Summary of Selective Empirical Studies on Stock Market Integration

Author Methodology Data & Period of Study Results

Kasa (1992) Johansen’s maximum

likelihood approach

Monthly and quarterly data

for five major industrialized

countries from January 1974

to August 1990.

Found single common

stochastic trend, and the

existence of cointegration

relationship.

Corhay et al.

(1993)

VAR-based maximum

likelihood framework

Weekly stock indices for

five European stock

markets, from 3/75 to 9/91

Cointegration between the

stock prices, except for Italy

Macdonald

(1995)

Engle-Granger two-step

cointegration method,

and Johansen maximum

likelihood frame work

Monthly stock indices for

16 developed stock markets

from 1970 -1992

Cointegration between

Australia and (Canada, UK,

Hong Kong) when using

Engle-Granger method.

When using Johansen

approach, cointegration

between Australia and

(Germany and Switzerland)

Masih and

Masih (1997)

Multivariate

cointegration, vector

error correction model,

forecast error variance

decomposition, and

impulse response

functions

Monthly data for: Taiwan,

South Korea, Singapore

Hong Kong, Japan, US, UK

and Germany, from 1/ 82 to

6/ 94

Cointegration between Asian

markets and developed

markets. Hong Kong led the

other Asian markets.

Choudhry

(1997)

Multivariate

cointegration, error

correction model

Weekly stock indices for

Six Latin American

countries and US, from 1/89

to 12/93

Presence of common

stochastic trends between the

different indices.

58

Garrett and

Spyrou (1997)

Johansen and Juselius

(1990)

Monthly data for Argentina,

Brazil, Chile and Mexico

during: 1976-1994, and

monthly data for India,

Malaysia, Philippines, south

Korea, Taiwan and Thailand

during: 1985-1994.

The composition of common

trend is limited, so these

markets offer benefit in terms

of diversification for

investors in USA and UK.

Chan et al.,

(1997)

Johansen (1988, 1991) Monthly data for 16

developed markets, in

addition to India and

Pakistan during the period

1/ 1961-12/ 1992.

Cointegration between small

number of stock markets and

that affects international

diversification positively.

Janakiramanan

(1998)

Using VAR, the (VAR)

model is transformed to

its moving average

form.

Daily return from eight

Pacific-Basin countries and

the US, during 1988- 1996.

The US influences all other

markets expect for Indonesia,

and significant influence

between the markets that are

geographically and

economically close to each

other.

Phylaktis (1999) Multivariate

cointegration, impulse

response analysis, and

multivariate Granger

causality test

Monthly data for Six Pacific

Basin countries, started on

different dates for each

country and ended on 9/93

Existence of integration

among countries, all countries

have grater capital integration

with Japan than US.

Sheng and Tu

(2000)

Multivariate

cointegration and

multivariate Granger

causality test

Daily stock prices for the

US and 11 Asian – Pacific

equity markets, before and

during the Asian Financial

Existence of integration

among the equity markets

during the crisis, but not

before. Also USA market

59

Crisis. causes some Asian markets

during the crisis.

Rangvid (2001) A recursive common

stochastic trends

analysis

Quarterly observations of

the share prices for France,

UK and Germany, during

the period 1960- 1999

Common stochastic trends

over the whole sample, and

increase in number of

cointegration vectors.

Pascual (2003) Estimate the time path

followed by the

coefficient of the error

correction term (ECT)

Quarterly observations of

the stock indices for the UK,

Germany and France, during

the period 1964- 1999

No increasing financial

integration in UK and

Germany, but increasing in

the case of France

Wang et al.

(2003)

Generalized impulse

response analysis

Daily stock indices prices

for five African countries

and the US during the

period 1 January 1996 to 5

May 2002

Limited interdependence

between African countries.

USA influences just South

Africa. Degree of integration

between emerging markets

tends to change over time.

Yang et al.

(2003)

Generalized impulse

response analysis

Daily stock indices prices

for ten Asian countries, US

and Japan during the period

2 January 95 to 15 May

2001

Long-run cointegration

relationship and short-run

casual linkages among

markets were strengthened

during the crisis. Markets

have been more integrated

after the crisis. The degree of

integration is changing over

time. USA influences all

countries.

Tahai, et al

(2004)

Johansen (92) )2(I Monthly stock indices for

the G7, during the period

Comovements of equity

returns of market indices of

60

VAR model 3/78 to 12/97 the G7. No gains from

diversification to investors

with long holding periods in

perfectly cointegrated

markets

Narayan, et al.

(2004)

ARDL approach Daily data for four South

Asian countries during the

period 2 January 1995 to 23

November 2001

Long-run relationships

between the four markets in

South Asia - India, Pakistan,

Bangladesh and Sri Lanka –

when stock prices in Pakistan

is the dependent variable

3.4 Efficient Market Hypothesis The efficient market hypothesis (EMH) - or sometimes called efficient market theory - was

formulated by Eugene Fama in 1970. It is based on the assumption that - at any given time -

prices of securities reflect fully all available information about securities. According to this,

a stock market is seen as more efficient the faster market relevant information is

incorporated into assets prices. Under fully efficient markets, past information should not

affect returns in present period (Fratzscher, 2002). In other words the “efficient stock

markets do not allow investors to earn more above-average returns without accepting

above-average risks” (Malkiel, 2003, p. 60). However, the main assumption of the efficient

market hypothesis is that prices of securities fully reflect all available information about

securities. It is believed that this theory is an application of rational expectation to the

pricing of securities.

61

Fama (1970, p. 387) suggests sufficient - but not necessary - conditions for market

efficiency. These conditions are. Firstly, no transaction costs in trading securities; secondly

all information is available for free to all market participants; and thirdly, there is universal

agreement on the implications of the current information for current prices and distributions

of future prices of each security. EMH is classified into three categories according to the

notions of what is meant by the term “all available information”:

1- Weak efficient market hypothesis: asserts that all the information contained in the

history of past stock prices are reflected in today’s stock prices. This means that

technical analysis can not be used to obtain systematic gains1.

2- Semi-strong efficient market hypothesis: states that all publicly available relevant

information regarding the firm is fully reflected in the price of the stock, therefore

neither fundamental nor technical analysis can therefore be used by investors to

achieve gains2.

3- Strong efficient market hypothesis: states that all kinds of information, whether

public or private, are fully reflected in the stock prices. Therefore, no information -

even insider information - could give an investor an advantage of achieving gains.

3.4.1 Cointegration and Stock Market Efficiency Based on the previous discussion in the last section, the existence of any profits depends on

having different kinds of relevant information. This could comprise past, publicly or

privately available information. The essence of EMH is that stock prices should follow a

1 Technical analysis is to study past stock price data in an attempt to predict future price (Malkiel, 2003, p.59) 2 Fundamental analysis is the analysis of financial information of the firm to help investors select undervalued stock (Malkiel, 2003, p.59)

62

random walk, where the future price changes should be - for all practical purposes - random

and therefore unpredictable (Mishkin, 1998, p. 173). A consequence of this is that past

movements of the stock price cannot be used to predict future prices movements.

Malkiel (2003, p. 60) states that the logic of the random walk idea is that if the flow

of information is unimpeded and information is immediately reflected in stock prices, then

tomorrow’s prices change will reflect only tomorrow’s news and will be independent of the

price changes today. Thus the random walk hypothesis is associated with weak form of the

efficient market hypothesis, which asserts that all the information contained in the history

of yesterday’s stock prices are reflected in today’s stock prices.

However, the implications of finding cointegration for the efficient market

hypothesis (EMH) are unclear. Different points of views related to this issue have been

introduced over the last two decades. The first comment on the relationship between

cointegration and market efficiency was given by Granger (1986), who stated:

“If tx , ty are a pair of series from jointly efficient speculative markets, they

cannot be cointegrated, if the two prices were cointegrated, one can be

used to help forecast the other and this would contradict the efficient market

assumption” (Granger, 1986, p. 218).

This proposition simplifies that predictable movement in stock prices would provide

evidence of stock market inefficiency because the ability to predict prices would indicate

that all available information was not already included in stock prices. The implication of

this is that stock prices cannot be cointegrated (based on a definition of efficient markets)

as markets in which changes in stock prices is unpredictable (Dwyer and Wallace, 1992).

Moreover, the inefficiency is reflected in the share price residuals measured as the

63

difference between the actual share prices and the share price that would have been in the

absence of new information (Agarwal, 2000).

The relationship between cointegration and market efficiency has been under

investigation by several studies, and controversial results have been provided. Some of

these results emphasize Granger’s theory, others contradict it, whilst some provide with

different explanations for this relationship. This contradiction has made the EMH as one of

the most controversial areas in modern financial economics.

Despite a significant number of studies which have tested and analyzed the efficient

market hypothesis in different stock markets, only few studies have examined the

relationship between cointegration and market efficiency. Baillie and Bollerslev (1989)

conclude that Granger’s result can be interpreted as a violation of weak form efficiency.

Other studies such as McDonald and Taylor (1989), Coleman (1990), Chan and Lai (1993),

Arshanapalli and Doukas (1993), Palac-McMiken (1997) and Chan et al. (1997) indicate

that if stock markets are collectively efficient in the long-run, then stock prices for these

markets are not cointegrated, that means they have no long-run relationship.

In an empirical study of the efficiency and integration of eighteen national stock

markets (developed stock markets in addition to India and Pakistan), Chan et al. 1997,

argue that there is a contradiction between the efficiency of markets and the existence of

cointegration. In another words, if two markets are cointegrated, then they are not efficient

because the weak form of efficient market hypothesis will be violated. Possible arbitrage

profits can be therefore explored. The study examines the weak–form efficient market

hypothesis by using unit root tests of the null hypothesis that stock prices follow a random

walk. Non rejection means the stock market is weak-form efficient. Tests for cointegration

provide some evidence of cointegration between some stock markets. However, many stock

64

markets do not have long-run comovements which positively affect international

diversification.

Palac-McMiken (1997), in a similar study to Chan (1997), uses cointegration

analysis to test for the efficiency of five Asian stock markets. The results indicate that most

of these markets are linked with each other, and are therefore not collectively efficient. In

general, the study argues that if asset (stock) markets are collectively efficient in the long

run, then the prices of these assets are not cointegrated, with no long run relationship.

This point of view has been criticized by several other studies such as Dwyer and

Wallace (1992), Baffes (1994), Engel (1996), Masih and Masih (2001, 2002) and Ahlgren

and Antell (2002). All of these studies indicate that market efficiency does not preclude

cointegration, and because no empirical evidences have confirmed that predictability from

cointegration can lead to arbitrage opportunities, so cointegration does not necessarily

imply market inefficiency. In this context Dwyer and Wallace state that:

“Market efficiency defined as the lack of arbitrage opportunities, there is

no general equivalence between market inefficiency and cointegration or,

for that matter lack of cointegration” (Dwyer and Wallace, 1992, p. 325).

Moreover, Ahlgren and Antell (2002), contradict Granger’s theory, they state that:

“Cointegration does not necessary rule out stock market efficiency, and

that is because market efficiency does not rule out predictable stock returns

but rule out arbitrage opportunities from predictable returns” (Ahlgren and

Antell, 2002, p. 852).

However, it should be recognized that even if some anomalous behavior of stock

prices may exist, it does not create a portfolio trading opportunity that enables investors to

65

earn extraordinary risk adjusted returns (Malkiel, 2003, p 60). Even if EMH might lead to

drastically incorrect interpretations of events, such as major stock market bubbles (Shiller,

2003 p. 101), one should realize that EMH could still have credibility.

According to the relationship between stock markets efficiency, market

liberalization and integration, it is believed that as stock markets become more liberalized

and more integrated, prices should reflect the increased availability of information more

efficiently. Accordingly, the current price of the asset should embody all available

information (Laopodis, 2003).

Another explanation for this relationship has been introduced by Masih and Masih

(2002). The authors contradict Granger’s theory that in the presence of cointegration the

market efficiency hypothesis will be violated, as the predictability of one stock price can be

enhanced significantly by utilizing information on the other stock prices. At the same time,

they question whether predictability necessarily implies market efficiency or not. They state

that:

“Predictability implies nothing necessarily about inefficiency. A market is

inefficient only if by using the predictability one could earn risk-adjusted

excess returns. Therefore, one must be careful in concluding that

cointegration implies anything about market inefficiency or efficiency”

(Masih and Masih, 2002, p. 87).

Following Masih and Masih’s (2002) argument, Narayan et al. (2004) emphasize

that although the gains from international diversification for investors with long holding

periods are limited, investors can still reap arbitrage profits through international

diversification of stocks in the short-run. Even in the case of cointegration, when one can

predict stock prices through utilizing information on the other stock prices, the

66

predictability does not imply any thing about the risk-adjusted excess return. This does not

necessarily tell anything about market efficiency (Narayan, et al., p. 431). So it could be

said that predictability and market efficiency are basically two different themes. Therefore,

one should be careful to imply that cointegration implies efficiency or not.

3.4.2 More Evidences on Stock Markets Efficiency A valuable addition to the literature in this subject is the definition of efficient financial

markets introduced by Malkiel (2003, p. 60). He states that efficient financial markets are

markets which do not allow investors to earn above-average returns and arbitrage

opportunities without accepting above-average risk. Malkiel (2003) argues that despite

some anomalous behavior of stock prices does create portfolio trading opportunities which

enable investors to earn extraordinary risk adjustment return. This means that stock markets

are still efficient even if some interruptions or impediments happen.

Despite some studies have found short-run serial correlations and that the existence

of too many successive moves in the same direction, which means a rejection of the random

walk hypothesis for these markets, Malkiel (2003) opposes these views and defends the

random hypothesis of stock markets. He states that:

“While the stock markets may not be a mathematically perfect random walk,

it is important to distinguish statistical significant from economic

significant. The statistical dependencies giving rise to momentum are

extremely small and are not likely to permit investors to realize excess

returns” (Malkiel, 2003, p. 62)

Moreover, Malkiel (2003) criticizes some studies that argue that if the reaction for

new information, or important news, announcement is only grasped over a period of time,

67

stock prices will exhibit the positive serial correlation found by investors. He shows that the

evidence of efficiency occurring systematically in the stock market is often rather thin (see

Fama (1988) and Fama and French (1998)).

3.4.3 A new Approach for the Relationship between Cointegration and

Efficiency It is well-known that all financial instruments in the integrated stock markets tend to get the

same rate of return if these returns are adjusted for the risk and maturity. This rules out any

chance of arbitrage possibilities between stock markets, and indicates that efficient markets

are perfectly competitive and therefore integrated.

What this study tries to bring out and add to this discussion is that stock markets are

efficient in the long-run because the existence of cointegrated vector implies the law of one

price (LOOP). Therefore little or no arbitrage opportunities or possible benefit can be

achieved from the diversification portfolio from one market to another. Even if some

predictability exists, it does not imply any thing about arbitrage opportunities, however, it

may reflect time varying risk premiums and required rate of return for stock investors rather

than inefficiency (Malkiel, 2003).

So, market efficiency defined as the lack of arbitrage opportunities. Based on this

argument, the existence of cointegration between stock markets implies that there are no

arbitrage opportunities in the long-run. This means that these markets are efficient in the

long-run. Despite “bubbles” sometimes exist in the stock markets; these events happen for a

short period of time and do not last long, so it is less rational to conclude the inefficiency

from this event. However, with the short-run error correction model (ECM), arbitrage

68

opportunities and possible benefits could exist from diversification, as the LOOP may not

hold in the short run.

69

3.5 The Integration of the Emerging Stock Markets in the MENA Region

Numerous amount empirical studies have focused on the integration of developed stock

markets in different parts of the world, especially the US and Europe, and the linkage

between these two main regions and other emerging stock markets in Latin America, South

Asia and Pacific-Basin. However, stock market integration in the MENA region has not

been discussed deeply despite the exceptional economic importance of this region.

The integration among the MENA stock markets is essential because these markets

as a group could offer more investment opportunities that are not possible by each one

individually. Very few empirical studies have examined the integration among the

emerging stock markets in the MENA region. These studies have been conducted over

shorter periods of time and small number of markets (Neaime, 2002). Butler and Malaikah

(1992) examine the behavior of individual stock returns in two stock markets, Saudi Arabia

and Kuwait, over the period 1985-1989. They use serial correlation and run several tests to

evaluate the weak form of efficiency in these two stock markets. The study tries to

investigate the similarities and dissimilarities of these stocks, regarding exchange

mechanisms and efficiency. They conclude that institutional factors contribute to

operational inefficiency in Saudi Arabia stock market. This inefficiency includes trading

delay, illiquidity, market fragmentation and the absence of official markets makers.

Although the study concentrates on these important aspects, it does not investigate the

integration or liberalization issues in these markets.

70

One of the earliest studies that examine stock markets integration among the MENA

countries is by Darrat, et al. (2000). The study estimates the linkages among three emerging

markets in the MENA region; namely, the Cairo (Egypt), Casablanca (Morocco ) , and

Amman (Jordan), and then between them and the US stock market as a world market.

Cointegration techniques and error correction model are employed by the study. The study

uses a time-series cross sectional estimation for the three emerging markets and the US

during the period from October 1996 to August 1999. The study comes to the following

results:

1- It is possible for one market to drift away from other markets in the short-run, but

the Johansen-Juselius cointegration test shows the existence of long-run relationship

between the three MENA markets. In other words, the indices of these markets are

moving together in the long run. These long run comovements - according to the

study - are due to socio-economic or political factors that force all markets to move

together.

2- No significant cointegration has been found between these countries and the US.

This indicates that these markets are segmented from the international market the

(US market). Therefore, it is a good opportunity for investors to target these markets

to get the benefit from international diversification of financial risks.

3- Based on Gonzalo-Granger (1995) test, the results show that the Egyptian market as

the main driving force in the region, so it plays an important role in the financial

stability.

4- The Granger-causality test reveals that short–run causality primary runs from

Jordanian and Egyptian markets to the Moroccan’s market, without feedback.

71

However, the small size of the sample and the short period of time that is covered

are the main weak points in the study. Usually, the Johansen-Juselius approach is valid for

large sample size not for small sample like this sample. Other techniques such as the

autoregressive distributed lag (ARDL) is more robust and performs well for such a sample.

In his study about liberalization and financial integration in a group of stock

markets in the MENA region, namely Bahrain, Egypt, Jordan, Kuwait, Morocco, Saudi

Arabia and Turkey, Neaime (2002) uses Johansen (1991, 1995) efficient maximum

likelihood test to examine the existence of the long-term relationship among the MENA

markets themselves and between the MENA markets and the world markets represented by

the US, UK and French markets. By using weekly data, the study covers the period up to

December 2000, and starts differently according to the availability of data for each country

as follow: Amman, Istanbul since 1990, Morocco since 1992, Saudi Arabia, Egypt since

1993, Bahrain since 1995, Kuwait since 1994 (due to the Gulf war). The MENA markets

are divided into two groups. The first consists of three GCC1 countries: Bahrain, Kuwait,

and Saudi Arabia. The second: the rest of the countries. In order to identify the number of

cointegration vectors, the study uses the following multivariate cointegration test:

∑=

−− +∆++=∆k

itititt PPP

1121 εδββ (3.10)

where tP is a vector of nonstationary variables.

In order to investigate the direction of the relationship between the world main

stock markets and the MENA stock markets, the study employs the Engle and Granger

causality test as follows:

1 GCC related to Gulf Cooperation Council countries, they are oil-producing countries.

72

∑=

−−− +∆+∆++=∆3

1

*12110

mtmtmttt PPECP εαααα (3.11)

where tP represent the MENA index, 1α is the coefficient of the error correction term,

which reflects the adjustment of the dependent variable in the short run to its long-run

position, *tP is the series of the world financial markets. To examine the possibility of a bi-

directional relationship between the MENA index ( tP ) and the series of the world financial

markets ( *tP ), equation 3.11 is re-estimated after switching the place of tP∆ and *

tP∆ as

following:

∑=

−−− +∆+∆++=∆3

1,1

*12110

*

mtmtmttt PPECP εαααα (3.12)

The findings of the study can be summarized as follows:

1- The stock markets of Egypt, Jordan, Morocco and Turkey show a significant level

of cointegration with the world financial markets, which means that these markets

do not offer good opportunities for international investment that are seeking

diversification.

2- The financial integration between the previous markets is still weak.

3- GCC stock markets are integrated with each other, and the results indicate one co-

integrating vector at the 5 per cent significant level.

4- GCC stock markets are segmented from the rest of the world, which means they can

offer diversification potentials to foreign investors.

5- The results of using the Engle and Granger-causality test indicate that there is a

unidirectional effect from the world financial markets to the MENA markets.

73

6- The study indicates that the increase in liberalization between the MENA stock

markets will increase the efficiency of these markets and provide the investors with

a good opportunity to diversify their portfolios and reduce risks. It also mentions

other benefits of liberalizing the MENA stock markets that are reducing the

borrowing costs of local firms and enhancing economic growth.

Based on the previous analysis, Neaime (2002) study is considered more

comprehensive than earlier studies due to the number of countries involved and the period

of time covered. The main criticism of this study is that there is the asymmetry between the

title and the contents. The title includes the concept of liberalization, thereby giving the

indication that there should be a detailed discussion and analysis of this concept. However,

the study just mentions general results without relying on any deeply theoretical or

empirical basis. Moreover, the study indicates that the increase in liberalization will have

positive effect on the efficiency of the markets. However, this study does not mention how

this result has been reached.

Maghyereh (2003) examines the integration among four MENA emerging markets,

namely: Egypt, Jordan, Morocco and Turkey. According to the study, the investigation of

stock markets integration in the same geographical region helps providing evidence

concerning the degree of intra-regional trade and macroeconomic coordination. The study

employed a daily data for the national stock indices of the four markets during the period

from 28 November 1997 to 12 December 2002. In order to capture the dynamic interaction

among these four countries, the study uses a vector autoregression model (VAR), which

takes the following general formula:

∑=

− +Π+=m

stststt vzDz

1

(3.13)

74

The main finding of the study shows weak linkages among the four markets, but

still no market is found to be completely isolated. The Turkish stock market affects all other

markets, because it is relatively bigger than others. The Jordanian stock market is the most

open one, 3.5 per cent of its innovations are explained by other markets, and that indicates a

high degree of openness in comparison to other stock markets. The study suggests that this

weak integration among MEEMs stock markets gives the international and regional

investors many opportunities for portfolio diversification by investing in the MEEMs.

However, the study presents the following suggestions to make these markets more

attractive to the domestic and foreign capital (Maghyereh, 2003, p. 20):

1- Introducing a single region-wide electronic trading system.

2- Developing a uniform investment law and market oriented legal and regulatory

system.

3- Introducing transparent and accessible accounting and taxation treatment.

4- Providing appropriate mechanisms to settle disputes.

By comparing the results of this study with that of Neaime (2002), we notice

similarity regarding the weak linkages between stock markets of Egypt, Jordan, Morocco

and Turkey. However Neaime’s study seems more comprehensive than Maghyereh (2003),

as it includes three other Gulf countries. It also studies the integration not only among the

regional stock markets, but also between the stock markets in the MENA and the world

stock markets. However, the main criticism of Maghyereh (2003) is that it does not

examine the integration between the MENA stock markets and developed markets, as it

concentrates just on the geographical integration. Also, the study covers a short period of

time, which means the conventional cointegration approaches are not appropriate to be

75

used. Other technique such as ARDL is more robust and performs well for short periods

than conventional approaches.

The relationship between capital market liberalization and integration is

investigated by Darrat and Benkato (2003). Specifically, the authors examine the impact of

capital market liberalization on the integration of Istanbul Stock Exchange (ISE) in the

global market represented by the US, UK, Japan and Germany markets. However a more

important focus of the study have been whether and to what extent the removal of capital

controls makes emerging stock markets more integrated in the global market. This

important topic has not been investigated deeply enough. By using data set that covers a

period of more than 14 years, from January 1986 to March 2000, the study employs the

Johansen-Juselius (1990) efficient approach to test cointegration among the five stock

markets, and GARCH approach to measure volatility in stock returns of the five stock

markets. The sample is divided into two sub periods; before and after the removal of capital

controls in July 1989. This timeframe mark the liberalization date. The study incorporates

(0,1) dummy variables in the JJ testing model. It takes the value of one for every month

following the removal of capital controls, and zero otherwise. The results indicate the

following:

1- There is a robust cointegration relationship binding the ISE with the four matured

markets over the full estimated period.

2- There is no cointegration relation linking the ISE with the four matured markets

over the pre-market liberalization period.

3- There is a strong cointegration relation linking the ISE with the four matured

markets over the post-market liberalization period.

76

According to these results, it is clear that stock market liberalization in 1989 has a

substantial and positive impact on the integration between the ISE and matured markets.

Another important result, from the GARCH processes, suggests that ISE has become

considerably less volatile in the post-liberalization period.

It becomes much clearer that stock markets in the MENA region have not been

studied deeply enough, either with regard to the integration among these markets or their

interplay with other markets. More precisely, most of the studies that dealt with integration

among stock markets in the MENA region or between MENA markets and developed

markets have suffer either from shorter periods of time or smaller number of markets.

In contrast to previous studies, this current study will extend the period of the study

and the number of markets either the markets from MENA region or the developed

markets. Moreover, this study will use more recent approaches to test for the integration.

These approaches include testing for the unit root in the presence of structural changes and

the Autoregressive Distributed Lag (ARDL) model to test for the integration. Both of these

models will be discussed in the following chapters.

77

3.6 Conclusion This chapter presents and analyzes the extensive literature relating to stock market

integration in both developed and emerging markets. It covers most various schools of

thoughts that have been developed for measuring stock market integration, focusing on

theoretical background and empirical studies. The use of cointegration approach for

measuring the integration is also discussed through the chapter. Different cointegration

approaches have been employed by several studies for measuring stock market integration.

Numerous studies use the conventional approaches such as Engle-Granger and Johansen

and Juselius, others use the VAR model, and more of recent studies start using the ARDL

approach for the many advantages it has over other conventional approaches.

The relationship between the existence of cointegration between stock markets and

the efficiency of these markets is also reviewed in this chapter. Several studies provide

controversial results about this relationship. A new point of view regarding this issue is

adopted by this current study.

Finally, this chapter sheds more light on the integration of the emerging stock

markets in the MENA region, which has net been discussed deeply enough despite the

exceptional international importance of this region from economic and political

perspectives. Only three studies, discuss the integration among stock markets in the MENA

region. This shortage of studies gives incentive to discuss the stock markets integration in

the MENA region more deeply using recent techniques, including more stock markets and

covering long period of time, and this is what this current study tries to do.

78

Chapter Four

Features and Characteristics of the Emerging Stock

Markets in the MENA Region.

4.1 Introduction There is a growing recognition among all countries in the world regarding the need to

strengthen their economic performance in a suitable manner. This will enable them to

maintain their competitiveness in an extremely harsh international economic

environment. A highly developed financial system promotes efficiency and increases

economic growth. At the same time, macroeconomic stability is crucial for a well

developed financial system (Creane, et. al., 2004, p. 2).

Stock markets are an important part of the overall financial markets, and that is

for the following reasons (Rousseau and Wachtel, 2000):

1. Stock markets provide investors and entrepreneurs with a potential exit

mechanism. Countries with vibrant stock markets are more attractive to

venture capital than countries without stock markets.

2. The existence of stock markets facilitates capital inflow for both foreign

direct investment and portfolio investment. As international portfolio

investments have grown rapidly in recent years, the portfolio managers

have started looking for ways of diversifying their investments.

3. Stock markets provide the much needed liquidity for domestic and

international investors.

79

The emerging stock markets in the MENA region have achieved positive

developments during the last decade despite that they are established recently. They are

experiencing exceptional growth and favourable returns. Market capitalization in these

markets has been boosted by the privatization of public enterprises in most MENA

countries during the 1990’s especially in Egypt, Turkey, Jordan and Morocco. Despite

this development, and in regards to the market indicators, these markets are still far

behind developed stock markets and emerging stock markets in South East Asia and

Latin America.

The objective of this chapter is to outline the specific characteristics of four

main stock markets in the MENA region, namely, Egypt, Turkey, Jordan and Morocco.

These markets will be examined spaning from December 1994 to June 2004. The

discussion will also provide details of the structure and the main indicators of these

markets, such as the market capitalization, trading value, turnover ratio and number of

listed companies1. Moreover, the chapter presents an analytical review for the portfolio

equity flow to the MENA region over the period of the study.

This chapter is organized as follows: Section 2 presents general features of the

MENA region. Section 3 presents an overview of the emerging stock markets in the

MENA region, and is divided into four parts, each part providing an analysis of the

performance of each stock market during the period of study. The stock markets are:

Egypt Stock Exchange, Istanbul Stock Exchange, Amman Stock Exchange and

Casablanca Stock Exchange. The last section concludes the chapter.

1 The turnover ratio is defined as the value traded of shares in a year as a percentage of market capitalization of listed shares on an exchange (http://www.stockriders.com/en/help/glossary.asp).

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4.2 General Economic Features of the MENA Region

The MENA region covers many countries extending from the Arabian Sea in the east to

the Atlantic Ocean in the west1. Most of these countries share a homogeneous culture,

religion, history and language. The MENA region is exceptionally rich in natural

resources, qualified labour force and has a large GDP and population.

During the last two decades, most countries in the MENA region, in particular

the countries that are considered in this study, have adopted several sound

macroeconomic policies. The reason for these kinds of policies is to achieve a higher

economic growth and to overcome some macroeconomic imbalances in their

economies. These policies include trade liberalization, financial liberalization, openness

to foreign direct investment, implementation of sound economic management and

privatization programs.

In addition to the economic importance of the region, it is playing a very

important role on the international political stage. The political developments in the

region have a marked effect on the international politics. This current study focuses on

the stock markets from four countries in this region namely Egypt, Turkey, Jordan and

Morocco. These markets are based in low and middle-income countries, as defined by

the World Bank. These countries have relatively active stock markets compared to other

markets in the region.

In the following section, a general review of the economic performance of these

countries is presented. Table 4.1 reports the main economic indicators for each country.

Turkey has the biggest economy among all MENA countries used by this study. Its

Gross Domestic Product (GDP) reached $ 257.59 billion in 2003, while the GDP in

1 Region is defined in terms of geography, it covers a continuous geographical land mass or share a common littoral (See El-Erian and Fischer, 1996, p. 2)

81

Egypt reached $ 82.43 billion. Both countries have almost the same population and

share many common features. Jordan is the smallest country amongst the group with a

GDP of $ 9.86 billion in the year 2003 and a population of around 5.47 million. The

GDP in Morocco was $ 43.73 billion in 2003.

The Egyptian economy witnessed macroeconomic imbalances during the

nineties due to fiscal and current account imbalances. Structural adjustment reforms and

an International Monetary Fund (IMF) stabilization program were launched to correct

these imbalances in all economic sectors and to improve the performance of the

economy. These programs include different policies such as privatization, liberalization

and the relaxation of restrictions on foreign investments. These programs have achieved

some success, such as reducing the budget deficit and inflation rates. There have also

been positive effects such as attracting an increased amount of foreign investment.

Despite this positive performance during the nineties, the Egyptian economy

was under enormous pressure during 2000 and 2003. International events including a

global downturn, the September 11 attacks and a sharp drop in oil prices, besides

national and regional events such as a sharp decrease in tourism revenue after Luxor

events1, and the continuing violence and political crisis in the Middle East have all

badly affected the economy by harming exports, foreign direct investments and tourism.

All of these factors together with the excessive spending on national infrastructure have

played a vital role in weakening the performance of the Egyptian economy. By January

2001, the Egyptian Pound devalued by 9.6%. After that, the central bank adopted more

flexible exchange rate policy; it again devaluated the Pound by 6.4% in August 2001

1 Luxor is an ancient city in southern Egypt. Thousands of international tourists visited Luxor each year. On the 17 November 1997 a massacre took place in Luxor when a group of radical Muslims massacred 62 tourists at the attraction. The tourist industry in Egypt was seriously affected by the resultants lump in visitors and remained depressed for at least the following five years. (http://www.wikipedia.org).

82

(The Hong Kong and Shanghai Banking Corporation (HSBC), 2003). The inflation rate,

measured by the CPI, increased from 2.3% in 2001 to 4.5% in 2003. The nominal GDP

decreased from $ 98.48 billions in 2001 to $ 89.85 billion in 2002 and kept decreasing

until reached $ 82.43 billion in 2003.

Regarding the Turkish economy, the most important events that affected its

performance were the Asian and Russian crises during 1997 and 19981, respectively.

Following the Russian crisis, the Turkish economy witnessed a high level of capital

outflow amounting to $ 6.8 billion as well as a reduction in domestic consumption and

external demand. As a result, export declined 4.2% in 1999 (The Central Bank of the

Republic of Turkey, 1999). Moreover, the deficit rose by 3.9 % in 1999, with most of

this deficit being financed by domestic borrowing. The financial and political crises in

November 2000 and February 2001 both had a huge negative impact on the Turkish

economy as real GDP decreased by 7.5%. Following these series of crises, a new

economic program was launched to reduce uncertainty in the financial markets,

complete the structural reform to promote economic efficiency, reduce the

hyperinflation rate, and re-structure the banking sector. However, the attack on the US

on the September 11 had also a negative impact on the Turkish economy. As a result of

these events, the economic program was revised to cover the period 2002-2004. This

program had a positive effect on the economy. During the period of implementing the

program, the Turkish Lira appreciated around 8% in nominal terms. Moreover, receding

uncertainties in the financial markets improved market expectations and the level of

expected inflation. By the end of 2002 the GDP growth rate reached 7.8% (Central

1 For more information on the Asian crisis, see section 3.3. The Russian crisis is a financial crisis started in August 1998. After recording its first year of positive economic growth since the fall of the Soviet Union, Russia experienced a comprehensive macroeconomic collapse, involving its exchange rate, the banking system and public debt. Russia was forced to default on its sovereign debt, devaluate the rouble and declared a suspension of payments by commercial banks to foreign creditors. (http://www.wikipedia.org).

83

Bank of Republic of Turkey, 2002). The year 2003 witnessed an improvement in the

Turkish economy. The nominal GDP rose to $ 240.38 billions in 2003 compared with $

183.89 billions in 2002. The main reasons behind this improvement were the ending of

the Iraqi operation in April 20031, the good performance in fiscal accounts and the

adjustment of the short-term interest rate. During the period 2002-2003, the inflation

rate was also harnessed from 45% to 25.3%. The main factors for this reduction were

the appreciation of the Turkish Lira against foreign currencies, the decline in real

wages, and the improvement in the cost conditions as a result of productivity

improvements (Central Bank of Republic of Turkey, 2003, p. 14).

Jordan has a small open economy. It is highly vulnerable to external shocks and

disturbances in the region. It was affected by the first Iraqi war with more than 300,000

returning Jordanian from the Gulf countries exacerbating unemployment. The high

unemployment ratio strained the government’s ability to provide essential infrastructure

such as education, health and other relevant services (Kanaan and Kardoosh, 2002, p.

10). During the period 1996-1999 economic growth in real terms was lower than

population growth; this had a negative impact on the standard of living. In 2000, the

economic growth in real term reached 4.7% and during this year Jordan also joined the

World Trade Organization (WTO). The September 11, 2001 attacks on the U S had

negative impacts on the Jordanian economy. The fourth quarter of 2001 witnessed a

decline in economic growth; it reached 3.1% compared with 4.65% in the first three

quarters compared to previous quarters. In the year 2002, the macroeconomic indicators

showed improvements in the performance of economic activities. GDP in fixed prices

grew by 4.8% in 2002 compared with 4.2% in 2001. At the same time however, the

1 The invasion of Iraq is known as “Iraqi operation”. This military operation against Iraq began on March 20 by the United States and Britain supplied 98% of the invading forces. The Iraqi Military was defeated and Baghdad fell on April 9, 2003. On May 1, 2003, U.S. President George Bush declared the end of this operation, terminating the Iraqi regime. (http://www.wikipedia.org).

84

external debt increased from (Jordanian Dinar) JD 4743 million in 2001 to JD 5123

million. In the year 2003, real GDP grew by 3.3% compared to 4.8% in 2002. The

mains reasons behind this lacklustre performance are the unfavourable economic and

political developments in the region. The main developments that affected the Jordanian

economy were the continuing increase in oil prices in international markets and the

continuing violence in the West Bank, Gaza and Iraq which had clear negative effects

on the Jordanian exports to both markets.

Morocco’s economic performance during the 1990’s was extremely poor; the

economic growth was less than 2 per annum. The main reason for this low level of

economic growth pertained to the deterioration in the agricultural sector. Morocco’s

economy depends heavily on the agricultural sector, which is normally affected by

severe droughts. This makes Morocco’s economy one of the most weather dependent in

the MENA region. The agricultural sector contributes around 20% of Morocco’s GDP.

Statistics show that almost 50% of Morocco's population depends directly on agriculture

production. Over the long term, Morocco will have to diversify its economy away from

agriculture to develop a more stable economic basis for growth

(http://www.traveldocs.com/ma/economy.htm). A trade agreement with the European

Union was signed in 1996. This agreement pushed for more improvements in the

private sector. The growth rate reached 6.5% in 1998. However, due to drought

conditions and insufficient rainfall, the growth rate fell to around -0.1% in 1999. This

deterioration in economic growth affected other sectors in the economy. In 2001,

favourable rainfall led to economic growth of 6%. Despite this high economic growth,

Morocco needs to improve other sectors in the economy, not just to depend on unstable

agricultural sector. Privatization programs, that took place in Morocco in the year 1993,

increased the capital inflow and thus enhanced the economy with regard to foreign

85

exchange. Morocco reports large foreign exchange inflows from the sale of a mobile

telephone license and partial privatization of the state-owned telecommunications

company. In 2003, real GDP growth reached to 6%, the nominal GDP reaches $ 43.73

billion. The primary economic challenge for Morocco is to accelerate growth in order to

reduce high levels of unemployment and underemployment. While overall

unemployment stands at 11.6%, this figure masks significantly higher urban

unemployment (currently at about 18%) (http://www.state.gov/r/pa/ei/bgn/5431.htm).

86

Table 4.1 Economic Overview of the MENA Countries

Sources: 1. Standards & Poor’s, 2002, Emerging Stock Market Factbook, New York, US 2. Standards & Poor’s, 2002, Global Stock Market Factbook, New York, US. 3. International Monetary Fund, 2004, International Financial Statistics, Yearbook, Washington DC.

87

4.3 An Overview of the Emerging Stock Markets in the MENA Region

The stock markets in the MENA region are considered as emerging markets. The phrase

“emerging market” is defined as a stock market that has started a transition process,

growing in size and the level of sophistication. According to the International Financial

Corporation of the World Bank and to Standard & Poor’s Emerging Markets Database,

a stock market is classified “emerging” if it is located in a low or middle income

“developing country”, and its investable market capitalization is low relative to its gross

domestic product (GDP) per capita. Despite investors’ perceptions of emerging markets

as being dominated by economic, social and political uncertainty, it is essential to

recognize that being classified as emerging market does not imply that these markets are

insignificant; on the contrary, some emerging stock markets are very active on the world

economic stage. A clear example is the emerging stock markets in South East Asian

countries (See Piesse and Hearn, 2002, p. 1711). In this context, Levine (2003)

emphasizes that countries with better financial systems are likely to grow faster than

those with less developed financial and banking sectors.

The emerging stock markets in developing countries and in the MENA region in

particular have improved rapidly in the last decade. Several factors have played vital

role in their improvements. These factors include the achievement of higher economic

growth, monetary stability, stock market reforms, privatisation, financial liberalization

and an institutional framework for investors (Claessens, et al., 2004). However, despite

these improvements in MENA emerging stock markets, some factors still hinder fast

progress and weaken portfolio equity flow to the region, which to some extent

undermines economic growth.

88

4.3.1 Stock Market Liberalization of the Emerging Stock markets in MENA Region

Stock market liberalization is a policy taken by a country’s government that allows

foreign investors to buy domestic shares from local stock market, and domestic

investors to invest in foreign stock markets. It has become a common financial

phenomenon during the last two decades. In fact, stock markets liberalization is

considered to be a main tool in financial liberalization that aims at integrating local

economy into the world economy (see: Bekaert et al, 2003 and Laopodis, 2003).

MENA countries started to liberalize their stock markets during the late eighties

and mid nineties. Table 4.2 reports the date of liberalization and a description of

liberalization for each country. The first country to liberalize its stock market was

Morocco in 1988; it allowed foreign investors to have complete access to the

Casablanca Stock Exchange. In 1989, Turkey also liberalized its stock market by

removing all restrictions for foreign investment. Egypt and Jordan liberalized their stock

markets in 1992 and 1995, respectively.

According to the World Bank (2004, p. 90), portfolio equity investment takes

place when investors purchase shares of a company through an International Public

Offering (IPO), or buy American or Global Depositary Receipts (ADRs or GDRs).

Moreover, venture capital investments and convertible bonds that give investors an

option to convert to equity at a later date are used as vehicles for portfolio equity flows.

89

Table 4.2 Openness of Stock Markets in the MENA Region

* ADR is the American Depositary Receipt. A negotiable certificate issued by a US bank representing a specific number of shares of a foreign stock traded on a US stock exchange. ADRs make it easier for Americans to invest in foreign companies, due to the widespread availability of dollar-denominated price information, lower transition costs, and timely dividend distribution (www.investorwords.com). Sources: Bekaert, et al. (2003) and www.worldbank.org/data.

The portfolio equity inflows to the MENA region have been low during the last

decade. Several reasons play major role for this low level of inflows compared to other

regions. One main reason is the small size of these markets compared with other

emerging markets (such as, China, India, Indonesia and many other markets) as well as

the small size of the market capitalization as a share of GDP. Countries in the MENA

region are prone to macroeconomic shocks and this causes investors in portfolio equity

some concern. Some studies point out that international financial crises have weakened

the global linkage of the emerging stock markets and made them more vulnerable to

external shocks (See Darrat and Benkato, 2003, p. 1090 and Phylaktis 1999).

Also, when talking about emerging stock markets in general and MENA markets

in particular, most portfolio investors are minority shareholders and may fear that they

will find their interests subordinated to these local owners (The World Bank, 2004). On

the other hand, more recent literature asserts that stock market developments depend on

a good legal system, which protects the rights of minority shareholders (Demirguc-Kunt

90

and Levine, 2001). Furthermore, and as has been mentioned previously, political

development in the MENA region has a large impact on the portfolio equity flow1.

Regarding the portfolio equity flow to stock markets in MENA region, table 4.3

shows that Turkey and Morocco received most of the portfolio equity flows during the

period 1994-1996. Egypt received significant flows of the equity portfolio during 1997

and that is as a result of privatization programs. After 1999, portfolio equity continues

to flow to all countries as a result of liberalization and privatization policies taken by

these countries. The reason why the equity inflow increases after the privatization is that

in the initial phases of privatization some shares of the public enterprises are bought by

non-resident investors, and as a result of this, the portfolio equity inflow increases (The

World Bank, 2004). However, in the year 2002 the MENA stock markets witness an

increase in the equity outflow from these markets to other markets. Generally, the

MENA emerging stock markets are still ranked far behind other emerging markets, such

as the East Asia stock markets, in related to portfolio equity flow.

Table 4.3 Portfolio Equity Net Flows to Stock Markets in the MENA Region ($US Million)

Source: The World Bank, Global Development Finance, CD ROM, 2004.

The financial sector in the MENA region is dominated by the commercial banks,

and the nonbank financial sector, which comprises stock market, corporate bond market,

1 The issue of portfolio equity flow will be discussed in details in chapter 6.

91

insurance companies, pension funds, and mutual funds, needs further development

(Creane, et. al., 2004, p. 5). The stock markets in the MENA region are considered

relatively small despite the region containing some of the developing world’s largest

institutional investors in the international bond market (El-Erian and Kumar, 1994, p.

15).

In the following sections, an inclusive review of the four stock markets in the

MENA region will be presented. To ensure comparability and consistency between all

stock markets, the study relies on Emerging Stock Markets Factbook, published by

Standard & Poor’s, for the main stock market indicators, and on Morgan Stanley’s

Capital International (www.msci.com) for the price indices.

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4.3.2 The Stock Market in Egypt Formal stock market activity in Egypt began in 1888 when the Alexandria Stock

Exchange was established while Cairo Stock Exchange was established in 1903. Cairo

Stock Exchange became one of the most active markets in the world during the 1940’s

until a comprehensive nationalization program started in 1959. Since that date and up

till 1991 trading in the two markets was effectively dormant. In 1992, the government

introduced new legislation to enhance the capital market and to open the markets to

foreign portfolio investment; this legislation is known as the market law No. 95/19921.

This development has a positive impact on the market capitalization (Azab, 2002, p.

27). In December 1993, the two markets were linked electronically to form the Cairo

and Alexandria Stock Exchange (CASE) and screen trading began (HSBC, 2003, p. 6).

The CASE is quasi-government supervised by a securities regulator.

The Egyptian Stock Market has witnessed several improvements in its main

indicators over the last decade. It serves as a good example of a newly emerging stock

market with significant growth potential. Table 4.4 shows that the market capitalization

increased from $4,263 million in 1994 $20,830 million in 1997 and reached $32,838

million in 1999, it then reduced slightly $28,741 million in 2000 and $24,335 million in

2001. However, it increased from $26,338 in 2002 $27,847 in 2003. Figure 4.1 shows

significant increases in market capitalization during the period 1994-1999. The reasons

for this big jump in market capitalization are the liberalization and the privatization

1 The Capital Market Law No. 95/1992 includes the following main points (www.egyptianstocks.com):

1. Establishing an independent Capital Market Authority (CMA) charged with supervising and regulating the securities industries.

2. Establishing a legal framework to govern specialized capital markets companies. 3. Authorizing corporate bonds and eliminating the 7% ceiling. 4. Taxes on income from most stocks and bonds were eliminated. 5. Investors’ rights were strengthened by provisions prohibiting unfair market practices and foreign

investors were given full access to market. 6. Financial disclosure was strengthened. Publicly traded companies were required to follow

international accounting and auditing standard and companies were required to issue semiannual financial statements.

93

programs that were launched during the early nineties. The liberalization of the stock

market in 1992 followed by the first ADR introduction in 1996 opened the door for

foreign portfolio equity to flow into the stock market. In 2004, the market capitalization

has grown by 42.3% compared with 2003. In regards to the market capitalization as a

percentage of GDP, it is found to be very low in a comparison with developed markets

especially in 1994 when it reached 8%. Just in the last two years (2003 and 2004), this

percentage has increased significantly to 33% and 51%, respectively.

Regarding the number of listed companies, it declined from 700 in 1994 to 649

in 1996, and then started increasing from 861 in 1998 to 1,110 by the end of 2001.

Despite the number of listed companies increased to 1150 in 2002, it dropped off to 976

and 792 during 2003 and 2004, respectively.

The trading value increased from $757 million in 1994 to $5,859 million in

1997, despite a reduction by 14% in 1998; it increased to $9,038 million and $11,120

million in 1999 and 2000, respectively. However, the trading value dropped off to a

very low value in 2001, it reached $3,897 million, and kept decreasing in the following

year when it reached $3,278 million. In 2004, the stock market achieved some progress

which is reflected by the increase in the trading value to $5,608. Figure 4.2 shows the

trading value during the period 1994-2004.

The turnover ratio decreased from 18.7% in 1994 to 10.9% in 1995. After the

year 1995 and as a result of several privatization programs during the second half of the

nineties until the end of the year 2000, the CSAE received additional boost. This was

reflected in a significant increase in the turnover ratio, which is considered as an

indicator of the liquidity of the market. It increased from 22.2% to 33.5% in 1996 and

1997, respectively. By the end of the year 2000, it reached 34.7%. However, after this

94

increase in 2000, there was a dramatic drop to 10.2% in 2002. Over the last two years,

2003 and 2004, the turnover ratio increased significantly from 13.7% to 17.3%.

The year 2001, as it is reported in Table 4.3, witnessed a sharp drop in all market

indicators. Many factors caused this abysmal performance. The Egyptian economy was

under enormous pressure during 2000 and 2001. Some factors caused this pressure such

as sharp drop in oil prices, sharp decrease in tourism revenue after the Luxor events, and

the continuing violence and political crisis in the Middle East. All of these factors

played vital role in weakening the performance of the Egyptian economy. By January

2001, the Egyptian Pound had devalued by 9.6%. After that the central bank adopted a

more flexible exchange rate policy; it again devaluated the Pound by 6.4% in August,

2001. All of these events had a negative impact on the Egyptian Stock Market

indicators. At the end of 2000, the Egyptian stock market index dropped by 44%

compared to the beginning of the year, and by 36% in December, 2002 compared to its

performance in January 2001 (www.msci.com).

The Egyptian Stock Market, like other markets in the MENA region, is

dominated by commercial banks. Regarding companies with the largest market

capitalization, the “Commercial Intl. Bank” was in the front with a market capitalization

of $318.87 million at the end of 2001. This was followed by “MobiNil” and “Suez

Cement” with $274.32 million and $246.85 million, respectively

(www.egyptianstocks.com).

95

Table 4.4 Egypt Stock Market Indicators ($US Millions)

* The GDP in this ratio is taken from Table 4.1. Sources:

1. Standard & Poor’s, 2002, Emerging Sock Markets Factbook, Yew York, US. 2. Standard & Poor’s, 2005, Global Stock Markets Factbook, New York, US.

96

Figure 4.1

Market Capitalization in Egypt Stock Exchange 1994-2004, $US Million

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: Table 4.4

Figure 4.2

Trading Value in Egypt Stock Exchange 1994-2004, $US Million

0

2000

4000

6000

8000

10000

12000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: Table 4.4

97

4.3.3 The Stock Market in Turkey The origin of the securities market in Turkey goes back to the second half of the 19th

century during the Ottoman Empire. The first securities market was established in 1866

under the name of the Dersaadet Securities Exchange. Since that date, numerous

political and economic events have occurred in Turkey and the region, which affected

all of the economic and financial systems in Turkey.

The Istanbul Stock Exchange (ISE) is the largest stock market in MENA region.

It was established in early 1986. It provides trading in equities, bonds and bills,

revenue-sharing certificates and international securities (www.ise.org). During the last

decade, the ISE has experienced different economic circumstances. They vary from a

cycle of growth to a crisis then a reform, each of these circumstances having a different

impact on the Turkish economy, and in particular on the Istanbul Stock Exchange.

Table 4.5 provides the main market indicators for the Istanbul Stock Exchange.

The ISE is considered as the region’s dominant stock market. The market

capitalization in the ISE is the largest among all stock market in the region; it

significantly increased from $21,605 million in 1994 to $61,090 million in 1997 and

kept increasing until the end of 1999 when it reached $112,716 million. One of the main

reasons for this huge increase in 1999 was the stand-by agreement that Turkey signed

with the IMF. The year 2002 witnessed a sharp decrease in market capitalization as it

reached $33,958 million. However, in the following two years (2003 and 2004) it

increased again to $68,379 million and $98,299 million, respectively. Figure 4.3 shows

market capitalization during the period 1994-2004. The market capitalization as a

percentage of the GDP was below 50% over the whole period. This is considered a low

level in comparison with developed markets. The year 1999 was the exceptional over

98

the whole period, in this year the market capitalization as a percentage of the GDP

reached 79%.

The trading value witnessed some volatility during the last decade; it increased

from $21,692 million in 1994 to $51,392 million in 1995 and despite some reductions in

1996, the trading value kept increasing until 2000. This increase was a reflection of the

optimistic atmosphere following the expectations of Turkey’s candidacy for the

European Union and the stand by agreement with the IMF. The growth rate of the

trading value between the years 2002 and 2004 reached 108.6%, which is already

reflected in the huge increase in the market capitalization during this period. Figure 4.4

shows the trading value for the whole period 1994-2004.

The turnover ratio in the ISE, which is an index of the market liquidity, is the

most active among all stock markets in the region. It increased from 94.2% in 1994 to

226.0% 1995. During 1996 and 1997 it decreased to 133.3% and 113.5% respectively.

Despite improvements in this ratio in 1998, it dropped again to 102.8% in 1999. In 2000

the turnover ratio reached 206.2%, which is a very high level compared with the year

before. Table 4.5 shows that after 1999 and until 2004, the turnover ratio witnessed

some volatility.

An important issue to be mentioned in this context is that in 1999 the ISE index

witnessed the best performance of the last decade; it increased by 119% between

January and July 1999, and increased by 172% between July and December 1999.The

rising continued until early 2000 (www.msci.com).

A main reason for this remarkable positive performance is the signing of several

bilateral protocols and memorandums between ISE and other local and foreign

organizations and stocks exchange markets in the Euro-Asian region during 1998 and

99

19991. Both the Asian financial crisis in 1997, and the Russian crisis in 1998 spread to

Turkey. The economy declined in six out of twelve quarters from 1998 to 2000. In

addition to the economic crises, internal political obstacles in the year 2000 had

negative impact on the economy as a whole and on the financial sector in particular

(www.erf.org.eg). As a result of these two financial crises it appeared that Turkey began

its own economic and financial crisis in 2000. As a result of this uncertainty, investors

in 2000 transferred around $7 billion out of the economy and between January and

August 2000 the ISE index crashed by 19%. In the year 2001 all market indicators

declined significantly, the market capitalization fell to $47,150 million compared with

$69,659 million in 2000. The trading value also crashed to $77,937 million compared

with $179,209 million in 2000, resulting in a 130% reduction in only one year. Different

factors played vital roll in this deterioration. These factors included the ongoing

political tension, and the collapse of the crawling-rate monetary regime, which ended in

60% lira devaluation. The ISE index fell by 63% during 2000. The number of listed

companies has increased from 176 companies in 1994 to 310 in 2001. It is clear that all

stock market indicators declined dramatically during 2001 as a result of the continuing

economic crisis.

1 The following memorandums and protocols have been signed between the Istanbul Stock Exchange and others markets (www.ise.org/about/majordev.htm):

1. In March 1998, a Memorandum of Understanding was signed between ISE and Baku Interbank Currency Exchange.

2. In May 1998, a protocol was signed between ISE and the Organization for Economic Cooperation and Development (OECD) in order to initiate a three-year ‘Securities market Development Program’ with the aim of providing assistance to the development of the securities markets in the Euro-Asian region and financing the relative projects.

3. In December 1998, the ISE signed a Memorandum of Cooperation with Small and Medium Industries Development Organization of Turkey (KOSGEB). Within the framework of this protocol, bilateral work is carried out in order to enable SMEs to offer their stock to the public and derive benefits from the capital market.

4. In June 1999, the ISE signed a Memorandum of Cooperation with London Stock Exchange. 5. In June 1999, the ISE signed a Memorandum of Cooperation with the Istanbul Chamber of

Industry, with an aim to increase the efficiency of capital markets in allocating funds for the Turkish Industry and increase its competitiveness thereof.

6. In June 1999, the ISE signed a Memorandum of Cooperation with the Stock Exchange of Republics of Kazakstan, Kyrgyzstan and Uzbekistan.

100

Table 4.5 Istanbul Stock Exchange Indicators ($ US Millions)

* The GDP in this ratio is taken from Table 4.1. Sources:

1. Standard & Poor’s, 2002, Emerging Sock Markets Factbook, Yew York, US. 2. Standard & Poor’s, 2005, Global Stock Markets Factbook, New York, US.

101

Figure 4.3

Market Capitalization in Istanbul Stock Exchange 1994-2004, $US Million

0

20,000

40,000

60,000

80,000

100,000

120,000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: Table 4.5

Figure 4.4

Trading Value In Istanbul Stock Exchange 1994-2004, $US Million

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: Table 4.5

102

4.3.4 The Stock Market in Jordan The Amman Financial Market (AFM) was established in 1976, and started its first day

of business on January the 1st, 1978. It was the only stock exchange in Jordan. The

establishment of the Amman Financial Market was a major step on the path of

developing the financial sector in Jordan. It allowed the country to achieve the best

utilization of available financial resources (Al-Refa’i, 1999, p. 9). The main objectives

of the AFM were the followings (Amman Financial Market, 1976):

1. To mobilize savings by encouraging investments in securities, thereby

channelling savings to serve the interests of the national economy.

2. To regulate and control the issuance of securities and dealings. This is to

ensure the soundness, ease, and speed of transactions in accordance with

the financial interests of the country.

3. To collect statistical data, that is important to achieve market objectives.

The government introduced a modern securities law in June 1997. This law

separated the regularity function from the technical side of the market. However, in

1999, the Amman Financial Market was legally split up to create three capital market

institutions: the Jordan Securities Commission (JSC), the Securities Depository Centre

(SDC), and Amman Stock Exchange (ASE). On March 26, 2000 another significant

change was made, that is the Amman Stock Exchange switched from traditional face-to-

face trading between brokers to an electronic trading system. By instituting this change,

the ASE is one step closer to being a commercial entity controlled by brokers. Due to

these changes the trading system in ASE was able to operate more efficiently and is

now similar to most developed stock markets. It also provides a technical foundation for

future growth and development (Hayter, 2001).

103

Jordan’s stock market has witnessed several improvements in its main

indicators. Table 4.6 shows that the market capitalization increased from $4,594 million

in 1994 to $5,838 million in 1998 and to $6,316 million in 2001. However, in the year

2000 the market capitalization dropped to $4,943 million. The main reasons for this

reduction in performance are the limited in the investor confidence and political

development in the region. Most notable was the deterioration in the peace process and

the acceleration of violence in the Middle East at the end of 2000. These events had a

large negative impact on the ASE activity (Jordan Securities Commission, 2000). The

relaxation of monetary policy by the central bank did not help the market. In the year

2000, 42.5% of total market capitalization was owned by non-Jordanian entities, 8%

was owned by foreign governments and public intuitions and 13% by the Jordanian

government and public institutions (Saket, 2000, p. 50). It was only by the end of 2000

when the ASE began to witness significant improvements. Regarding the companies

with the largest market capitalization, the Arab Bank was still in the front, with a market

capitalization of $2,257.11 million at the end of 2001. The Housing Bank and the

Petroleum Refinery followed with $287.7 million and $107.69 million, respectively. For

the years 2002 and 2003, Figure 4.5 shows that market capitalization increased during

this period. In 2003, the ASE indicators showed remarkable performance not seen since

the establishment of the bourse in 1978. Market capitalization increased by 55% during

2002 and 2003. The upsurge in trading value by 95%, during the same period, reflects a

significant increase in the share price index. This outstanding performance was

concentrated in both the industrial and service sectors (Central Bank of Jordan, 2003). A

main reason for this high level of market capitalization was a significant increase in the

foreign portfolio equity flows, which reached JD 145.2 million in 2003 as compared to

JD 33.4 million in 2002 (Central Bank of Jordan, 2003). The year 2004 witnessed the

104

best performance for the ASE over the whole period (1994-2004). As it is shown in

table 4.6, all market indicators achieved the highest level. The market capitalization as a

percentage of the GDP increased from 76% in 1994 to 111% and 146% by the end of

2003 and 2004, respectively. By reaching to this high level in 2004, the ASE is breaking

the record since the establishment of the market in 1978. This very high rate could

reflect the importance of the stock market in the national economy. Moreover, it could

also reflect the importance of ASE from both regional and international perspectives.

The banking and finance sector leads the market capitalization.

Regarding the price index of the Amman Stock Exchange, during the year 2000,

it decreased from 147.304 in January 2000 to 116.059 in December 2000

(www.msci.com). This is a clear indication of the deterioration of the ASE performance

by the end of the year 2000. In the year 2001, the ASE performance was the strongest in

the Middle East. This is despite a two-week decline following the September 11 attacks.

The fourth quarter in 2001 was the year’s best performance.

The trading value as shown in Figure 4.6 experienced a large fall from $626

million in 1994 to $416 million in 2000 which is a reduction of 50%. But in the year

2000, the trading value in the secondary market, bonds market, the transaction-off-the

trading floor and the investment funds markets increased by 124.3% and reached $933

million. From this $933 million, 24% of the total trading value was by non-Jordanians

and 66% of the non-Jordanian trading value was by non-Arabs (Saket, 2000, p. 50). A

remarkable increase in trading value happened in 2003, when it reached $2,607 million

compared with $1,335 million in 2002. This is equal to a 95% increase in one year. The

significant increase in the foreign portfolio equity flows to the stock market also played

a major role in this increase. However, the year 2004 has witnessed the best

105

performance ever, as the trading value reached $ 5,328 million, which is equivalent to a

104.4% per annum growth.

The turnover ratio, which is an index for the liquidity of the market, varied from

13.0% in 1994 to 6.4% in 1996. In 2000, and as a result of limited investor confidence,

political instability in the neighbouring countries, and weak performance of the

economy, the ASE witnessed a sluggish in terms of trading value and turnover ratio (the

trading value reached $US 416 compared to $US 548 in 1999, also the turnover ratio

reached7.7% compared to 9.4% in 1999. However, the following years witnessed

improvements in all market indicators (see table 4.6). This boom at the ASE is believed

to be attributed to the improvement in the legislative and technical advancement of the

market and the bourse in general. In 2004, and as all other market indicators, turnover

ratio continued increasing as it reached 36.3% which is the highest level over the period

1994-2004.

106

Table 4.6 Amman Stock Exchange Indicators ($US Millions)

* The GDP in this ratio is taken from Table 4.1 Source: 1. Standard & Poor’s, 2002, Emerging Stock Markets Factbook, Yew York, US. 2. Standard & Poor’s, 2005, Global Stock Markets Factbook, New York, US.

107

Figure 4.5

Market Capitalization in Amman Stock Exchange 1994-2004, $US Million

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: Table 4.6

Figure 4.6

Trading Value in Amman Stock Exchange 1994-2004, $US Million

0

1000

2000

3000

4000

5000

6000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: Table 4.6

108

4.3.5 The Stock Market in Morocco The Casablanca Stock Exchange in Morocco (CSE) was established in 1929, it is the

third oldest stock market in Africa. The CSE was a state institution under the authority

of the Ministry of Finance. The CSE was a broker/dealer market, dominated by the

banking sector. It was characterized by a lack of transparency and small number of

individual investors. The exchange was extremely illiquid as many stocks did not trade

for several weeks (Ghysels and Cherkaoui, 2003).

A major financial reform for the financial sector, simultaneously with the

privatization program, took place in Morocco during 1993. One of the main objectives

of the program was to make fundamental changes in the operations of the stock market.

As a result of this program a dealer/market maker structure was created and a securities

commission was established to ensure shareholder protection and the market maker

became legally and financially autonomous (Ghysels and Cherkaoui, 2003). Also, as a

result of reform there are no restrictions for foreign participation.

Regarding the achievement of the CSE, Table 4.7 reports the main market

indicators over the period 1994-2004. The market capitalization increased consistently

until 1998. It increased from $4,376 million in 1994 to $5,951 million in 1995, and

continued to $12,177 million then $15,676 million during 1997 and 1998 respectively.

The remarkable increase during these five years was mainly because of a massive

privatization program and a positive progress of the financial reform that started in

1993, it was also aided by other political and economic developments. The Asian crisis

in 1997 and the crisis in Russia 1998 both had no effect on CSE. The main reason for

this was the low level of foreign activity during that period. This low level of foreign

activities had a positive effect of shielding the CSE from these crises. Due to a number

109

of taxes imposed during 19981, the performance of CSE was weakened during the

following two years. The market capitalization in 1999 decreased to $13,695 million

compared with $15,676 million in 1998. All market indicators had declined

significantly during the year 2000 and market capitalization reached $10,899. During

2001, the CSE also witnessed a decline in market capitalization and trading value. The

weak performance of the CSE during the last three years, particularly the year 2000,

was a result of different factors. The main factors were the stagnation in 2000 and the

instability in Western Sahara. Both of them caused a reduction in economic growth in

2001 (Standards & Poor’s, 2002, p. 241). After four years (1998-2002) of continuing

reduction in market capitalization and a near collapse in liquidity, the market

capitalization, as it shown in Figure 4.7, reached a remarkable level of $13,152 million

and $25,064 million in 2003 and 2004 respectively. This reflected a return of investor

confidence as portfolio equity flows increased during this period. As a percentage of

GDP, the market capitalization has varied from 14% in 1994 to 30% and 50% by the

end of 2003 and 2004 respectively.

Other market indicators showed an increase in value. The trading value

increased from $788 million in 1994 to $2,426 million in 1995. Despite a reduction of

82% in 1996, the trading value continued increasing for the following three years,

reaching $2,530 million in 1999. As Figure 4.8 shows, the trading value decreased

dramatically over the period 2000 to 2002 as compared with 1999. In 2004, trading

value reached $1,677 million, which is the highest level over the period 1994-2004.

This reflects a return of investor confidence in the stock market.

1 There are three different taxes imposed on the CSE:

1. National solidarity tax levied on all listed companies. 2. Capital gains tax of 10% on individuals and mutual funds, imposed by the Ministry of Finance. 3. A 20% tax on bond funds and a 15% tax on mixed funds.

110

The turnover ratio increased from 22.1% in 1994 to 45.9% in 1995, and sharply

decreased to 5.9% in 1996. During the following three years the average turnover ratio

was 12.6%, which is 0.27 of its value in 1995. During the years 2000, 2002 and 2003,

the turnover ratio witnessed a continuing reduction, while in 2004 it increased to 9.1%.

Regarding the number of listed companies, they did not increase significantly.

The increase was from 51 in 1994 to 55 in 1999. In 2004, this number decreased to 52

companies. In regards to the price index, it increased significantly during (1994-1998).

In December 1994 it increased from 100 to 300.651 in December 1998

(www.msci.com). As a consequence of the factors mentioned previously, (the new taxes

imposed in the CSE, the political instability in Western Sahara and stagnation during

2000), the price index declined to180.078 in December 2001.

111

Table 4.7 Casablanca Stock Exchange Indicators ($US Millions)

* The GDP in this ratio is taken from Table 4.1 Source:

1. Standard & Poor’s, 2002, Emerging Stock Markets Factbook, Yew York, US. 2. Standard & Poor’s, 2005, Global Stock Markets Factbook, New York, US.

112

Figure 4.7

Market Capitalization in Casablanca Stock Exchange 1994-2004, $US Million

0

5,000

10,000

15,000

20,000

25,000

30,000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: Table 4.7

Figure 4.8

Trading Value in Casablanca Stock Exchange 1994-2004, $US Million

0

500

1000

1500

2000

2500

3000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: Table 4.7

113

4.4 Conclusion In this chapter, the features of the emerging stock markets in the MENA region are

revised and analysed. These markets include the Egypt Stock Exchange (ESE), Istanbul

Stock Exchange (ISE), Amman Stock Exchange (ASE) and Casablanca Stock Exchange

(CSE). The chapter starts with reviewing the general economic features in these four

small open economies in the MENA region, namely; Egypt, Turkey, Jordan and

Morocco.

The analysis shows that all of these countries witnessed macroeconomic

imbalances during the last decade. These imbalances are due to international and

domestic factors. The international factors are related to the Asian financial crisis in

1997, the Russian crisis in 1998, the drop in oil prices and the September 11 attacks in

2001. The domestic factors are different from one country to another, but the main

factors are the continuing violence and political crisis in the Middle East, the war in

Iraq, and the domestic macroeconomic imbalances in each of these countries.

The emerging stock markets in the MENA region have achieved considerable

improvements in the last decade due to several factors such as the achievement of

higher economic growth, monetary stability, stock markets reforms, privatisation,

financial liberalization and an institutional framework for investors. Most of the stock

markets in the MENA region have been liberalized during the late eighties and nineties.

As a result of this liberalization, foreign investors are allowed to purchase shares

without restrictions.

Regarding the main indicators for the stock markets, the analysis shows that

Turkey has the largest stock market among all other markets in the MENA region. The

market capitalization in the Istanbul Stock Exchange reached $98,299 million in the

year 2004, compared with $38,516 million for Egypt, $18,383 million for Jordan and

114

$25,064 million for Morocco for the same year. The market capitalization as a share of

GDP for all MENA stock markets, except for Jordan, is considered to be low in

comparison with developed markets. This sheds more light on the importance for these

markets to develop its market capitalization by attracting more of the international

equity portfolio flow.

Moreover, over the whole period of study, the turnover ratio indicates that the

Turkish stock market is the most active among all others. In 2004, the turnover ratio for

Turkish stock market reached $147,426 million, while in Egypt it just amounted to

$5,608 million, in Jordan it reached $5,325 million and in Morocco $1,677 million. In

general, this chapter provides a general review for the macroeconomic performance of

the MENA countries, and an inclusive analysis of the performance of the stock markets

in the MENA region over the period 1994-2004.

115

Chapter Five

Structural Changes and Efficiency in the MENA Stock

Markets

5.1 Introduction A significant amount of economic and statistical literature has focused on the unit root

hypothesis when using time series data. It becomes a preliminary procedure to test the

null hypothesis of a unit root against the alternative hypothesis of stationarity. For a

time series, stationarity in this context means that the fundamental form of the data

generating process remains the same over time. The unit root test considered as a

necessary preliminary step in testing for cointegration or causality because these

procedures required the series to be integrated of the same order. Different methods

have been used to test for stationarity; the commonly used methods are the Augmented

Dickey-Fuller (ADF) and Phillips and Perron (PP).

However, Perron (1989) argues that most economic time series are characterized

by stochastic rather than deterministic non stationarity. He shows that many apparent

non-stationarity macroeconomic variables are indeed stationary if one allows for

structural changes in the intercept or trends. When there are structural breaks present,

the Dickey–Fuller statistics are biased towards the non rejection of the existence of a

unit root (Ender, 2004, p. 200).

Two main aims for this chapter, Firstly, to test the unit root hypothesis using

both the conventional unit root tests and the unit root tests in the presence of structural

change at an unknown time of break. Secondly, investigate the efficient market

116

hypothesis in the MENA markets. This chapter is divided into seven sections. Section 2

presents a descriptive analysis of the statistical characteristics of the stock price indices

in the MENA region and selected developed markets. Section3 presents the results of

the ADF and PP unit root tests, with the theoretical background and the procedure of

testing the unit root hypothesis included in Appendix A. Section 4 presents a summary

of testing for stationarity in the presence of structural change. The procedures for

selecting the order of the lag, and endogenously determining the time of the break are

considered. Section 5 tests for the unit root hypothesis in the presence of structural

change. Section 6 analyzes the relationship between the random walk behavior and the

efficiency of the MENA stock markets. The last section concludes the chapter analysis.

5.2 Data and Descriptive Statistics This study employs monthly stock market indices for four major emerging stock

markets in the MENA region, namely Egypt, Turkey, Jordan, and Morocco. As

representative developed markets, the study uses monthly data for the US, UK, and

Germany.

As previously discussed in chapter four, the main indicators show the stock

markets in the MENA region are relatively active when compare them to other

emerging markets. Nevertheless, these markets have not been discussed deeply despite

the exceptional importance of this region and its economies on a world wide level.

The US and UK stock markets are included here because they are among the

largest stock markets in the world and play vital role in their economies. Moreover the

US is the largest economy in the world and all countries from the MENA region

(especially those used by this study) have strong economic relationships with the US.

117

Germany is also included because it is the largest economy in Europe and has a very

strong economic relationship with all countries in the MENA region, especially with

Turkey.

The data is obtained from Morgan Stanley Capital International (MSCI)

(www.msci.com) and covers 115 monthly observations for the period from December

1994 to June 20041. Following Koutmos (1996), Darrat (2003) and Tahai et al. (2004)

all stock prices are expressed in local currencies. Darrat (2003) asserts that

denominating stock prices in local currency incorporates hedging activities of investors

against foreign exchange rates. However, for an inclusive view, this study also

expresses all stock price indices in $US.

Monthly stock prices indices are used to avoid distortions common in weekly

and daily data arising from non-trading and non-synchronous trading. Less frequent

monthly observations provide a clearer picture of movements (see, Hung and Cheung,

1995 and Piesse and Hearn, 2002). Figure 5.1 shows the monthly stock price indices in

three countries from MENA region expressed in local currencies, namely Egypt, Jordan

and morocco2. Figure 5.1 shows that the three markets relatively have similar

movements during the period of study, despite some disturbances. By the mid of

nineties Egypt and Morocco experienced significant increase in their index, while

Jordan experienced modest increase during the period (1996-1997). The stock price

index in Egypt experienced another significant increase in its value during the second

half of 1999. This increase (as mentioned before in chapter four) was a result of several

privatization programs during that period. However, the three markets have also

experienced an increase in their indices during the period (2002-2004).

1 The reason for using data for ten years is because for some markets in the MENA region the data is not available before the December 1994. 2 Turkey is not included in figure 5.1 because the Turkish currency has experienced a large depreciation in its value during the period of study. Technically it could not be drown in the same figure as other countries. For a clear idea see Figures 5.6 and 5.7.

118

Figure 5.1

Stock Price Indices in MENA Region (Local Currencies)

0

50

100

150

200

250

300

350

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x lv

el

Egypt Morocco Jordan

Figure 5.2 shows the monthly stock price indices expressed in $US for the four

stock markets in the MENA region. It shows similar movement for all indices.

However, Turkey experienced high volatility over the period 1998-2000. This was due

to several effects such as the Asian crisis and the Russian crisis in 1997 and 1998,

respectively. Also part of this volatility during the year 2000 was a reflection of the

optimistic atmosphere following the expectations of Turkey’s candidacy for the

European Union and the stand by agreement with the IMF. Other countries experienced

growth in their indices in the mid nineties due to several privatization programs and

structural markets reforms.

119

Figure 5.2

Stock Price Indices in MENA Region ($US)

0

100

200

300

400

500

600

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

Egypt Turkey Jordan Morocco

Figure 5.3 shows the monthly stock price indices expressed in $US for all seven

countries included in this study. It is clear that there is a kind of comovements among

all developed markets over the period of study. However, Germany experienced some

volatility by the end of 1990’s and during 2000. Also it is clear that there is a similarity

in the movements of the indices in Germany and Turkey. This reflects the strong

economic relationship between these tow countries.

120

Figure 5.3

Stock Price Indices in All Countries ($US)

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

Egypt Turkey Jordan Morocco USA UK Germany

121

Figures 5.4 and 5.5 show the monthly price indices for Egypt in both local

currency and $US. In figure 5.4, it is clear that the stock price index in Egypt has

experienced significant increase in its value during the years 1996, 1999 and 2002 to

2004. Most of these increases were a result of several privatization programs during

those periods, which were reflected in an increase in the trading value in the market.

Figure 5.4

Monthly Stock Price Index in Egypt (Local Currency)

0

50

100

150

200

250

300

350

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

Figure 5.5

Monthly Stock Price Index in Egypt ($US)

0

50

100

150

200

250

300

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

122

Figures 5.6 and 5.7 show the monthly price indices for Turkey in both local

currency and $US. After a poor performance following the Asian and Russian crises in

1997 and 1998 respectively, the index achieved a significant improvement in 1999 and

2000. The reasons behind this improvement were the signing of several bilateral

protocols and memorandums between ISE and other stocks exchange markets during

1998 and 1999.

Figure 5.6

Monthly Stock Price Index in Turkey (Local Currency)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

Dec

30,

199

4

Apr 2

8, 1

995

Aug

31,

199

5

Dec

29,

199

5

Apr 3

0, 1

996

Aug

30,

199

6

Dec

31,

199

6

Apr 3

0, 1

997

Aug

29,

199

7

Dec

31,

199

7

Apr 3

0, 1

998

Aug

31,

199

8

Dec

31,

199

8

Apr 3

0, 1

999

Aug

31,

199

9

Dec

31,

199

9

Apr 2

8, 2

000

Aug

31,

200

0

Dec

29,

200

0

Apr 3

0, 2

001

Aug

31,

200

1

Dec

31,

200

1

Apr 3

0, 2

002

Aug

30,

200

2

Dec

31,

200

2

Apr 3

0, 2

003

Aug

29,

200

3

Dec

31,

200

3

Apr 3

0, 2

004

Time

Inde

x le

vel

Figure 5.7

Monthly Stock Price Index in Turkey ($US)

0

100

200

300

400

500

600

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

123

In the case of the stock market of Jordan, both figures 5.8 and 5.9 show smooth

movement of the index over the period of study. However, in some years the stock

market in Jordan has been affected by the limits to investor confidence and political

developments in the region. Nevertheless, the stock market witnessed continuous

improvements after the year 2000. Despite some interruptions after the 11th September

2001 attacks, the stock market continues to achieve considerable growth.

Figure 5.8

Monthly Stock Price Index in Jordan (Local Currency)

0

50

100

150

200

250

300

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

Figure 5.9

Monthly Stock Price Index in Jordan ($US)

0

20

40

60

80

100

120

140

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

124

Figures 5.10 and 5.11 both show the monthly stock price index in Morocco over

the period of the study. The stock price index witnessed an exceptional improvement

during the period 1996-1998. It is believed that the signing of the trade agreement with

the European Union in 1996, beside several privatization programs played an important

role for this achievement.

Figure 5.10

Monthly Stock Price Index in Morocco (Local Currency)

0

50

100

150

200

250

300

350

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Yime

Inde

x le

vel

Figure 5.11

Monthly Stock Price Index in Morocco ($US)

0

50

100

150

200

250

300

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

125

Figures (5.12-5.16) show the monthly stock price indices in the three developed

markets, namely, United Kingdom, Germany and the United States.

Figure 5.12

Monthly Stock Price Index in United Kingdom (Local Currency)

0

500

1000

1500

2000

2500

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

Figure 5.13

Monthely Stock Price Index in United Kingdom ($US)

0

200

400

600

800

1000

1200

1400

Dec

30,

199

4M

ar 3

1, 1

995

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5M

ar 2

9, 1

996

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6M

ar 3

1, 1

997

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7M

ar 3

1, 1

998

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8M

ar 3

1, 1

999

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9M

ar 3

1, 2

000

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0M

ar 3

0, 2

001

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1M

ar 2

9, 2

002

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2M

ar 3

1, 2

003

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3M

ar 3

1, 2

004

Jun

30, 2

004

Time

Inde

x le

vel

126

Figure 5.14

Monthly Stock Price Index in Germany (Local Currency)

0

200

400

600

800

1000

1200

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

Figure 5.15

Monthly Stock Price Index in Germany ($US)

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

127

Figure 5.16

Monthly Stock PricenIndex in United States

0

200

400

600

800

1000

1200

1400

1600

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Inde

x le

vel

Figures 5.17 – 5.29 show the monthly rate of return for each country expressed

in both local currencies and $US. The monthly stock rates of return are computed as

logarithmic differences based on the following formula:

)/log( 1−= titiit PPR

where, tiP is the value of the stock price index for country i at time t . The time period

is from December 1994 to June 2004.

128

Figure 5.17

Monthly Rate of Return in Egypt (Local Currency)

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn (%

)

Figure 5.18

Monthly Rate of Return in Egypt ($US)

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn (%

)

129

Figure 5.19

Monthly Rate of Return in Turkey (Local Currency)

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn (%

)

Figure 5.20

Monthly Rate of Return in Turkey ($US)

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Dec

30,

199

4M

ar 3

1, 1

995

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5M

ar 2

9, 1

996

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6M

ar 3

1, 1

997

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7M

ar 3

1, 1

998

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8M

ar 3

1, 1

999

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9M

ar 3

1, 2

000

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1Ju

n 29

, 200

1Se

p 28

, 200

1D

ec 3

1, 2

001

Mar

29,

200

2Ju

n 28

, 200

2Se

p 30

, 200

2D

ec 3

1, 2

002

Mar

31,

200

3Ju

n 30

, 200

3Se

p 30

, 200

3D

ec 3

1, 2

003

Mar

31,

200

4Ju

n 30

, 200

4

Time

Rat

e of

retu

rn (%

)

130

Figure 5.21

Monthly Rate of Return in Jorndan (Local Currency)

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12D

ec 3

0, 1

994

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn (%

)

Figure 5.22

Monthly Rate of Return in Jordan ($US)

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn(%

)

131

Figure 5.23

Monthly Rate of Return in Morocco (Local Currency)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn (%

)

Figure 5.24

Monthly Rate of Return in Morocco ($US)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn (%

)

132

Figure 5.25

Monthly Rate of Return in United Kingdom (Local Currency)

-0.15

-0.1

-0.05

0

0.05

0.1D

ec 3

0, 1

994

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

time

Rat

e of

retu

rn (%

)

Figure 5.26

Monthly Rate of Return in United Kingdom ($US)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

rate

of r

etur

n (%

)

133

Figure 5.27

Monthly Rate of Return in Germany (Local Currency)

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn (%

)

Figure 5.28

Monthly Rate of Return in Germany ($US)

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29, 1

995

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30, 1

996

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30, 1

997

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30, 1

998

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30, 1

999

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29, 2

000

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28, 2

001

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30, 2

002

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30, 2

003

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn (%

)

134

Figure 5.29

Monthly Rate of Return in United States

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Dec

30,

199

4

Mar

31,

199

5

Jun

30, 1

995

Sep

29,

199

5

Dec

29,

199

5

Mar

29,

199

6

Jun

28, 1

996

Sep

30,

199

6

Dec

31,

199

6

Mar

31,

199

7

Jun

30, 1

997

Sep

30,

199

7

Dec

31,

199

7

Mar

31,

199

8

Jun

30, 1

998

Sep

30,

199

8

Dec

31,

199

8

Mar

31,

199

9

Jun

30, 1

999

Sep

30,

199

9

Dec

31,

199

9

Mar

31,

200

0

Jun

30, 2

000

Sep

29,

200

0

Dec

29,

200

0

Mar

30,

200

1

Jun

29, 2

001

Sep

28,

200

1

Dec

31,

200

1

Mar

29,

200

2

Jun

28, 2

002

Sep

30,

200

2

Dec

31,

200

2

Mar

31,

200

3

Jun

30, 2

003

Sep

30,

200

3

Dec

31,

200

3

Mar

31,

200

4

Jun

30, 2

004

Time

Rat

e of

retu

rn (%

)

135

The descriptive statistics of monthly stock returns in local currency for

the seven stock markets are reported in table 5.1. The highest average returns are

for Turkey of 3.83% with Egypt has the lowest average returns of 0.18%.

Regarding volatility, the Turkish stock market shows the highest volatility of

16.5% (as measured by the standard deviation), while Egypt has the lowest

volatility of 1.6%. Accordingly, it can be observed that the existence of high

volatility in a stock market is correlated with the existence of high returns. Egypt

and Turkey are very clear examples of this relationship. In terms of higher

moments, the distributions for all MENA monthly stock returns are skewed to

the right, which indicates that there is a greater probability of higher returns.

This compares with monthly stock returns in developed stock markets which are

skewed to the left, indicating a greater probability of lower returns. In addition,

most of the stock markets show an excess of kurtosis, with the coefficients of the

kurtosis grater than 3, indicating fat tails and sharp peaks. Jordan is the

exception with a low coefficient of kurtosis of 2.53, indicating the monthly stock

returns in Jordan are platykurtic (Thin tails). For testing normality, the Jarque-

Bera (JB) test shows that the null hypothesis, that the residuals are normally

distributed, can be rejected1.

1 Jarque-Bera (JB) statistics

−+24

)3(6

22 KSn follows the chi – square distribution with 2 degree of

freedom. However, for normally distributed variable, the skewness coefficient = 0, and the kurtosis coefficient = 3.

136

Table 5.1

Descriptive Statistics for Monthly Stock Returns in (Local Currency).

Egypt Turkey Jordan Morocco United Kingdom Germany United States Mean 0.001771 0.038312 0.002499 0.005370 0.003484 0.007742 0.004760 Std. Dev. 0.016143 0.164961 0.041015 0.043531 0.040613 0.046688 0.071897 Skewness 0.739257 0.200666 0.282872 0.376613 -0.792864 -0.692147 -0.846748 Kurtosis 3.805127 4.327637 2.529505 3.446691 3.933312 3.441499 5.090812 Minimum -0.027009 -0.473690 -0.084778 -0.094760 -0.127693 -0.151131 -0.286742 Maximum 0.055411 0.589942 0.092702 0.156839 0.084902 0.094247 0.179559 Jarque-Bera 13.46262 9.137508 2.571799 3.642696 16.08161 10.02815 34.38725 Observations 114 114 114 114 114 114 114

Note: The Kurtosis of the normal distribution is 3. If the kurtosis exceeds 3, the distribution is peaked (leptokurtic) relative to the normal, if the kurtosis is less than 3, the distribution is flat (platykurtic) relative to the normal. The skewness of a symmetric distribution is zero. Positive skewness means that the distribution has a long right tail, while negative skewness means that the distribution has a long left tail.

137

For an inclusive view, table 5.2 reports descriptive statistics for monthly stock

returns in $US. It shows that Turkey has also the highest average returns of 5.63%,

while Egypt has average returns of 3.93%. For Jordan and Morocco, it is found that the

average returns are very similar whether expressing the indices in local currencies or in

$US. Regarding the volatility, Turkey shows the highest volatility of 17.8%, while

Egypt shows a volatility of 8.1%. For both Jordan and Morocco, the volatility was

around 4% which is very low rate compared with other emerging markets. In terms of

higher moments, the distributions for Egypt, Jordan and Morocco are skewed to the

right. This is similar to the results in table 5.1. However, the distribution for Turkey is

skewed to the left when expressing the index in $US. This indicates the probability of

lower returns. Also, in similar results to table 5.1, most of the stock markets show an

excess of kurtosis, with the coefficients of the kurtosis grater than 3, indicating fat tails

and sharp peaks. Jordan is the exception with a low coefficient of kurtosis of 2.50,

indicating the monthly stock returns in Jordan are platykurtic (Thin tails).

138

Table 5.2

Descriptive Statistics for Monthly Stock Returns in ($US).

Egypt Turkey Jordan Morocco United Kingdom Germany United States Mean 0.003928 0.005625 0.002415 0.005501 0.004492 0.004064 0.007962 Std. Dev. 0.081060 0.178007 0.041377 0.046879 0.039723 0.067833 0.046711 Skewness 0.697979 -0.132968 0.265081 0.158081 -0.311832 -0.811737 -0.704848 Kurtosis 3.887154 3.923452 2.504332 3.177830 3.056976 5.803612 3.448310 Maximum 0.280374 0.544089 0.092705 0.162845 0.095737 0.202033 0.094247 Minimum -0.151109 -0.531772 -0.084780 -0.108392 -0.111216 -0.279071 -0.151131 Jarque-Bera 12.99478 4.386553 2.502105 0.625016 1.862963 49.85557 10.39405 Observations 114 114 114 114 114 114 114

Note: The Kurtosis of the normal distribution is 3. If the kurtosis exceeds 3, the distribution is peaked (leptokurtic) relative to the normal, if the kurtosis is less than 3, the distribution is flat (platykurtic) relative to the normal. The skewness of a symmetric distribution is zero. Positive skewness means that the distribution has a long right tail, while negative skewness means that the distribution has a long left tail.

139

Tables 5.3 and 5.4 provide the correlation matrices for the respective monthly stock

market indices and returns in local currencies. It can be seen from table 5.3 that Egypt

and Morocco have the highest correlation coefficient of 0.64, while Jordan and Turkey

have the lowest correlation coefficient of -0.30. Moreover, Jordan is found to have

negative correlations with all indices except with Egypt it has a low positive correlation

of 0.20. However, according to some previous studies, to have a negative correlation

indicates the ability to benefit from portfolio diversification (see Maghyereh, p. 11,

2003). Also, it is found that all developed stock markets have high correlation among

themselves, while correlation among stock markets in the MENA is relatively low.

However, it becomes clearer that relying on correlation analysis for testing integration

or other phenomena is quite questionable. Many studies have pointed to the problems of

using this technique. One of them is that it does not eliminate the spurious relationships.

Some studies also show that “the conventional cross-correlation coefficients are biased

upwards during a period of increased volatility” (Wilson et. al., 2002, p. 8). In general,

we consider these results as a preliminary step for a further analysis in the context of

measuring stock market integration in the MENA region.

Table 5.4 shows the correlation coefficients matrix among the indices returns.

Again, the highest correlation coefficient among the stock market returns in the MENA

region is between Egypt and Morocco (0.22), which is still low compare to the

correlation coefficients among developed markets. Also, Morocco and Turkey have a

negative correlation coefficient equal to -0.01. Finally, the Turkish returns show higher

correlation coefficients with developed returns (the correlations are around 0.46) than

with MENA returns.

140

Table 5.3

Correlation Coefficients for Monthly Stock Indices in (Local Currency).

Egypt Turkey Jordan Morocco United Kingdom Germany United StatesEgypt 1 Turkey 0.352745 1 Jordan 0.200164 -0.299265 1 Morocco 0.641591 0.457494 -0.399593 1 United Kingdom 0.460158 0.573022 -0.623021 0.868060 1 Germany 0.461347 0.630631 -0.644691 0.811120 0.969159 1 United States 0.510091 0.813355 -0.520275 0.812346 0.924185 0.922352 1 Table 5.4

Correlation Coefficients for Monthly Rate of Returns in (Local Currency).

Egypt Turkey Jordan Morocco United Kingdom Germany United StatesEgypt 1 Turkey 0.189199 1 Jordan 0.115030 0.095558 1 Morocco 0.218674 -0.009154 -0.014811 1 United Kingdom 0.065865 0.455746 0.107911 0.139331 1 Germany 0.085684 0.456762 0.104913 0.149434 0.743879 1 United States -0.033071 0.462927 0.094016 0.095969 0.784358 0.751970 1

141

Tables 5.5 and 5.6 provide the correlation matrices among all monthly

stock market indices and returns in $US. These tables give relatively similar

picture as tables 5.3 and 5.4. It can be seen from table 5.5 that the highest

correlation coefficient among the monthly stock indices in the MENA markets is

between Egypt and Morocco of 0.75. The lowest correlation is between Jordan

and Morocco of -0.21. Turkish stock price index is highly correlated with the

developed stock indices that with the MENA markets.

In regards to table 5.6, which reports the correlation coefficients for the

monthly rate of returns in $US, a gain it gives similar results to table 5.4. Egypt

and Morocco have the highest correlation coefficient among the stock market

returns in the MENA region of 0.23. At the same time, stock returns in Morocco

have very low and negative correlation with stock returns in Turkey.

To sum up, it is found that there are low correlation coefficients among

the MENA markets either in related to stock indices or stock returns. At the

same time, correlation coefficients among developed markets are extremely

high. Also it is found that Turkey has higher correlation coefficients with the

developed markets than with the MENA markets.

142

Table 5.5

Correlation Coefficients for Monthly Stock Indices in ($US).

Egypt Turkey Jordan Morocco United Kingdom Germany United StatesEgypt 1 Turkey 0.738664 1 Jordan -0.002687 -0.203515 1 Morocco 0.753071 0.558372 -0.209617 1 United Kingdom 0.609291 0.692408 -0.454966 0.853561 1 Germany 0.643287 0.767938 -0.490810 0.776013 0.936693 1 United States 0.350892 0.612498 -0.554204 0.651824 0.916886 0.847988 1

Table 5.6

Correlation Coefficients for Monthly Rate of Return in ($US).

Egypt Turkey Jordan Morocco United Kingdom Germany United StatesEgypt 1 Turkey 0.261658 1 Jordan 0.203079 0.122243 1 Morocco 0.231826 -0.080116 0.037292 1 United Kingdom 0.155983 0.474997 0.162990 0.135637 1 Germany 0.192889 0.469218 0.136042 0.120205 0.728078 1 United States 0.263471 0.487994 0.098090 0.015199 0.744288 0.725540 1

143

5.3 The Conventional Augmented Dickey-Fuller (ADF) and Phillips–Perron (PP) Unit Root Tests

The using of time series data for empirical work requires that the underlying time series

is stationary. In this context, stationary means that the fundamental form of the data

generating process remains the same over time. The unit root test is the most widely

used test for stationary, it is also considered as a preliminary step in testing for

cointegration, as all series need to be integrated to the same order.

There are several traditional methods to test for stationary. The commonly used

methods are the Augmented Dickey-Fuller tests (1979, 1981), Said and Dickey (1984)

and Phillips and Perron tests (1988). However, the theoretical background of unit root

tests using these approaches is explained in details in Appendix (A). The traditional

Augmented Dickey-Fuller (ADF) and Phillips–Perron (PP) tests will be the starting

point for testing the unit root hypothesis. The study begins with estimating the

following ADF models:

∑=

−− +∆+++=∆m

itittt YYtY

1121 εδβαα (5.1)

∑=

−− +∆++=∆m

itittt YYY

111 εδβα (5.2)

∑=

−− +∆+=∆m

itittt YYY

11 εδβ (5.3)

where the first model represented by equation 5.1 includes a constant term ( 1α ) and a

trend term ( t2α ) together with am mth order autoregressive term. The second model,

represented by equation 5.2, includes just a constant term only, and the third model does

not include intercept and trend terms. The null hypothesis of stationarity for all

144

specifications is 0=β . The autoregressive term ( ∑=

−∆m

iitY

1

δ ) is included to ensure the

residual ( tε ) is serially uncorrelated.

The Phillips and Perron (PP) test introduces a non parametric method to

overcome the problem of serial correlation in the error term1, using the following

specification:

ttt uyy ++=∆ −1ρα (5.4)

Equation 5.4 is estimated by using the ordinary least square (OLS) method. In most

cases, (PP) test gives similar results as the (ADF) test and suffers from most of the same

important limitations.

The unit root results using the ADF and the PP tests for the variables in levels, in

local currency are displayed in table 5.7. Table 5.8 displays the unit root results for

variables in levels in $US. Tables 5.9 and 5.10 present the unit root results using both

tests for the returns of the variables using local currency and $US respectively.

1 For more details about Philips and Perron (PP) test, see Appendix (A).

145

Table 5.7

Estimated Results of ADF and PP Unit Root Tests (Local Currency)

ADF Test Statistics PP Test Statistics Variables in levels

Test Statistics

5% C.V. Lag order

Test Statistics

5% C.V. Lag order

lnE -2.145 -2.888 4 0.756 -1.944 6 lnT -2.011 -2.887 0 -2.009 -2.887 3 lnJ -0.376 -3.45 1 -0.21 -3.449 4 lnM -1.656 -3.449 0 -1.663 -3.449 4 lnUK -2.205 -2.887 0 -2.2 -2.887 4 lnG -1.755 -2.887 0 -1.756 -2.887 1 lnUS -2.654 -2.887 0 -2.671 -2.887 3

where, Eln , Tln , Jln , Mln , UKln , Gln and lnUS are the natural log of the monthly stock price indices for Egypt, Turkey, Jordan, Morocco, the United Kingdom , Germany and United States..

Table 5.8

Estimated Results of ADF and (PP) Unit Root Tests ($US).

ADF Test Statistics PP Test Statistics Variables in levels

Test Statistics

5% C.V. Lag order Test Statistics

5% C.V. Lag order

lnE -2.011 -2.887 4 0.234 -1.944 6 lnT -2.483 -2.887 0 -2.561 -2.887 4 lnJ -0.308 -3.45 1 -0.082 -3.449 3 lnM -1.964 -2.888 6 -1.831 -2.887 4 lnUK -2.058 -2.887 0 -2.057 -2.887 4 lnG -1.846 -2.887 0 -1.834 -2.887 1 lnUS -2.664 -2.887 0 -2.683 -2.887 3

where, Eln , Tln , Jln , Mln , UKln , Gln and lnUS are the natural log of the monthly stock price indices for Egypt, Turkey, Jordan, Morocco, the United Kingdom , Germany and United States..

146

Table 5.9

Estimated Results of ADF and PP Unit Root Tests (Local Currency)

ADF Test Statistics PP Test Statistics Variables in first differences Test Statistics 5% C.V. Lag order Test Statistics 5% C.V. Lag order ∆ lnE -8.622 -1.944 0 -8.972 -1.944 6 ∆ lnT -10.601 -2.887 0 -10.603 -2.887 3 ∆ lnJ -8.788 -3.45 0 -8.73 -3.45 3 ∆ lnM -9.289 -3.45 0 -9.376 -3.45 3 ∆ lnUK -10.567 -3.45 0 -10.567 -3.45 1 ∆ lnG -10.572 -1.944 0 -10.572 -1.944 1 ∆ lnUS -10.896 -3.45 0 -10.912 -3.45 4

Table 5.10

Estimated Results of ADF and PP Unit Root Tests ($US)

ADF Test Statistics PP Test Statistics Variables in first differences Test Statistics 5% C.V. Lag order Test Statistics 5% C.V. Lag order ∆ lnE -8.292 -1.944 0 -8.645 -1.944 6 ∆ lnT -10.982 -1.944 0 -11.004 -1.944 2 ∆ lnJ -8.739 -3.45 0 -8.716 -3.45 2 ∆ lnM -2.66 -1.944 5 -9.176 -1.944 3 ∆ lnUK -9.921 -1.944 0 -9.934 -1.944 4 ∆ lnG -10.975 -1.944 0 -10.974 -1.944 1 ∆ lnUS -10.889 -3.45 0 -10.903 -3.45 4

147

The results reported in tables 5.7 and 5.8 fail to reject the null hypothesis of a

unit root for all monthly stock price indices (in the levels) denominated in both local

currency and $US. This indicates that all indices are non-stationary.

The same tests are applied to the first differences of the indices expressed in

both local currency and $US. The results from both tests reported in tables 5.9 and 5.10

reject the null hypothesis of non-stationarity for all indices. This means that all indices

become stationary if they differenced once and they therefore follow unit root process,

meaning they are integrated of order one I(1).

However, it is now well known that the conventional ADF and PP unit root tests

are biased towards the non-rejection of the unit root null hypothesis in the presence of

structural breaks. These tests lack power in the presence of structural breaks in the

series and they may fail to show whether a series is first difference stationary (Wilson,

et. al. 2003, p. 445). Structural change may occur in a time series for different reasons

such as economic and political crises, environmental crisis, institutional changes and

policy changes.

Perron (1989) perceives this phenomenon and proposes a unit root test that

allows for a structural change at a known date by incorporating dummy variables for the

structural change into the Augmented Dickey-Fuller (ADF) test. Subsequently a

significant amount of research which followed Perron (1989) suggests it is better to

consider a structural change at an unknown date in which the choice of the break point

is considered as endogenous. This is preferred because any arbitrary fixed date can be

subject to criticism of data mining (Lai, 2004). The theoretical background of these

newer tests of stationarity with structural changes is presented in the following section.

148

5.4 The Development of Testing for Structural Change During the last two decades, the properties of macroeconomic and financial time series

have received attention from both theoretical and empirical perspectives. A major

debate started since Nelsson and Plosser (1982) argued that current shocks have

permanent effects on the level of most macroeconomic and financial time series.

Nelsson and Plosser used the Dickey and Fuller (1979) test to find that most

macroeconomic and financial variables have a univariate time series structure with a

unit root. It is well known that random shocks have permanent effects under the

hypothesis of a unit root.

Since these results of Nelsson and Plosser (1982) there has been more attention

given to the effect of structural change on the level of macroeconomic and financial

time series. Structural change is usually interpreted as a change in the regression

parameters (Maddala and Kim, 2003, p. 399). This change can be viewed as a big shock

or infrequent events that have permanent effect on the level of the series.

Perron (1989) continued the debate by pointing out that in the presence of a

structural break, the various Dickey-Fuller test statistics are biased towards the non

rejection of a unit root (Enders, 2004). He argued that macroeconomic fluctuations are

indeed stationary if one allows for structural changes to affect the trend function. Perron

(1989) extended the unit root strategy to ensure a consistent testing procedure against

shifting trend functions (Mills, 1999).

Perron (1989) proposed a unit root test in which he incorporated dummy

variables into the Augmented Dickey-Fuller (ADF) test to allow for a single change in

the intercept of the trend function and/or a single change in the slope of the trend

function. This structural change is allowed at a known break date BT ( TTB <<1 ),

149

where BT is the time of a structural change. Determining the time of the break a priori

means that the date chosen was uncorrelated with the data and is related to an

exogenous event. Usually economic theory suggests that this event has an effect on the

series (Perron, 1997).

Perron (1989) introduced three different models under the null hypothesis:

Model (1) tttt eyTbdDy +++= −1)(µ (5.5)

Model (2) tttt eDUyy +−++= − )( 1211 µµµ (5.6)

Model (3) ttttt eDUTbdDyy +−+++= − )()( 1211 µµµ (5.7)

where 1)( =tTbdD if 1+= BTt , 0 otherwise, and tDU =1 if BTt > and

,)()( tt LBeLA υ=

),,0.(..~ 2συ diit with )(LA and )(LB pth and qth order polynomials, respectively, in

the lag operator L. The innovation series { te }is taken to be the ARMA ( p, q) type with

the orders p and q possibly unknown. This postulate allows the series { ty }to represent

quite general processes. More general conditions are possible and will be used in

subsequent theoretical derivations.

The first model (5.5) permits an exogenous change in the level of the series,

which is referred to as a “crash model”. Under the null hypothesis of a unit root a

dummy variable is included, and takes the value one at the time of the break. The

second model (5.6) permits an exogenous change in the rate of the growth and is

referred to as a “changing growth model”. Under the null hypothesis, the drift parameter

µ changes from 1µ to 2µ at time BT . The third model (5.7) permits an exogenous

change for both.

Under the alternative hypothesis, Perron (1989) introduces three alternative

models as follow:

150

Model (1) ttt eDUy +−++= )( 1211 µµβµ (5.8)

Model (2) ttt eDTty +∗−++= )( 121 βββµ (5.9)

Model (3) ttt eDTDUty +−+−++= )()( 1212111 ββµµβµ (5.10)

where Bt TtDT −=* and tDTt = if BTt > and 0 otherwise.

BT refers to the time of the break, i.e., the period at which the change in the parameters

of the trend function occurs.

The first model (5.8) permits a one-time change in the intercept of the trend

function. Whilst the second model (5.9) allows for a change in the slope of the trend

function. Model (5.10) allows for both changes in the level and the slope to take place

simultaneously (Perron, 1989, p. 1364).

Two different models that have different implications with respect to this

structural effect have since been introduced. The first is the Additive Outlier model that

allows for a sudden change to the trend function. The second is the Innovational Outlier

model that allows for a gradual change to the trend function. Perron (1989)

implemented tests for the presence of a unit root in a framework that directly extends

the Dickey-Fuller strategy by adding dummy variables to the regression. He employed

the following three Augmented Dickey-Fuller equations which he called models (1), (2)

and (3):

tit

k

iit

At

AAt

AAt eycyTbDdtDUy ˆˆˆ)(ˆˆˆˆ

11 +∆+++++= −

=− ∑αβθµ (5.11)

tit

k

iit

Bt

BBt

BBt eycyDTtDUy ˆˆˆ*ˆˆˆˆ

11 +∆+++++= −

=− ∑αγβθµ (5.12)

tit

k

i

Cit

Ct

Ct

CCt

CCt eycyTbDdDTtDUy ˆˆˆ)(ˆˆˆˆˆ

11 +∆++++++= −

=− ∑αγβθµ (5.13)

151

For model (1), the “crash hypothesis” involves 1=Aα , 0=Aβ , 0=Aθ ; Model

(2), the “breaking slope with no crash”: 1=Bα , 0=Bγ , 0=Bβ ; and Model (3) where

both effects are allowed: 1=Cα , 0=Cγ , 0=Cβ . Under the alternative hypothesis of

a “trend stationary” process, we expect 1,, <CBA ααα ; 0,, ≠CBA βββ ;

0,, ≠CBA θθθ . Finally, under the alternative hypothesis, ,, CA dd and Bθ should be

close to zero while under the null hypothesis they are expected to be significantly

different from zero.

The results of Perron’s (1989) tests surprisingly rejected the unit root hypothesis

for 11 out 14 series analyzed by Nelson and Plosser (1982). These results confirm the

idea that where there is a structural break, the Dickey-Fuller tests are biased towards the

non-rejection of a unit root. Nevertheless, the statistical approach by Perron (1989) has

been criticized by several subsequent studies for the assumption of a known break,

which is assumed to be given exogenously. The problem with this assumption is that the

choice of the break point is based on visual inspection of the data and that the problems

to pre-testing or data-mining can arise. The criticism was first pointed out by Christiano

(1992) and followed later by several others including Zivot and Andrews (1992),

Banerjee, Lumsdaine and Stock (1992), Perron and Vogelsang (1992) and Perron

(1997). All of these studies follow new procedures, in which the choice of the break

point is considered as endogenously determined.

The new strategy in these studies endogenizes the choice for the time of the

break. They used different approaches to implement this strategy. Zivot and Andrews

(1992) extended Perron’s (1989) test to allow for structural change at an unknown

break. They consider the following null hypothesis for the series )( ty in the model:

ttt eyy ++= −1µ (5.14)

152

Zivot and Andrews assert that with this null hypothesis there is no need for the dummy

variable BDT . The alternative hypothesis stipulates that y can be represented by a

trend-stationary process with possible structural change occurring at an unknown point

in time. They followed Perron’s models but without including the dummy variable

tTbD )( in models (1) and (2). Under the alternative hypothesis, Zivot and Andrews

(1992) introduce three alternative models:

Model (A) tjt

k

j

Ajt

AAt

AAt eycytbTDUy ˆˆˆˆ)ˆ(ˆˆ

11 +∆++++= −

=− ∑αβθµ (5.15)

Model (B) tjt

k

j

Bjt

Bt

BBBt eycybTDTty ˆˆˆ)ˆ(ˆˆˆ

11 +∆++++= −

=− ∑αγβµ (5.16)

Model (C) tjt

k

j

Cjt

Ct

CCt

CCt eycybTDTtbTDUy ˆˆˆ)ˆ(ˆˆ)ˆ(ˆˆ

11 +∆+++++= −

=− ∑αγβθµ

(5.17)

where 1)ˆ( =bTDU t if bTt > , 0 otherwise and bt TtbTDT −=)ˆ( if bTt > .

The k extra regressors in the preceding regressions are added to eliminate possible

nuisance-parameter dependencies in the limit distributions of the test statistics caused

by temporal dependence in the disturbances.

The critical values in Zivot and Andrews (1992) are different to the critical

values of Perron (1989). The reason for this difference is that the selection of the time of

the break ( )bT is treated as the outcome of an estimation procedure, rather than

predetermined exogenously. Perron’s critical values are too small in absolute value and

hence biased towards rejecting the unit root null (Mills, 1999, p. 93).

Zivot and Andrews’s results show less conclusive evidence against the unit root

hypothesis than Perron (1989). They couldn’t reject the unit root hypothesis for 5 of the

11 Nelson and Plosser series for which Perron rejected the unit root hypothesis at the

153

5% level. However, Zivot and Andrew emphasized that the reverse results for these 5

series should not be considered as providing evidence for the unit root hypothesis, in

stead the reverse should be viewed as they are less evidence against the unit root

hypothesis for many of the series.

The testing for the unit root hypothesis that allows for a possible change in the

level of the series occurring at an unknown break point was continued by Perron and

Vogelsang (1992). In their study they considered similar models to those of Perron

(1989) and Zivot and Andrews (1992) Additive Outlier model (AO) for a sudden

change and the Innovational Outlier model (IO) for a gradual change. These tests are

based on the minimal value of the t statistics on the sum of the autoregressive

coefficients over all possible breakpoints in the appropriate augmented autoregression

(Perron and Vogelsang, 1992, p. 303).

In the case of the Additive Outlier model (AO), the null hypothesis of a unit root

is parameterized as follows:

tttt wyTbDy ++= −1)(δ (5.18)

where 1)( =tTbD if 1+= bTt and 0 otherwise. The sequence of errors { tw } is

specified to be a stationary and invertible (autoregressive moving average) ARMA

(p, q) process. More specifically, twLA )(* = teLB )( , where te is iid (0, 2σ ) with finite

fourth moment. )(* LA and )(LB are polynomials in L of order p and q, respectively,

whose roots are assumed to be strictly outside the unit circle.

Under the alternative hypothesis, the series ( ty ) does not contain a unit root, and

it is represented as:

ttt DUcy υδ ++= (5.19)

154

where 1=tDU if bTt > and 0 otherwise. The sequence of errors { tυ } is a stationary

and invertible ARMA (p+1, q) process of the form tt eLBLA )()( =υ . The mean of the

series is c up to time bT and δ+c afterward.

In the case of using the Innovational Outlier model (IO), the null hypothesis of a

unit root is parameterized:

))()((1 tttt TbDeLyy θψ ++= − (5.20)

where )()()( 1* LBLAL −=ψ defines the moving average representation of the noise

function with )(* LA and )(LB as defined in (5.18).

The alternative hypothesis of the model is represented by:

))(( ttt DUeLay δφ ++= (5.21)

Models (5.20) and (5.21) can be approximated by the following regression:

(5.22)

under the null hypothesis of a unit root, α is equal to 1 (which also implies 0=δ ).

Hence the appropriate testing strategy is to use the t statistics for testing 1=α when the

previous regression is estimated by ordinary least square (OLS).

Perron (1997) re-examines his (1989) findings, with the time of the break

unknown. He presents a statistical procedure used to test for a unit root allowing for the

presence of structural change in the trend function occurring at most once. This change

is assumed to occur gradually (Innovational Outlier model) by including dummy

variables for the structural change into the Augmented Dickey-Fuller test. Three

parameterizations of structural break models have been considered, model 1, model 2

and model 3. Both models 1 and 2 are Innovational Outlier models taking the following

specifications:

∑=

−− +∆+++ + =k

ititittt t eycyTbDDU y

11)( αθδ µ

155

Model (1) allows for a one time change in the intercept of the trend function:

t

k

ititttt eycyTbDDUty ∑

=−− +∆+++++=

111)( αδθβµ (5.23)

where =tDU 1 if BTt > , 0 otherwise, and 1)( =tTbD if 1+= BTt , 0 otherwise.

Model (2) is the most inclusive model, allowing for the occurrence of both

changes in the intercept and in the slope of the trend function. It is performed using the

t-statistic for the null hypothesis: 1=α

t

k

itittttt eycyTbDDTDUty ∑

=−− +∆++++++=

111)( αδγθβµ (5.24)

where tDT = 1(t) if BTt > , 0 otherwise.

Model (3) is an Additive Outlier model that allows for a sudden and rapid

change to the trend function. This model uses a two-step procedure. First, the series is

de-trended as follows:

ttt yDTty ~* +++= γβµ (5.25)

where )(1*bt TtDT −= if bTt > , 0 otherwise.

The test is then performed using the t-statistic for 1=α in the regression:

∑=

−− +∆+=k

itititt eycyy

11

~~~ α (5.26)

In all of these regressions, Tb and k are treated as unknown.

Perron’s (1997) method is different from Zivot and Andrews (1992) in the

following aspects. First, the model estimated by Perron (1997) is considered the most

inclusive model in comparison with either Zivot and Andrews (1992) or Perron and

Vogelsang (1992). Perron retains the one-time dummy tTbD )( in regressions (1) and

(2). Second, he considers both the F-sig and t-sig procedures to select the truncation lag.

Third, he selects the break date using a test of significance on the coefficient of the

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change in slope. The findings of Perron (1997) confirm the results presented in Perron

(1989), that is most of the rejections reported in Perron (1989) are confirmed. So,

Perron’s (1997) study is considered the most inclusive among all studies in testing for a

unit root in the presence of a structural change at an unknown time of the break. It is

therefore decided for this study to use Perron’s (1997) procedure.

5.4.1 Procedures for Selecting the Order of the Lag1 An important issue that should be considered when implementing the ADF test is how

to select the lag length. Several studies have observed that the size and the power

properties of the ADF test are sensitive to the lag length, (Hall (1994) and Ng and

Perron (1995)). Regarding this matter, there have been several methods on how to best

select the lag length. The most widely used are the Bayesian Information Criterion

(BIC) method by Schwartz (1978), the procedure used by Said and Dickey (1984) and

the general-to-specific method by Hall (1994). The method used by Said and Dickey

(1984) uses an F-test for the joint significance of the coefficient on the lagged first

differences of the data. The general-to-specific method based on the significant t-

statistic of the coefficient associated with the last included lag in the estimated

regression. In other words, we test the significance of the highest order lag, starting with

a maximum order of lag and reducing the order until the last lag becomes significant.

This determines the lag k and if no lags are significant then k is set to 0. According to

Ben-David and Papell (1997) the maximum order of lag should be set at 8 but most of

the statistical packages set the lag at 12. A two-sided 10% test based on the asymptotic

normal distribution is used to assess the significant of the last lags (Perron 1997). The

general to specific procedure is more preferable than other methods based on

1 These procedures have been used by most of the previous mentioned literature.

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information criteria. Perron (1994, p. 139) indicates that “methods based on information

criteria tend to select very parsimonious models leading to tests with sometimes serious

size distortions”. However, this study will consider a general to specific procedure

rather than other methods based on information criteria.

5.4.2 Procedures for Determining the Time of the Break In order to determine the time of a break endogenously at an unknown break point, two

methods have been suggested by most of the previous studies such as Banerjee et al.

(1992), Zivot and Andrew (1992), Perron and Vogelsang (1992), Perron (1997), and

Vogelsang and Perron (1998). The first method is that the time of the break bT is

selected as the value, which minimizes the t-statistic for testing 1=α . The reason for

this selection is to make it more likely to reject the null hypothesis of 1=α (Wilson,

2004, p.16). In this context, Zivot and Andrews (1992) choose the break point that gives

the least favorable result for the null hypothesis.

The second method is that bT is selected as to minimize either the t-statistic on

the parameter associated with the change in the intercept ( θt ), or the t-statistic on the

change in the slope ( γt ), or both of them. According to Perron (1997) the break point is

selected using the maximum of the absolute value of ( θt ) or ( γt ), or both of them. After

calculating the t-statistic for 1=α , it is compared with their critical values. The critical

value depends on the proportion of the observation occurring priori to the break; this

proportion is denoted by TTb=λ , where T is the total number of observations. The

values of λ ranges between 0 and 1and the critical values are identical to the Dickey-

Fuller statistics when 0=λ and 1=λ . The structural change occurs when 10 << λ . If

158

the t-statistic is grater than the critical value calculated by Perron then reject the null

hypothesis of a unit root (Enders, 2004, pp. 203-204).

However, before applying these tests on the stock markets in the MENA region,

two comments are important to be mentioned at this stage. First, the power of these tests

has been questioned by Perron himself and others. They raise the issue of the trade-off

between the power of the test and the amount of information incorporated with respect

to the choice of break point (Perron, 1997, p. 378). That is, assuming an unknown break

point will be less powerful than if the break point is already known. Second, these tests

were recently extended again by allowing for the possibility of multiple endogenous

break points in the linear regression model. Several studies argue that allowing for the

possibility of two endogenous breaks points show more evidence against the unit root

hypothesis (Maddala and Kim, 2003). However, it is believe that when testing for a unit

root and allowing for structural change at an unknown break, by doing this, we are

finding the most significant break. Consequently the debate is still continuing and this

important area of research is attracting more researchers to join the debate.

5.5 Testing for Structural Changes in MENA Stock Markets Based on the brief review of the literature, it appears that Perron’s (1997) method is the

most inclusive. Perron proposes models (1) and (2) as Innovational Outlier models and

model (3) as an Additive Outlier model. All of these models have null hypothesis of I(1)

against the alternative hypothesis of I(0) with a structural change at an unknown time of

the break. This study will start with the least restrictive model (2), represented by

equation 5.24. This model allows for a gradual change in the trend function and

provides the most general specifications. It also allows for the occurrence of changes in

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both the intercept and in the slope of the trend function. If the γ̂t is statistically not

significant, then the results of an (IO1) is considered.

The results of estimated model (2) are reported in table 5.11. These results

indicate that the stock price indices for all markets (in levels) show evidence of a non

stationarity, as the values of α̂t (in absolute value) for all variables are less than the

critical values. The α̂t for Egypt is -3.074, for Turkey is -4.671, for Jordan is -4.581,

and for Morocco is -3.487. These values are less than the critical value at 5%, which

equals to -5.57. Also, in the case of Turkey and Jordan, it is found that the coefficients

for all dummy variables and trends are significant. These results of model (2) for all

stock markets confirm the results of ADF and PP tests.

The time of the break for Turkey is found to be in 1999:7, while for Jordan is in

2001:12. Both of these breaks are represented by dotted lines in figure 5.30. These

breaks are found to be coinciding with the real events affecting both indices. This is also

clear when comparing the time of the break with the plot for each index. The stock

market in Turkey (ISE) has been affected by both of the Asian financial crisis in 1997

and the Russian crisis in 1998. Both crises had negative impacts on the Turkish

economy as a whole and on the financial sector in particular. This negative impact was

very clear over most of 1999. Despite some improvements in the beginning of 2000,

investors in 2000 (as a result of this uncertainty) transferred around $7 billion out of the

economy and between January and August 2000 the ISE index had crashed by 19%. In

the year 2001 all market indicators had declined significantly, the market capitalization

fell to $47,150 million compared with $69,659 million in 2000.

The stock market in Jordan has been affected by the limits to investor

confidence and political developments in the region especially the “Second Palestinian

160

Intifada”1. This Intifada casts its shadow on the Jordanian market during 2000. The

stock market witnessed continuous improvements after the year 2000. Despite some

interruptions after the 11th September 2001 attacks, the stock markets continued to

achieve significants growth in its indicators.

In the case of two monthly stock price indices, namely Egypt and Morocco, it is

found that the coefficients for all dummy variables are not significant. Specifically, the

coefficient of the γ̂t is statistically not significant. In this case, Model (1), which is

represented by equation 5.23, is applied for these two indices. As it is known, model (1)

allows for a one time change in the intercept of the trend function. The results for model

(1) are reported in table 5.122. All coefficients for both variables (Egypt and Morocco)

are significant. The α̂t for Egypt and Morocco are -3.205 and -3.525, respectively.

These values are less than the critical value (in absolute value) at 5%, which equals to -

4.80. These results of model (1) for stock markets in both Egypt and Morocco confirm

the results of ADF and PP tests.

Regarding the time of the break for each index, it is found that the time of break

in case of Egypt is in 2001:4, while for Morocco is in 1996:11. These times of the

breaks are represented by dotted lines in figure 5.30. These breaks are found to be

coinciding with the real events affecting both indices. The stock market in Egypt has

witnessed a sharp drop in all market indicators in the year 2000. The Egyptian economy

was under enormous pressure during 2000 and 2001 because of a sharp drop in oil

1 Intifada came into common usage in English as the popularized name for two recent Palestinian campaigns directed at ending the Israeli military occupation. These two uprisings have been significant aspects of the Israeli Palestinian conflict in recent years: 1. The first Intifada began in 1987. Violence declined in 1991 and came to an end with the signing

of the Oslo accords (August 1993) and the creation of the Palestinian National Authority. 2. The Al-aqsa Intifada (also known as the Second Palestinian Intifada or the Second Intifada) was

the violent Palestinian-Israeli conflict that began in September of 2000. (http://www.wikipidia.com).

2 Model (1) is run for all variables, even though our main aim is to show the results for the stock market indices for Egypt and Morocco.

161

prices, a sharp decrease in tourism revenue after Luxor events, and the continuing

violence and political crisis in the Middle East. All of these factors played a vital role in

weakening the performance of the Egyptian economy. By January 2001, the Egyptian

Pound was devalued by 9.6%. After that the central bank adopted a more flexible

exchange rate policy and further devalued the Pound by 6.4% in August 2001. All of

these events had a negative impact on the Egyptian Stock Market indicators.

The stock market in Morocco has witnessed a sharp drop in trading value

during 1996, which has a negative impact on the performance of Casablanca Price

index. Despite the huge reduction in the trading value during 1996, it is believed that the

signing of the trade agreement with the European Union in 1996 pushed for further

improvements in the private sector. This was reflected in the improved performance of

the Casablanca stock Exchange at the end of 1996 and during 1997 Moreover, the

results show that the time of the break for Morocco is the same when using both models

(1) and (2), is 1996: 11.

162

Figure 5.30 Plots of the Series and Estimated Timing of Structural Breaks.

4.4

4.6

4.8

5.0

5.2

5.4

5.6

5.8

95 96 97 98 99 00 01 02 03

lnE

Tb=2001:M4

8

9

10

11

12

13

95 96 97 98 99 00 01 02 03

lnT

Tb=1997:M7

4.7

4.8

4.9

5.0

5.1

5.2

5.3

5.4

5.5

5.6

95 96 97 98 99 00 01 02 03

lnJ

Tb=2001:M12

4.6

4.8

5.0

5.2

5.4

5.6

5.8

95 96 97 98 99 00 01 02 03

lnM

Tb=1996:M11

Note: The time ( bT ) of structural breaks for Turkey and Jordan is based on the IO2 model (impacting on both the intercept and the slope of each series). While for Egypt and Morocco is based on the IO1 model (impacting on the intercept only).

163

Table 5.11.

Empirical Results, Perron’s (1997) Model (IO2), (Local Currency)

Series: monthly stock price index

Time of the break ( )bT

k β βt θ θt γ γ̂t α

α̂t Result*

Egypt 1996: 7 11 0.015 1.184 0.319 1.458 -0.016 -1.202 0.872 -3.074 Unit root Turkey 1999: 7 10 0.021 4.223 1.179 4.255 -0.018 -4.146 0.653 -4.671 Unit root Jordan 2001: 12 12 -0.003 -4.156 -1.058 -4.672 0.012 4.731 0.473 -4.581 Unit root Morocco 1996: 11 9 -0.000 0.313 0.091 1.376 -0.002 -0.521 0.903 -3.487 Unit root United Kingdom 1998: 8 12 0.003 2.356 0.244 3.248 -0.004 -2.902 0.788 -3.731 Unit root Germany 1999: 8 12 0.005 3.187 0.619 3.549 -0.009 -3.538 0.724 -3.998 Unit root United State 1999: 8 12 -0.008 -3.135 0.495 2.972 -0.008 -3.135 0.700 -3.424 Unit root

* The results are significant at 5%. Critical values = -5.57, -5.08 and –4.82 for 1%, 5% and 10%, respectively.

Table 5.12

Empirical Results, Perron’s (1997) Model (IO1), (Local Currency)

Series: monthly stock price index

Time of the break ( )bT

k β βt θ θ̂t α̂ α̂t Result*

Egypt 2001: 4 11 0.001 1.684 -0.083 -1.789 0.838 -3.205 Unit root Turkey 2001: 12 10 0.009 3.022 -0.258 -2.772 0.811 -3.307 Unit root Jordan 2003: 2 12 -0.001 -1.787 0.081 3.347 0.845 -2.850 Unit root Morocco 1996: 11 9 -0.002 -2.630 0.059 2.355 0.907 -3.525 Unit root United Kingdom 2000: 12 0 -0.001 1.771 -0.048 -2.619 0.932 -2.849 Unit root Germany 2002: 3 6 0.002 2.720 -0.140 -3.198 0.859 -3.629 Unit root United State 2000: 9 4 0.001 2.758 -0.081 -3.568 0.919 -3.533 Unit root

* The results are significant at 5%. Critical values = -5.41, -4.80 and –4.58 for 1%, 5% and 10%, respectively.

164

In order to test for robustness, the Innovational Model (IO) by Perron and

Vogelsang (1992), represented by equation 5.22, is applied. This model allows for a

structural break in the mean of the series with the change supposed to affect the level of

the series gradually. The results for this approach are reported in table 5.13. Again the

results indicate that all variables show evidence of a non stationarity, as the values of α̂t

for all variables (in absolute value) are less than the critical values computed by Perron

and Vogelsang (1992). The coefficients of the dummy variable for the time of the break

tTbD )( for five variables, except for Morocco and Germany, are not significant. The

coefficient of the tTbD )( for Morocco is 2.843, while for Germany is 1.961. In general,

these results confirm the previous results by both the conventional unit root tests (ADF)

and (PP), and Perron (1997) models (2) and (1).

165

Table 5.13

Empirical Results, Perron and Vogelsang (1992) (IO), (Local Currency)

Critical values = -5.33, -4.58 and 4.27 for 1%, 5% and 10%, respectively.

Series: monthly stock price index

Time of the break ( )bT

k δ̂ δ̂t θ θ̂t α̂ α̂t Result

Egypt 1996:3 11 0.075 1.670 -0.050 -0.588 0.885 -3.143 Unit root Turkey 1998:9 10 0.192 2.636 -0.256 -1.399 0.909 -3.245 Unit root Jordan 2003:3 10 0.046 2.899 -0.008 -0.197 0.931 -2.452 Unit root Morocco 2001:1 12 -0.014 -1.362 0.126 2.843 0.939 -3.251 Unit root United Kingdom 2002:2 0 -0.019 -2.06 0.049 1.198 0.953 -2.549 Unit root Germany 2002:2 6 -0.031 -1.839 0.148 1.961 0.945 -2.573 Unit root United State 1996:7 0 0.029 1.582 -0.019 -0.400 0.936 -2.897 Unit root

166

All of these tests were based on expressing the stock market indices in local

currency. However, the same tests are redone by expressing the indices in $US. Tables

5.14 - 5.15 show the results using Perron (1997) Innovational Outlier models (2) and

(1). Starting with Innovational Outlier model (IO2), the results, reported in table 5.14,

indicate that all variables show evidence of a non-stationarity. The values of α̂t (in

absolute value) for all stock price indices in the MENA markets are less than the critical

values. However, the coefficients of the γ̂t are statistically not significant for Egypt,

Turkey and Morocco (just Jordan is found to be significant). In this case Innovational

Outlier model (1), which is represented by equation 5.23, is applied for these three

indices. As it is known, model (1) allows for a one time change in the intercept of the

trend function. The results for model (1) are reported in table 5.15. The values of α̂t (in

absolute value) for all stock price indices in the MENA markets are less than the critical

values. All dummy variables are significant except the coefficient of the trend in the

case of Egypt is statistically not significant.

In order to test for robustness, the Innovational Model (IO) by Perron and

Vogelsang (1992) is applied. This model allows for a structural break in the mean of the

series with the change supposed to affect the level of the series gradually. The results

for this approach are reported in table 5.16. The results indicate that all variables show

evidence of a non-stationarity.

The results of conducting the unit root test in the presence of structural change

by expressing the indices in $US and local currency indicate that all variables show

evidence of a non-stationarity. However, some differences are observed between the

$US and the local currency results. These included the time of the structural break of the

estimated levels of significance of some of the tests. The reasons for observing these

differences could be because local currency incorporates hedging activities of investors

167

against foreign exchange rates, while expressing indices in $US does not incorporate

such activities. Moreover, the local currency could include variations such as exchange

rate movements, domestic inflation, domestic monetary policy and political

developments, all of them are incorporated in local currency but not $US. In terms of

detecting structural breaks, the local currency results are much closer to observed real

changes experienced by each country. This is consistent with the detected high levels of

significance of important estimated parameters. For these reasons, this study will

continue with local currency.

168

Table 5.14

Empirical Results, Perron’s (1997) Model (IO2), ($US)

Series: monthly stock price index

Time of the break ( )bT

k β βt θ θt γ γ̂t α

α̂t Result*

Egypt 1996:5 4 0.000 0.150 0.088 1.116 -0.002 -0.281 0.921 -2.949 Unit Root Turkey 2001:12 12 0.005 3.342 -0.322 -0.786 -0.002 -0.350 0.474 -5.060 Unit Root Jordan 2001:10 12 -0.004 -4.347 -1.122 -4.811 0.013 4.857 0.424 -4.784 Unit Root Morocco 1995:7 6 0.000 -1.275 0.083 1.768 0.000 NA 0.956 -2.632 Unit Root United Kingdom 1998:8 12 -0.005 -2.824 0.287 3.595 -0.005 -2.824 0.749 -3.541 Unit Root Germany 1999:6 12 0.004 2.509 0.461 2.808 -0.007 -2.809 0.705 -3.307 Unit Root United State 1999:8 12 0.006 3.249 0.495 2.972 -0.008 -3.315 0.701 -3.424 Unit Root

* The results are significant at 5%. Critical values = -5.57, -5.08 and –4.82 for 1%, 5% and 10%, respectively.

Table 5.15

Empirical Results, Perron’s (1997) Model (IO1), ($US)

Series: monthly stock price index

Time of the break ( )bT

k β βt θ θ̂t α̂ α̂t Result*

Egypt 2000:12 4 -0.000 1.555 -0.097 -2.307 0.897 -3.216 Unit Root Turkey 2001:12 12 0.005 3.596 -0.459 -4.105 0.487 -5.283 Unit Root Jordan 2003:2 12 -0.000 -1.809 0.082 3.353 0.843 -2.884 Unit Root Morocco 1999:11 6 -0.000 1.767 -0.052 -2.296 -.944 -2.911 Unit Root United Kingdom 2000:12 0 -0.000 2.033 -0.052 -2.547 0.919 -3.021 Unit Root Germany 2000:12 9 0.001 2.297 -0.101 -2.987 0.887 -3.232 Unit Root United State 2000:9 4 0.001 2.758 -0.081 -3.567 0.919 -3.533 Unit Root

* The results are significant at 5%. Critical values = -5.41, -4.80 and –4.58 for 1%, 5% and 10%, respectively.

169

Table 5.16

Empirical Results, Perron and Vogelsang (1992) (IO) Model, ($US)

Critical values = -5.33, -4.58 and 4.27 for 1%, 5% and 10% respectively

Series: monthly stock price index

Time of the break ( )bT

k δ̂ δ̂t θ θ̂t α̂ α̂t Results

Egypt 1999:12 4 -0.040 -2.410 0.107 1.317 0.933 -2.989 Unit root Turkey 2002:3 12 -0.079 -1.641 -0.077 -0.387 0.759 -3.534 Unit root Jordan 2003:3 10 0.046 2.906 -0.009 -0.226 0.931 -2.473 Unit root Morocco 2001:8 9 -0.009 -0.831 -0.024 -0.484 0.947 -2.604 Unit root United Kingdom 1996:6 12 0.033 1.61 -0.037 -0.849 0.925 -2.717 Unit root Germany 2001:3 9 -0.027 -1.718 0.099 1.393 0.929 -2.329 Unit root United State 1996:7 0 0.029 1.582 -0.019 -0.400 .936 -2.897 Unit root

170

5.6 The Random Walk Behavior and the Efficiency of the MENA Stock Markets

In the pervious sections, we test for unit roots in the presence of structural

change using the Innovational Outlier models proposed by both Perron (1997) and

Perron and Vogelsang (1992). The results verify the main findings drawn form the ADF

and PP unit root tests. All the variables show evidence of a non-stationarity, which

means that all indices are integrated of order one I(1).

This indicates the efficiency of the MENA stock markets. The efficient stock

market is characterized by a random walk process, where the future prices should be

random and unpredictable. The random walk hypothesis is associated with the weak

form of the efficient market hypothesis. This asserts that all the information contained

in the history of yesterday’s stock prices are reflected in today’s stock prices.

The result of this study is identical to several previous that studies have

examined random walks in different stock markets in the world. These studies include

Dezelan (2000), Lee et al. (2001), Cheung and Coutts (2001), Smith et al. (2002) and

Narayan and Smith (2004). However, based on the previous results, all stock markets in

the MENA region are characterized by a unit root, which is consistence with the

efficient market hypothesis.

The presence of random walks in these stock markets plays an important role in

attracting foreign equity portfolio, boosting domestic saving and improving the pricing

and availability of capital. This has important implications for the allocation of capital

within an economy and hence overall economic development (Worthington and Higgs,

2003, p. 2).

171

5.7 Conclusion The main purpose of this chapter is to test for structural changes in selected stock

markets of the MENA region. The chapter starts with presenting some descriptive

statistics for the monthly returns of four emerging stock markets in the MENA region,

namely Egypt, Turkey, Jordan and Morocco and for three developed stock markets in

the US, UK and Germany. The study covers the period of December 1994 to June 2004.

The statistics show that Turkey has the highest average returns of 3.83%, and

volatility of 16.5% as measured by the standard deviation among all seven stock

markets. In comparison, Egypt has the lowest average returns of 0.18%, and the lowest

volatility of 1.6%. Moreover, the correlation matrices among all monthly stock markets

indices and returns show that most stock markets in MENA region are correlated with

each other, especially the Egyptian stock market is found to be highly correlated with all

MENA markets. However, Turkish index returns show higher correlation coefficients

with returns from developed countries than with MENA countries returns.

The conventional Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP)

unit root tests are then conducted. The results fail to reject the null hypothesis of a unit

root for all monthly stock price indices using both local currency and $US. This

indicates that all indices are non stationary. The same tests are applied to the first

differences of the indices expressed in both local currency and $US and the results from

both tests reject the null hypothesis of non-stationarity across all indices. This means

that all indices become stationary if they are first differenced once. They therefore

follow a unit root process, which means that all indices are integrated of order one I(1).

The main aim of the chapter is to test for a unit root in the presence of structural

change at an unknown time of the break. The reason for this test is that when

performing these conventional unit root tests for a time series where there are structural

172

breaks, these tests are believed to be biased towards the non-rejection of the unit root

hull hypothesis. We use Innovational Outlier models proposed by Perron’s (1997) and

Perron and Vogelsang (1992). These more appropriate tests reinforce the main findings

drawn form the ADF and PP unit root tests that all the variables are integrated of order

one I(1). Regarding the endogenously determined time of the break for each stock

market, the results show that they coincide with the real events affected each stock price

index.

Finally, based on the result that all stock markets in the MENA region are

characterized by a random walk hypothesis, this indicates that these markets are

efficient. The reason behind this conclusion is because random walk hypothesis is

associated with the weak form of the efficient market hypothesis.

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Chapter Six

Stock Market Integration in the MENA Region:

Cointegration and Causality Tests

6.1 Introduction A significant amount of recent research in empirical economics and finance has been

proposed to test for stock market integration. For this purpose a number of tests for

cointegration have been introduced over the past two decades. The most commonly

used cointegration tests are the Engle-Granger test (1987) that uses a two-step residual-

based procedure for testing the null of a no cointegration and Johansen’s test (1988,

1991, and 1995) that uses a system-based reduced rank regression approach1. In

addition to these two techniques, several others have been used such as Park’s

techniques (1990), and those of Phillips and Hansen (1990), Shin (1994) and Gregory

and Hansen (1996a, 1996b). The main feature for all of these methods is that they

concentrate on cases in which the underlying variables are integrated of order one.

However, a more advanced cointegration approach has been used recently by other

studies. This approach is the Autoregressive Distributed Lag (ARDL) approach to

cointegration.

The purpose of this chapter is to empirically estimate the integration among

stock markets in the MENA region, namely Egypt, Turkey, Jordan and Morocco, and

between the MENA region and the developed stock markets in the US, the UK and

Germany. The study will utilise the ARDL approach. The study of integration among

1 These two approaches have been analyzed in details in Appendix B.

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the MENA stock markets and between these markets and developed stock markets is

essential to analyze the consequences of this integration, as the stock market integration

has several direct and indirect potential benefits. Also, an analysis will be presented

related to the implication of finding integration among these markets. An important

contribution made by this chapter is to shed more light on the effect of the existence of

cointegration among stock markets on the efficiency of the stock market. Moreover, this

chapter will test for the Granger-causality within a Vector Error Correction Model

(VECM). The main objective of this test thus is to explore the short and long-run

dynamic relationships among the MENA stock markets and between these markets and

developed markets.

This chapter is divided into seven sections. Section 2 presents an analytical

review for the Autoregressive Distributed Lag (ARDL) model. Section 3 describes the

specification of this model. The interpretation of the results is presented in section 4.

The implications of the empirical results are presented in section 5. Section 6 examines

the Granger causality test results, while the final section concludes the chapter.

6.2 The Autoregressive Distributed Lag (ARDL) Approach to Cointegration

This study utilizes the newly proposed autoregressive distributed lag (ARDL) approach,

recently developed and introduced by Pesaran and Shin (1995 and 1998), Pesaran et al.

(1996), Pesaran (1997) and Pesaran et al. (2001). More of the recent studies indicate

that ARDL approach is preferred in estimating the cointegration relation. It is preferred

over other methods like Engle and Granger (1987), Johansen (1988), Phillips and

Hansen (1990) and Gregory Hansen (1996).

175

The ARDL approach has been recognized as a more preferable approach than

other cointegration approaches for the following reasons:

1. Conventional cointegration methods concentrate on those cases in which the

underlying variables are integrated of order one, “which introduce a further

degree of uncertainty into the analysis of levels relationships” (Pesaran, et al.

2001, p. 289). The ARDL approach is applicable irrespective of whether the

underlying regressors are purely )0(I , purely )1(I or mutually cointegrated. The

statistic underlying this procedure is the Wald or F-statistic in a generalized

Dickey-Fuller type regression, which is used to test the significance of lagged

levels of the variables under consideration in a conditional unrestricted

equilibrium error correction model (ECM) (Pesaran, et al. 2001, pp. 289-290).

2. The pre-testing procedure in the unit root cointegration literature is particularly

problematic, where the power of the unit root tests is typically very low, and

there is a switch in the distribution function of the test statistics as one or more

roots of the tx process approach unity (Pesaran, 1997, p. 184).

3. Unlike most of the conventional cointegration procedures, which are valid for

large sample size, the ARDL approach is more robust and performs well for

small sample sizes – such as this study - than other cointegration techniques. In

this context, this study uses monthly data for 115 observations during the period

December 1994 to June 2004 and it is considered as a small sample size.

However, increasing the number observations by using daily or weekly data

does not add robustness to the cointegration results, because what matters is the

length of the period not the number of observations (Narayan, 2004).

4. More of the Monte Carlo results indicate strongly that the ARDL approach is

preferable to other methods of estimating the long-run coefficients in the light of

176

the sensitivity of the diagnostic test of the specification of the ECM to

alternative estimation methods (Gerrard and Godfrey, 1998).

5. By using the ARDL approach, one can estimate the long-run and the short-run

components of the model simultaneously. Furthermore, this method can

distinguish which series is the dependent variable from the F-test when

cointegration exists (Narayan and Narayan, 2003, 11). The ARDL approach

allows for the inclusion of deterministic or exogenous regressors in the

cointegration relation.

6. The ARDL approach is robust against simultaneous equation bias and

autocorrelation on the condition that the orders of the ARDL are adequately

selected on the basis of a priori knowledge or estimated using a model selection

procedure such as Akaike Information Criterion (AIC) or Schwarz-Bayesian

Criterion (SBC) (Laurencson et al., 2003, p. 28). However, Pesaran and Shin

(1998) insist that the using of SBC performs to some extent better than AIC, and

that is as the SBC is considered as amore consistent model selection criterion

than AIC.

7. Most of applied models, such as those based on the VAR methodology, are very

sensitive to the non-normality of the data. Several previous and recent studies,

such as Groslambert and Kassibrakis (1999), Hwang and Satchell (1999) and

Piesse and Hearn (2002), have questioned the validity of these models for

analyzing non-normal data.

For all the reasons mentioned before, this study will adopt the ARDL approach

to examine the long-run equilibrium relationship among stock price indices in the stock

markets in the MENA region and between these market and developed markets.

177

Based on the results of conducting unit root tests in the presence of structural

change, it is found that the times of the breaks for all indices coincided with real events

when expressing the indices in local currency. In this chapter, the investigation of the

long-run equilibrium relationship will be carried on using stock prices indices expressed

in local currency.

The ARDL approach involves estimating conditional error correction version of

the ARDL model for variables under estimation.

According to (Pesaran and Pesaran 1997 and Pesaran and Shin 2001) the

augmented ARDL ( ),...,,, 21 kqqqp is given by the following equation:

∑=

+′++=k

ittitiit wxqLaypL

10 ),(),( ελβα nt ,...,1=∀ (6.1)

where pp LLLpL αααα −−−−= ...1),( 2

21 ,

i

i

qiqiiiii LLLqL βββββ ++++= ...),( 2

210 ki ,...,2,1=∀ ,

ty is the dependent variable,

0a is the constant term,

L is the lag operator such that 1−= tt yLy , and

tw is 1×s vector of deterministic variables such as the intercept term, a time

trend variable, or exogenous variables with fixed lags.

The long-run elasticities are estimated by:

p

qiiiiii p

q

ˆ21

ˆ10

ˆ...ˆˆ1

ˆ...ˆˆ

)ˆ,1()ˆ,1(ˆ

αααβββ

αβφ

−−−+++

== ki ,...,2,1=∀ (6.2)

where p̂ and kiqi ,...,2,1,ˆ = are the selected (estimated) values of p̂ and kiqi ,...,2,1,ˆ = .

The long-run coefficients are estimated by:

178

p

kqqqp

ˆ21

21

ˆ...ˆˆ1)ˆ,...,ˆ,ˆ,ˆ(ˆ

αααλπ

−−−−= (6.3)

where )ˆ,...,ˆ,ˆ,ˆ(ˆ 21 kqqqpλ denotes the OLS estimates of λ in equation (6.1) for the

selected ARDL model.

The error correction model (ECM) related to the ARDL )ˆ,...,ˆ,ˆ,ˆ( 21 kqqqp can be

obtained by writing equation (6.1) in terms of lagged levels and the first difference of

ktttt xxxy ,...,,, 21 and tw :

∑ ∑ ∑∑=

−

= =

−

=−−

∗

− +∆−∆−∆′+∆+−∆=∆k

i

p

j

k

i

q

jtjtiijjttititt

i

xyjwxECpay1

1ˆ

1 1

1ˆ

1,010 )ˆ,1( εβαλβα

(6.4)

where ECM is the error correction model and it is defined as follows:

∑ ′−−−= tititt wxayECM λβ̂ˆ (6.5)

tx is the k -dimensional forcing variables which are not cointegrated among

themselves. tε is a vector of stochastic error terms, with zero means and constant

variance-covariance.

The existence of an error-correction term among a number of cointegrated

variables implies that changes in the dependent variable are a function of both the level

of disequilibrium in the cointegration relationship (represented by the ECM) and the

changes in other explanatory variables. This tells us that any deviation from the long-

run equilibrium will feed back in to the changes in the dependent variable in order to

force the movement towards the long-run equilibrium (Masih and Masih, 2002, p. 69).

According to Pesaran et al., (2001), the ARDL approach involves two steps for

estimating the long-run relationship. The first step is to examine the existence of a long–

run relationship among all variables in the equations under estimation. The second step

179

is to estimate the long-run and the short-run coefficients of the same equation. The

second step is run only if a long-run relationship is found in the first step.

6.3 Model Specification This study uses a more general formula (least restrictive) of ECM with an unrestricted

intercept and an unrestricted trend (Pesaran et al., 2001, p. 296):

ttt

p

iitxyxtyyt uxwzXytccy +∆′+∆′++++=∆ −

−

=−− ∑ 1

1

11.110 ψππ (6.6)

where 00 ≠c and 01 ≠c . The Wald test (F-statistics) for the null hypotheses

0:0 =yyyyH ππ , 0: .0

. ′=xyxxyxH ππ , and the alternative hypotheses 0:1 ≠yy

yyH ππ ,

0: .1. ′≠xyxxyxH ππ . Hence the joint null hypothesis of interest in the above equation is

given by: xyxyy HHH .000ππ Ι= , and the alternative hypothesis is correspondingly stated

as: xyxyy HHH .110ππ Ι= .

In order to examine the long-run relationship among the four stock markets in

the MENA region namely Egypt, Turkey, Jordan and Morocco, and the interplay with

those of developed markets, namely US, UK and Germany, a long-run multivariate

model is estimated as follows:

Egypt:

+++++++= tEtEtEtEtEEEt UKUSMJTtaE lnlnlnlnlnln 6543210 λλλλλβ EttEtEtE DUTbDG ελλλ +++ 987 )(ln (6.7)

Turkey:

+++++++= tTtTtTtTtTTTt UKUSMJEtaT lnlnlnlnlnln 6543210 λλλλλβ TttTtT DUG ελλ ++ 87 ln (6.8)

180

Jordan:

+++++++= tJtJtJtJtJJJt UKUSMTEtaJ lnlnlnlnlnln 6543210 λλλλλβ JttJtJ DUG ελλ ++ 87 ln (6.9)

Morocco:

+++++++= tMtMMtMtMMMt UKUSJTEtaM lnlnlnlnlnln 6543210 λλλλλβ MttMtM DUG ελλ ++ 87 ln (6.10)

where, Eln , Tln , Jln , Mln , USln , UKln and Gln are the natural log of the

monthly stock price indices for Egypt, Turkey, Jordan, Morocco, the US, the UK and

Germany, respectively. ε is a vector of random error terms. However, based on the

results of testing for a unit root in the presence of structural change, using Perron’s

(1997) procedure, dummy variables are included in the above equations1. In equation

6.7 the dummy variable EtTbD )( takes the value 1 on 2001:5 and 0 otherwise. EtDU

takes the value 0 until 2001:5 and the value 1 afterward. In equation 6.8, the dummy

variable TtDU takes the value of 0 until 1999:8 and 1 afterward. In equation 6.9, the

dummy variable JtDU takes the value of 0 until 2002:1 and 1 afterward. In equation

6.10, the dummy variable MtDU takes the value of 0 until 1996:12 and 1 afterward2.

The ARDL approach to cointegration involves estimating the conditional error

correction version of the ARDL model for the monthly price indices for each stock

market in the MENA region as follows:

1 The times of these breaks are deeply justified in chapter 5. 2 According to Harvey, et al. (2001, pp. 565-566), choosing the break date at one observation later such as

1+Tb rather than the suggested (Tb ) will overcome any asymptotic size distortion.

181

Egypt:

∑ ∑ ∑ ∑∑= = = =

−−−−=

− ∆+∆+∆+∆+∆+=∆p

i

p

i

p

i

p

iitEitEitEitE

p

iitEEt USaMaJaTaEaaE

0 0 0 05432

110 lnlnlnlnlnln

∑ ∑= =

−−−−−−− ++++++∆+p

i

p

itEtEtEtEtEitEitE USMJTEGaUKa

0 0151413121176 lnlnlnlnlnln λλλλλ

tEtEtEtEtE tDUTbDGUK εβλλλλ ++++++ −− 981716 )(lnln (6.11)

Turkey:

∑ ∑ ∑ ∑∑= = = =

−−−−=

− ∆+∆+∆+∆+∆+=∆p

i

p

i

p

i

p

iitTitTitTitT

p

iitTTt USaMaJaTaEaaT

0 0 0 05432

110 lnlnlnlnlnln

∑ ∑= =

−−−−−−− ++++++∆+p

i

p

itTtTtTtTtTitTitT USMJTEGaUKa

0 0151413121176 lnlnlnlnlnln λλλλλ

tTtTtTtT tDUGUK εβλλλ +++++ −− 81716 lnln (6.12)

Jordan:

∑ ∑ ∑ ∑∑= = = =

−−−−=

− ∆+∆+∆+∆+∆+=∆p

i

p

i

p

i

p

iitJitJitJitJ

p

iitJJt USaMaJaTaEaaJ

0 0 0 05432

110 lnlnlnlnlnln

∑ ∑= =

−−−−−−− ++++++∆+p

i

p

itJtJtJtJtJitJitJ USMJTEGaUKa

0 0151413121176 lnlnlnlnlnln λλλλλ

tJtJtJtJ tDUGUK εβλλλ +++++ −− 81716 lnln (6.13)

Morocco:

∑ ∑ ∑ ∑∑= = = =

−−−−=

− ∆+∆+∆+∆+∆+=∆p

i

p

i

p

i

p

iitMitMitMitM

p

iitMMt USaMaJaTaEaaM

0 0 0 05432

110 lnlnlnlnlnln

∑ ∑= =

−−−−−−− ++++++∆+p

i

p

itMtMtMtMtMitMitM USMJTEGaUKa

0 0151413121176 lnlnlnlnlnln λλλλλ

tMtMtMtM tDUGUK εβλλλ +++++ −− 81716 lnln (6.14)

182

For equations (6.11)-(6.14), the null hypothesis of no cointegration for each of the

dependent variables is: ( 0: 76543210 ======= λλλλλλλH ) and it is tested

against the alternative hypothesis ( 0: 76543211 ≠≠≠≠≠≠≠ λλλλλλλH ) by means

of F-statistics. The asymptotic distributions of the F-statistics are non-standard under

the null hypothesis of no cointegration relationship between the examined variables.

This is irrespective of whether the variables are purely )0(I or )1(I , or mutually

cointegrated.

Two sets of asymptotic critical values are provided by Pesaran and Pesaran

(1997). The first set assumes that all variables are )0(I while the second category

assumes that all variables are )1(I . If the computed F-statistics is greater than the upper

bound critical value, then we reject the null hypothesis of no cointegration and conclude

that there exists steady state equilibrium between the variables. If the computed F-

statistics is less than the lower bound critical value, then we can not reject the null of no

cointegration. If the computed F-statistics falls within the lower and upper bound

critical values, then the result is inconclusive. In this case, following Kremers, et al.

(1992) the error correction term will be a useful way of establishing cointegration. The

second step is to estimate the long-run coefficients of the same equation and the

associated ARDL error coercion models.

183

6.4 Interpretation of the Results The ARDL model requires a priori knowledge or estimation of the orders of the

extended ARDL. This appropriate modification of the orders of the ARDL model is

sufficient to simultaneously correct for residual serial correlation and the problem of

endogenous regressors (Pesaran and Shin, 1998, p. 386). The order of the distributed lag

on the dependent variable and the regressors is selected using either the Akaike

Information Criterion (AIC) or the Schwartz Bayesian Criterion (SBC). However,

depending on Monte Carlo evidence, Pesaran and Smith (1998) find that the SBC is

more preferable than the AIC; it is a parsimonious model that selects the smallest

possible lag length, while (AIC) selects the maximum relevant lag length. This study

will use the SBC as a lag selection criterion. However, given the small sample of

observations and the relatively large number of explanatory variables in each regression,

the maximum number of lags is set equal to 31. Recall that a significant F-statistics for

testing the joint level significance of the lagged level indicates the existence of long-run

relationship among variables involved in equations 6.11-6.14.

The results of the F-statistics are reported in Table 6.1. These results show that

in the case of equation 6.11, where stock price index of Egypt is dependent variable, we

have inconclusive outcome because the calculated F-statistics is less than the upper-

bound critical value but greater than the lower-bound. In this case, as has been

1 As this current study uses monthly data covering the period December 1994 to June 2004, the use of 12 as a maximum lag length means that the effective period is December 1995 to June 2004. In each equation we have 6 regressors and this means that the total number of regression to be estimated is equal to 809,825,4)112( 6 =+ . This is technically impractical, and the Microfit statistical software cannot compute this number. The only results that could be obtained are for 4 lags or less. However, all the results reported and estimated in this study are for 3 lags as they are an improvement over the results of 1, 2 or 4 lags. The study depends on a main contention to choose a low number of lag., that is according to Obben and Nugroho (2004, p. 12) in the case that the diagnostic tests are plausible, the potential cost of effecting 12 lags would far outweigh the benefit of getting more precise coefficients.

184

mentioned before, we continue with the ARDL procedure, as the error correction term is

a useful way of establishing cointegration.

Table 6.1 F-Statistics for Testing the Existence of a long-Run Relationship

Stock Market Equation The Calculated F-Statistics

Egypt (6.11): ),,,,,/( GUKUSMJTEF 3.2493

Turkey (6.12): ),,,,,/( GUKUSMJETF 3.9203*

Jordan (6.13): ),,,,,/( GUKUSMTEJF 5.3635**

Morocco (6.14): ),,,,,/( GUKUSJJTMF 2.6116

Note: The relevant critical value bounds are obtained from Pesaran and Shin (2001). We use Table CI (v) Case V (with unrestricted intercept and unrestricted trend), where the critical values in the case of 6 regressors are 2.53 - 3.59 at a 10% significance level and 2.87 - 4.00 at a 5% significance level. * denotes that the F-statistic falls above the 90% upper bound. ** denotes that the F-statistic falls above the 95% upper bound.

In the case of equations 6.12 and 6.13, where the stock price indices of Turkey

and Jordan are dependent variables, the results indicate that the calculated F-statistics

for both indices are greater than the upper critical values at a 10% and 5% significant

level respectively. Thus, the null hypothesis of no cointegration cannot be accepted.

Therefore, there are long-run relationships when stock price indices of Turkey and

Jordan are treated as dependent variables.

Finally, in the case of equation 6.14, where stock price index of Morocco is a

dependent variable, the results indicate that we have an inconclusive outcome since the

calculated F-statistics is less than the upper bound critical value but greater than the

lower bound. In this case, we continue with the ARDL procedure and our decision on

whether the long-run relationship exists or not depends on the significance of the error

correction term. Following the establishment of the existence of cointegration, we move

to the second stage, where we retain the lagged level of variables and estimate equations

6.11-6.14.

185

6.4.1 Stock Market of Egypt The conditional error correction version of the ARDL model for the monthly price

index of Egypt is given by equation 6.11, where the stock price index of Egypt (lnE) is

the dependent variable. The equation is based on the ARDL model selected by the

Schwarz Bayesian Criterion (SBC). As it is shown in Appendix C, the overall goodness

of fit of the estimated equations is high, showing 2R = 0.95, the F-statistics measuring

the joint significance of all regressors is statistically significant. Regarding the

diagnostic tests, the model passes these tests for serial correlation, functional form and

heteroscedasticity. The SBC lag specification is ARDL (1,0,0,0,1,1,0), where the

numbers in parenthesis represent the lags for the variables which are listed in the same

order. The long-run coefficient estimates are reported in Table 6.2.

Table 6.2 Long-Run Coefficients Estimated Based on

ARDL (1,0,0,0,1,1,0) Model Selected Based on SBC.

Dependent Variable: Egypt (lnE)

Regressors Coefficient Standard Error T-Ratio Intercept -0.3684 3.4319 -1.0735 lnT 0.3979 0.1287 3.0922*** lnJ 1.4353 0.2318 6.1912*** lnM 0.7502 0.2089 3.5918*** lnUS 1.4782 0.7797 1.8959* lnUK -2.5054 1.0071 -2.4879** lnG -0.2213 0.4613 -0.4797 t -0.0102 0.0055 -1.8767* D(Tb) Et 0.6833 03152 2.1678**

DU Et -0.5174 0.1827 -2.8319***

* Significant at a 10% level ** Significant at a 5% level *** Significant at a 1% level

Table 6.2 shows that the long-run coefficients for the three regressors namely

lnT, lnJ and lnM are positive and highly significant at 1% per cent level, while both

lnUS and lnUK are significant at a 10% and 5% respectively. This implies that there is a

significant long run impact of these regressors on lnE. An increase in the stock price

186

index of Turkey (lnT) by 1% will have a significant long-run impact on the stock price

index of Egypt (lnE) by 0.4%, whereas an increase in the stock price index of Jordan

(lnJ) by 1% will have a positive long run impact on the stock price index of Egypt by

1.44%. Also, an increase in the stock price index of Morocco (lnM) by 1% will have a

long-run impact on the stock price index of Egypt by 0.75%. This long-run impact of

the stock markets indices in the MENA regions on the Egyptian stock market indicates

that all of these indices are cointegrated with each other throughout the long term. In the

case of developed stock markets in the US, the UK and Germany, the results show that

both stock price indices in the US and the UK markets have long-run impact on the

stock price index of Egypt. An increase in stock price index of the US by 1% will have

a long-run impact on stock price index of Egypt by 1.48%. Also, an increase of stock

price index of the UK by 1% will have a negative impact on stock price index of Egypt

by 2.51%. The stock price index in Germany has no long-run effect on Egypt1.

Both dummy variables are highly significant. This indicates that the structural

change, which happens at the time of the break, has a long-run impact on stock price

index. In fact, the Egyptian economy was under enormous pressure during 2000 and

2001. Influencing factors on this pressure were: a sharp drop in oil prices, a sharp

decrease in tourism revenue after the events in Luxor city, and the continuing violence

and political crisis in the Middle East. All of these factors played a vital role in

weakening the performance of the Egyptian economy in general and the stock market in

particular. By January, 2001, the Egyptian Pound devalued by 9.6%. After that the

central bank adopted more flexible exchange rate policy; it again devaluated the Pound

by 6.4% in August, 2001.

1 Where the long-run coefficient is not significant, it maybe due to change valuation via bilateral exchange rate. Where trend is significant then this maybe picking up trend changes via bilateral exchange rate.

187

The estimated error correction model (ECM) of the selected ARDL

(1,0,0,0,1,1,0) is presented in Table 6.3, and the results show that the coefficient of the

ECM is negative, as expected, and highly significant at a 1% level. This confirms the

existence of a stable long-run relationship and points to a long-run cointegration

relationship between variables (see Bannerjee et al. 1998). The ECM represents the

speed of adjustment to restore equilibrium in the dynamic model following a

disturbance. The coefficient of the ECM is -0.2563, this implies that a deviation from

the long-run equilibrium following a short-run shock is corrected by about 26% after

one month.

Table 6.3 Error Correction Model (ECM) Results for the Selected

ARDL (1,0,0,0,1,1,0) Selected Based on SBC

Dependent Variable: ∆lnE

Regressors Coefficient Standard Error T-Ratio Intercept -0.0944 0.87953 0.1074 ∆lnT 0.1020 0.0364 2.7988*** ∆lnJ 0.3679 0.0832 4.4233*** ∆lnM 0.1923 0.0637 3.0198*** ∆lnUS -0.4315 0.2569 -1.7198* ∆lnUK 0.2066 0.3086 0.66953 ∆lnG -0.0567 0.1141 -0.4971 t -0.0026 0.0014 -1.8495* ∆D(Tb) Et 0.1751 0.0781 2.2425 **

∆DU Et -0.1326 0.0517 -2.5653***

Ecm 1−t -0.2563 0.0566 -4.5325***

* Significant at a 10% level ** Significant at a 5% level *** Significant at a 1% level

188

6.4.2 Stock Market of Turkey To measure the long-run relationship between the stock price index in Turkey (lnT) and

other stock price indices in the MENA region and developed markets, equation 6.12 is

estimated. This represents a conditional error correction version of the ARDL model for

monthly price index of Turkey. The equation is estimated based on the ARDL model

selected by the Schwarz Bayesian Criterion (SBC). The overall goodness of fit of the

estimated equation, as shown in Appendix C, is extremely high where 99.02 =R ; the F-

statistics measuring the joint significance of all regressors is statistically significant.

Regarding the diagnostic tests, the model shows no serial correlation and no

heteroscedasticity. The SBC lag specification is ARDL (1,0,0,0,0,0,1), where the

numbers represent the lags for the variables which are listed in the same order. The

long-run coefficient estimates are reported in Table 6.4

Table 6.4 Long-Run Coefficients Estimated Based on ARDL

(1,0,0,0,0,0,1) Model Selected Based on SBC

Dependent Variable: Turkey (lnT)

Regressors Coefficient Standard Error T-Ratio Intercept 4.4820 5.8782 0.7625 lnE 0.8808 0.2928 3.0085*** lnJ 0.4741 0.5987 0.7919 lnM -1.5724 0.5169 -3.0429*** lnUS -1.0361 1.2573 -0.8241 lnUK 0.9347 0.6801 1.1516 lnG 0.7832 0.6801 1.1516 t 0.0798 0.0197 4.0459*** DU Tt -0.0767 0.0298 -2.5733**

** Significant at a 5% level *** Significant at a 1% level

The results show the estimated long-run coefficients of six regressors. Two

regressors have a significant long-run impact on the stock market of Turkey, namely the

stock markets in Egypt and Morocco. The results show that a 1 % increase in lnE will

have a significant long-run impact on lnT by 0.88 %, also an increase in lnM by 1 %

189

will have a significant negative impact on lnT by 1.6 %. This gives the indication that

only stock markets in the MENA region have a long-run impact in the stock market of

Turkey. The coefficient of lnG is statistically not significant, which means that

Germany has no long-run impact on Turkey. However, the coefficient of ∆lnG in Table

6.5 is statistically significant at a 1 % level. This implies that although there is no

significant long-run impact of the stock price index of Germany on the stock price index

of Turkey, a change in the stock price index of Germany has a significant short-run

impact on the change of the stock price index of Turkey. This result confirms the

existence of a strong economic relationship between the two countries. However, the

statistics show that around 2.5 million people of Turkish origin live in Germany, and

Germany is the most important trading partner to Turkey. The volume of bilateral trade

in 2003 reached EUR 16 billion. Moreover, the largest number of foreign companies

investing in Turkey is German (see www.auswaertiges-amt.de).

The dummy variable in Table 6.4 is statistically significant at a 5 % level and

has a negative sign. This indicates that the structural change has a negative long-run

impact on the stock price index of Turkey. This structural change took place in 1999:7.

All events indicate that both the Asian and Russian crises in 1997 and 1998,

respectively, had a negative impact on the performance of the Istanbul Stock Exchange

(ISE). This negative impact can be observed during the 1999’s and early 2000 when the

ISE witnessed excessive volatility.

The error correction model ECM of the selected ARDL (1,0,0,0,0,0,1), as shown

in Table 6.5, is significant at a 1% level with the expected negative sign. The ECM

represents the speed of adjustment of the ∆lnT to its long-run equilibrium following a

shock. Moreover, this significant of the ECM confirms the existence of a stable long-

run relationship between the significant regressors and the dependent variable. The

190

ECM suggests that 30 % of the adjustment back to long-run equilibrium is corrected

after one month.

Table 6.5 Error Correction Model (ECM) Results for the Selected

ARDL (1,0,0,0,0,0,1) Selected Based on SBC

Dependent Variable: ∆lnT

Regressors Coefficient Standard Error T-Ratio Intercept 1.3614 1.8982 0.7172 ∆lnE 0.2675 0.1040 2.5734** ∆lnJ 0.1440 0.1716 0.8390 ∆lnM -0.4777 0.1457 -3.2789*** ∆lnUS -0.3147 0.3898 -0.8074 ∆lnUK 0.2839 0.4662 0.6090 ∆lnG 1.0875 1.8982 0.7172*** t 0.0242 0.0068 3.5439*** ∆DU Tt -0.0233 0.0086 -2.7052***

Ecm 1−t -0.3037 0.0741 -4.0991***

** Significant at a 5% level *** Significant at a 1% level

6.4.3 Stock Market of Jordan Given the existence of a stable long-run relationship when Jordan is a dependent

variable (lnJ), the second stage is to estimate the ARDL model in order to derive the

long-run and short-run estimates. For this purpose, equation 6.13 which represents a

conditional error correction version of the ARDL model for monthly price index of

Turkey is estimated. The results in Appendix C show that the overall goodness of fit of

the estimated equation is fairly high, it shows 97.02 =R , the F-statistics measuring the

joint significance of all regressors is statistically significant. Moreover, the underlying

ARDL model passes all the diagnostic tests such as serial correlation, functional form

and heteroscedasticity. The SBC lag specification is ARDL (1,0,1,0,0,2,0), where the

numbers represent the lags for the variables which are listed in the same order.

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The long-run coefficient estimates are reported in Table 6.6. The results show

that among MENA markets just stock markets in Egypt and Turkey have a positive

long-run impact on Jordan. An increase in lnE by 1 % will have a significant long-run

impact on lnJ by 0.42 %, while an increase in lnT by 1 % will have a negative long-run

impact on lnJ by 0.53 %. Regarding the developed markets, the results show no long-

run impacts from all of these markets toward Jordan.

Table 6.6 Long-Run Coefficients Estimated Based on ARDL

(1,0,1,0,0,2,0) Model Selected Based on SBC

Dependent Variable: Jordan (lnJ)

Regressors Coefficient Standard Error T-Ratio Intercept 10.094 3.4786 2.9018*** lnE 0.4244 0.1356 3.1298*** lnT -0.5270 0.1778 -2.9639*** lnM -0.0526 0.2341 -0.2250 lnUS -0.7309 0.5926 -1.2334 lnUK -0.1884 0.9650 -0.1953 lnG 0.6469 0.4948 1.3073 t 0.0232 0.0079 2.9174*** DU Jt -0.0464 0.1664 -0.2786

*** Significant at a 1% level

The error correction model (ECM) of the selected ARDL (1,0,1,0,0,2,0) is

included in Table 6.7. The coefficient of ECM, which is statistically significant with the

correct negative sign, represents the speed of adjustment of lnJ back to its long-run

equilibrium. The significance of this coefficient points to a long-run cointegration

relationship between lnJ and other significance regressors. The ECM suggests that

following a shock, about 14 % of the adjustment back to long-run equilibrium is

completed after one month.

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Table 6.7 Error Correction Model (ECM) Results for the Selected

ARDL (1,0,1,0,0,2,0) Selected Based on SBC

Dependent Variable: ∆lnJ

Regressors Coefficient Standard Error T-Ratio Intercept 1.4029 0.4702 2.9837*** ∆lnE 0.0589 .02657 2.2204*** ∆lnT -0.0057 0.0255 -0.2232 ∆lnM -0.0073 0.0329 -0.2222 ∆lnUS -0.1016 0.0818 -1.2413 ∆lnUK 0.0767 0.1452 0.5284 ∆lnUK(-1) 0.3735 0.0926 4.0315*** ∆lnG 0.0899 0.0610 1.4749 t 0.0032 0.7297E-3 4.4219*** ∆DU Jt -0.0064 0.0222 -0.2898

Ecm 1−t -0.1389 0.0425 -3.2711***

*** Significant at a 1% level

6.4.4 Stock Market of Morocco To measure the long-run relationship between stock price index in Morocco (lnM) and

other stock price indices in MENA region and developed markets, equation 6.14 which

represents a conditional error correction version of the ARDL is estimated. The

equation is estimated based on the ARDL model selected by the Schwarz Bayesian

Criterion (SBC). The overall goodness of fit of the estimated equation, as shown in

Appendix C, is high, showing 98.02 =R . The F-statistics measuring the joint

significance of all regressors is statistically significant. Moreover, the underlying ARDL

model passes all the diagnostic tests such as serial correlation, functional form and

heteroscedasticity. The SBC lag specification is ARDL (1,0,0,0,0,0,0), where the

numbers represent the lags for the variables which are listed in the same order. The

long-run coefficient estimates are reported in Table 6.8.

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Table 6.8 Long-Run Coefficients Estimated Based on ARDL

(1,0,0,0,0,0,0) Model Selected Based on SBC

Dependent Variable: Morocco (lnM)

Regressors Coefficient Standard Error T-Ratio Intercept -0.2954 3.6397 -0.0812** lnE 0.5779 0.2375 2.4338** lnT -0.2895 0.1526 -1.8978* lnJ -0.2397 0.3260 -0.7354 lnUS 0.1188 0.7275 0.1634 lnUK 1.1174 1.0831 1.0835 lnG -0.4634 0.4855 -0.9544 t 0.0049 0.0056 0.8739 DU

tM 0.4740 0.1697 2.7927***

* Significant at a 10% level ** Significant at a 5% level *** Significant at a 1% level,

The results in Table (6.8) show that two regressors have a significant long-run

impact on the stock market of Morocco, namely, the stock markets of Egypt and

Turkey. The results show that a 1% increase in lnE will have a significant long-run

impact on lnM by 0.58%. Also, an increase in lnT by 1% will have a significant long-

run impact on lnM by -0.29%. This gives an indication that only stock markets of the

MENA region, namely, Egypt and Turkey, have long-run impacts on the stock market

of Morocco, while developed markets have no long-run effect on the stock market of

Morocco.

The dummy variable is found to be significant at 1% level with a positive sign.

This indicates that the structural change occurred in 1996:11 had a positive long-run

impact on the stock market of Morocco. However, despite a huge reduction in the

trading value during 1996, it is believed that the signing of trade agreement with the

European Union in 1996 had pushed for more improvement in the private sector, which

was reflected in the stunning performance of the Casablanca stock Exchange at the end

of 1996 and during 1997.

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The error correction model (ECM) of the selected ARDL model (1,0,0,0,0,0,0)

is reported in Table (6.9). The coefficient of ECM, which is statistically significant with

the correct sign, represents the speed of adjustment of lnM back to its long-run

equilibrium. The significance of this coefficient points to a long-run cointegration

relationship between lnM and other significance regressors. The ECM suggests that

following a shock, about 14 % of the adjustment back to long-run equilibrium is

completed after one month.

Table 6.9 Error Correction Model (ECM) Results for the Selected

ARDL (1,0,0,0,0,0,0) Selected Based on SBC

Dependent Variable: ∆lnM

Regressors Coefficient Standard Error T-Ratio Intercept -0.0398 0.4914 -0.0811*** ∆lnE 0.0779 0.0276 2.8165*** ∆lnT -0.0390 0.0222 -1.7557* ∆lnJ -0.0323 0.0435 -0.7425 ∆lnUS 0.0160 0.0995 0.1611 ∆lnUK 0.1582 0.1437 1.1006 ∆lnG -0.0625 0.0625 -0.9989 t 0.0006 0.0007 0.8862 ∆DU

tM 0.0639 0.0290 2.2019**

Ecm 1−t -0.1348 0.0455 -2.9623***

* Significant at a 10% level ** Significant at a 5% level *** Significant at a 1% level

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6.5 Implications of the Empirical Results Based on the results obtained in section 6.4, it is clear that there are long-run

relationships among all stock markets in the MENA region. More precisely, the indices

of these markets move together in the long-run. Table 6.10 summarizes the results of

the estimated equations 6.11-6.14.

Table 6.10 The long-Run Impacts on Stock Markets in the MENA Region

Equation Dependent Variable The significant regressors that affect the dependent variable

6.11 Egypt Turkey, Jordan, Morocco, US and UK 6.12 Turkey Egypt and Morocco 6.13 Jordan Egypt and Turkey 6.14 Morocco Egypt and Turkey

The results reported in Table 6.10 indicate the existence of a significant level of

cointegration among all stock markets in the MENA region when Egypt is a dependent

variable. This means that all of these markets move together in the long-run. So at the

regional level all markets are integrated. In the long-run, any movement occurs in

Turkey, Jordan or Morocco will have a long-run impact on Egypt. A significant level of

cointegration is found between Egypt and both US and UK. This means that there is a

long-run relationship between the Egyptian stock market and both the US and UK stock

markets when Egypt is a dependent variable. In the long-run any movement occurs in

the US or UK will have a long-run impact on Egypt. In the case that Turkey is a

dependent variable, the results show that Jordan has no long-run impact on Turkey, just

Egypt and morocco have this impact. In the case that Jordan is a dependent variable;

both Egypt and Turkey have the long-run impact on Jordan, so these three markets are

cointegrated with each other when Jordan is the dependent variable. Finally, when

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Morocco is a dependent variable, long-run impacts have been found from the stock

markets of Egypt and Jordan toward the stock market of Morocco.

Based on the previous results, it is found that cointegration does exist among all

MENA stock markets in the case where the Egypt stock market is the dependent

variable. This means that all markets in the region are integrated with each other. In the

case where Turkey is a dependent variable, the results show that Jordan has no long-run

impact on Turkey whereas Egypt and Morocco do. However, in the case that Jordan is

the dependent variable; both Egypt and Turkey have long-run impacts on Jordan, so

these three markets are cointegrated with each other when Jordan is the dependent

variable. Finally, when Morocco is the dependent variable; long-run impacts flow from

the stock markets of Egypt and Jordan towards the stock market of Morocco. So, the

Egyptian stock market has an impact on all other MENA markets. Also, Turkey is

found to have a similar impact on all markets. This means that any movement in stock

price indices in these two markets (Egypt and Turkey) will be followed by similar

movements from other indices in the region and this indicates the existence of

integration among these markets.

This integration is essential as it could offer investment opportunities not

possible by each market separately. However, there is no long-run relationship between

MENA stock markets and developed markets represented by US, UK and Germany.

This means that the MENA stock markets are segmented from developed markets.

Egypt is the only exception; the study found that the stock market of Egypt has long

run-relationship with US and UK markets. It is believed that one of the reasons for the

integration between the stock market in Egypt and the stock market in the UK is that in

the year 2000, seven companies in telecommunication, construction and tourism sectors

listed their shares on the London Stock Exchange

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(www.erf.org.eg/html/economic_00html/top_main.html). The reason for the integration

between the stock market in Egypt and the stock market in the US is that the Egyptian

economy is highly tied with the US economy.

According to Bekaert and Harvey (1995), segmentation is greatly influenced by

the economic and financial polices, and regulatory institutions in each country. Also it is

believed that differences in privatization methods between countries cause this

segmentation. Moreover, the low level of economic integration between these countries

play vital role in this segmentation. This study suggests that MENA countries should

adopt more sound economic and financial polices, such as the implementation of

liberalization and privatization programs in order to be more integrated with the

developed markets and the rest of the world.

The implication of finding that stock markets in the MENA region are

cointegrated with each other and just Egypt cointegrated with developed stock markets

will be analyzed at two levels; the regional level and the international level.

First, at the regional level, the results show that stock markets in the MENA

region are integrated with each other, which implies that these stock markets do share

long-run equilibrium relationship. This implies the existence of the law of one price

(LOOP) among them. This means that the potential of regional investors for obtaining

abnormal profits through portfolio diversification is limited in the long-run. The reason

for this is that as the MENA stock markets are cointegrated, abnormal profits will be

arbitraged away in the long-run. Moreover, in the absence of barriers or potential

barriers generating country risk and exchange rate premium, financial assets of similar

risk and liquidity are expected to achieve similar yields, irrespective of nationality or

location (see, Von Furstenberg & Jeon 1989 and Narayan et al. 2004). However, despite

no arbitrage opportunities in the long-run, investors can still achieve arbitrage profits

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through portfolio diversification in the MENA stock markets in the short-run. This

depends on the speed of adjustment which is represented by the error correction term

(ECT). The coefficients of ECT’s for the MENA stock markets are as follows, For

Egypt it is (-0.26), for Turkey it is (-0.30), for Jordan it is (-0.14) and for Morocco is (-

0.14). These coefficients show that the speed of adjustment is slow which means the

short-term can last for a longer period, and there is a high possibility of achieving

arbitrage profits as the LOOP may not hold.

Second, at the international level, the results show that stock markets in Turkey,

Jordan and Morocco are not integrated with developed markets, represented by US, UK

and Germany. This means that there is no long-run impact from developed stock

markets towards these markets. However, a long-run relationship is found between

Egypt and both US and UK when Egypt is a dependent variable. Based on these results,

there are opportunities for international investors to obtain long-run gains through

international portfolio diversification in Turkey, Jordan and Morocco stock markets.

Also at the same time, investors from these three countries have the opportunities to

obtain long-run gains through investing in developed markets. The existence of long-

run relationships between Egypt and both US and UK implies that the potential for

investors from the Egyptian stock market to obtain abnormal profit through portfolio

diversification in US and UK is limited in the long-run. The reason for this is that the

abnormal profit will be arbitraged away in the long-run. However, there are

opportunities for achieving abnormal profit by investing in Germany as it is not

cointegrated with the MENA markets. With the short-run error correction model

(ECM), arbitrage opportunities and possible profits may also be achieved from

diversification as the LOOP may not hold in the short-run. In the case when Egypt is a

dependent variable, the coefficient of ECM is -0.26, which shows that the speed of

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adjustment is not very fast (ie. it shows that following a shock, about 26% of the

adjustment back to the long-run equilibrium is completed after one month, which means

it takes around 4 months to complete the whole adjustment process). This indicates that

the short-term could last for a period of time, and there is a high possibility of achieving

arbitrage profits during this period as the LOOP may not hold.

The finding based on the previous section is that most stock markets in the

MENA region (except for Egypt) are not integrated with developed stock markets. This

situation gives the possibility of an increase in the portfolio equity flows to these stock

markets in the long-run. This increase based on the notion of achieving more profits

from international diversification as stocks in the MENA markets are not exposed to the

same risk factors as developed markets. However, statistics for portfolios equity flow in

table 6.11 show different story. The MENA region was the least region in the world to

receive portfolio equity flows from developed countries over the period (1995-2003).

Table 6.11 shows the net inward portfolio equity flows to developing countries during

the period 1995 to 2003.

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Source: The World Bank, 2004, Global Development Finance: harnessing cyclical gains for development, Washington DC., US.

Different factors play major role for the low level of inward portfolio equity

flows to the MENA region. These factors are as follows:

1. Most of the stock markets in the MENA region are still, from some

perspectives, underdeveloped. Market capitalization as a share of GDP is

still small in most of these markets compare to developed countries1.

Furthermore, the stock markets in the MENA region tend to lag technology

behind developed markets. Technology plays crucial role in the trading,

clearance and settlement process. Also, some regulations such as limits on

foreign ownership in the MENA stock markets impede the inflow of

portfolio equity from developed markets (World Bank, 2004, p. 95).

2. Developing countries in the MENA region are more vulnerable to

macroeconomic shocks; this is a matter of concern for investors in portfolio

equity (World Bank, 2004). As has been mentioned before in chapter four,

all MENA countries were affected by international events including a global

1 The stock market in Jordan (ASE) is an exceptional case. The market capitalization as a share of GDP reached to around 111% by the end of 2003 and to around 164% at the end of 2004 (see table 4.6).

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downturn as a result of the Asian financial crises, the 11th of September

attacks, the war in Iraq, and sharp drop in oil prices in several occasions.

3. One of the main characteristics of the MENA region is the political

instability. The political and military conflicts in the region have affected the

economic environment for the last fifty years. These conflicts include the

Palestinian-Israeli ideological military conflict, the wars in Iraq in 1990 and

2003, and many national political conflicts in some of MENA countries. All

of these events caused political instability in the region, which had and still

negative effects on the economic environment in general, and on the equity

portfolio flows in particular.

4. Some emerging stock markets in different regions are more attractive than

the emerging stock markets in the MENA region. The main emerging stock

markets that attract most of the portfolio equity flows from developed

countries are China, India, Brazil, and some other markets in Latin America

and South Asia. These markets located in more stable region than MENA

region, and considered more developed than stock markets in MENA region.

All of these factors will increase the risk premium in the MENA markets. Based

on this, huge efforts should be carried on to improve the institutional reforms and

increase the degree of openness for foreign capital in the MENA stock markets.

Increasing the market capitalization, adopting new technology and increasing the

liberalization in the MENA stock markets, all of these policies are expected to attract

more equity portfolio to the region. Moreover, these markets need to develop structural

relations with major foreign and regional markets. As such, MENA stock markets have

the potential of offering distinctive risk-return characteristic to investors seeking

international diversification.

202

Moreover, economic policy makers should strengthen their national economies,

so they become less vulnerable to macroeconomic shock, as this is a matter of concerns

for portfolio managers. A stable economy in a stable political environment has a strong

ability to attract equity portfolio more than others. Another important issue that

economic policy makers should be aware of is the culture of their societies and how this

culture affects the economic environment there. On the other hand, how these societies

are perceived by the others. Investment decisions depend not just on economic factors

but also on political and social factors as well. The MENA region is one of the most

dynamic regions in the world from both economic and political perspective. Still to say

that integration by itself is an important goal for all emerging stock markets, even it

could reduce the international equity portfolio diversification to these markets in the

long-run.

Regarding the effect of the existence of cointegration among stock markets in

the MENA region on efficient market hypothesis (EMH), the contribution of this

current study is as follows. First, based on a significant number of previous studies,

such as Wallace (1992), Baffes (1994), Engle (1996) and Ahlegren & Antell (2002),

this study does not support Granger’s theory. This theory asserts that the existence of

cointegration between two stock prices implies the ability to predict each price’s

movement, which indicates market inefficiency. Secondly, what this study tries to bring

out is that in the case of the existence of cointegration between two or more stock

markets, this indicates that these markets are efficient in the long-run. The reason for

this is because the existence of cointegrated vector implies the law of one price (LOOP).

Note that it is actually the law of one vector of prices in that the prices do not have to be

the same, but the differences reflecting variations: including transaction costs and risk

premiums have to be constant in the long-run (Malkiel, 2003). If this is true, then there

203

are little or no arbitrage opportunities. Possible benefits can be achieved from the

diversification of the portfolio from one market in MENA region to another in the long-

run. Malkiel (2003) defines market efficiency as the lack of systematic arbitrage

opportunities which rules out opportunities of earning risk adjusted excess return. Then

the finding of cointegration with the LOOP interpretation implies there maybe no

general equivalence between cointegration and market inefficiency in the long-run.

However, the existence of the VECM may imply that the LOOP does not hold and the

market could be inefficient in the short-run.

Based on the previous argument, a result could be reached that as all stock

markets in the MENA region are cointegrated; this indicates that these markets are

efficient in the long-run. Nevertheless, as the LOOP may not hold in the short-run,

arbitrage opportunities or possible benefit can be achieved in these markets.

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6.6 Granger Causality The results of the bound tests indicate the existence of cointegration among emerging

stock markets in the MENA region, i.e. there is a long-run equilibrium relationship

among the MENA stock markets. Also the results indicate the existence of cointegration

between the stock market of Egypt and developed markets represented by US and UK.

The following step is to construct a standard Granger causality tests. This test will be

augmented with a lagged error correction term when variables are cointegrated, and

then estimated within a Vector Error Correction Model (VECM). The conducting of this

test helps to explore the short and long-run dynamic relationships among the MENA

stock markets, and between these markets and developed markets. The estimated long-

run causal relationship among stock markets is based on the error correction model

(ECM).

Based on the theoretical background, if two variables, i.e. ty , tx are cointegrated

with each other, then the error correction term is required in testing Granger causality as

follows (see Granger et. al. 2000):

∑ ∑= =

−−−− +∆+∆+−+=∆k

i

k

ititiitittt xyxyy

1 11211110 )( εααφγα (6.15)

∑ ∑= =

−−−− +∆+∆+−+=∆k

i

k

ititiitittt xyxyx

1 12211120 )( εββφγβ (6.16)

where 1γ and 2γ represent the speed of adjustment , and the ( 11 −− − tt xy φ ) represents the

error correction term. The null hypothesis for equation 6.15 is that 0H :

0... 22221 === iααα and 01 =γ , in case of rejecting the null hypothesis, this implies

that tx does Granger cause ty . The null hypothesis for equation 6.16 is that: 0H :

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0... 22221 === iβββ and 02 =γ , in case of rejecting the null hypothesis, this implies

that ty does Granger cause tx .

The standard causality test is based on the past changes in one variable that

explain current changes in another. However, if cointegration exists between two

variables such as ty and tx , i.e. they share a common trend in the long-run, then current

adjustments in one variable towards its long-run equilibrium are partially the result of

current changes in the other variable. This causality could be detected based on the

significant error correction term (Nell, 1999). According to (Granger, 1988, p. 199) if

these two variables are cointegrated, then there must be causation in at least one

direction between them that is not always detected by using the standard Granger

causality. Moreover, Granger (1988) asserts that neglecting the error correction term

(ECT) when testing for causality among cointegrated variables leads to serious biases

due to filtering out low-frequency (long-run) information.

The test for causality will be misspecified if relevant countries are excluded. It is

well known that the bias of incorrectly excluding a relevant explanatory variable is

greater than the efficiency cost of including an irrelevant explanatory variable. As the F-

statistics in table 6-1 show, the MENA and developed markets should be included in the

Granger-Causality test for Jordan and Turkey (at a 5% and 10% level of significance).

However, the F-statistics for Egypt and Morocco fall within the inconclusive region. As

mentioned, we need to include these variables to avoid potential serious

misspecification bias. The only cost in doing this is a loss of statistical efficiency, which

should not be large because of the relatively large sample size 115 observations1.

1 We could also use stock return variables instead of the share price indices although the Granger-causality results presented in table 6-12 show that the developed countries are important according to the share price indices.

206

There are other reasons for including developed stock markets in the analysis;

the study includes the US, the UK and the German stock markets. The US, the UK and

Germany are among the largest economies in the world. They have mature and well

functioning markets constituting a significant portion of capitalization in the global

market. All countries in the MENA region have strong economic relationships with

these three countries. This study explores whether MENA stock markets can offer

international investors from these developed countries unique risk and returns

characteristics to diversify international portfolios. In the last decade and as a result of

liberalization and privatization policies in the MENA region, portfolio equity continues

to flow to the region from those countries. In addition, more multinational corporations

such as banks, insurance companies and industrial companies have started their

operations in the region recently. For all of these reasons it is very important to include

them in the study.

The Granger Causality within VECM approach is applied for stock price indices

in MENA markets and developed markets. The results are presented in Table 6.12. For

the short-run causality, the Chi-square test of the explanatory variables indicates the

significance of the short-run causal effect, while the t-statistics on the coefficient of the

lagged ECT indicates the significance of the long-run causal effect. The optimal number

of lag is used based on two different criterions, namely Schwarz Bayesian Criterion

(SBC) and Akaike Information Criterion (AIC). The lag structure is sensitive to the

model selection criterion. As mentioned before, SBC is a parsimonious model that

selects the smallest possible lag length, while (AIC) selects the maximum relevant lag

length.

In the case of stock price index in Egypt is dependent variable, the results show

that the coefficient of the ECM term is statistically highly significant which indicates

207

the existence of long-run causality from MENA and developed stock markets towards

Egypt stock market. Moreover, as has been mentioned before the coefficient of the

ECM term is negative as expected, indicating that stock price index in Egypt adjusts

back towards long-run equilibrium following a shock in the previous month. In the

short-run, based on SBC, the Chi-square statistics on the explanatory variables suggest

that just US stock price index Granger causes stock price index in Egypt, as we cannot

accept the null hypothesis of non causality, while in the case of stock price index of UK,

the null hypothesis cannot be rejected, which means that UK stock price index does not

Granger cause stock price index of Egypt. Based on using the AIC, the results

strengthen the previous results based on SBC. The price indices of US, UK, Morocco

and Turkey Granger cause stock price index of Egypt. This result reveals that these

markets could provide useful information to forecast stock price index in Egypt, but not

the reverse as Egypt does not Granger cause any other stock market.

Moreover, this short-run causality from the stock price indices in these markets

toward Egypt could be interpreted based on the strong economic relationship between

Egypt and all of these countries, especially with the US. The Egyptian economy is

highly tied with the US economy. Egypt is the second largest recipient of American aid

after Israel. Table 6.13 reports the recent American aids to Egypt during the period

1998-2003.

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6.12 Granger Causality Results Based on Vector-Error Correction Model (VECM)

Short-run lagged differences (Chi-Square) ECT Dependent Variable

Criterion used

∆lnE ∆lnT ∆lnJ ∆lnM ∆lnUS ∆lnUK ∆lnG Coefficient t-statistics

SBC _ 2.958* (1)

0.448 (1)

-0.256 -4.532*** ∆lnE

AIC _ 3.429* (2)

8.178** (3)

10.492*** (3)

10.812*** (3)

-0.222 -3.323***

SBC _ 13.477*** (1)

-0.304 -4.099*** ∆lnT

AIC _ 3.308 * (2)

6.418 ** (1)

-0.313 -4.272***

SBC 0.0498 (1)

_ 17.391*** (2)

-0.139 -3.271*** ∆lnJ

AIC 7.273* (3)

_ 13.104 *** (2)

-0.171 -3.852***

SBC _ -0.135 -2.962*** ∆lnM

AIC _ 5.535 * (2)

-0.116 -2.545**

Notes: 1- The symbols ***, ** and * indicate significance at 1, 5 and 10% level, respectively. 2- The orders of lags are reported in the parentheses.

209

Table 6.13 Recent American Aids to Egypt $US million

Source: Congressional Research Service, 2003, http://www.fas.org/man/crs/IB93087.pdf

.

In the case when stock price index of Turkey is dependent variable, the results

show that the coefficient of the ECM is highly significant which indicates the existence

of long-run causality from MENA and developed markets towards Turkey stock market.

In the short run, and based on both SBC and AIC, Chi-square statistics on the

explanatory variables suggest that the stock price index in Germany Granger causes

stock price index in Turkey as the null hypothesis of non-causality cannot be accepted at

1% significant level based on SBC and at 5% significant level based on AIC. Also,

based on AIC, the results show that stock price index in US Granger cause the stock

price index in Turkey in the short-run. In related to the economic relationship between

Turkey and Germany the statistics show that Germany is the most important trading

partner to Turkey. The volume of bilateral trade in 2003 reached EUR 16 billion.

Moreover, the largest number of foreign companies investing in Turkey is German (See

www.auswaertiges-amt.de). The short-run Granger causality from US and Germany

towards Turkey indicates that these markets could provide useful information to

forecast stock price index in Turkey.

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When stock price index in Jordan is dependent variable, the coefficient of the

error correction term shows a significant long-run causality at 1% level based on both

SBC and AIC. This long-run causality goes from MENA and developed stock markets

towards Jordan stock market. On the other hand, the Chi-square statistics on the

explanatory variables suggest that stock price indices in both UK and Turkey Granger

cause stock price index in Jordan. This reveals that both UK and Turkey stock markets

could provide some information to predict the stock price index in Jordan.

The stock market of Morocco is found to be the most exogenous among all

MENA markets despite one channel of short-run Granger causality from the German

towards the Moroccan market at 10% significant level based on AIC; this result could

be interpreted based on the strong economic relationship between Morocco and some

European economies, especially Germany as it considered one of the main economic

partners to Morocco. So, the German stock market could provide information to

forecast the stock price index in Morocco. Based on SBC, no short-run causality is

found towards Morocco. However, the coefficient of the ECT is found to be significant,

this indicates the existence of long-run causality from MENA and developed markets

towards the stock market in Morocco.

Finally, it is worth mentioning that as the coefficient on the lagged error

correction term for each price index in the MENA markets is significant with a negative

sign, this means that in the long-run the stock price index in each market in MENA

region bears the brunt of any disturbance in the long-run equilibrium relationship either

in other MENA stock markets or in developed markets.

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6.7 Conclusion The main purpose of this chapter is to examine the stock markets integration in MENA

region, represented by Turkey, Egypt, Jordan, and Morocco. At the same time, it

examines the integration between these markets and some developed markets

represented by US, UK and Germany. Moreover, the chapter explores the short and

long-run dynamic relationship between these markets.

To achieve the previous objectives, the study utilizes the newly proposed

autoregressive distributed lag (ARDL) cointegration approach. This approach is more

preferable in estimating the cointegration among variables than other conventional

approaches for many reasons like being more robust for small sample sizes, and being

applied irrespective of whether the regressors are I(0) or I(1). The existence of

cointegration among stock prices indicates stock markets integration; this implies that

one of the markets will help predicting the other markets returns. Moreover, to test for

the short and long-run dynamic relationship, the study conducts the Granger-causality

test within a Vector Error Correction Model (VECM).

The results indicate the existence of integration among MENA stock markets,

especially when Egypt stock market is dependent variable. At the same time no

integration between MENA stock markets and the developed markets has been found,

except between Egypt market and these developed markets.

The results of conducting the Granger-causality test within a Vector Error

Correction Model show the existence of long-run causality from developed markets

towards all stock markets in MENA Region, and also long-run causality among MENA

stock markets themselves. The coefficient of the ECT is found to be strongly significant

for all markets. However, in the short-run, based on SBC and AIC, it is found that US,

UK, Turkey, and Morocco Granger-cause stock price index in Egypt. In the case of

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Turkey, the results show that Germany and US Granger-cause stock price index in

Turkey in the short-run. For Jordan, the results show that there is a Granger-causality

from UK and Turkey towards Jordan. Finally, in the case of Morocco, it is found that

Germany Granger-cause stock price index in Morocco in the short-run.

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Chapter Seven

Summary and Conclusions 7.1 Introduction This study empirically investigates stock market integration in the Middle East and

North Africa (MENA) region. The study focuses on four emerging stock markets in the

region, namely, Egypt, Turkey, Jordan and Morocco. The interrelationships between

these emerging markets and developed markets, represented by the US, UK and

Germany, are also investigated. Two main definitions for stock market integration are

commonly adopted in the literature. The first is related to the early theoretical models

(ie. the capital asset pricing models) of stock market integration. According to these

models, national markets are considered to be integrated if securities with the same risk

characteristics are priced the same, even if they are traded on different markets. In other

words, if two or more markets are integrated then the identical securities should be

priced identically within both markets. The existence of stock market integration

implies that stocks in all markets are exposed to the same risk factors and the risk

premia on each factor is the same in all markets.

The second definition is related to the recent literature of stock market

integration. Most recent studies have adopted an alternative view of stock market

integration. They rely on recent econometric techniques, such as cointegration, the

generalized autoregressive conditional heteroscedasticity (GARCH) model, Granger

causality and vector autoregressive (VAR) models, to measure stock market integration

among national stock markets. According to these studies, stock markets are considered

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integrated if they share a long-run equilibrium relationship. In other words, the

existence of co-movements between the stock price indices indicates stock market

integration. This co-movement, which indicates the existence of cointegration between

two stock markets, implies that one of the markets will help predicting the other

market’s returns, since a valid error correcting representation will exist.

This chapter is divided into five sections. Section 2 presents a summary of the

chapters in the study including the main empirical findings. Section 3 analyses the

implication of the study. Section 4 outlines the specific contributions made by this study

and discusses the implications of the empirical findings for the MENA region. Finally,

section 5 presents some suggestions for future research.

7.2 Summary of the Study

This study started with an overview of the early theoretical models related to stock

market integration in chapter 2. The main aim of the chapter was to analyse the

literature related to stock market integration that used different versions of asset pricing

models, such as capital asset pricing model (CAPM). Also this chapter reviewed the

literature related to the Arbitrage Pricing theory (APT). According to the asset pricing

models, stock markets are considered to be integrated if securities with the same risk are

priced the same. So in the case of implementing CAPM, there will be a unitary price

risk, and the price of all assets will reflect the level of systematic risk they possess so

the assets are considered to be integrated. Also, the chapter reviewed another asset

pricing model that is the consumption-based asset pricing model. This model shows that

the simple relation between consumption and assets return captures the implication of

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the complex dynamic international multi factor asset pricing model. The chapter also

reviewed the arbitrage pricing theory (APT) model, which unlike the CAPM, depends

on no arbitrage conditions. It assumes that stock prices can be influenced not only by

the market risk, but also by several sources of systematic risk in the economy. These

sources can be thought of as factors such as integration, aggregate output, industry

effects and interest rate. Moreover, beside the asset pricing models and the arbitrage

pricing theory, some studies used what is called a conditional regime-switching model.

This model allows for switching between segmentation and integration by attaching

probabilities to the respective asset pricing models. However, most of recent studies

rely on recent econometrics techniques, such as cointegration techniques, generalized

autoregressive conditional heteroscedasticity (GARCH) model, Granger-causality,

vector autoregressive (VAR) model and variance decomposition, to measure the stock

market integration among national stock markets. These approaches are reviewed and

analyzed extensively in chapter three.

Cointegration approaches become the most popular techniques to be used for

testing stock market integration. Chapter 3 reviewed a significant amount of literature

that used different cointegration approaches. The idea behind using cointegration

techniques is that if stock markets are integrated, it is expected for the indices in these

markets to display common trends. Many studies try to calculate the number of

common stochastic trends, but since these indices are nonstationary, then using

cointegration method becomes necessary. The existence of co-movements between the

securities prices indicates stock market integration. This co-movement implies that one

of the markets will help predicting the other market’s returns, since a valid error

correcting representation will exist. Also, they share at least one common stochastic

trend and they will tend to drift together over time. This chapter reviewed the most

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popular cointegration approaches such as, Engel-Granger two-step method, Johansen’s

maximum likelihood procedure and finally the recently developed autoregressive

distributed lag (ARDL) approach. The studies, which are reviewed in this chapter, have

obtained different findings on financial integration among stock markets. This chapter

also reviewed some studies that investigated the effect of the Asian financial crisis on

stock market integration in different parts of the world. The relationship between the

existence of cointegration and stock market efficiency was analyzed. The chapter

covered the debate about this issue started by Granger in 1986. He asserted that the

existence of cointegration between two stock markets implies that these markets are not

efficient. This point of view has been criticized by several studies. These studies

indicate that market efficiency does not preclude cointegration, and because no

empirical evidences have confirmed that predictability from cointegration can lead to

arbitrage opportunities, so cointegration does not necessarily imply market inefficiency.

However, this study asserts that the existence of cointegration between stock markets

implies stock market efficiency. This is because existence of cointegration vector

implies the law of one price (LOOP) in the long-run. Therefore, little or no arbitrage

opportunities or possible benefit can be achieved from the diversification portfolio from

one market to another. However, with the short-run error correction model (ECM) there

could exist arbitrage opportunities and possible benefits from diversification, ie. the

LOOP may not hold in the short run.

The last part of the chapter reviewed most of the empirical studies that examined

the stock market integration in the MENA region. Most of these studies found that

cointegration exists among the MENA stock markets, which means that these markets

are integrated with each other. At the same time, some of these studies found that these

markets are not integrated with developed markets. However, these studies are criticized

217

for using conventional cointegration approaches, which are not appropriate for small

sample sizes, and for covering few numbers of markets.

Chapter 4 outlined the specific characteristics of the four main emerging stock

markets in the MENA region. The chapter started by giving a brief analysis of the

general economic features of these four countries. All of these countries are considered

as low and middle-income countries. Following the brief analysis regarding the

economic features in these countries, the stock market liberalization in these markets

was reviewed. MENA countries started to liberalize their stock markets during the late

eighties and mid nineties. The first country to liberalize its stock market was Morocco

in 1988; it allowed foreign investors to have complete access to the Casablanca Stock

Exchange. In 1989, Turkey also liberalized its stock market by removing all restrictions

for foreign investment. Egypt and Jordan liberalized their stock markets in 1992 and

1995, respectively. As a result of these liberalization and several privatization programs,

portfolio equity started to flow to the stock markets in the region. A summary of the net

flows to the stock markets during the period 1994-2002 was reported in the chapter.

The stock markets in the MENA region have achieved considerable

improvement in the last decade due to several factors such as the achievement of higher

economic growth, monetary stability, stock market reforms, privatization, financial

liberalization and institutional framework for investors. Regarding the development of

these stock markets in the region, an inclusive analysis of the main market indicators for

each stock market was presented. These indicators include market capitalization, trading

value, turnover ratio and the number of listed companies in each market. It was found

that the Istanbul Stock Exchange in Turkey is the region’s dominant stock market; the

market capitalization is the largest among all stock markets. Also, the turnover ratio in

Turkey, which is an index of the market liquidity, is considered to be the most active

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among all stock markets. The highest level of market capitalization as a percentage of

the GDP was in Jordan. This high rate could reflect the importance of the stock market

in the national economy; it could also reflect the importance of the stock market in

Jordan from both regional and international perspective.

Chapter 5 tested the unit root hypothesis using both the conventional unit root

tests and the unit root tests in the presence of structural change at an unknown time of

the break. The chapter started with presenting a preliminary descriptive analysis of the

statistical characteristics of the stock price indices1. Based on the correlation coefficient

matrices, it is found that the correlation coefficients among the MENA markets either in

related to stock indices or stock returns are low. At the same time, correlation

coefficients among developed markets are extremely high. Also, it is found that Egypt

and Morocco have the highest correlation coefficient among MENA markets, while

Turkey has a high correlation coefficient with the developed markets.

The conventional Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP)

unit root tests were then conducted. The results failed to reject the null hypothesis of a

unit root for all monthly stock price indices using both local currency and $US mesures.

This indicates that all indices are non-stationary. The same tests were applied to the first

differences of the indices expressed in both local currency and $US and the results from

both tests rejected the null hypothesis of non-stationarity across all indices. This means

that all indices become stationary if they are first differenced. However it is known that

when performing the conventional unit root tests for a time series where there are

structural breaks, these tests are biased towards the non-rejection of the unit root hull

hypothesis.

1 For an inclusive view, the study expressed all stock price indices in both local currency and $US.

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The study then tested for a unit root in the presence of structural change at an

unknown time of the break. These tests were conducted by using the Innovational

Outlier (IO) models proposed by Perron (1997) and Perron and Vogelsang (1992).

These more appropriate tests reinforced the main findings drawn form the ADF and PP

unit root tests that all the variables are integrated of order one I(1). Rregarding the

endogenously determined time of the break for each stock market, the results showed

that they coincided with the real events which affected each stock price index. The time

of the breaks in the MENA stock markets were as follows: for Egypt it was in 2001:4,

for Turkey it was in 1999:7, for Jordan it was in 2001:12 and for Morocco it was in

1996:11.

Finally, the results of conducting unit root tests in the presence of structural

change indicated that all stock markets in the MENA region are characterized by a unit

root. This is consistence with the efficient market hypothesis as the random walk

hypothesis is associated with weak form of the efficient market hypothesis. This result

emphasises that the stock markets in the MENA region are efficient.

In the final chapter 6, the study used the newly proposed autoregressive

distributed lag (ARDL) test of cointegration. This approach has been recently developed

and introduced by Pesaran et al (2001) based on previous studies by Pesaran and others.

The ARDL approach has been recognized as a more preferred approach than other

cointegration approaches. Some of the reasons for preferring the ARDL procedure over

others are as follow: First, it is applicable irrespective of whether the underlying

regressors are purely )0(I , purely )1(I or mixed )0(I and )1(I process. Second, it is

more robust and performs well for small sample sizes – such as in this study - than other

cointegration approaches. Third, by using the ARDL approach one can estimate the

long-run and the short-run components of the model simultaneously.

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The cointegration test results show that there are long-run equilibrium

relationships among all stock markets in the MENA region. This indicates that stock

markets in the MENA region move together in the long-run. So, at the regional level all

markets are integrated. At the same time no long-run equilibrium relationship was found

between MENA markets and developed markets. This means that MENA stock markets

are segmented from developed markets. However, Egypt was the exceptional case; the

study found that the stock market of Egypt has long-run equilibrium relationship with

the US and UK markets. The main reason for the integration between the stock market

in Egypt and the stock market in the UK is that in the year 2000, several companies in

the telecommunication, construction and tourism sectors listed their shares on the

London Stock Exchange. The reason for the integration between the stock market in

Egypt and the stock market in the US is that the Egyptian economy is highly tied with

the US economy. This strong economic relation is reflected in the Egyptian stock

market.

In comparison with the previous studies conducted over the stock markets in the

MENA region, this study is mostly consistent with Darrat (2000). Darrat found

cointegration among the MENA stock markets but no evidence of cointegration

between these markets and the US. The results of this study are consistent with Darrat’s

(2000) results, yet we used different sample and different techniques. Also, this study

contradicts Neaime (2002). In his study he found that MENA stock markets are

cointegrated with developed markets but no evidence of cointegration among

themselves. This inconsistency is possibly due to different sample periods covered in

his study. Also, it is possibly due to using three developed markets by this study, while

in Neaime’s study only two markets, the US and UK are considered. Finally the results

of this study are partly consistent with Maghyereh (2003). In his study, Maghyereh

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found that the integration among the MENA markets is still weak. However, he did not

study the integration between MENA markets and developed markets.

In regards to stock markets efficiency, this study asserted that MENA stock

markets are efficient in the long-run. The reason for this is because the existence of

cointegration vector implies the law of one price (LOOP). However, with the short-run

error correction model (ECM) there could exist arbitrage opportunities and possible

benefits from diversification, ie. the LOOP may not hold in the short run.

Finally, the study conducted Granger-causality tests. This test was augmented

with a lagged error correction term when variables are cointegrated, and then estimated

within vector error correction model (VECM) for stock price indices in the MENA

markets and developed markets. The conducting of this test helps to explore the short

and long-run dynamic relationships among MENA stock markets, and between MENA

markets and developed markets. The optimal lag length used was based on two different

criterions, namely the Schwarz Bayesian Criterion (SBC) and the Akaike Information

Criterion (AIC). The reason for using these two criteria is because the lag structure is

sensitive to the model selection criterion. As mentioned before, the SBC is a

parsimonious model that tends to select the shorter lag length, while the AIC typically

selects longer lag lengths.

The results of Granger-causality test based on vector error correction model

(VECM) reveal the existence of long-run causal relationships among the MENA

markets. This means that these markets influence each other. Also, the results show that

developed markets influenced stock markets in the MENA region. In the short-run,

there is unidirectional Granger-causality running from stock prices in Turkey, Morocco,

The US and UK to Egypt. Also, there is unidirectional Granger-causality running from

Germany and the US towards Turkey. In addition, The UK and Turkey are found to

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Granger-cause the stock prices in Jordan. Finally, there is a unidirectional Granger-

causality from Germany to Morocco.

7.3 Implications of the Study The empirical findings of this study indicate that the stock markets in the MENA region

are found to be integrated with each other (regionally), but not with the developed

markets (internationally). However, Egypt was the exceptional case as it is integrated

with the US and UK. These empirical findings have several important implications in

regards to portfolio diversification. These implications were analyzed at two levels; the

regional level and the international level.

First at the regional level, the results showed that stock markets in the MENA

region are integrated with each other. This implies the existence of the law of one price

(LOOP). This means that the potential of regional investors for obtaining abnormal

profits through portfolio diversification is limited in the long-run. The reason for this is

that as MENA stock markets are cointegrated, abnormal profits will be arbitraged away

in the long-run. However, despite no arbitrage opportunities in the long-run, investors

can still achieve arbitrage profits through portfolio diversification in the MENA stock

markets in the short-run. This depends on the speed of adjustment which is represented

by the error correction term . The coefficients of error correction term for the MENA

stock markets are as follows, for Egypt it is -0.26, for Turkey it is -0.30, for Jordan it is

-0.14 and for Morocco is -0.14. These coefficients show that the speeds of adjustment is

slow which means the short-term corrections can last for longer periods, and there is a

high possibility of achieving arbitrage profits as the LOOP may not hold.

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Second, at the international level, the results showed that stock markets in

Turkey, Jordan and Morocco are not integrated with developed markets, represented by

the US, UK and Germany. This means that there is no significant long-run impact from

developed stock markets towards these markets. However, a long-run relationship is

found between Egypt and both the US and UK when Egypt is a dependent variable.

Based on these results, there are opportunities for international investors to obtain long-

run gains through international portfolio diversification in Turkey, Jordan and Morocco

stock markets. Also at the same time, investors from these three countries have the

opportunities to obtain long-run gains through investing in developed markets. The

existence of long-run relationships between Egypt and both US and UK implies that the

potential for investors from the Egyptian stock market to obtain abnormal profit through

portfolio diversification in US and UK is limited in the long-run. However, there are

opportunities for achieving abnormal profit by investing in Germany as it is not

cointegrated with the MENA markets. In the short-run, arbitrage opportunities and

possible profits may also be achieved from diversification as the LOOP may not hold.

However, despite these results, it is found that the MENA region is not attracting

more international portfolio investment. Different factors cause this low level of inward

portfolio equity flow to the region. One of the main factors is that most of these markets

are still underdeveloped compared with developed markets as the market capitalization

is still small in most these markets. Another factor is that most of countries in the

MENA region are more vulnerable to macroeconomic shocks and this is a matter of

concern for investors in portfolio equity. Moreover, the political instability in the region

plays a vital role in reducing the equity portfolio flow to the stock markets in the region.

Based on these results, efforts should be carried out to improve the institutional

reforms and increase the degree of openness for foreign capital in the MENA stock

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markets. Increasing market capitalization, adopting new technology and increasing the

liberalization in the MENA stock markets are expected to attract more equity portfolio

to the region. Also, these markets need to develop structural relations with major

foreign and regional markets. Moreover, economic policy makers should strengthen

their national economies, so they become less vulnerable to macroeconomic shocks, as

this is a matter of concern for portfolio managers. Still to say that a stable economy in a

stable political environment has a strong ability to attract equity portfolio.

7.4 Contribution of the Study This study has made significant contributions to the analysis of stock market integration

in the MENA region. First, this study is believed to be the first study to address the

issue of structural change when testing for the unit root hypothesis in the MENA stock

markets. According to our knowledge, and after conducting an inclusive survey, no

study has addressed this issue before when examining stock market integration in the

MENA region. As has been mentioned before when performing the conventional unit

root tests, such as Dickey-Fuller and Phillips-Perron tests for a time series where there

are structural breaks, these tests are biased towards the non-rejection of the unit root

hypothesis in the presence of structural breaks. These tests lack power in the presence of

potential structural breaks in the series and they may fail to show whether a series is

first difference stationary. For conducting unit root test in the presence of structural

change at an unknown time of the break, the study used the Innovational Outlier models

proposed by Perron (1997) and Perron and Vogelsang (1992). The results of conducting

these models indicated that the endogenously determined times of the breaks for all

225

variables coincides with observed real events for each country, such as the Asian crisis,

fluctuations in oil prices and political conflicts in the MENA region.

Secondly, this study used larger sample of stock markets than previous studies.

Three developed markets have been included for the first time, beside four emerging

markets form the MENA region. Previous studies include either one developed market

or at most two. Also, the period of the study has been conducted over a longer period of

time than previous studies. Moreover, when presenting descriptive statistics, or

conducting the conventional unit root tests and the unit root test in the presence of

structural change test, all stock price indices were expressed in both local currency and

$US. This gives an inclusive view more than previous studies.

Thirdly, most previous studies have used conventional cointegration approaches

such as Engle-Granger (1987) and Johansen-Juselius (1990). These approaches

concentrate on cases in which the underlying variables are integrated of order one. Also,

they are valid for large sample size and not for small samples. However, this study has

overcome these difficulties by utilizing the newly proposed autoregressive distributed

lag (ARDL) approach. The ARDL approach is applicable irrespective of whether the

underlying regressors are )0(I , )1(I . Also, the ARDL approach is more robust and

performs well for small sample sizes, such as this study, than other cointegration

approaches. This study is believed to be the first to examine stock market integration in

the MENA region by using the ARDL approach.

Fourthly, this study contradicted Granger’s (1986) theory on the relationship

between the existence of cointegration and market efficiency. Granger (1986) asserted

that the existence of cointegration between two stock prices implies the ability to predict

each price’s movement, which indicates market inefficiency. Also, this study did not

fully agreed with other perspectives, such as those found in Wallace (1992), Baffes

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(1994), Engle (1996), Ahlgren and Antell (2002), and Masih and Masih (2002) in which

they asserted that cointegration does not necessarily imply market inefficiency or

efficiency. What this study tried to bring out was that if cointegration exists between

two stock markets then these markets are efficient in the long-run because the existence

of cointegrated vector implies the law of one price (LOOP). Therefore, little or no

arbitrage opportunities or possible benefit can be achieved from the diversification

portfolio from one market to another. However, with the short-run error correction

model (ECM), there could exist arbitrage opportunities and possible benefits from

diversification, ie. the LOOP may not hold in the short run.

7.5 Suggestions for Future Research There are several avenues for future research that stem from this study. These could be

summarized as follows:

1. Although this study has used Perron’s (1997) model, which is one of the most

advanced techniques to test for a unit root in the presence of an unknown

structural break, other recent techniques allow for multiple structural breaks.

These techniques could be implemented in future studies when studying stock

markets in the MENA region. Moreover, a panel data unit root tests could be

implemented too by which we can test for common shocks affecting the MENA

stock markets.

2. The sample of the study could be extended to include more stock markets.

Several countries in the MENA region (such as the United Arab Emirates, Qatar,

Bahrain, Oman, Kuwait and Saudi Arabia) have established their stock markets

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recently. These markets are playing increasing roles in their economies. Also,

the Israel stock market (as a developed market) could be included in a further

study to test whether it is integrated with other stock markets in the region or

not.

3. This study has used stock price indices to test for stock market integration.

However, it could be interesting to see whether different sectors in those

countries are integrated. For example, rather than concentrating on general

stock markets, one can study whether specific sectors in different markets are

integrated.

4. Future studies could be conducted to examine the stock market efficiency in the

region. This issue has been attracting a great deal of interest recently. These

studies could be conducted either on a group of stock markets or on an

additional market.

5. The liberalization of the stock market is another important topic to be

investigated. Future studies could examine the relationship between

liberalization, integration and the efficiency of stock markets.

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Appendix (A)

Conventional Unit Root Tests

A.1 Stationary Time Series The using of time series data for empirical work requires that the underlying time series

is stationary. Stationary means that the fundamental form of the data generating process

remains the same over time. Many economic time series are nonstationary. The use of

standard inference procedures for the estimation of nonstationary series could lead to

spurious results. These spurious regressions usually exhibit a high 2R but low Durbin

Watson ( DW ) statistic. The rises of this spurious results is because if the time series

involved in the regression exhibit strong trend, then the high 2R , which observed, is due

to presence of the trend not to a true relationship. Any time series can be thought of as

being generated by a stochastic (random) process. According to Gujarati, (1995):

“A stochastic process is considered to be stationary if its mean and

variance are constant over time and the value of covariance between two

time periods depends only on the distance or lag between the two time

periods and not on the actual time at which the covariance is computed.”

The mean stationary means that the expected value of the process is constant over

time:

Let tY be a stochastic time series, then:

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The Mean:

tYE t ∀= µ)( (1)

Also, variance stationary means that the variance is temporally stable:

The Variance:

tYEY Ytt ∀=−= 22)()var( σµ (2)

And the covariance stationary is similar:

The covariance:

[ ]))(( µµγ −−= +kttk YYE (3)

),cov( ktt YY += (4)

If 0=k , we obtain 0γ , which is the variance of )( 2σ=Y .

This form of stationary is called “weak stationary”. Another form of stationary is

called strict stationary and that is if the joint probability distribution of any set of

observations }{ tYYY ,..., 21 is the same as that for }{ ktkk YYY +++ ,..., 21 , for all t and k .

A.2 Unit Root Tests The unit root test is the most widely used test for stationary; it was first presented by

Fuller (1976), and Dickey and Fuller (1979). These tests are referred to as Dickey-Fuller

(DF) tests. A simple form of the DF test is based on the following model:

ttt uYY += −1ρ (5)

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where tu is a white noise error term with zero mean, constant variance 2σ , and

nonautocorrelated. In the case that the coefficient of 1−tY ( ρ ) is equal to 1, then we have

unit root problem, in other words the stochastic variable tY has a unit root or what is

known as a random walk time series, which it is an example of a nonstationary time

series. Still to say that the test is named unit root because it is the root of a polynomial.

Equation (5) alternatively expressed as:

( ) ttt uYY +−=∆ −11ρ (6)

ttt uYY +=∆ −1β (7)

where 1−= ρβ , and 1−−=∆ ttt YYY

the null hypothesis for a unit root is 0=β (equivalently 1=ρ ) and it is tested against

the alternative of 0<β or stationary series.

Dickey fuller test can be applied not just for equation (7) but for more extended

formulas as follows:

ttt uYY ++=∆ −11 βα (8)

where tY is a random with drift and 1α is a constant.

the null hypothesis 0H : 0=β

tttt uYY +++=∆ −121 βαα (9)

where tY is a random walk with drift around a stochastic trend, t is the time or the trend

variable. Again the null hypothesis is 0H : 0=β

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A.3 Augmented Dickey Fuller (ADF) Test In the last two equations both constant ( 1α ) and trend term ( t ) have been included

assuming that the error term is nonautocorrelated. In the case that the error term ( tu ) is

autocorrelated (not white noise), then to make the error term nonautocorrelated (serially

independent), Dickey and Fuller have augmented equation (9) by including the lagged

value of the dependent variable tY∆ , the result is known as Augmented Dickey Fuller

(ADF) test, it has the following form:

∑=

−− +∆+++=∆m

ititttt YYY

1121 εδβαα (10)

Equation (10) is the most extended form of Augmented Dickey Fuller (ADF) test;

it can be reduced to the following forms:

∑=

−− +∆++=∆m

itittt YYY

111 εδβα (11)

∑=

−− +∆+=∆m

itittt YYY

11 εδβ (12)

In all previous equations 10, 11 and 12, the null hypothesis is that 0=β , that is

there is unit root.

ADF and DF tests statistics have the same asymptotic distribution; therefore they

have the same null hypothesis and same critical values. However, two important things

should be considered, first not to include too few lags because this will leave

autocorrelation in the errors, and second not to include too many lags because that will

reduce the power of the test statistics. Couples of different ways have been suggested to

overcome this problem. Practically, one of the best options is to estimate models with a

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range of values for ( m ), and then use one of the following tests: the Akaike Information

Criterion (AIC) or Bayes Information Criterion (BIC) to determine which the best option

is1.

The Akaike Information Criterion (AIC) takes the following formula:

NmmAIC m

2ˆln)( 2 += σ (13)

where m is the number of parameters in the model and N is the number of observations

in the regression.

The Bayes Information Criterion (BIC) takes the following formula:

NNmmBIC m

lnˆln)( 2 += σ (14)

A.4 Phillips-Perron Tests Phillips and Perron (1988) develop a more comprehensive theory of unit root non-

stationarity. They introduce a different method to overcome the problem that the error

term is serially correlated (not white noise), without including lagged difference error

term as the Augmented Dickey– uller (ADF) tests. The Phillips-Perron (PP) tests take the

following formula:

ttt uyy ++=∆ −1ρα (15)

Still as (ADF) tests, a constant and time trend can be included in the equation (15). This

equation is estimated by using OLS. In general (PP) tests give the same conclusions as 1 There are more than AIC and BIC tests, but these two have received substantial acceptance in recent applied econometrics.

233

(ADF) tests and suffer from most of the same important limitations (Brooks, 2002,

p.381).

However, in order to avoid spurious regression, the nonstationary time series

should be transformed to a stationary time series. One way2 for doing this is to

differentiate the nonstationary time series d times to achieve stationary and this series is

called integrated of order d , denoted )(~ dIxt . Still to say that if the time series is

already stationary then it is called )0(I (Granger, 1986).

2 The transformation process depends on whether the time series are difference stationary or trend stationary, for more details about the transforming process, see Gujarati (2003).

234

Appendix B

Cointegration and Causality Tests

B.1 Cointegration Cointegration is an economic tool for examining the long run equilibrium relationship

among a set of time series variables. According to Granger (1986): “the term

equilibrium in this definition describes the tendency of an economic system to move

towards a particular region of the possible outcome space, and does not imply

anything a bout the behavior of economic agents”, in the case there is two times series

tx and ty , and both of them are nonstationary, that they have stochastic trend, and

integrated of order 1. Now if the linear combination of these two time series is

stationary )0(I , then the two variables are considered cointegrated. So cointegration is

considered as a linear combination of nonstationary variables.

Consider the relationship between two times series ty and tx represented by:

ttt uxy ++= 10 ββ (1)

equation (1) can be written as:

ttt xyu 10 ββ −−= (2)

if the tu is found to be stationary )0(I , then the other side of the equation (the linear

combination tt xy 10 ββ −− ) must also be stationary. Depending on this, both

variables are considered cointegrated. This example can be extended to a more

general case by assuming tx to be an 1×n column vector of economic variables that

235

represents the transpose of the t th row of a nt × matrix of variables, X . As the vector

tx will be in long-run equilibrium when:

0...2211 =+++ ntntt xxx βββ (3)

any deviations from this relationship will represent equilibrium errors such that:

tt xe δ= (4)

where δ is an 1×n row vector of linear weights referred to as the cointegration

vector (Grieb, 1996, p. 59).

Engle and Granger (1987) adapted a formal definition for cointegration from

Granger (1981) and Granger and Weiss (1983) as follows:

“ The component of the vector tx are said to be cointegrated of the order

bd , , denoted ),(~ bdCIxt , if (i) all components of tx are )(dI ; (ii)

there exists a vector )0(≠α so that )(~ bdIxz tt −′=α , 0>b . The vector

α is called the cointegration vector”.

B.2 Error Correction Model (ECM) The main idea behind cointegration is that if two variables have a long-run

equilibrium relationship, then they are considered cointegrated. But, what is about the

short run? It has been widely accepted that shocks in the short-run disturb this

relationship causing disequilibrium. Error Correction Model (ECM) has been used to

describe the short run dynamics between these two variables. The core idea of the

Error Correction Model (ECM) is that “a proportion of the disequilibrium from one

period is corrected in the next period” (Granger, 1987, p. 254). So changes in one of

these variables are related to past changes in both variables and to past equilibrium

236

error. Now consider a system of two variables tx , ty and both of these variables are

)1(I , then tx∆ and ty∆ are )0(I , depending on that, a simple form of (ECM) can be

shown as follows:

txtxtxttt yaxayxx εδβα +∆+∆+−+=∆ −− 121111 )( (5)

tytytyttt yaxayxy εδβα +∆+∆+−+=∆ −− 121122 )( (6)

where txε and

tyε ~ ),0( 2σIN ,

)( tt yx δ− is the error correction term that measures past equilibrium error.

β represents the speed with which the model adjusted it self to its equilibrium level.

So, from economic point of view any deviation from long run equilibrium must be

corrected to keep the stability of the system as a whole.

B.3 Testing for Cointegration: Engle and Granger (1987)

Approach Engle and Granger (1987) suggest a set of seven test statistics for testing

cointegration, as follows:

1- Cointegration Regression Durbin Watson (CRDW) test. This test uses Durbin-

Watson critical value obtained from the cointegration regression. The null

hypothesis is that 0=DW which means that the residuals are not stationary

and the variables are not cointegrated.

2- Dickey-fuller (DF) test. This test depends on running a regression on the

residuals from a cointegration regression:

ttt eu εβ +=∆ −1 (7)

237

now this test is known as Engle-Granger (EG), since they have calculated the

critical significant values. So the rejection of the null hypothesis ( 0=β )

means that the residuals do not contain a unit root, which indicates that these

residuals are stationary and the variables are cointegrated.

3- Augmented Dickey-Fuller (ADF) test. This test is similar to (DF) test, that it

assumes the same null hypothesis as (DF), and follows the same asymptotic

distributions as (DF), the difference is that it contains a lagged value for the

dependent variable, which is in this case te∆ (Gujarati, 2003).

4- Restricted Vector Autoregression (RVAR) test. This test estimates whether the

error correction term is significant, conditional on the estimated residuals from

the cointegration regression. The test estimates the following regressions

equations:

ttt ey 111 εβ +=∆ − (8)

tttt yex 212 εδβ +∆+=∆ − (9)

The null hypothesis is that the coefficients of the error correction term

( 21 , ββ ) are jointly zero.

5- Augmented Restricted Vector Autoregression (ARVAR) test. It is similar to

(RVAR) except that it contains lagged values of the dependent variable.

6- Unrestricted Vector Autoregression (UVAR) test. This test estimates the

following regressions equations:

tttt xycy 112111 εββ +++=∆ −− (10)

ttttt yxycx 214132 εγββ +∆+++=∆ −− (11)

7- Augmented Unrestricted Vector Autoregression (AUVAR) test

238

B.4 A maximum Likelihood Approach for Testing

Cointegration

Engle and Granger (1987) suggested several test statistics for testing cointegration,

but their procedure suffers from several important defects (see, Cheung, (1998),

Sheng & Anthony (2000) and Enders (2004)). First, Engle and Granger use a system

of two variables, and in reality there could be more than one cointegration vector,

Enders, 2004, p. 347) states that the Engle–Granger method “has no systematic

procedure for the separate estimation of the multiple cointegration vectors”. Second,

they use two steps to run the cointegration regression, starting by estimating the

regression using the Ordinary Least Square (OLS) approach, and then by applying test

statistics on the residuals from the regression, one problem associated with this

procedure is that in the case of the existence of any measurement error, this error will

be shifted from one step to another. Another deficiency is that estimated cointegrating

relationship between two variables has different test results sometimes, depending on

which one of the variables is dependent and which one is independent. According to

Enders (2004) in the case of reversing the order of the variables, it possible to end up

with different results (i.e. in the first case the variables are cointegrated, and in the

second case they are not cointegrated).

Johansen (1988), Stock and Watson (1988) and Johansen and Juselius (JJ)

(1990) develop what is knows as a maximum likelihood approach for testing and

evaluation of multiple cointegrated vectors. This approach is preferred to the Engle

and Granger (1987) for the reasons mentioned before, and also because of some other

reasons; like it has better asymptotic properties than the latter and therefore yields

more robust estimates (Phillips and Ouliaris, 1990), in addition it identifies the space

239

spanned by cointegration vectors, which the pervious approach could not. Also it

assumes all variables to be endogenous and it explicitly tests for number of

cointegrating relationships. So, a maximum likelihood approach is considered to be a

multivariate generalization of Dickey- Fuller test.

The starting point of their approach is the following )(kVAR model:

tktkttt eyAyAyAy +++++= −−− ...2211µ (12)

where ty is an 1×n vector of stochastic nonstationary variables or )1(I , and µ is an

intercept vector.

Now, consider L−=∆ 1 , where L the lag operator is, equation (12) is rewritten as

follows:

tktktktt eyyyy +Π+∆Γ++∆Γ+=∆ −+−−− 1111 ...µ (13)

where

),...( 1 iAAI −−−−=Γ 1,...,1 −= ki

)...( 1 kAAI −−−−=Π

=I identity vector

Π is an nn × long run response matrix. This matrix summarized all the long run

relationships in ty . The number of cointegration vectors is determined by the rank r

of the matrix Π , which is equal to number of its characteristic root. Three possible

cases have to be considered:

1- If the matrix Π has full rank nr = , all the variables in ty are stationary.

2- If the matrix Π has zero rank 0=r , i.e. null matrix, then there are on

stationary long run relationships among variables in ty .

240

3- If the rank of the matrix Π is nr <<0 , then there are r linear combinations

of nonstationary variables that are stationary, and there are multiple

cointegrating vectors, and the matrix Π can be expressed as βα ′=Π , where

α and β are rn × matrices with r cointegration vectors.

Johansen and Juselius propose two statistic tests in order to determine the number

of cointegration vectors in the matrix Π . The first statistic test is called the maximum

eigenvalue, and it takes the following formula:

)1ln()1,( 1max +′−−=+ rTrr λλ (14)

where T the number of observations, iλ ′ the estimated values of the characteristic

roots (also called eigenvalues) and nr λλ ′′+ ,...,1 the rn − smallest squared canonical

correlations.

The maximum eigenvalue statistic tests the null hypothesis of r cointegration

relations

rRankH yr =Π )(: (15)

against the alternative hypothesis

1)(:1 +=Π+ rRankH yr (16)

The second statistic test is called the Trace statistic, and it takes the following

formula:

∑+=

′−−=n

riitrace Tr

1)1ln()( λλ (17)

It tests the null hypothesis of r cointegration relations

rRankH yr =Π )(: (18)

against the alternative hypothesis

241

yyn nRankHy

=Π )(: (19)

B.5 The Multi-Cointegration Approach Multi-cointegration is a situation in which a linear combination of )2(I and )1(I

variables is integrated of order zero (Enders, 2004). In this case the maximum

likelihood approach is invalid because in the long run covariance matrix defined for

the cointegration relations and the differenced variables will be singular (Tahai, et al.,

2004, p. 331). For testing multicointegraion, a Johansen’s approach can be used. The

model takes the following formula:

∑−

=−−− +∆Ψ+Γ∆+Π=∆

2

1

211

2k

ititittt eyyxy (20)

where ∑−

=

Γ−=Γ1

1

k

iiI , ∑

−

=

Γ−=Ψ1k

iji and 2,...,1 −= ki

Enders (2004, p. 343) states that: “there are two steps to check for

multicointegraion. First, to search for cointegration among )2(I variables, then use

this relationship to check for a possible cointegration relationship with the remaining

)1(I variables”. However, an important thing to be considered is that “this procedure

is effective only if the cointegration vector for the first step is known; otherwise the

second step is contaminated with the errors generated in the first” (Engsted, Gonzalo

and Haldrup, 1997, in Enders, 2004)

242

B.6 The Granger Causality The general idea about Granger Causality test is that it measures the dependency

between two (or more) variables, and which one causes the other. Before applying

this test to any times series, one should be a ware that the time series is stationary. So,

if the time series of any variable is not stationary, the Granger Causality test cannot be

applied. According to Granger first definition “… we say that Y is causing X ,

denoted by tt XY ⇒ if we are better able to predict tX using all available

information that if the information apart from tY had been used” (Granger, 1969, p.

428). A simple causality model is given as follows:

∑ ∑= =

−− +++=n

i

n

ititiiti YbXaY

1 10 εβ (21)

∑ ∑= =

−− +++=n

i

n

ititiitit eYdXcX

1 10β (22)

whereε , e are uncorrelated white noise series, i.e., [ ] [ ] ,0 stst eeΕ==Ε εε and

[ ] 0=Ε stεε for all st, .

Equation (21) indicates that tX is causing tY if ia is not zero, and tY causing

tX if id is not zero. If both events occur then it is said to be a feed back relationship

between tX and tY . If both variables fail to Granger-cause other, this means that both

variables are independent. In general this kind of causality is called bilateral causality,

since it deals with two variables. However it can be extended to multivariable

causality by using (VAR) technique (Gujarati, 2003).

243

Appendix C

Diagnostic Tests

Appendix C.1 Autoregressive Distributed Lag Estimates

ARDL(1,0,0,0,1,1,0) selected based on Schwarz Bayesian Criterion ********************************************************************* Dependent variable is LE 109 observations used for estimation from 1995M3 to 2004M3 ********************************************************************* Regressor Coefficient Standard Error T-Ratio[Prob] LE(-1) .74370 .056547 13.1520[.000] LT .10197 .036436 2.7988[.006] LJ .36787 .083168 4.4233[.000] LM .19228 .063671 3.0198[.003] LUS -.43147 .25088 -1.7198[.089] LUS(-1) .81034 .25924 3.1258[.002] LUK .20658 .30855 .66953[.505] LUK(-1) -.84873 .29662 -2.8614[.005] LG -.056709 .11408 -.49709[.620] INPT -.094424 .87953 -.10736[.915] t -.0026239 .0014187 -1.8495[.067] EtTBD )( .17513 .078098 2.2425[.027] EtDU -.13261 .051695 -2.5653[.012] ********************************************************************* R-Squared .95558 R-Bar-Squared .95003 S.E. of Regression .071042 F-stat. F( 12, 96) 172.1149[.000] Mean of Dependent Variable 5.0138 S.D. of Dependent Variable .31781 Residual Sum of Squares .48451 Equation Log-likelihood 140.5056 Akaike Info. Criterion 127.5056 Schwarz Bayesian Criterion 110.0119 DW-statistic 1.6630 Durbin's h-statistic 2.1796[.029] ********************************************************************* Diagnostic Tests ********************************************************************* * Test Statistics * LM Version * F Version ********************************************************************* * A:Serial Correlation*CHSQ( 12)= 14.4525[.273]* F( 12, 84)= 1.0700[.396] * B:Functional Form *CHSQ( 1)= 1.7278[.189]* F( 1, 95)= 1.5301[.219] * C:Normality *CHSQ( 2)= 6.0984[.047]* Not applicable * D:Heteroscedasticity*CHSQ( 1)= .99814[.318]* F( 1, 107)= .98888[.322] ********************************************************************* A:Lagrange multiplier test of residual serial correlation B:Ramsey's RESET test using the square of the fitted values C:Based on a test of skewness and kurtosis of residuals D:Based on the regression of squared residuals on squared fitted values

244

Appendix C.2

Autoregressive Distributed Lag Estimates ARDL(1,0,0,0,0,0,1) selected based on Schwarz Bayesian Criterion

********************************************************************* Dependent variable is LT 109 observations used for estimation from 1995M3 to 2004M3 ********************************************************************* Regressor Coefficient Standard Error T-Ratio[Prob] LT(-1) .69626 .074100 9.3962[.000] LE .26753 .10396 2.5734[.012] LJ .14401 .17164 .83901[.404] LM -.47772 .14570 -3.2789[.001] LUS -.31472 .38978 -.80742[.421] LUK .28390 .46617 .60900[.544] LG 1.0875 .29622 3.6711[.000] LG(-1) -.84957 .20328 -4.1792[.000] INPT 1.3614 1.8982 .71719[.475] t .024234 .006838 3.5439[.001] DU Tt -.023296 .0086118 -2.7052[.008] ********************************************************************* R-Squared .98965 R-Bar-Squared .98860 S.E. of Regression .13415 F-stat. F( 10, 98) 937.3561[.000] Mean of Dependent Variable 11.1669 S.D. of Dependent Variable 1.2563 Residual Sum of Squares 1.7637 Equation Log-likelihood 70.0897 Akaike Info. Criterion 59.0897 Schwarz Bayesian Criterion 44.2873 DW-statistic 2.1166 Durbin's h-statistic -.96040[.337] ********************************************************************* Diagnostic Tests ********************************************************************* * Test Statistics * LM Version * F Version * ********************************************************************* * * * * * A:Serial Correlation*CHSQ( 12)= 13.5562[.330]* F( 12, 86)= 1.0179[.440]* * B:Functional Form *CHSQ( 1)= 6.0521[.014]* F( 1, 97)= 5.7024[.019]* * C:Normality *CHSQ( 2)= 2.4942[.287]* Not applicable * * D:Heteroscedasticity*CHSQ( 1)= .059101[.808]* F( 1, 107)= .058048[.810]* ********************************************************************* A:Lagrange multiplier test of residual serial correlation B:Ramsey's RESET test using the square of the fitted values C:Based on a test of skewness and kurtosis of residuals D:Based on the regression of squared residuals on squared fitted values

245

Appendix C.3

Autoregressive Distributed Lag Estimates ARDL(1,0,1,0,0,2,0) selected based on Schwarz Bayesian Criterion

********************************************************************* Dependent variable is LJ 109 observations used for estimation from 1995M3 to 2004M3 ********************************************************************* Regressor Coefficient Standard Error T-Ratio[Prob] LJ(-1) .86102 .042487 20.2656[.000] LE .058985 .026565 2.2204[.029] LT -.0056926 .025507 -.22317[.824] LT(-1) -.067553 .026508 -2.5484[.012] LM -.0073171 .032932 -.22219[.825] LUS -.10158 .081832 -1.2413[.218] LUK .076719 .14519 .52839[.598] LUK(-1) .27059 .12835 2.1082[.038] LUK(-2) -.37350 .092644 -4.0315[.000] LG .089907 .060958 1.4749[.144] INPT 1.4029 .47019 2.9837[.004] t .0032186 .7279E-3 4.4219[.000] DU Jt -.0064434 .022231 -.28984[.773] ********************************************************************* R-Squared .96672 R-Bar-Squared .96256 S.E. of Regression .035900 F-stat. F( 12, 96) 232.3624[.000] Mean of Dependent Variable 5.0958 S.D. of Dependent Variable .18553 Residual Sum of Squares .12373 Equation Log-likelihood 214.9009 Akaike Info. Criterion 201.9009 Schwarz Bayesian Criterion 184.4072 DW-statistic 1.9196 Durbin's h-statistic .46819[.640] ********************************************************************* Diagnostic Tests ********************************************************************* * Test Statistics * LM Version * F Version * ********************************************************************* * A:Serial Correlation*CHSQ( 12)= 13.1569[.358]* F( 12, 84)= .96093[.492]* * B:Functional Form *CHSQ( 1)= 4.0849[.043]* F( 1, 95)= 3.6988[.057]* * C:Normality *CHSQ( 2)= 1.1282[.569]* Not applicable * * D:Heteroscedasticity*CHSQ( 1)= 1.7382[.187]* F( 1, 107)= 1.7340[.191]* ********************************************************************* A:Lagrange multiplier test of residual serial correlation B:Ramsey's RESET test using the square of the fitted values C:Based on a test of skewness and kurtosis of residuals D:Based on the regression of squared residuals on squared fitted values

246

Appendix C.4

Autoregressive Distributed Lag Estimates ARDL(1,0,0,0,0,0,0) selected based on Schwarz Bayesian Criterion

********************************************************************* Dependent variable is LM 109 observations used for estimation from 1995M3 to 2004M3 ********************************************************************* Regressor Coefficient Standard Error T-Ratio[Prob] LM(-1) .86519 .045507 19.0122[.000] LE .077907 .027661 2.8165[.006] LT -.039027 .022229 -1.7557[.082] LJ -.032315 .043524 -.74246[.460] LUS .016021 .099476 .16105[.872] LUK .15819 .14374 1.1006[.274] LG -.062463 .062530 -.99893[.320] INPT -.039827 .49137 -.081052[.936] TIME .6535E-3 .7374E-3 .88622[.378] DUMM3 .063900 .029020 2.2019[.030] ********************************************************************* R-Squared .98168 R-Bar-Squared .98001 S.E. of Regression .040869 F-stat. F( 9, 99) 589.3119[.000] Mean of Dependent Variable 5.2206 S.D. of Dependent Variable .28906 Residual Sum of Squares .16535 Equation Log-likelihood 199.0958 Akaike Info. Criterion 189.0958 Schwarz Bayesian Criterion 175.6391 DW-statistic 1.9061 Durbin's h-statistic .55704[.578] ********************************************************************* Diagnostic Tests ********************************************************************* * Test Statistics * LM Version * F Version * ********************************************************************* * A:Serial Correlation*CHSQ( 12)= 16.2714[.179]*F( 12, 87)= 1.2722[.250]* * B:Functional Form *CHSQ( 1)= 3.8085[.051]*F( 1, 98)= 3.5481[.063]* * C:Normality *CHSQ( 2)= .94527[.623]* Not applicable * * D:Heteroscedasticity*CHSQ( 1)= .76189[.383]*F( 1, 107)= .75318[.387]* ********************************************************************* A:Lagrange multiplier test of residual serial correlation B:Ramsey's RESET test using the square of the fitted values C:Based on a test of skewness and kurtosis of residuals D:Based on the regression of squared residuals on squared fitted values

247

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