QinYafeng 210906 Paper

Doctoral Thesis Proposal

Liquidity and Commonality in Emerging Markets

QIN Yafeng

Department of Finance and Accounting School of Business

National University of Singapore 1 Business Link

Singapore 117592 Email Yafengqinnusedusg

September 2006

Abstract

Emerging markets share many distinct features that separate them from more developed markets including a low level of liquidity In this study we investigate the extent to which the illiquidity of emerging market stocks co-moves with other stocks in the same market We document a significantly higher commonality in illiquidity in emerging markets than in developed markets Our analysis shows that individual stock illiquidity is more affected by fluctuations in market prices than by uncertainty in individual stock returns We find commonality in liquidity is positively related with synchronicity in prices and level of development of the financial markets These findings reinforce the idea that liquidity commonality is related to market-wide factor and provide an explanation to the higher co-movement in liquidity in emerging markets We also document that liquidity co-movement across markets has a strong geographic component and is related to correlation in market-wide volatility Our initial results do not support the presence of a global liquidity factor

Acknowledgements I would like to thank my supervisor Professor Allaudeen Hameed and my committee members AP Inmoo Lee and Dr Kang Wenjin for their invaluable guidance insightful feedback and encouragement that helped make this thesis possible I would also like to thank participants at NUS Brown-Bag seminar for their insightful comments All errors are mine

Table of Contents

Chapter 1 Introductionhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip1

Chapter 2 Liquidity and Commonality in Liquidity in Emerging Marketshelliphelliphellip7

21 Liquidity and Intra-Market Commonality in Liquidity in Emerging Marketshellip7

22 Inter-market Commonality in Liquidity helliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip11

Chapter 3 Data and Liquidity Proxieshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip13

Chapter 4 Empirical Analysishelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip18

41 Intra-Market Commonality in Liquidity of Emerging Marketshelliphelliphelliphelliphelliphelliphellip18

42 Common Sources of Illiquidity at Individual Security Levelhelliphelliphelliphelliphelliphelliphellip20

43 Sources of Commonality at Aggregate Market Levelhelliphelliphelliphelliphelliphelliphelliphelliphelliphellip24

44 Inter-Market Commonality in Liquidityhelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip26

Chapter 5 Conclusion helliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip28

Referencehelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip30

Tableshelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphelliphellip32

Chapter 1 Introduction

There has been an extensive market microstructure literature on the role of liquidity in the

price formation process of individual securities Some studies show that liquidity on average

is priced (Amihud and Mendelson 1986 and Brennan and Subrahmanyam 1996) Other

research documents that liquidity can predict future returns and liquidity shocks are positively

related to returns (Chordia Roll and Subrahmanyam 2002 and Amihud 2002) More

recently a new stream of studies has shown that liquidity more than just an attribute of

single asset co-moves with each othermdasha phenomenon called commonality in liquidity

( Chordia Roll and Subrahmanyam 2000 Hasbrouck and Seppi 2001 and Huberman and

Halka2001) Findings on commonality in liquidity have raised a new issue of whether shocks

in liquidity constitute a source of non-diversifiable priced factor This is important because

even if liquidity affects the risk of an asset it should not be a priced risk factor if it is

idiosyncratic and can be diversified away at portfolio level If there is non-diversifiable

liquidity risk securities with high liquidity risk should have high expected return Acharya

and Pedersen (2005) develop a liquidity-adjusted CAPM by assuming a random variation in

liquidity over time Their model decomposes the net beta into the standard market beta and

three betas representing different forms of liquidity risk including commonality with the

market liquidity Their empirical test shows that these three different risk premia are highly

significant in US data Pastor and Stambaugh (2003) investigate whether market liquidity is

a state variable for asset pricing Also using US stocks as sample they construct their

aggregate monthly liquidity measure and find that expected stock returns are related cross-

sectionally to the sensitivities of stock returns to innovations in aggregate liquidity even after

controlling for other risk factors

However in contrast to the burgeoning literature on liquidity in the US market the role

of liquidity in emerging markets has long been ignored which leaves us a line of interesting

research to investigate the liquidity and liquidity risk in emerging markets In particularly

we focus on one specific aspect of liquidity risks proposed in Acharya and Pedersen (2005)mdash

commonality in liquidity in emerging markets because the existence of systematic liquidity

is a precondition for liquidity to be priced

Study on liquidity liquidity risk and its implication on asset pricing in emerging markets

is particularly important because liquidity effect should be more acute in emerging markets

than in developed markets (Bekaert Harvey and Lundblad 2006) Most of the standard asset

pricing models such as CAPM APT and consumption based CAPM have some

presumptions of perfect capital markets such as complete and symmetric information no

transaction costs homogeneous expectation et al which actually assume that the underlying

problems of liquidity and price discovery have been completely solved This assumption is

more applicable to developed markets like US stock market which is the most liquid

market in the world and is most close to the ldquoperfect capital marketrdquo But such assumption is

actually counterfactual among the thinly traded stocks as those in emerging markets where

the impact of liquidity on asset pricing will be much more acute The illiquidity will be more

of a concern for investors in emerging markets than those in the liquid and developed markets

Besides the high liquid feature of US market the vast number of traded securities and very

diversified ownership structure result in a clientele effect in portfolio choice that mitigate the

pricing of liquidity But such diversity in both securities and ownership is lacking in

emerging markets making liquidity effects potentially more acute (Bekaert Harvey and

Landblad 2006)

Despite the extreme importance of liquidity in emerging markets little research has been

done in this field The most important reason why liquidity has not received as much

attention in emerging markets as in developed markets is that most studies rely on high

frequency or transaction data to measure liquidity such as bid-ask spread or market depth

But these data are usually unavailable for most countries especially for emerging markets

Recent literature proposes some new measures of liquidity using only daily observations

which overcomes the transaction-level data limitation and makes possible the study in a

broader setting Bekaert Harvey and Lundblad (2006) constructing liquidity measure from

daily return series study the pricing of liquidity in emerging markets They find that market

liquidity significantly predicts future returns But before we draw the conclusion that liquidity

is a priced risk factor in emerging markets we have to show that there is systematic liquidity

risk that cannot be diversified away which gives the initial motivation for this study

This paper is going to extend the current literature on market microstructure and

international markets by investigating liquidity and liquidity commonality in emerging

markets Our first objective is to investigate whether securities from emerging markets also

co-move with each other in liquidity as those in developed markets Given the illiquid feature

of emerging markets answer to this question becomes especially critical If there is no

comovement in liquidity in emerging markets ie there is enough variation in liquidity

among securities the liquidity exposure of investors can be easily diversified away by

constructing portfolios Then the finding from Bekaert et al (2006) of priced aggregate

liquidity could also be ascribed as an omitted variable correlated with liquidity proxy

However if securities also co-move in liquidity with each other as those from developed

markets diversification becomes less likely and investors have to bear systematic liquidity

risk which will make emerging market securities even less attractive to investors Therefore

our primary task is to test the existence of commonality in liquidity in emerging markets In

our empirical test following recent literature we construct five liquidity measures and use

each of them to investigate the intra-market comovement in liquidity in 18 emerging markets

Emerging markets have many distinct features one of which is the high synchronicity in

returns documented by Morck Yeung and Yu (2000) Their study shows that security prices

co-move with each other more in emerging markets than in developed markets The higher R2

from market model indicates that a larger proportion of variation in individual prices is

attributable to market wide variation Since both securityrsquos recent performance and its

variation influence its liquidity by affecting inventory risk of liquidity providers in financial

markets or their funding abilities (Copeland and Galai 1983 Chordia Roll and

Subrahmanyam 2003 Hameed Kang and Viswanathan 2006) covariation in price and in

volatility should also induce a covariation in the provision of liquidity

Such conjecture motivates the second objective of this papermdashto investigate what are the

possible reasons driving commonality in liquidity in emerging markets So far there have

been several studies documenting the existence of commonality in US (Chordia Roll and

Subrahmanyam 2000 Hasbrouck and Seppi 2001 and Huberman and Halka 2001) Hong

Kong (Brockman and Chung 2002) and Austrian security markets (Sujoto Kalev and Faff

2005) But none of them looks at the reasons why such phenomenon exists Coughenour and

Saad (2004) document the covariation in liquidity among securities handled by the same

specialist firm They believe that shared capital and information among specialists within a

firm cause co-movement in their provision of liquidity Hameed Kang and Viswanathan

(2006) suggest that market states can affect the funding ability of financial intermediaries

and thus inducing the covariation in their provision of liquidities Our paper extends this

stream of research further First we investigate another candidate factor that could induce

market-wide comovement in liquidity in emerging marketsmdashmarket uncertainty If as we

discussed above covariation in price and in volatility could induce covariation in the

provision of liquidity we shall see that the market uncertainty is another driving force of

intra-market commonality And this effect should be weaker in developed markets where

security prices do not co-move much with each other We will empirically test this conjecture

by looking at the impact of market uncertainty on the time series variation of individual

securities And we also compare this effect with that from developed market to see if there is

a difference Second Morck et al (2000) attribute the high synchronicity of returns in

emerging markets to the poor property rights protection which deter risk arbitrage cause

more noise trading and thus causing more market-wide stock price variation If this is also the

underlying reason for commonality in liquidity in emerging markets we shall see a link

between the country governance or market development and intra-market covariation in

liquidity Emerging markets do have some macro economic features that could induce higher

commonality in liquidity For example emerging markets usually do not have many

alternative investments (for example bonds) Or even if they have the markets may not be

well developed As a result investors facing liquidation needs cannot easily diversify their

liquidity shock among several asset classes thus causing the covariation in liquidity in one

asset market Therefore beyond studying at the individual security level we also investigate

the impact of some market or country features on intra-market commonality in liquidity

Third it has been well acknowledged that liberalization of emerging markets and

international fund flows have reduced cost of capital and increased liquidity of these markets

(Bekaert and Harvey 2000) However how does the liberalization process affect the risk of

liquidity is still unknown If international fund flows also reduce the commonality in liquidity

there should not be any problem However if they cause more commonality which increases

the liquidity risk in emerging markets it would become a concern for both investors and

regulators Therefore an investigation into the impact of international fund flow on the

market liquidity risk is both necessary and valuable

Our last objective is to investigate the inter-market linkage in liquidity This is

important because if liquidity co-moves across markets liquidity dry up in several markets

might lead to a widespread financial crisis (Stahel 2005b) Stahel (2005a) documents

commonality in liquidity both within and across countries However this study uses sample

stocks only from Japan the UK and the US which are the most developed and integrated

markets What he finds may not totally apply to emerging markets as they are not well

integrated with world financial markets yet Stahel (2005b) takes a more comprehensive

study among 18 developed and emerging markets He finds that there exist global factors But

his analysis of the comovement of changes in liquidity and liquidity shocks shows that the

correlation across markets is relatively low Brockman Chung and Perignon (2006) also

document a global component in bid-ask spread and depths in their study among 47 security

markets However all these studies assign a special role to the global portfolios In our study

we investigate the cross-market linkage in liquidity among our sample emerging countries

Different from previous studies we do not assign any prior restriction to the global factor but

use common factor analysis to investigate whether market aggregate liquidities especially

those from the same region are subject to the same factors In order to analyze whether such

cross-border linkage is related to volatility spillover effect documented by previous studies

we also apply the same procedure to test the common factors in market volatility and see if

these common factors are correlated

We have several interesting findings in this study Firstly we document a

significantly higher commonality in liquidity in emerging markets than in developed markets

And this finding is robust to all five liquidity proxies we construct Secondly the time-series

analysis at individual security level shows that individual liquidity is more affected by market

uncertainty than by individual security idiosyncratic uncertainty which is in contrast to

securities from developed markets This could partially explain the higher covariation in

liquidity in emerging markets And consistent with this explanation we find commonality in

liquidity is positively related with stock synchronicity in price Thirdly we find that countries

with less developed equity markets less developed bond markets poorer country governance

or more noise traders have higher intra-market covariation in liquidity Finally we document

inter-market commonality among countries from the same geographical region And such a

link is closely related to the volatility spillover effect among these markets We fail to find

any covariation in aggregate liquidity across different regions which shed some doubt on the

existence of global liquidity factor

Illiquidity is an especially important feature of emerging markets A better

understanding of its dynamics within and across markets should be valuable to both domestic

and international investors for constructing their portfolios successfully This study also has

practical implications for regulators The knowledge of liquidity risk as well as its driving

mechanisms is of critical importance for designing well-functioning markets to improve the

liquidity condition of emerging markets and to promote global integration of financial

markets The findings of this study should shed light on literature in market microstructure

and liberalization and integration of emerging markets

In what follows the theoretical motivation for the study and relevant previous

literature will be discussed in Section 2 followed by the data and construction of liquidity

proxies in Section 3 Section 4 designs research methodology and presents empirical results

Section 5 concludes the paper and draws lessons for future research

Chapter2 Liquidity and Commonality in Emerging Markets In this section we first review some theories on liquidity to analyze the sources of

illiquidity Then based on the analysis especially combing some unique features of emerging

markets we try to find the plausible common factors that affect all individual liquidities and

cause commonality in liquidity Finally we list the plausible reasons for inter-market linkage

in aggregate market liquidity

21 Liquidity and Intra-Market Commonality in Emerging Markets Liquidity generally referring to the ability to trade large size quickly at low cost

when one wants to trade is a very important feature of financial markets This is a ldquoslippery

and elusiverdquo concept (Kyle 1985) encompassing five dimensions Tightness refers to low

transaction costs Immediacy refers to how fast an order can be settled Depth refers to the

size of the trade at a give cost Breadth means the impact of large trade on prices And

Resiliency refers to the speed with which prices recover from a random uninformative shock

(Kyle 1985 Sarr and Lybek 2002) It is generally acknowledged that there is no single

unambiguous theoretically correct or universally accepted definition of liquidity Therefore

there is no single measure that can precisely capture all these dimensions of liquidity

Liquidity is a complex concept And it is affected by many factors Liquidity

providers such as market makers dealers or precommitted traders who submit limit orders

face certain risks when they provide liquidity These risks influence their bid-ask quotes or

the limit order and thus affect the liquidity provision of the security

One of the most important risks the liquidity providers face is inventory risk

Liquidity providers buy from security sellers and sell to security buyers later Before they sell

they have to bear inventory risk of change in security price and require compensation by

quoting bid-ask spread (Stoll 1978) The most important factor that affects inventory risk is

the securityrsquos uncertainty If the price of a security is very volatile the probability that the

value of the security falls increases Thus liquidity providers are less willing to hold illiquid

asset when they expect a high volatility and therefore increase their bid-ask spread or submit

a more conservative limit order which reduces the liquidity of the security Copeland and

Galai (1983) developed a model on the quoting decision of a profit-maximizing market

maker defining the profit as the difference between the gain from liquidity traders and the

loss to informed traders One important implication of their model is that increased

uncertainty (volatility) widens the bid-ask spread and induce illiquidity which is consistent

with empirical evidence

Morck et al (2000)lsquos finding that emerging markets have greater synchronicity than

developed markets have several implications for liquidity providersrsquo inventory risks Firstly

high R2 of the market model suggests that a large portion of the individual volatility comes

from market-wide volatility When market is volatile high synchronized securities also

become more volatile And due to the increased expected inventory risk liquidity providers

will increase the bid-ask spread and reduce the liquidity of the security Secondly high

synchronicity also indicates that the price of asset reflects more of the market-wide

information than the firm-specific information This could be due to the poor information

environment of emerging markets where not much firm-specific information is publicly

available Then market makers who are uninformed investors have to form their expectation

on the security and its inventory risk based on market-wide information Thirdly as Morck et

al (2000) suggest high synchronicity could be caused by the insufficient informed trading

from arbitrageurs Arbitrageurs not only help incorporating firm-specific information to asset

prices and preventing security prices from deviating too far away from the assetsrsquo

fundamental values they also play an important role in transmitting liquidity among different

markets One effect of arbitrageurrsquos trading is to connect demands for liquidity made in one

market with offers of liquidity made in another market They demand liquidity in the market

where it is most available and supply that liquidity in the market where traders demand it

(Harris 2003) In emerging markets with poor property rights protection high transaction

cost and high information searching cost arbitrageurs are less willing to participate This

could also deter the diversification of liquidity shocks among markets and aggravate the

intra-market liquidity covariation All these implications suggest an empirically testable

hypothesis highly synchronized securities and markets are likely to have high commonality

in liquidity

Besides high synchronicity there are some other features of emerging markets that

could also induce higher covariation of liquidity within market

1) Insufficient investment instruments make diversification of liquidity shock more

difficult in emerging markets If some event causes a liquidity problem on one asset it may

induce a corresponding liquidity inflow in another asset Examples of this could be the ldquoflight

to qualityrdquo observed periodically in the bond markets However emerging markets are not

well developed in a sense that they generally have less alternative investments than in

developed markets Hence when faced with an unexpected need to liquidate assets investors

in emerging markets cannot effectively diversify the liquidity shock by liquidating alternative

investments (like bonds) and thus causing liquidity comovement among same assets on one

market (for example stock market) Therefore countries with more developed alternative

financial markets like bond markets are less likely to have commonalty in liquidity in equity

markets

2) The development of the equity markets themselves also affect the commonality in

liquidity within these markets For example many emerging markets are not well developed

in a sense that they do not have the breadth of industrial sectors that developed countries have

All firms come from very few industries that dominate the whole market Thus it is very

likely that we will find a stronger within industry commonality in liquidity in emerging

markets relative to what Chordia et al (2000) document in US markets Also less developed

equity markets usually have a less transparent information environment This will make

security prices less efficient in reflecting the firm-specific information or their fundamental

values Therefore development of equity markets should to be positively related with the

intra-market covariation in liquidity

3) Investment style also affects the covariation of liquidity Different investors with

different trading style could have different impact on the commonality in liquidity For

example index traders or portfolio investors are more likely to cause covariation in liquidity

among securities in their portfolio But the impact of stock-picking investors especially

individual traders is more difficult to predict If the stock-picking behavior is based on firm-

specific information or the true fundamental value of the asset then such trading is not

correlated with each other and it wonrsquot cause covariation in liquidity among different

securities If however the stock-picking comes mainly from individual investors who are

uninformed noise traders their trading behavior are more likely to be based on the same

market-wide information and are more likely to be correlated which is so called herding

behavior This is especially true in emerging markets where firm-specific information is not

always publicly available Thus we conjecture that in these less transparent markets stock-

picking behavior is very likely to increase the market-wide comovement in liquidity

We therefore expect to see a higher commonality in liquidity among emerging markets

than in developed markets A comprehensive analysis on inter-market comovement in

liquidity as well as its driving force helps to gain more insights into the liquidity and

liquidity risk of emerging markets

22 Inter-Market Commonality in Liquidity There has been a large literature on the international integration of financial markets

and its implication for asset pricing Some studies have examined the correlation in price

movements (synchronicity or contagion) and volatility across markets (spillover) and tried to

identify the underlying mechanisms that drive this interdependence within or among markets

Recently the cross-border linkage in liquidity has received some attention However the

empirical findings are mixed Some studies document global liquidity risk factors (Stahel

2005a Brockman Chung and Perignon 2006) but some find that the cross border

correlation in liquidity is low (Stahel 2005b) There are some mechanisms that could

possibly drive the inter-market comovement in market aggregate liquidity

1 Trading activities of global investors are correlated across markets which may

affect inventory costs of different markets at the same time For broadly diversified investors

it is reasonable to believe that when faced with an unexpected need to liquidate assets they

will choose to liquidate assets in a number of markets It is also possible that when they

encounter liquidity problem in one market they may increase liquidity inflow in other

markets at the same time Both of the behavior will cause co-variation in international

portfolio flows across markets and thus result in co-variation in stock liquidity

2 Strong volatility linkages across markets can induce comovement in the inventory risk

in different markets As volatility is one important determinant factor of inventory risk

global co-variation of volatility may also induce global co-variation of inventory cost and

level The financial literature offers much research on stock market volatility over time and

linkages that exist among world markets (Eun and Shim 1989 Hamao Masulis and Ng 1991

Lin Engle and Ito 1994 et al) If inventory fluctuations were correlated across markets

market liquidity should also be expected to exhibit similar co-movement

3 Other common fundamentals across markets that may also give rise to global

commonalities in liquidity On one hand economy-wide shocks such as unanticipated interest

rate changes may impact aggregate liquidity directly by altering the cost of inventory

financing for market markers (Chordia Roll and Subrahmanyam 2001) On the other hand

factors such as unanticipated interest rate changes productivity declines and excessive

inflationary pressures are likely to influence liquidity indirectly by inducing fund outflows

price declines and increased volatility for the stock market and exacerbating inventory risks

(Fujimoto 2004) Fujimotorsquos (2004) empirical work confirms the substantial role of

economic fundamentals in the time series variation of US stock market liquidity With the

integration of global market economy-wide fundamentals such as short-term interest rate

macroeconomic coordinated monetary policy business cycle inflation rate are also linked

across markets These correlated fundamentals across economies may also induce global

commonality in liquidity

Stahel (2005) investigates commonalities in liquidity in a multi-country setting and

finds that individual stock liquidity exhibit commonalities within and across countries His

asset pricing analysis suggests that global liquidity is also a priced risk factor However his

sample stocks are drawn only from Japan the UK and the US markets namely the most

liquid and best integrated markets Given the relative segmentation feature of emerging

markets and their restriction on capital flows as well as some other features that prevent

foreign investors from investing in these markets such as poor liquidity and high uncertainty

it is hard to conclude whether there is such a significant cross-border comovement in liquidity

among emerging markets especially in early 90rsquos when these markets are relatively

segmented However many emerging markets experienced the market liberalization during

the past decades After the liberalization many foreign investors are attracted to emerging

markets for various purposes such as portfolio diversification benefit Many literatures on the

integration of emerging markets document the increasing linkage of these markets with

global markets in return and volatility Investigation of linkage in liquidity among emerging

markets as well as its driving mechanism may have extra contribution to this stream of

research

Chapter 3 Data and Liquidity Proxies Liquidity usually defined as the ability to buy or sell an asset quickly and in large

volume without substantially affecting the assets price is not directly observable and even

harder to measure Several proxies have been proposed in the empirical literature to measure

liquidity such as bid-ask spread (quoted or effective) market depth and the price impact

However the absence of bid-ask quotes or intraday transaction data for a sufficiently long

period of time makes it impossible to use these proxies to measure liquidity in emerging

markets Following the recent literature we use daily price and volume data to construct

several proxies to capture the different dimensions of liquidity in emerging markets

Our data are obtained from several sources All our measures are derived from daily

data including price and trading volume We constrain our sample countries to those defined

by IMF as emerging markets and those with sufficient number of stocks in our sample period

January 1990 to November 2005 This rule leaves us 18 sample markets Daily price and

trading volume monthly number of shares outstanding and annual market capitalization for

each stock are obtained from Datastream for countries Argentina Brazil Chile Greece India

Israel Mexico Pakistan Peru Philippines Poland South Africa and Turkey I obtain data

from PACAP database for Asian markets Indonesia Korea Thailand Malaysia and Taiwan

of China To facilitate our illustration and comparison I also include securities traded on

New York Stock Exchange (NYSE) in my sample and the data are obtained from CRSP We

only use ordinary common shares in our study and constrain our sample securities to those

traded in their domestic markets only The annual market economic data such as GDP

capitalization of equity and bond market and international fund flows are obtained from

International Financial Statistics produced by IMF

Ince and Porter (2004) study the quality of Datastream data and identify many

instances of errors Besides filtering data based on security type and geographic location they

also suggest some other screening procedures that can greatly improve the quality of the data

We follow their suggestion by further filtering our data as follows

1) We remove the padded zero return records at the end of each stockrsquos time series caused

by suspension of trading

2) For any stock if monthly return exceeds 300 and reverses within one month then

returns for both months will be set to missing

Apart from the screening procedures above we also filter our data as

3) All securities from Datastream are those included in WorldScope constituent list

WoldScope has a very broad coverage with ldquo more than 90 of the worldrsquos market value

is representedhelliprdquo and ldquoinclusion in Worldscope is predicated on criteria such as benchmark

index membership market capitalization and IBES International estimates coveragerdquo For

US stocks we restrain to those traded on NYSE and filter on size at the beginning of each

sample year we rank all securities based on their market capitalization at the end of previous

year and assign them to each of the ten size-ranking deciles Stocks fall into the smallest

decile will be removed for the following sample year We also tried to remove the smallest

5 stocks in each year and the results are quite the same

4) For any market if on any particular day all stocks have zero returns orand all stocks

have zero trading volume then all return for any individual security will be set missing on

this particular day

5) To remedy the IPO effect at the beginning of each year we exclude stocks that are not

traded during the previous 6 month

6) The extreme 1 observations on each of our several liquidity measures within a

market are removed

The first measure follows Lesmond Ogden and Trzcinka (1999) and has been used in

several studies on liquidity among markets where microstructure data are not readily

availablemdashproportion of zero returns (PZR) The intuition is that if the value of an

information signal is insufficient to outweigh the cost associated with transaction the

investors will choose not to trade resulting in an observed zero return Therefore PZR is a

comprehensive estimate of transaction cost capturing ldquonot only the spread but also

commission costs a portion of the expected price impact costs and possible opportunity

costs of informed trade (Lesmond 2005)rdquo For each individual security in our sample weekly

PZR is calculated as the proportion of trading days with zero return during a week For each

market the aggregate PZR is calculated as the equally weighted average PZR of all securities

Bekaert Harvey and Lundblad (2006) calculate their market monthly PZR in a slightly

different waymdashthey first find the proportion of zero returns across all securities on each

trading day then calculate the time-series average over a month We also applied their

methodology and find that the market monthly PZR calculated in both ways are quite the

same (the correlation of these two series data is above 099)

The second measure follows Amihud (2002)rsquos illiquidity measure (ILLIQ) which is

defined as the ratio of the daily absolute return to the dollar trading volume in million This

illiquidity measure mainly captures the response of price to order flow and closely follows

the Kyle (1985) price impact definition of liquidity But while Kylersquos λ measures the return

impact of a cumulative signed order flow ILLIQ captures the absolute return impact of a

cumulative unsigned volume One problem with this measure is that when zero volume

weeks occur which is common in emerging markets as thin trading is a pervasive phenomena

the illiquidity ratio ILLIQ will be undefined In order to solve this problem we calculate this

measure at a weekly frequency RETit is defined as cumulative weekly return and VOLit is

cumulative weekly trading volume On each week t for each stock i Amihudrsquos illiquidity

ratio is constructed as titi

titi VOLP

RETILLIQ

= where RETit is weekly return with cash

dividend Pit is unadjusted closing price on week t and VOLit is trading volume over the

week The aggregate market illiquidity ratio is the equally weighted average of individual

securities illiquidity ratios sum=

ititmkt ILLIQ

NILLIQ

As the denominator of the ILLIQ ratio is dollar trading value which is dominated by

local currency of each country it is impossible to compare this ratio cross markets Therefore

we made some adjustment on this illiquidity ratio to make it more unified and comparable In

so doing we collect the exchange rate to US dollars for each markets to construct the US

dollar dominated illiquidity ratio ILLIQusdit Notice that we not only adjusted the share price

in the denominator we also use the price in US dollar to calculate the absolute return in the

numerator Therefore the return comes not only from the change in share price in local

currency but also from the appreciation or depreciation of the currency

Another proxy for liquidity we use is weekly turnover ratio for each security We

collected the number of shares outstanding for each stock and calculate the turnover ratio

(TNV) as weekly trading volume to total number of shares outstanding ti

titi NOSH

VOLTNV

Again the market aggregate turnover ratio is calculated as equally weighted average of

turnover ratios of individual stocks This measure is used in Rouwenhorst (1999) Bekaert et

al (2006) as well as many other researches Turnover ratio captures the trading frequency

But it does not reflect the cost per trade which varies considerably across assets Lesmond

(2005) states that ldquoGiven the specific focus on only trading volume turnover is likely to

increase during liquidity crunches such as occurred during the Tequila Crisis the Asian

Crisishelliprdquo However it is still used in many researches for it is easy to construct and has

intuitive appeal

The last proxy we use is Amivest liquidity ratio (AMI) calculated as ratio of trading

volume to absolute returnti

titi RET

VOLAMI

= It is based on the intuition that in a liquid security

a large trading volume may be realized with small change in price Like for other proxies we

calculate the Amivest ratio for each security on each week with non-zero returns and average

across all stocks to find the aggregate market measure

Table 1 Panel A-E report the time series descriptive statistics for our five primary

liquidityilliquidity measures at the aggregate market level We also include the descriptive

statistics for US markets for comparison purpose From the tables we can see that on general

emerging markets are much less liquid than US market For measures proportion of zero

returns (PZR) Amivest ratios (AMI) and turnover ratio (TNV) NYSE securities are twice as

liquid as securities from emerging markets For the measure ILLIQusd this difference is even

higher Therefore high illiquidity is a stylized fact of emerging markets

Each measure captures different aspects of liquidity and each has its strength and

weakness In order to better assess the efficiency of these proxies in measuring liquidity we

conduct the paired Pearson correlation analysis between any two of these five proxies Table

2 shows the average correlation coefficient as well as the P-value As we can see all the

mean correlation coefficients show the correct sign and on average are significant Looking

at the correlation coefficients from each individual market (table available upon request) we

find that sometimes the correlation coefficients are quite low or even have the ldquowrongrdquo sign

This indicates that these proxies do capture different aspects of liquidity Also we can find

that all the correlation coefficients look better for US stocks than for emerging markets

suggesting that measuring liquidity in emerging markets are particularly difficult

Lesmond (2005) analyzes the efficiency of various liquidity measures in emerging

markets He concludes that the proportion of zero returns (PZR) and Amihudrsquos Illiquidity

ratio (ILLIQ) perform better than other measures Bekaer et al (2006) point out that

proportion of zero returns may ignore the return ldquocatch-uprdquo effect2 Therefore in all the

empirical tests in this paper we focus on ILLIQ as our main liquidity measures

Chapter 4 Empirical Analysis In this chapter we design our empirical test for each research questions and discuss

the empirical results

2 Bekaert et al (2006) states that ldquoLengthly periods of consecutive non-trading days should be associated with greater illiquidity effects than non-consecutive periodsrdquo For example a security with no trading for the first 3 days in a week and another security traded only on Monday Wednesday and Friday have the same proportion of zero returns But they obviously are different in liquidity However proportion of zero return cannot capture this effect

41 Intra-Market Commonality in Liquidity of Emerging Markets When investigating the intra-market commonality in liquidity in emerging markets

we follow Chordia et al (2000)rsquos procedure We first calculate change in liquidity for each

individual security i for each week t as

minusminus=

tititi LIQ

LIQLIQDLIQ

where LIQit denotes our liquidity measure of PZRit ILLIQit or ILLIQusdt TNVit and

AMIit Then on each week the aggregate market illiquidity is calculated as equally average

of all individual stock liquidity measure

ititmkt LIQ

and change in illiquidity is measured as

minusminus=

tmkttmkttmkt LIQ

LIQLIQDLIQ

Then we use a market model to regress the percentage change in the liquidity proxy for an

individual stock on the percentage change in the market wide liquidity proxy (equal weighted

average of all individual stock liquidity excluding the stock in the dependent variable)

which is specified as

tjtmktjjtj DLIQDLIQ εβα ++= (1)

Taking into account the time variation feature of the loading factor jβ we run this regression

for each individual security in each sample year Table 3 reports the percentage of jβ s that

are positive the percentage of jβ s that are significantly positive at the 95 and 90 level

for a one-sided test of whether the coefficient is smaller or equal to zero and the cross-

sectional equally-weighted averages of the 2jR from the above regression From Table 3 we

can see that with different measures all tests show that emerging markets have significantly

higher commonality than the US market in terms of both average 2R and percentage of

positive β s The average 2R for emerging markets ranges from 885 ( for the PZR

measure) to 1876 (for log transformation of ILLIQ measure) While those for US market

are all below 6 The percentage of positive β also indicate that stocks in emerging markets

on average have a higher commonality in liquidity than US stocks with only one exception

where liquidity is measured as logarithm transformation of AMI Overall the above results

indicate that there also exists intra market comovement in liquidity for individual stocks in

most of the emerging markets And the extent to which stocks from emerging markets co-

vary with each other in liquidity is significantly higher than those of US stocks This result

suggest that liquidity if treated as a risk factor is more difficult to be diversified away in

emerging markets and should get compensation

42 Common Sources of Illiquidity at Individual Security Level In this section we investigate the sources for commonality in liquidity at the

individual security level We look at the time-series determinants of individual liquidity In

particular we separate the market-wide factors from firm-specific factors to see how the

different factors affect individual liquidity

For each individual security monthly illiquidity measure ILLIQit (calculated as

average of weekly Amihudrsquo illiquidity ratio over each month) is regressed on explanatory

variables suggested by previous literature (see Hameed Kang and Viswanathan 2006 and

Chordia Roll and Subrahmanyam 2003)

11111211 _ minusminusminusminusminusminus ++++++= titmtmtitmtiti IRSTSTDSTDRbRbILLIQ εα (2)

where Rit-1 is monthly return of security i at time t-1 Rmt-1 is the monthly return of market

that security i belongs to The recent performance of security as well as the market could

affect liquidity providersrsquo expectation on performance of the security as well as its liquidity

risk They also affect the funding ability of the market makers or dealers Therefore we

include these two explanatory variables in our regression STD it-1 is the standard deviation of

daily returns for security i during month t-1 STDmt-1 is the standard deviation of daily returns

of market m that security i belongs to during month t-1 Market uncertainty as well as

individual securityrsquos volatility influence investorsrsquo inventory risk and thus affect the

individual securityrsquos liquidity ST_IRmt-1 is the short-term interest rate for market m at t-1

Previous studies suggest that market performance has an asymmetric impact on

liquidity To capture this effect we follow Hameed et al (2006) to separate the positive and

negative lagged returns to allow their asymmetric impact

)_()_()_( 121112111 minusminusminus +++= tmtititi RposiAbsbRnegaAbsbRposiAbsbILLIQ α

1111122 _)_( minusminusminusminusminus +++++ titmtmtitm IRSTSTDSTDRnegaAbsb ε (3)

where Abs_Posi(Rit-1) is absolute value of monthly return of security i at time t-1 if it is

positive and zero otherwise Abs_Nega(Rit-1) is absolute value of monthly return of security

i at time t-1 if it is negative and zero otherwise Abs_Posi(Rmt-1) is market return when it is

positive and zero otherwise Abs_Nega(Rmt-1) is market return when it is negative and zero

otherwise

We also replace the individual security total volatility measure STD it-1 from the

above regression for the idiosyncratic volatility measure STDidio t-1 where STDidio t-1 is the

standard deviation of daily idiosyncratic returns for security i during month t-1 and the

idiosyncratic return is the residual term of the market model

Table 4 reports the cross-sectional equally weighted average of all the coefficients

across all securities To make a comparison we also reported the results for the same test on

the US market at Table 4 Panel B From Panel A we can see that market-wide factors have a

greater influence on individual liquidity in emerging markets than firm-specific factors do

For example comparing model 4 and 5 we can see that while both firm-specific return and

volatility significantly affect the expected illiquidity the magnitude of their coefficients are

smaller than the market-wide return and volatility In particular adding the market-wide

factors makes the coefficient of firm-specific volatility no longer significant Model 6-9 also

indicate that market factors are more important than firm-specific factors in affecting the

individual liquidity especially market uncertainty

However if we look at Panel B we see different results Though the market return

still have a greater impact than firm-specific return individual volatility is playing a very

significant role in affecting individual liquidity as compared with that from emerging

markets Firm-specific uncertainty only affects inventory risk of the single security It wonrsquot

cause covariation in liquidity However market uncertainty influences the inventory risk as

well as the liquidity of all securities within the market Since securities in emerging markets

are more subject to market uncertainty any variation in market volatility will cause all

securities co-moves in liquidity in the same direction

Our test does not show a significant role of short-term interest rate both in emerging

markets and in developed markets suggesting this may not be the factor inducing intra-

market commonality in liquidity

The above results suggest that securitiesrsquo liquidity will be affected by market-wide

variation and thus move in the same direction Thus securities more affected by market-wide

information should have greater commonality in liquidity which provides us a testable

implicationmdashhighly synchronized securities are more likely to co-move in liquidity We thus

test this hypothesis in the following way Firstly we run the CAPM model for each

individual security from emerging markets in each sample year and calculate the R2 from

regression as an indicator of its synchronicity (denoted as SYNCH) We also run regression (1)

for the same security in each year and calculate the R2 from regression as the measure of its

commonality (denoted as COMO) Secondly for each year we assign each security from the

same market into one of five portfolio based on the magnitude of its COMO to construct

commonality portfolios Then within each portfolio we calculate the mean and median

SYNCH across all securities Table 5 Panel A and B report the results for this univariate test

on both emerging markets and NYSE

We can see that the average COMO does increases monotonically with SYNCH in

emerging markets suggesting that high synchronized securities show high comovement in

liquidity simultaneously However test results from NYSE reject this conclusion There is

not an obvious relation between synchronicity and commonality

In order to have a clear picture of the relation between synchronicity and

commonality we run a panel regression for each security markets across all the firm-year

observations

titiitiiti SIZESYNCHCOMO

εγβα +++= (4)

where COMOit is the R2 from the market liquidity regression for individual security

i in year t and and SYNCHit is the R2 from regression of the market model for the same

security in the same year But since both measures are bounded within the intervals [01] we

apply logistic transformations to them as

⎟⎟⎠

⎞⎜⎜⎝

minus= 2

tjtiti

orSYNCHCOMO (5)

SIZEit is the log market value of each individual securities in year t I use the size as a

control variable to test whether the impact of synchronicity on commonality is simply due to

size effect

From Panel C of table 5 we can see that commonality in liquidity is positively related

with synchronicity among 17 out of these 18 emerging markets and 12 of the coefficients are

significant at the 90 level and 10 are significant at 95 level Size on average are

negatively correlated with commonality (in 16 out 18 markets) and the effect is significant at

90 level in 7 markets But size effect does not explain the impact of synchronicity on

commonality

We also ran the Fama-McBeth regression as a robustness check For each year we

run a cross-sectional regression of (4) among all securities within the same market then

calculate the average coefficients across all sample years Results (available upon request)

show the same pattern SYNCH is positive among 16 markets and SIZE is negative in 15

markets

The above analysis on individual security level suggests a strong link between

synchronicity and commonality which is probably the reason why emerging markets have a

higher comovement in liquidity

43 Sources of Commonality at Aggregate Market Level In this section we further investigate whether there are other macro economic factors

that induce covariation in liquidity As we discussed earlier some market structure or

behaviour could also cause commonality in liquidity Based on our previous discussion we

test our hypothesis on the link between intra-market commonality and market macro features

by running the following regression

titmkttmkttmkttmkttmkttmkt SPCPIGDPBGDPECOMO 4321 εδββββα +Χ+++++= (5)

where COMOmktt is the cross-sectional average of individual COMOit in the same market

in year t Our explanatory variables are EGDPmktt measured as total capitalization of equity

market i over the domestic GDP of market i in year t This variable measures the

development of equity markets relative to the whole economy As the more developed equity

markets have broader industry structure more transparency in information and better country

governance Therefore we expect to see a negative relation between this measure with

commonality in liquidity BGDPmktt calculated as total capitalization of bond market i over

the domestic GDP of market i in year t It captures the development of alternative investment

instruments We also expect to see a negative relation between this variable and commonality

CPImktt is the Corruption Perception Index (CPI) released by ldquoTransparency Internationalrdquo

and used in Morck et al (2000)rsquos paper to measure country governance The ICPI assesses

the degree to which public officials and politicians are believed to accept bribes take illicit

payment in public procurement embezzle public funds and commit similar offences Low

scores of this index indicate a high perceived level of corruption and poor country

governance We conjecture that it will have a negative impact on the commonality in

liquidity SPmktt measures the investment style in a market This measure is developed by

Utpal and Galpin (2005) and measures the maximum fraction of volume explained by stock

picking in each market during each year If most stock-picking investors in emerging markets

are individual investors or noise traders who trade on market-wide information instead of

firm-specific information we shall see a positive relation between stock-picking behaviour

X are control variables such as market return and volatility

Table 6 Panel A shows the supportive evidence for our conjecture Overall the

development of equity markets and bond markets can reduce the commonality in liquidity

The more corrupted countries seem to have greater commonality in liquidity though the

correlation is not significant And the significant and positive relation between stock-picking

and commonality suggest that individual investorsrsquo trading induce greater comovement in

liquidity

Another interesting question is how international fund flows affect the commonality

International investors are usually big institute investors who usually invest in portfolios

rather than do stock-picking as most individual small investors do their trading behaviour

does affect more than just a couple of stocks when they balance their portfolio For example

when faced with an unexpected need to liquidate assets big portfolio investors may choose to

liquidate several assets from the portfolio thus causing liquidity comovement among these

securities On the other hand previous literature documents that international and institution

investors tend to herd They buy or sell with each other which can also cause the covariation

in liquidity among many securities Therefore we shall see a positive relation between

international fund inflow and comovement of liquidity

In empirical tests we run a similar pooled regression of the commonality measure

COMOmktt on the international fund flows

titmkttmkttmkt FLOWCOMO 1 εδβα +Χ++= (6)

FLOWmktt are international portfolio flows We have PortInmt (EquityInmt) measuring the

international portfolio (equity) inflows into country m in year t and PortNetmt (EquityNetmt)

measuring the international net portfolio (net equity) inflows into country m in year t X is a

vector of control variables including market performance and volatility

The empirical regression results are reported in Table 6 Panel B All these four

international fund flow measures have a significantly positive impact on market commonality

in liquidity Suggesting that market integration process actually increase the liquidity risk in

emerging markets

44 Inter-Market Commonality in Liquidity In this section we investigate the covariation of aggregate market liquidity across

markets Instead of using the same methodology in investigating the intra-market

commonality where we assign a priori role to market liquidity we employ common factor

analysis to see whether there is any common factor affecting the aggregate market liquidities

of several markets In particular we divide our sample markets according to the regions they

geographically located Among our 18 emerging markets 10 are from Asia 5 are from Latin

America 1 from Africa and 2 are from Europe Therefore we will only look at markets in

Asia and Latin America regions

Our starting point is a hypothesis that a set of common factors underlies market

liquidity In particular we assume that the cross-section of aggregate liquidity from a set of n

markets can be represented statistically by the linear factor model

tttm FLIQ εθ += (7)

where LIQmt is a column n-vector of the aggregate liquidity of the n markets at time t Ft is a

column vector of liquidity common factors

The results for Asian markets are shown in Table 7 Panel A Results shows that there

are three common factors affecting all the market liquidity of these 10 countries However

the Eigenvalues of the second and third factor are less than 1 indicating that they are

negligible The first Eigenvalue of 34124 implies that 3412410=3412 of the total

variation in market liquidity can be explained by a single common factor

Previous researches document volatility spillover across markets Such effect could

induce covariation in inventory risk of different markets thus causing commonality in

liquidity In order to investigate whether there are spillover effects among our sample markets

we apply the same procedure on market volatility to find the common factors Gt as in

equation (8)

tttm GSTD εφ += (8)

where STDmt is a column n-vector of the market volatility of the n markets at time t Gt is a

column vector of market volatility common factors

Table 7 Panel A shows that there also exist common factors affecting volatility of

these 10 markets and 3843 of the total variation in market volatility can be explained by a

single common factor with the other two factors negligible

Finally we want to see whether the common factor in volatility spillover is related to

the common factor in commonality We therefore extracted these two factors and calculate

their correlation Panel A shows that these two factors are significantly positively correlated

with a Pearson correlation coefficient of 05087

In Table 7 Panel B we replicate the same procedure for the 5 Latin America countries

The results also indicate a common factor explaining 4807 of total variation in market

liquidity and another common factor explaining 5607 of total variation in market volatility

These two factors are also positively correlated with a lower correlation coefficient of 01036

but still significant

We also test the cross-region linkage in liquidity and volatility The regional liquidity

and volatility are calculated as equally weighted average market liquidity or volatility of

countries from the same region Panel C shows that these two regions are quite segmented in

a sense that both the liquidity and volatility are unrelated

Chapter 5 Conclusion

Emerging markets have many features that could induce greater commonality in

liquidity than developed markets A comprehensive study on commonality as well as its

underlying driving forces could produce more powerful results than in developed markets

Our study in such setting generates several interesting findings 1) we document a

2) The time-series analysis at individual security level shows that individual liquidity is more

affected by market uncertainty than by individual volatility which is in contrast to securities

from developed markets This could partially explain the higher covariation in liquidity in

emerging markets And consistent with this explanation we find commonality in liquidity is

positively related with synchronicity in prices 3) We find that countries with less developed

equity markets less developed bond markets poorer country governance or more noise

traders have higher intra-market covariation in liquidity 4) We document inter-market

commonality among countries from the same geographical region And such a link is closely

related with the volatility spillover effect among these markets We fail to find any

covariation in aggregate liquidity across different regions which shed some doubt on the

In future study we can test the implication of our finding on asset pricing The current

finding on pricing of liquidity risk in US market could always be criticized as an omitted

variable correlated with a liquidity proxy (Bekaert et al 2006) An empirical test in emerging

markets could help to provide out of sample evidence and we expect to see a stronger results

as liquidity is more acute in these illiquid markets Such research should contribute to

extension of current literature in market microstructure and asset pricing

Reference Acharya V V and L H Pedersen 2005 Asset pricing with liquidity reisk Journal of Financial Economics 77 375-410 Amihud Y 2002 Illiquidity and stock returns Cross-section and time series effects Journal of Financial Markets 5 31-56 Amihud Y and H Mendelson 1986 Asset Pricing and the bid-ask spread Journal of Financial Economics 17 223-249 Bekaert G and C R Harvey 2000 Foreign speculators and emerging equity markets Journal of Finance 55 565-614 Bekaert G C R Harvey and C Lundblad 2006 Liquidity and expected returns Lessons from emerging markets working paper Brennan M J and A Subrahmanyam 1996 Market microstructure and asset pricing On the compensation for illiquidity in stock returns Journal of Financial Economics 41 441-464 Brockman P and D Y Chung 2002 Commonality in liquidity Evidence from an order-driven market structure Journal of Financial Research 25 521-539 Chordia T R Roll and A Subrahmanyam 2000 Commonality in liquidity Journal of Financial Economics 56 3-28 Chordia T R Roll and A Subrahmanyam 2002 Order imbalance liquidity and market returns Journal of Financial Economics 65 111-130 Chordia T R Roll and A Subrahmanyam 2003 Determinants of daily fluctuations in liquidity and trading activity working paper Copeland T E and D Galai 1983 Informational effects on the bid ask spread Journal of Finance 38 1457-1469 Coughenour J F and M M Saad 2004 Common market makers and commonality in liquidity Journal of Financial economics 73 37-69 Eun C and S Shim 1989 International transmission of stock market movements Journal of Financial and Quantitative Analysis 24 241ndash56 Fujimoto A 2004 Macroeconomic sources of systematic liquidity working paper Yale University Hamao Y R Masulis and V Ng 1991 The effect of the 1987 stock crash on international financial integration Japanese Financial Market Research Amsterdam Elsevier Science Hameed A W Kang and S Viswanathan 2006 Stock market decline and liquidity working paper

Harris L 2003 Trading and Exchanges Oxford University Press Hasbrouck J and D J Seppi 2001 Common factors in prices order flows and liquidity Journal of Financial Economics 59 383-411 Huberman G and D Halka 2001 Systematic liquidity Journal of Financial Research 24 161-178 Ince O R B Porter 2004 Individual equity return data from Thomson DataStream Handle with care working paper Kyle A S 1985 Continuous auctions and insider trading Econometrica 53 1315-1335 Lesmond D 2005 Liquidity of emerging markets Journal of Financial Economics 77 411-452 Lesmond D J Ogden and C Trzcinka 1999 A new estimate of transaction costs Review of Financial Studies 12 1113-1141 Lin W R F Engle and T Ito 1994 Do bulls and bears move across borders International transmission of stock returns and volatility Review of Financial Studies 7 507ndash38 Morck R B Yeung and W Yu 2000 The information content of stock market why do emerging markets have synchronous stock price movements Journal of Financial Economics 58 215-260 Pastor L and R Stambauth 2003 Liquidity risk and expected stock returns Journal of Political Economy 111 642-685 Stahel C W 2005a Is there a global liquidity factor working paper George Mason Univeristy Stahel C W 2005b Liquidity across developed and emerging markets working paper George Mason University Stoll H 1978 The supply of dealer services in securities markets Journal of Finance 33 1133-1151 Sujoto C P S Kalev and R W Faff 2005 Commonality in liquidity Further Australian evidence working paper

Table 1 Descriptive statistics on time series liquidity measures

These tables report in each sample market the total number of weeks (T) total number of stocks (Total N) and average number of stocks within sample weeks (Aver N) and the time-series descriptive statistics of aggregate liquidityilliquidity measures We include the same statistics on sample securities from NYSE for comparison A Proportion of Zero Return (PZR)

Market T Total

N Aver

N Mean Std Dev Min Q1 Median Q3 Max 1 Argentina 620 87 67 05219 01213 01250 04415 05107 05890 10000 2 Brazil 672 352 232 06183 00849 01854 05635 06167 06698 08950 3 Chile 776 166 134 06691 00792 04015 06168 06732 07241 08986 4 Greece 776 361 222 02824 01731 00075 01540 02696 03884 08442 5 India 776 418 328 02669 02017 00046 01054 02429 03757 10000 6 Israel 620 121 87 02327 01015 00581 01659 02145 02732 06984 7 Mexico 776 159 92 05402 00873 01342 04879 05491 05929 08730 8 Pakistan 672 96 79 04812 01644 01633 03539 04959 05913 10000 9 Peru 672 82 52 07415 00973 04459 06652 07506 08238 09493

10 Philippines 776 217 145 06832 01049 04214 05967 06896 07668 09539 11 Poland 464 99 75 02339 00924 00844 01782 02172 02638 06877 12 South Africa 776 665 400 06108 01459 01946 04869 05736 07675 09563 13 Turkey 672 224 177 03807 02139 00382 02073 03068 05522 10000 14 Indonesia 513 290 112 03436 00983 00556 02784 03333 04059 06589 15 Korea 517 855 686 01191 00532 00229 00768 01174 01428 03826 16 Thailand 571 408 121 01547 00461 00000 01250 01563 01874 02679 17 Taiwan 516 484 306 01105 00363 00000 00875 01101 01314 03031 18 Malaysia 518 751 490 01695 00502 00417 01358 01690 02037 03034

MEAN 649 324 211 03978 01084 01325 03182 03887 04694 07596 19 NYSE 780 2567 1568 01275 00846 00146 00326 01105 02132 02670

B Amihudrsquos Illiquidity measure (ILLIQ) local currency

Market T Mean Std Dev Min Q1 Median Q3 Max 1 Argentina 618 12961 09115 01501 06668 10621 16274 79997 2 Brazil 672 21014 16317 00199 08602 18129 28966 148653 3 Chile 776 00081 00058 00005 00039 00066 00109 00532 4 Greece 776 13060 10388 00071 04949 11267 18804 62318 5 India 568 01945 01501 00000 00597 01868 02901 09505 6 Israel 620 04782 04804 00151 01550 02855 06386 25665 7 Mexico 776 02209 01768 00059 00945 01789 03070 16157 8 Pakistan 671 03083 02608 00000 01031 02383 04531 17350 9 Peru 672 18988 15052 01259 09320 15182 24270 123404

10 Philippines 776 05858 04547 00154 02218 05003 08292 27810 11 Poland 464 03459 02744 00268 01408 02697 04702 14399 12 South Africa 776 27376 15467 02658 15502 24170 35292 108985 13 Turkey 665 16568 23996 00000 02060 06816 22356 169798 14 Indonesia 512 00003 00003 00000 00001 00002 00004 00028 15 Korea 517 00002 00001 00000 00001 00001 00002 00008 16 Thailand 571 00492 00422 00033 00179 00361 00688 02500 17 Taiwan 510 00048 00061 00003 00015 00024 00052 00362 18 Malaysia 517 01554 01958 -02327 00330 00867 01989 12305

19 NYSE 778 00300 00150 00058 00203 00287 00371 01064

C Amihudrsquos Illiquidity measure in US dollar (ILLIQusd) Market T Mean Std Dev Min Q1 Median Q3 Max 1 Argentina 618 16479 12920 00428 07469 13081 21086 101432 2 Brazil 672 38310 35576 00168 06820 31744 61415 239791 3 Chile 620 44846 27501 00000 22788 39945 61430 192927 4 Greece 776 30299 21879 00224 13687 28402 41435 151649 5 India 568 66906 49209 00456 19806 63410 104706 307458 6 Israel 620 14131 12111 00621 05509 09831 19521 63336 7 Mexico 579 21421 14618 00614 09932 18827 29801 124774 8 Pakistan 671 118608 103022 00000 37917 91509 166159 748609 9 Peru 672 42951 29133 03108 23507 37356 55028 214191

10 Philippines 776 214628 183610 00000 58827 171132 322579 940836 11 Poland 724 12146 19195 00022 01947 06583 14925 153728 12 South Africa 776 154628 102488 16736 73515 121460 226579 616510 13 Turkey 665 02242 02026 00000 00965 01722 02760 14214 14 Indonesia 511 19692 29330 00448 03325 07410 22885 199614 15 Korea 517 01779 01549 00233 00951 01402 02156 15737 16 Thailand 571 15093 14859 00845 04110 09717 20721 74662 17 Taiwan 510 01845 02220 00008 00434 00876 02493 12682 18 Malaysia 517 12614 10690 00650 05387 09669 16346 62363

MEAN 631 46034 37330 01365 16494 36893 66224 235251 19 NYSE 778 00300 00150 00058 00203 00287 00371 01064

D Amivest Ratio (Amivest) Market T Mean Std Dev Min Q1 Median Q3 Max 1 Argentina 617 311785 138060 31476 212000 288921 399944 843745 2 Brazil 671 1996520 2400650 03157 81837 236083 4022620 8715410 3 Chile 773 2609859 1698694 73840 1400352 2227943 3356891 10826200 4 Greece 776 112820 64936 07059 62203 104730 149931 374062 5 India 567 350454 200574 01775 178283 335869 496999 962716 6 Israel 620 333258 230782 08023 76535 342865 504065 1007080 7 Mexico 775 3170704 1599222 262555 1966875 2917511 4178576 8230186 8 Pakistan 666 3394737 3102374 43358 1077508 2620011 4624177 24342900 9 Peru 670 295570 178969 02341 166269 274109 399629 1375457

MEAN 635 1439244 922117 119954 726878 1200951 1998994 5324852 19 NYSE 778 691318 515865 81042 296025 520659 950267 2279162

E Turnover Ratio (TNV) Market T Mean Std Dev Min Q1 Median Q3 Max 1 Argentina 618 00480 00300 00070 00300 00400 00550 01890 2 Brazil 672 00900 00320 00210 00690 00790 01020 02210 3 Chile 776 00260 00100 00050 00180 00250 00320 00640 4 Greece 776 01310 00620 00340 00840 01200 01630 03550 5 India 568 00590 00260 00090 00380 00550 00760 01400 6 Israel 620 00640 00300 00080 00340 00660 00850 01490 7 Mexico 776 00820 00360 00070 00490 00810 01080 02140 8 Pakistan 671 00590 00220 00020 00430 00560 00740 01420 9 Peru 672 00970 00670 00050 00400 00830 01400 03230

MEAN 637 00956 00367 00143 00686 00913 01176 02293 19 NYSE 780 01800 00499 00804 01422 01628 02136 03071

Table 2 Pearson correlation analysis between different liquidity measures

We construct the time-series market aggregate liquidityilliquidity measure of each individual security market as in Table 1 and calculate the Pearson correlation coefficients between any two of these measures on each market Table 2 Panel A reports the average coefficients and p-values across all the emerging markets and Panel B reports the coefficients and p-value of NYSE market Panel A Emerging Markets

PZR TNV IL_$ AMIVEST

ILLIQ 03630 -03714 07982 -04205 P-value 00836 00734 00117 00149

PZR -04016 03373 -03851 P-value 00696 01242 00715 TNV -04124 02660

P-value 00983 00001 ILLIQ_IL$ -04205

P-value 00149

Panel B NYSE

ILLIQ 07314 -06577 10000 -05470 P-value 00000 00000 00000 00000

PZR -08747 07314 -08026 P-value 00000 00000 00000 TNV -06577 09480

P-value 00000 00000 ILLIQ_IL$ -05470

P-value 00000

Table 3 Intra market commonality in liquidity

When investigating the commonality in liquidity in emerging markets we strictly following Chordia et al (2000)rsquos procedure tjtmktjjtj DLIQDLIQ εβα ++= where

tiDLIQ denotes percentage change in individual weekly liquidity tmktDLIQ denotes percentage change in market weekly liquidity and the aggregate market illiquidity is calculated as

equally average of all individual stock liquidity measure excluding the stock in the dependent variable Taking into account the time variation feature of the loading factor jβ we run this

regression for each individual security in each sample year Table 3 reports the percentage of jβ s that are positive the percentage of jβ s that are significantly positive at the 95 and 90

level for a one-sided test of whether the coefficient is smaller or equal to zero and the cross-sectional equally-weighted averages of the 2jR from the above regression

PZR ILLIQ Ln(ILLIQ) Market RSQ Pct + Pct +90 Pct +95 RSQ Pct + Pct +90 Pct +95 RSQ Pct + Pct +90 Pct +95 1 Argentina 815 8714 5590 4395 695 8598 4621 3655 1348 7743 5131 4466 2 Brazil 677 8214 4420 3436 407 6620 2783 2068 1296 8412 6371 5730 3 Chile 527 7944 3873 2866 435 7240 2842 2240 1721 9780 8433 7720 4 Greece 996 9010 5942 4907 1390 9361 6786 5977 1575 6968 4741 4368 5 India 2545 9806 8925 8424 692 8570 4577 3626 1964 8995 7800 7404 6 Israel 1052 9149 6092 5034 592 8107 3993 3107 1454 8472 6658 6124 7 Mexico 599 8084 3911 3074 359 6460 2454 1735 1554 8981 7360 6815 8 Pakistan 1495 9254 6795 5997 572 7310 3776 2931 1865 8918 7216 6735 9 Peru 536 7418 3902 2740 627 7562 3134 2637 681 6313 2475 1919 10 Philippines 659 8083 4737 3659 883 8067 4636 3613 1758 8780 6935 6176 11 Poland 1074 9213 6531 5510 763 8696 5028 3855 1557 8456 6737 6042 12 South Africa 587 8286 4225 3182 586 7796 3651 2775 1183 8074 5694 4970 13 Turkey 1970 9655 7840 7105 2830 9925 9036 8415 2292 7126 5385 5122 14 Indonesia 514 7374 3308 2652 1062 9003 5704 4948 2490 9928 9388 8921 15 Korea 416 7079 2658 1607 1328 9610 7384 6460 2714 9934 9521 9219 16 Thailand 454 5825 2427 1650 1288 9317 6639 5675 2490 9729 9049 8585 17 Taiwan 586 7846 3538 2923 2068 9831 8449 7686 3310 9978 9774 9603 18 Malaysia 435 7342 2937 2025 1653 9219 7075 6234 2520 8692 7451 7067

MEAN 885 8239 4870 3955 1013 8405 5143 4313 1876 8627 7007 6499 19 NYSE 314 5758 1661 958 410 6268 2128 1402 566 7772 4184 3171

Table 3 Intra market commonality in liquidity (continued)

TNV AMI ln(AMI) Market RSQ Pct + Pct +90 Pct +95 RSQ Pct + Pct +90 Pct +95 RSQ Pct + Pct +90 Pct +95 1 Argentina 1657 9012 6367 5732 820 7895 4511 3584 871 7028 3602 2897 2 Brazil 526 7251 3625 2798 480 6681 2907 2213 723 7111 3540 2910 3 Chile 525 7275 3483 2772 422 5853 2305 1662 719 6887 3598 2731 4 Greece 1550 9248 7230 6383 1128 8669 5326 4521 865 6277 3148 2712 5 India 1105 9061 6399 5410 680 7793 3764 3007 691 5299 1656 1386 6 Israel 1211 8317 5503 4887 469 6814 2806 2022 602 6143 2651 2073 7 Mexico 1097 8209 5041 4408 630 7152 3507 2634 968 7842 5122 4362 8 Pakistan 602 7431 3586 2672 910 7394 4055 3394 863 6679 3321 2708 9 Peru 767 7115 3439 2885 1085 6919 3198 2558 522 6047 2151 1337

10 Philippines 778 7842 4182 3296 747 7747 3857 2952 808 7870 4626 3860 11 Poland 1221 8670 6138 5337 498 7553 3237 2408 360 5494 1285 949 12 South Africa 482 7145 3160 2347 537 6772 2757 2074 733 6309 2746 2228 13 Turkey 1334 8240 5147 4453 1736 9347 7112 6133 1916 7184 5081 4776 14 Indonesia 801 8035 4695 3681 1082 8491 5560 4655 1615 9083 6507 5721 15 Korea 1661 9543 7812 7065 949 8849 5405 4360 698 5681 1972 1585 16 Thailand 703 7586 3986 3071 1049 8727 5402 4417 1206 7318 4568 3956 17 Taiwan 3787 9830 9258 9035 1432 9437 7095 6174 2307 9855 9082 8647 18 Malaysia 2635 9695 8339 7809 1955 9458 7288 6500 1760 7784 5746 5194

MEAN 1247 8306 5410 4669 923 7864 4450 3626 1013 6994 3911 3335 19 NYSE 268 7661 6484 5993 385 6924 2691 1855 509 7337 3571 2705

Table 4 A Average time-series regression coefficients across all securities of 18 emerging markets (Dependent variable ILLIQ it ) Time series regression of monthly illiquidity measure ILLIQ it (calculated as average of weekly Amihudrsquo illiquidity ratio over each month) was run on the following explanatory variables for each individual security

1111122121112111 _)_()_()_()_( minusminusminusminusminusminusminusminus ++++++++= titmtmtitmtmtititi IRSTSTDSTDRnegaAbsbRposiAbsbRnegaAbsbRposiAbsbInterceptILLIQ εα (3)

The table presents the cross-sectional average of all the coefficients across all securities R it-1 is monthly return of security i at time t-1 Abs_Posi(R it-1) is absolute value of monthly return of security i at time t-1 if it is positive and zero otherwise Abs_Nega(R it-1) is absolute value of monthly return of security i at time t-1 if it is negative and zero otherwise R mt-1 is the monthly return of market that security i belongs to Abs_Posi(R mt-1) is market return when it is positive and zero otherwise Abs_Nega(R mt-1) is market return when it is negative and zero otherwise STD it-1 is the standard deviation of daily returns for security i during month t-1 STD mt-1 is the standard deviation of daily returns of market m that security i belongs to during month t-1 ST_IR mt-1 is the short-term interest rate for market m at t-1 STD idio t-1 is the standard deviation of daily idiosyncratic returns for security i during month t-1 A Average time-series regression coefficients across all securities in emerging markets (Dependent variable ILLIQ it)

Expected sign (1)

ILLIQit

(2) ILLIQit

(3) ILLIQit

(4) ILLIQit

(5) ILLIQit

(6) ILLIQit

(7) ILLIQit

(8) ILLIQit

(9) ILLIQit

Intercept 05870 06674 05898 05591 05648 05564 05623 05737 05560 t 2068 968 2045 1799 746 1788 742 743 721

+ ILLIQ it-1 04371 03936 04519 04344 03634 04332 03669 03674 03644 t 9282 8944 9143 8191 7407 8016 7389 8132 8062 - R it-1 -01774 -02308 -01941 t -556 -61 -501 - Abs_Posi(R it-1) -01870 -01714 -01887 -02294 t -377 -294 -319 -389

+ Abs_Nega(R it-1) 03224 02242 02222 01822 t 337 247 244 199 - R mt-1 -02929 -02756 t -207 -194 - Abs_Posi(R mt-1) 00628 00656 t 029 03

+ Abs_Nega(R mt-1) 07798 07838 t 23 231

+ STD it-1 10428 04313 09237 03832 04093 t 338 148 275 115 125

+ STD mt-1 45586 46932 33524 30426 t 239 25 176 16

+ STD idio t-1 11994 t 354

+ ST_IR mt-1 -28884 -19020 -18166 -21908 -20262 t -198 -134 -127 -152 -14 Mean_R2 2283 2341 2702 2637 2857 3535 2959 3655 3759

B Average time-series regression coefficients across all securities of NYSE (Dependent variable ILLIQ it)

Expected sign (1)

ILLIQit

(2) ILLIQit

(3) ILLIQit

(4) ILLIQit

(5) ILLIQit

(6) ILLIQit

(7) ILLIQit

(8) ILLIQit

(9) ILLIQit

Intercept 00246 00239 00316 00104 00159 00102 00152 00154 00147 t 1897 189 911 774 499 794 473 478 455

+ ILLIQ it-1 05055 05189 04675 04900 04194 05066 04324 04359 04337 t 10589 10729 9182 9661 7758 9844 7919 8009 7986 - R it-1 -00212 -00212 -00232 -00206 t -1206 -1231 -1281 -1158 - Abs_Posi(R it-1) -00415 -00391 -00380 -00390 t -1388 -1392 -1354 -1381

+ Abs_Nega(R it-1) 00464 00544 00399 00437 t 189 238 248 193 - R mt-1 -00523 t -73 - Abs_Posi(R mt-1) -00512 -00516 t -453 -455

+ Abs_Nega(R mt-1) 00515 00529 t 306 313

+ STD it-1 05963 05607 06723 06084 06179 t 1328 1269 1461 1368 1377

+ STD mt-1 02082 03377 02277 02525 t 18 292 192 213

+ STD idio t-1 06707 t 1448

+ ST_IR mt-1 -01113 -00651 -00443 -00531 t -189 -122 -082 -098 Mean_R2 3023 3209 3479 3385 3909 3523 3930 4128 4129

Table 5 Commonality and Synchronicity Firstly we run the CAPM model for each individual security from emerging markets in each sample year and calculate the R2 from regression as an indicator of its synchronicity(denoted as SYNCH) We also run regression (1) for the same security in each year and calculate the R2 from regression as the measure of its commonality (denoted as COMO) Secondly for each year we assign each security from the same market into one of five portfolio based on the magnitude of its COMO to construct commonality portfolios Then within each portfolio I calculate the mean and median SYNCH across all securities Panel A and B report the results for this univariate test on both emerging markets and NYSE A Univariate analysis on commonality and synchronicity in emerging markets

COMO Mean

SYNCH Median SYNCH N

Low COMO 00063 01605 01491 220 Q2 00266 01728 01595 220 Q3 00590 01801 01703 220 Q4 01176 01869 01750 220 High COMO 02676 01880 01764 220

B Univariate analysis on commonality and synchronicity in NYSE

COMO Mean

C Regression Analysis (Panel Regression) We run a panel regression for each security markets across all the firm-year observations

εγβα +++= (4)

where COMOit is the R2 from the market liquidity regression for individual security i in year t and and SYNCHit is the R2 from regression of the market model for the same security in the same year But since both measures are bounded within the intervals [01] we apply logistic transformations to them as

⎟⎟⎠

⎞⎜⎜⎝

minus= 2

tjtiti

orSYNCHCOMO

And SIZE it is the log market value of each individual securities in year t

Market Intercept t SYNCH t SIZE t AdjR2 N

1 Argentina -30532 -778 01458 181 -00696 -124 085 411 2 Brazil -49211 -2297 -00172 -035 00365 131 018 941 3 Chile -39332 -594 01930 294 -00361 -071 125 696 4 Greece -21975 -1721 02899 886 -00313 -129 313 2553 5 India -32936 -1280 00422 115 -00733 -278 027 2898 6 Israel -38063 -786 00788 127 -00262 -046 021 763 7 Mexico -43909 -707 00475 075 -00611 -089 015 627 8 Pakistan -19674 -274 04134 442 -02345 -287 362 533 9 Peru -39485 -815 00673 076 -00525 -065 052 178

10 Philippines -48406 -933 01217 189 01726 338 402 645 11 Poland -25774 -637 01391 227 -01266 -236 152 476 12 South Africa -39741 -1384 00840 291 -00081 -025 051 1915 13 Turkey -10636 -1668 02501 679 -00061 -043 276 1654 14 Indonesia -04136 -027 02698 198 -01761 -167 234 201 15 Korea -04403 -186 01286 716 -01755 -876 165 5411 16 Thailand -26964 -592 00750 137 -00035 -007 035 540 17 Taiwan -12800 -567 01415 543 -00630 -196 111 2665

18 Malaysia -07870 -520 02694 1403 -01969 -805 672 3415

MEAN -27547 -876 01522 364 -00629 -165 173 1473

Table 6 Commonality in Liquidity and Market Features A Commonality in Liquidity and Market Development We test our hypothesis on the link between intra-market commonality and market macro features by running the following regression

titmkttmkttmkttmkttmkttmkt SPICPIGDPBGDPECOMO 4321 εδββββα +Χ+++++= (5)

where COMOmktt is the cross-sectional average of individual COMOit in the same market in year t Our explanatory variables are EGDPmktt measured as total capitalization of equity market i over the domestic GDP of market i in year t BGDPmktt measured as total capitalization of bond market i over the domestic GDP of market i in year t It captures the development of alternative investment instruments ICPImktt is the Corruption Perception Index (CPI) released by ldquoTransparency Internationalrdquo SPmktt measures the investment style in a market This measure is developed by Utpal and Galpin (2005) and measures the maximum fraction of volume explained by stock picking in each market during each year X are control variables such as market return and volatility

COMO COMO COMO COMO COMO COMO Intercept -29227 -27260 -28971 -27635 -34623 -34667

t -1189 -1173 -1181 -1065 -1091 -1125 Abs_Posi(R mt) 00795 00322 00823 01626 02182 03407

t 02 009 002 004 006 15 Abs_Nega(R mt) 04947 03051 05186 04261 07790 06843

t 165 143 168 157 205 205 STD mt 202288 170409 194440 155914 185297 350054

t 202 189 196 166 194 363 EGDPmt -00256 -00028

t -371 -214 ICPImt -00037 -00031

t -132 -109 BGDPmt -00716 -00208

t -173 -055 Stock Pickingmt 08564 05846

t 256 176 98 98 98 98 98 98 Adj R2 609 2153 838 992 1410 3132

B Commonality in Liquidity and International Fund Flow We run a similar pooled regression of the commonality measure COMOmktt on the international fund flows as

FLOWmktt are international portfolio flows X is a vector of control variables including market performance and volatility

COMO COMO COMO COMO Intercept -65349 -65713 -5699 -58415

t -822 -862 -734 -866 Abs_Posi(R mt) 02023 01919 01906 0185

t 139 133 127 125 Abs_Nega(R mt) 03529 04025 04242 05196

t 049 057 057 071 STD mt 274671 260125 326953 302798

t 359 35 377 372 Port Inmt 02274

t 305 Port Netmt 02381

t 324 Equity Inmt 01554

t 201 Equity Netmt 01799

t 257 90 90 90 90 Adj R2 2703 2802 2232 2469

Table 7 Intra market commonality in liquidity and spillover of volatility (common factor analysis) A Common factor in aggregate liquidity among Asian markets common factor in market volatility among Asian countries and Pearson correlation between two common factors

Eigenvalues Variable

Region

NoMkts

Liquidity Asia 10 34124 08421 06307

Volatility Asia 10 38435 09409 04266

Pearson Correlation between 2 common factors

Correlation 050872 p-value lt0001

B Common factor in aggregate liquidity among Latin American markets common factor in market volatility among Latin American countries and Pearson correlation between two common factors

Region

NoMkts

Liquidity LA 5 24037 03726 01244 Volatility LA 5 28034 00476 -00778

Correlation 010363 p-value 00129

C Common factor in aggregate liquidity among Asia and Latin America common factor in market volatility among these two regions and Pearson correlation between two common factors

Region

NoMkts

Liquidity Asia vs LA 02410 -01604 NA Volatility Asia vs LA 07246 -02498 NA

Correlation NA p-value NA

Chapter2 Liquidity and Commonality in Emerging Markets

21 Liquidity and Intra-Market Commonality in Emerging Markets

22 Inter-Market Commonality in Liquidity

Chapter 3 Data and Liquidity Proxies

Chapter 4 Empirical Analysis

41 Intra-Market Commonality in Liquidity of Emerging Markets

42 Common Sources of Illiquidity at Individual Security Level

43 Sources of Commonality at Aggregate Market Level

