Realty Bubbles, Oil Crises and Market Crash in Dubai a ...globalbizresearch.org/files/d630_irrem_shruthi... · E-mail: [email protected] G. Naresh, Associate Professor, Department

International Review of Research in Emerging Markets and the Global Economy (IRREM)

An Online International Research Journal (ISSN: 2311-3200)

2016 Vol: 2 Issue: 2

823 www.globalbizresearch.org

Realty Bubbles, Oil Crises and Market Crash in Dubai a Reality?

Shruthi Murali,

Department of Management,

Birla Institute of Technology, Offshore Campus, UAE.

E-mail: [email protected]

G. Naresh, Associate Professor, Department of Management,

Birla Institute of Technology, Offshore Campus, UAE.

E-mail: [email protected]

___________________________________________________________________________________

Abstract

This paper analyses the impact of WTI Crude Oil spot prices and Dubai’s Real Estate market

on Dubai Financial Market. Daily returns of Dubai Financial Market General Index

(DFMGI), WTI Crude Oil spot prices and Dubai Real Estate and Construction Index

(DFMRE) were calculated and univariate linear regressions are fitted to the data, with

DFMGI returns as the dependent variable and WTI spot price returns and DFMRE returns as

the independent variables. Evidence of a long-run relationship between DFMGI returns and

both WTI Crude Oil spot price returns and DFMRE returns were found, with the latter having

a greater and more significant effect on DFMGI returns. In the short-run, the evidence

indicates that while DFMRE returns have a significant relationship with DFMGI and were

highly linearly correlated with it, WTI spot price returns were only statistically significant in

the short-term prior to a real estate market crash, not during or after. Even in periods where

WTI spot price returns were statistically significant, their extent of significance and extent of

linear correlation with DFMGI returns were very low as compared to that of DFMRE

returns.

___________________________________________________________________________

Key Words: Economic Meltdown, Real Estate Bubble, Stock Market Crash, Dubai Financial

Market

JEL Classification:G01, G10, L8

mailto:[email protected]

mailto:[email protected]





1. Introduction

Financial crises and bubbles were far from new phenomena in the global financial

markets. However, the crisis of the late 2000s was exceptional in terms of both magnitude and

globalisation. According to some estimates, the global market cap had fallen from $51 trillion

in October 2007 to $22 trillion in February 2009. While the Middle East does not in general

have a high level of financial integration with global markets, certain countries have

developed strong economic linkages with the global economy through oil exports, trade

investments in the Arab region, Arab investments in global asset markets, as well as tourism.

Through these and other channels, the financial crisis permeated to the Middle East, albeit

with a delay. The case of Dubai, a city-state in United Arab Emirates, is of particular interest.

In fact, the portfolio losses of the Dubai Financial Market (DFM) increased from 18% pre-

crisis to 42% post-crisis.

Dubai markets faced a real-estate bubble of their own due to heavy borrowing in the

period from 2004-08 by major holding companies such as Dubai World. This period was one

of exponential economic growth and DFM boomed (2005-06 were the best years on record

for DFM). However, along with the global financial crisis, the steep drop in oil prices and

foreign asset returns, drastically reduced investment in Dubai's real estate sector (Real estate

prices reached their highest in Dubai in 2008, after which they fell), the call for a debt

standstill of Dubai World in November 2009 and reduced tourism, the boom quickly turned

into a regional financial crisis that affected other Middle Eastern Nations as well.

Crude Oil as a commodity had been instrumental to the development of the world

economy. It feeds the world's growing energy needs and acts as a raw material for products or

consumed by the products of myriad industries around the world. It was a clear case of

economic globalization wherein the changes in Oil prices affected economies around the

globe. According to the Oil & Gas Journal estimates as of January 2015, UAE holds the

seventh-largest proved reserves of oil in the world at 97.8 billion barrels, with about 94% of

these reserves located in Abu Dhabi. Dubai was a distant second with about 4 billion barrels.

UAE holds approximately 6% of the world's proven oil reserves. Its economy relies majorly

on crude oil exports of which the major export stream was a light sweet crude known as

'Murban' (EIA Report, 2015).

2. Literature Review

Crude oil was one of the largest commodity market in the world, with huge volumes

being traded daily. Previous research focused on the relationship between crude oil prices and

macroeconomic factors such as GNP, while lately, around the nineties, the focus had been

shifted to include research on the effect of Crude Oil prices and price volatility on

international stock markets. Also, Dubai had been investing billions of dollars to build an





stupefying modern city nearly from scratch in a mere 15 years. However, research on the

relationship between oil prices, real estate investment and the stock market performance was

relatively sparse.

Oil prices affect economies on both the macro and micro levels. At the macro level,

Economic recessions, interest and exchange rates, GDP growth rates were all influenced by

oil prices (Davis and Haltiwanger, 2001; Hamilton and Herrera, 2002; Lee and Ni, 2002;

Hooker, 2002; Cunado and Perez de Garcia, 2005; Kilian 2008; Balcilar and Ozdemir, 2013,

as cited in Hayky and Naim, 2016). At the micro level, cost of goods and services, cost of

production (Arouri and Nguyen, 2010; Backus and Crucini, 2000; Kim and Loungani, 1992),

expected future cash flows, company returns, variance of company returns, profits and

consequently cash dividend pay-outs were all influenced by oil prices (Bouri, 2015; Naifar

and Al Dohaiman, 2013; Balcilar and Ozdemir, 2013; Jones et al., 2004, as cited in Hayky

and Naim, 2016). The mechanisms and extent of the effect of oil prices on these variables

varies.

Furthermore, market capitalisation of firms were affected because stock prices vary based

on estimates of expected future cash flows and the increase in the required rate of return

caused by the greater risk of investment brought on by oil price increases and fluctuations

(volatility) (Filis et al., 2011). For all these reasons, oil and its relationship to the global

economy and aggregate macro-economic indicators have been the focus of a great deal of

research. Hamilton's 1983 paper was considered as the pioneering work in this field of

research.The paper found that all but one recession in the US after World War 2 were

followed by a rise in Oil Prices. (Hamilton, 1983). This inverse relation between oil prices

and aggregate economic activity had been reaffirmed in subsequent studies (Hooker, 1996).

There exists a further extensive body of research on the macroeconomic effects of Oil Prices

(Burbridge and Harrison (1984), Gisser and Goodwin (1986), Hamilton (1988a, 1998b), Mork

et al. (1994), Ferderer (1996), Hondroyiannis and Papapetrou (2001), Papapetrou (2001),

Jones et al. (2004), Hamilton and Herrera (2004), Huntington (2007), Kilian (2008), Jimenez-

Rodriguez (2009), Berument et al. (2010), Du et al. (2010), Korhonen and Ledyaeva (2010),

Hammoudeh et al. (2010) and Doğrul and Soytas (2010); as cited in Degiannakis et al.

(2011)).

Various researchers had found that macroeconomic variables were impacted by oil price

increases and fluctuations across various economies (Rebeca and Sanchez, 2004, 2009;

Jacobs et al. (2009)). Researches also shown that oil price shocks had an impact on the

macroeconomic variables in G-7 (Cologni and Manera, 2008) and Asian economies (Cunado

and Perez de Gracia, 2005). Because oil was a sizeable input for many industries (in terms of

relative factor costs), oil price increases cause cost-push inflations and higher unemployment,

leading to economic crises (Eski et al., 2012). The increased costs get transferred to





consumers causing reduced consumer spending and thereby reduced demand (Hamilton,

1996; Bernanke, 2006).This further lowers production and thereby increases unemployment

(Davis and Haltiwanger, 2001; Lardic and Mignon, 2006).

Sadorsky (1999) using a dataset including USA industrial index, interest rates and oil

prices between 1947:1–1996:4 periods, concluded that oil prices have an effect on economc

activity and that the effects expand after 1986. The effect was unidirectional. Papapetrou

(2001) reaffirmed this conclusion for the 1989:1-1996:6 period. Cunado and Garcia (2005)

studied the effects of oil shocks on both economic output and the Consumer Price Index for

six Asian countries. In all cases, a significant causality existed in the short-run between oil

prices and economic activity as well as oil prices and inflation. However, these relationships

did not continue in the long-run.

Research indicates that there were two major transmission mechanisms of an oil price

shock to the stock market. One school of thought argues that the effects were indirect and

transmitted through macroeconomic indicators. An oil price increase will positively impact

the income of an oil exporter, thus increasing productivity and lowering unemployment; to

which stock markets respond positively (Bjornland (2009); Jimenez-Rodriguez and Sanchez

(2005). The opposite effect was also seen in the case of oil importing nations (LeBlanc and

Chinn, 2004; Hooker, 2002). The increased costs get transferred to consumers causing

reduced consumer spending and thereby reduced demand (Hamilton, 1996; Bernanke, 2006).

This further lowers production and thereby increases unemployment (Davis and Haltiwanger,

2001; Lardic and Mignon, 2006). Stock markets would react negatively in such case

(Sadorsky, 1999; Jones and Kaul, 1996).

The other mechanism of transmission of the effects of oil price shocks was through the

uncertainty that they generate in financial markets, depending on whether the shock originates

on the demand-side or the supply-side. Stock markets would respond positively to demand

side shocks and negatively to supply-side ones (Berk, Aydogan, 2014). A greater change in

energy prices creates uncertainty in the market about future energy price and consequently,

firms delay irreversible investment decisions in reaction to uncertain profit prospects

(Bernanke (1983); Pindyck (1991)).

Given that asset prices were the discounted present value of the future net earnings of a

firm; assuming that the stock market was efficient, the market would capitalise efficiently the

effects of any oil price shock on the real cash flows of various companies, especially those

companies for which oil is a significant operational cost or raw material. If, however, the

market was not efficient, a time lag between the price shock and the market reaction can be

expected.

Jones and Kaul (1996) were the first to reveal the negative impact of oil price on stock

markets. Other authors, such as Filis (2010), Chen (2009), Miller and Ratti (2009), Nandha





and Faff (2008), O'Neill et al. (2008), Park and Ratti (2008), Driesprong et al. (2008), Ciner

(2001) and Gjerde and Sættem (1999) also provided proof of such a negative relationship.

Oberndofer (2009) found a negative relationship between oil price volatility and European

stock markets, while Chiou and Lee (2009) found a similar relationship for the S&P 500.

Malik and Ewing (2009) also found a negative relationship between oil price return

volatilities and three sectoral returns of the US Stock market. Arouri and Nguyen (2010)

undertook a similar examination for European sectoral stock market index returns and found

relationships with varying directionality and magnitude, depending on the sector.

Driesprong et al. (2004) also found strong evidence that changes in oil prices forecast

stock returns in an economically significant manner that remains robust over time. This

predictability cannot be explained by calendar anomalies or well-known economic variables

that predict stock market returns. Basher and Sadorsky (2006), using a multifactorial model of

arbitration that allows for both conditional and unconditional risk factors, found robust

evidence that oil price risk impacts the performance of stock markets in emerging countries.

Park and Ratti (2008), using alternative VAR models, concluded that the real return of 13

European stock exchanges have a significant relationship, in addition to that between oil price

shocks and economic activity in these 13 countries for the period 1986:1-2005:12. Miller and

Ratti (2009) built a VECM using monthly data of OECD countries from 1971:1-2008:3 and

thereby concluded that a significant negative relationship existed between crude oil prices and

stock indices for all the countries; and that it weakens after 1999:9.

Aloui and Jammazi (2009), using a univariate regime-switching EGARCH model found

two episodes of series behaviour (low mean- high variance and high mean- low variance) of

which the former coincided with common recessions. Lee and Chiou (2011) applied a similar

model and found that significant fluctuations in oil prices (as happens in an oil price crash)

lead to negative impacts on S&P 500 returns while a similar impact was not seen during

regimes of lower oil price fluctuations (low volatility). Cifarelli and Paladino (2010) found

further evidence that oil price shifts were negatively related to stock price changes while Choi

and Hammoudeh (2010) found decreasing correlations between Brent oil, WTI oil and the

S&P 500.

Bashar (2006) and Arouri and Rault (2011) found that positive oil price shocks had a

positive relationship with the stock market performance of GCC countries. El Hedi (2006) on

the other hand found that oil prices significantly affect stock markets only in Qatar, UAE and

Oman. Arouri (2009) used an unrelated regression method to show that oil prices and GCC

stock markets were co-integrated. Hammoudeh and Aleisa (2004) examined the markets of 5

GCC countries and found that they react to oil prices, while Zarour (2006) investigated the

same countries during a later period and found that the sensitivity of these markets to oil price

shocks had increased. The GCC economies, with their heavy dependence on crude oil





exports, take cues on a daily/ weekly basis from WTI crude oil futures prices traded on the

New York Mercantile Exchange for their own oil prices. A linkage between oil price

volatility and GCC stock market returns had been empirically established (Adebiyi et al.

2010). Naifar and Al Dohaiman (2013) found a regime-dependent relationship between GCC

stock market returns and OPEC oil market volatility with the exception of Oman.

Huang et al. (1996), using a VAR model, found that while Granger causal effects exist

from oil futures to individual oil companies, no effect of oil futures on the S&P 500 exists.

Maghyereh (2004), using a VAR model, also found a similar absence of any relationship or

influence of oil shocks on stock index returns in 22 emerging economies in the period 1998-

2004. This finding contrasts with those related to developed economies, indicating that

emerging economies possibly do not rationally incorporate information on shocks in the crude

oil markets. Cong et al. (2008), Jammazi and Aloui (2010), Aspergis and Miller (2009) and

Chen et al. (1986) all found no relation between stock markets and oil prices. Al-Fayoumi

(2009) and Al Janabi et al. (2010) similarly found no relationship for oil-exporting (GCC) and

oil-importing countries respectively.

In addition to oil prices rise, the volatility of realty prices—the biggest purported

downside of “speculation”—is strongly related to supply conditions. Moreover, while demand

conditions in general, and speculation in particular, contribute to boom and bust cycles in

housing and real estate markets, the impact of speculation is dominated by the effect of the

price elasticity of supply. In fact, the large impacts of speculation were only observed when

supply is inelastic (Malpezzi & Wachter, 2005). However, the activities in the real estate

markets in Southeast and East Asian economies were an important contributing force to the

financial crises of 1997 in the Asian economies. Even though, the analysis relies upon

unpublished data reported contemporaneously by financial institutions and market watchers to

document the extent of the imbalances in the real property market that were evident to

informed observers at the time of the financial collapse. Given the recentness of the crisis,

they are certainly consistent with a financial collapse whose proximate cause was unchecked

activity in the property market (Quigley, 2001). Dubai had been spending billions of dollars

with an ambition to become a hub of global commerce, a top tourist spot and shopping Mecca

all rolled in to one. Around $100 billion worth of real estate under construction or in the

pipeline continues the boom. Given the rollercoaster nature of realty sector, the sustainability

mature of this market is highly questionable (Bagaeen, 2007). Farmer (2012) argues that the

stock market crash of 2008, triggered by a collapse in house prices, caused the Great

Recession. The financial crisis showed that neglecting real estate booms can have disastrous

consequences. In order to control the damage to the financial system and the economy from

real estate boom–bust episodes, the monetary policy may have to be used to lean against the

wind. (Crowe et. al. 2013). Having reviewed the impact of crude oil and realty prices on the





various stock markets and their listed stocks globally, also the impact on the developed as

well as developing economies in addition to the middle east economies in general, the present

study intends to look at the causal relationship among these three macro economic indicators

in the Dubai economy.

3. Methodology

3.1 Research Questions

Dubai emerged as a global business hub and always considered as the leading emirates

among the seven sheikhdoms. Dubai attracted the world attention apart from its oil reserves,

development in real estate and the financial services. Therefore the major research question is

to examine the relationship of Crude Oil prices and Real Estate market with the Dubai

Financial Market. The study intends to examine these relationships for the entire period from

January 2004 till December 2015, and also specifically examine them in the two quarters just

before, during and just after Dubai’s 2009 Real Estate Market crash. The date of the

beginning of the crash is taken as November 25th, 2009; the date on which Dubai World asked

its creditors for a debt standstill. Specifically, the short-term periods under examination are

2009:5 to 2009:11 (period 1), 2009:11 to 2010:5 (period 2) and 2010:5 to 2010:11 (period 3).

3.2 Model for Analysis

The univariate linear regression equation was ran with Dubai Financial Market General

Index (DFMGI) returns as the dependent variable. For one set of equations, crude oil price

returns (WTI spot price returns) was used as the independent variable while for the other,

Dubai Real Estate and Construction Index (DFMRE) returns was used as the independent

variable. The model used for analysis was:

YDFMGI = a1XDFMRE + c1 -------------------- eqn. 1

YDFMGI = a2XWTI + c2-------------------------- eqn. 2

Where Y is DFMGI returns, XWTI and XDFMRE are crude oil price returns (WTI spot price

returns) and DFMRE returns respectively.

3.3 Data

In this study, the DFMGI daily closing prices, DFMRE daily closing prices and WTI

(West Texas Intermediary) daily crude oil spot prices were used. DFMGI prices and DFMRE

prices are obtained from the official website for the Dubai Financial Market (DFM), while

WTI Crude Oil spot prices were obtained from the official website of the US Energy

Information Agency (EIA). Specifically, the model was run using the daily returns of each of

these variables, calculated as:

log10(Current price/ Previous Day’s Price).





4. Results and Discussion

The values of a1 and a2 (the coefficients of the independent variables in regression model)

indicate how much the dependent variable (DFMGI returns) will change for a unit change in

the respective independent variable. The p values associated with each coefficient indicate

how statistically significant its associated independent variable (WTI price returns or DFMRE

returns) is in explaining the variation in the dependent variable (DFMGI returns). For

example, in the table below, DFMGI returns increase by 0.611 for a unit change in DFMRE

returns in the long-term. Meanwhile, the Adj. R2 value indicates how good a fit the linear

model used for the data, i.e., how much of the variance in the dependent variable can be

explained by changes in the independent variable.

Time period Y(Dependent) X(Independent) Coefficient (a) p value Adj. R2

Long-term (2004:1

to 2015:12)

DFMGI returns

DFMGI returns

WTI spot price

returns

DFMRE returns

2.11E-03

0.611

3.59E-06

0

8.75E-02

0.798

Period 1 (2009:5 to

2009:11)

DFMGI returns

DFMGI returns

WTI spot price

returns

DFMRE returns

0.267

0.596

3.12E-02

6.57E-67

0.075

0.948

Period 2 (2009:11 to

2010:5)

DFMGI returns

DFMGI returns

WTI spot price

returns

DFMRE returns

0.217

0.606

0.112

2.70E-68

0.016

0.957

Period 3 (2010:5 to

2010:11)

DFMGI returns

DFMGI returns

WTI spot price

returns

DFMRE returns

0.0802

0.588

0.185

2.03E-57

7.80E-02

0.924

The analysis of the data showed that in all periods examined, both long-term and short-

term, WTI crude oil spot price returns and DFMRE returns had a positive correlation with

DFMGI returns, that is, the direction of movement of the variables is the same.

4.1 Crude Oil

The analysis of the data shows that WTI crude oil spot price returns have a statistically

significant relationship (at a 5% level of significance; p value < 0.05) with DFMGI returns in

the long-term (January 2004 to December 2015) as well as in the two quarters before the real

estate market crash (2009:5 to 2009:11). However, the relationship becomes statistically

insignificant in the two periods examined after this (2009:11 to 2010:5 and 2010:5 to

2010:11), that is the two quarters during the real estate market crash (i.e., two quarters

immediately after Dubai World’s call for a debt standstill) and two quarters post that. This

would indicate that while in the long run and when the real estate market is booming, DFM

takes cues from WTI crude oil spot prices. However, when the real estate market crashes,

WTI crude oil spot prices are no longer considered important indicators of stock performance





by investors in DFM. Instead, the ailing real estate market becomes a greater factor in their

valuations of DFM stocks, that is, investors weight the real estate market’s performance more

highly than the impact of WTI crude oil spot prices on firms in the market.

It should be noted however, that while WTI crude oil spot price returns have a statistically

significant relationship with DFMGI returns, in the aforementioned period, the correlation

coefficient (Adjusted R2) value is extremely low. This indicates that while WTI crude oil spot

prices do affect DFM, their effect was limited; rather, other factors affect DFM performance

to a greater extent. This was possibly due to the fact that Dubai’s economy depends on real

estate and tourism greatly as compared to oil exports. Most of the oil in UAE is in Abu Dhabi;

Dubai exports a much smaller percentage of UAE’s overall oil exports. It was also possible

that WTI crude oil spot price returns do not share a linear relationship with the DFMGI

returns; thus, even if the extent of correlation was stronger, it was not reflected in the value of

the Adj. R2 in the linear model used by this study.

4.2 Real Estate

The analysis of the data showed that DFMGI returns have a statistically significant

relationship (at a 1% level of significance; p value< 0.01) with DFMRE returns both in the

long-term and in all three short-term periods under examination. The Adjusted R2 value was

high in all cases, (0.80 in the long-term and above 0.90 in all short-term periods examined).

This would indicate that a major part of the variation in DFMGI returns could indeed be

explained by the DFMRE returns. Dubai’s real estate market had a lasting and strong impact

on Dubai’s stock market. This relationship is extremely well-characterized by a linear model.

It was observed that the Adj. R2 value was slightly lower in the long-term than in the short-

term periods examined. This could indicate that in the long-term, other factors (including

WTI spot prices) could play a greater role in Dubai’s stock market than they do in the short-

term.

It was also noted that the p value in the long term for the relationship between DFMGI

returns and WTI crude oil spot price returns was of the order of magnitude 10-6, which was

higher than that for the relationship between DFMGI returns and DFMRE returns which was

zero. A similar observation was made in the short-term periods as well. For those periods in

which WTI crude oil spot price returns were statistically significant to DFMGI returns, the p

value for this relationship was of the order of magnitude 10-2, whereas for the same periods,

the order of magnitude of the p value for the relationship between DFMGI returns and

DFMRE returns was 10-67 to 10-68. This large difference indicates that Real Estate market was

indeed more significant to Dubai stock market than WTI crude oil spot prices.





5. Conclusion

A survey by an U.S. global consulting firm rated Dubai as one of the best places to live in

the Middle East (Duncan, 2013). Dubai had changed over the last three decades, becoming a

major business gateway for the Middle East region, its economy was initially built on

revenues from Oil reserves. The realty sector boomed drastically thereby turning itself as a

global hub for tourism, aviation, real estate, and financial services. This study examined the

relationships between the Dubai Financial Market and two factors, the Dubai Real Estate

Market and WTI crude oil spot prices. It was found that in the long run, both factors (DFMRE

returns and WTI spot price returns) have a statistically significant relationship with DFMGI

returns, with DFMRE having a more significant relationship, a more linear relationship than

the WTI crude oil prices. This reflects that although the Dubai economy was initially built on

the revenues from Oil industry presently their revenues were only restricted to 5% or less.

However, in the short-term, it was found that DFMGI returns have a significant relationship

with WTI crude oil spot price returns only before a real estate market crash, after which, only

DFMRE returns have an impact on DFMGI returns. The DFMGI returns and DFMRE returns

relationship was higher in the short-term than in the long-term.

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Annexure

CAUSAL RELATIONSHIP BETWEEN DFMGI RETURNS AND WTI SPOT PRICE

RETURNS Long-term (2004:1 to 2015:12)

Regression Statistics

Multiple R 0.095802

R Square 0.009178

Adjusted R Square 0.008753

Standard Error 0.009281

Observations 2331

Equation

Components

Coefficients Standard

Error

t Stat P-value

Intercept (c1) 0.000211 0.000192 1.095763 0.273296

WTI spot price

returns

0.076065 0.016377 4.644725 3.59E-06


RETURNS Period 1 (2009:5 to 2009:11)


Multiple R 0.289853

R Square 0.084015



Observations 102

Equation

Components


Error

t Stat P-value

Intercept 0.000569 0.001016 0.560584 0.576335

WTI spot price

returns

0.266709 0.088065 3.028538 0.003127








Multiple R 0.161461

R Square 0.02607



Observations 98

Equation

Components


Error

t Stat P-value

Intercept (c1) -0.0011 0.00117 -0.94383 0.347625

WTI spot price

returns

0.216601 0.13512 1.603025 0.112214




Multiple R 0.133511

R Square 0.017825



Observations 100

Equation

Components


Error

t Stat P-value

Intercept (c1) 0.00019 0.00058 0.327424 0.744046

WTI spot price

returns

0.080181 0.060122 1.333628 0.185417





CAUSAL RELATIONSHIP BETWEEN DFMGI RETURNS AND DFMRE RETURNS Long-

term (2004:1 to 2015:12)


Multiple R 0.893564

R Square 0.798456



Observations 2332

Equation

Components


Error

t Stat P-value

Intercept (c2) 1.09E-05 8.67E-05 0.125379 0.900234

DFMRE returns 0.610732 0.006357 96.07673 0

CAUSAL RELATIONSHIP BETWEEN DFMGI RETURNS AND DFMRE RETURNS Period 1

(2009:5 to 2009:11)


Multiple R 0.973956

R Square 0.948591



Observations 103

Equation

Components


Error

t Stat P-value

Intercept (c2) 5.1E-05 0.000241 0.211807 0.832685

DFMRE returns 0.595575 0.013796 43.16992 6.57E-67






(2009:11 to 2010:5)


Multiple R 0.978481

R Square 0.957425



Observations 99

Equation

Components


Error

t Stat P-value

Intercept (c2) -0.00041 0.000242 -1.7051 0.091377

DFMRE returns 0.60569 0.012969 46.7045 2.7E-68


(2010:5 to 2010:11)


Multiple R 0.961637

R Square 0.924746



Observations 101

Equation

Components


Error

t Stat P-value

Intercept (c2) 0.000126 0.000168 0.751345 0.454228

DFMRE returns 0.588081 0.016861 34.87909 2.03E-57

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