barnes_2003_abnormal returns in emerging equity markets_dissertation

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BOSTON UNIVERSITY

GRADUATE SCHOOL OF ARTS AND SCIENCE

Dissertation

ABNORMAL RETURNS IN EMERGING EQUITY MARKETS

by

MARK A. BARNES

B.A., The Johns Hopkins University. 1987

M.A.. The University of Texas at Austin. 1991

M.A.. Boston University. 1995

Submitted in partial fulfillment of the

requirements for the degree of

Doctor o f Philosophy

2003

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UMI Number: 3054524

Copyright 2002 by

Barnes, Mark Allan

All rights reserved.

_ ___ (ft

UMIUMI Microform 3054524

Copyright 2002 by ProQuest Information and Learning Company.

All rights reserved. This microform edition is protected againstunauthorized copying under Title 17, United States Code.

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Copyright by

MARK A. BARNES

2002

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Approved by

First Reader

Andrew M. Weiss, Ph.D.

Professor of Economics

Second Readel

Third Reader

Jonathan Eaton, Ph.D.


Pierre Perron, Ph.D.


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ACKNOWLEDGMENTS

I will take the rare opportunity to express appreciation publicly for the invaluable help

that I received while working on my doctorate . Foremost is Andy Weiss, who has given me

unreasonable amounts of support and encouragement over the years. Jonathan Eaton and

Pierre Perron kindly agreed to act as readers on my committee and gave many valuable

suggestions. I would also like to thank Simon Gilchrist and Jerome Detemple for serving

on my committee and putting up with my last minute scheduling.

My experience at the economics department was enriched by many professors and staff

members, but a few demand special recognition. I would like to thank Uncle Bob Rosenthal

for keeping an eye on all of us. and Russ Cooper for somehow making macroeconomics

funny. I would not have finished my dissertation without the help of Sam Holmes who

always managed to solve my problems.

A number of people have given me much needed encouragement at times. They include

Joao Ejarque. Melissa Hieger. David Stewart, and of course Lee McKee. Finally. I would

like to thank my parents, Emmett and LaNell Barnes, for provided unconditional support

from as early as I can remember.

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ABNORMAL RETURNS IN EMERGING EQUITY MARKETS

(Order No. )

MARK A. BARNES

Boston University Graduate School of Arts and Sciences. 2003

Major Professor: Andrew M. Weiss, Professor of Economics

ABSTRACT

Understanding the risk and reward from investing in emerging equity markets is neces

sary for rational flows of equity financing to developing countries. Early research claimed

that investing in emerging markets significantly improved the performance of global stock

portfolios, but this may have been due to misinterpretations of the data.

In this dissertation, I analyze the period of financial liberalization of emerging markets,

including data up until Jun e 1997. By focusing on possible changes in the return patterns

directly associated with the emergence. I suggest that more modest expectations should

have been formed. In the first chapter. I present an introduction to the problem and a

literature review.

In the second chapter. I focus on ten markets that are found to have abnormally high

retu rns when analyzed using a simple Capital Asset Pricing Model. I find that most of

the abnormally high returns came either before the market opened to foreigners, or during

the liberalization period when changes in government policy opened the market to foreign

investors.

In the third chapter, I use a regime-switching model in which the probability of a change

in regime varies over the period. This approach provides a better explanat ion of why the

distribution of returns varies over time. I also present evidence that the abnormally high

returns are associated with the period around the time when the markets were opened to

outside investors.

In the fourth chapter, I use a regime-switching autoregressive conditional heteroskedas-

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ticity (ARCH) framework to model the variance of returns. This approach reduces the

effect of extremely large shocks, which are frequently seen in emerging markets. I present

evidence tha t a switching model tha t conditions the probability of switching on liberaliza

tion events provides better forecasts than commonly used models which do not allow for

such changes.

VI

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Contents

1 Introduction and Literature Review 1

1.1 Introduction ................................................................................................................ 1

1.2 Emerging Markets and PortfolioDecisions............................................................. 2

1.2.1 International in ves tin g ............................................................................... 3

1.2.2 The problem of portfolio th eory ............................................................... -I

1.2.3 The problem of time-variation ............................................................... 7

1.2.4 The problem of outliers - an illu str at io n ................................................ 10

1.2.5 O u tlie rs .......................................................................................................... 12

1.2.6 Robust outlier iden tificatio n....................................................................... 15

1.2.7 Modeling ou tli e rs .......................................................................................... 25

1.3 Literature re v ie w ...................................................................................................... 28

1.3.1 Static characterization and portfolioconsequences............................... 29

1.3.2 Integration and asset p r ic in g .................................................................... 31

1.3.3 Time-varying integration .......................................................................... 33

1.3.4 Time-varying characte ristics....................................................................... 34

1.3.5 Single break in te g ra tio n .............................................................................. 35

1.3.6 Characterization of the emergencepro cess ................................................ 36

1.4 A description of thefollowingchapters .................................................................... 38

1.4.1 Time-varying pricing and liberalizationepisodes ................................... 38

1.4.2 Outliers and libera liza tion .......................................................................... 39

1.4.3 Switching vo latil ity ....................................................................................... 40

1.4.4 Conclusion .................................................................................................... 41

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1.5 Appendix: Description of the outlier detection procedure ............................... 42

2 Exp ectations of Retu rns in Emerging Equity Markets 45

2.1 Introduction .............................................................................................................. 45

2.1.1 Portfolio th e o ry ........................................................................................... 45

2.1.2 Related R es ea rc h ........................................................................................ 47

2.1.3 The problem: perceived m isp ric ing ......................................................... 50

2.2 Two approaches to explaining mispr icing........................................................... 54

2.2.1 Time varying p a ra m e te rs ........................................................................ 56

2.2.2 Liberalization ep iso de s............................................................................... 62

2.2.3 Results ........................................................................................................ 65

2.3 Conclusion .............................................................................................................. 67

3 Ou tliers and Libe ralization in Em erging M arkets 72

3.1 Introduction .............................................................................................................. 72

3.2 Previous re se ar ch ..................................................................................................... 75

3.3 Discrete changes in policy and asset prices ...................................................... 77

3.4 Models and es tim at io n ........................................................................................... 87

3.5 Data and testing p ro c e d u re .................................................................................. 92

3.5.1 D a ta ............................................................................................................... 92

3.5.2 T e st in g ........................................................................................................ 92

3.6 Results........................................................................................................................ 95

3.6.1 Su m m ary ..................................................................................................... 95

3.6.2 Decomposition into a mixture of n o r m a ls ............................................. 97

3.6.3 Time variation of m o m e n ts ...................................................................... 99

3.7 Conclusions............................................................................................................... 107

4 A Regime-Sw itching Mod el of Conditional Variance in Emerging EquityMarkets 146

4.1 Introduction ............................................................................................................... 146

4.1.1 Volatility c lu s te r in g .................................................................................. 147

4.1.2 Volatility in emerging markets ................................................................ 148

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4.2 A regime-switching ARCH m odel........................................................................ 150

4.2.1 A time-varying probability ex tensio n ...................................................... 155

4.3 Within-sample forecasting re su lts ........................................................................ 157

4.4 Out-of-sample forecasts ........................................................................................ 160

4.5 Conclusion and ex ten sio ns ..................................................................................... 163

5 Con clusion 173

A D ata Description 174

B Return Statistics 176

C Graphs of Statistics Tim e Series 177

D Curriculum V itae 192

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List of Tables

1.1 Outlier example 1 .................................................................................................... 11

1.2 Outlier example 2 .................................................................................................... 12

1.3 Jarque-Berra test for n o rm a lity ........................................................................... 13

1.4 Return statistic s with and without outliers ..................................................... 18

1.5 Outlier s ta ti s ti c s .................................................................................................... 19

1.6 Identified ou tl ie rs .................................................................................................... 24

1.7 Hampel identifier critica l valu es ........................................................................... 44

2.1 Buckberg's b e t a s .................................................................................................... 50

2.2 Estimated a lp h a s .................................................................................................... 52

2.3 Estimated alphas and b e ta s ................................................................................. 53

2.4 Estimated alpha, beta, and d e l t a ........................................................................ 66

2.5 Stacked regression average alpha andpost-open d u m m y ................................. 68

2.6 Liberalization episodes used in chapter 2 ............................................................ 71

3.1 Portugal and Colombia liberalizationepisodes ................................................... 80

3.2 Examples of mixture d is tr ib u tio n s ..................................................................... 85

3.3 Model summary ..................................................................................................... 95

3.4 Estimated po (central mean) for the singleand three statemodels ............... 103

3.5 Liberalization episodes used in chapter 3 ............................................................ 109

3.6 Number of parameters, log likelihood. AIC. RAISE, andM A E ........................ 124

3.7 J-test ....................................................................................................................... 127

3.8 Davies test .............................................................................................................. 130

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3.9 Moment matching s ta ti st ic s ................................................................................. 134

3.10 Transitional probabilities .................................................................................... 137

3.11 Coefficients and standard e rr o rs ........................................................................... 145

4.1 Measure of persistence (A) and percent reduction inmean absolute error . . 159

4.2 Out-of-sample forecast percent reductionin mean absolute e r r o r .................. 167

4.3 Liberalization episodes used in chapter 3 ........................................................... 172

B.l Basic statistics on monthly returns. Jan . 1980-June 1997 ............................... 176

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List of Figures

2.1 Alphas and betas. 36-month w in dow .................................................................. 58

2.2 Alphas and betas. 36-month w in dow ................................................................... 59

2.3 Alphas and betas. 36-month w in dow ................................................................... 60

2.4 Alphas and betas, 36-month w in dow ................................................................... 61

3.1 Examples of mixture d ist rib u tio n s ....................................................................... 84

3.2 Brazil: histogram of re tu rn s ................................................................................... 86

3.3 Chile: histogram of r e tu r n s ................................................................................... 97

3.4 Colombia: histogram of re tu rn s ............................................................................. 98

3.5 Colombia: time series of mixture densi ty ............................................................. 99

3.6 Portugal: one-step ahead return densities for a three-regime TVPmodel . . 100

3.7 Portugal. One-step ahead return density for a non-switchingm o d e l............... 101

3.8 Time series of mixtures for Argentina and B r a z il ........................................... 110

3.9 Time series of mixtures for Chile and Co lom bia .............................................. I l l

3.10 Time series of mixtures for Greece and India ................................................. 112

3.11 Time series of mixtures for Indonesia and J o r d a n ........................................... 113

3.12 Time series of mixtures for Korea and M ala ys ia .............................................. 114

3.13 Time series of mixtures for Mexico and N ig e r ia .............................................. 115

3.14 Time series of mixtures for Pakistan and the Philip pin es ............................... 116

3.15 Time series of mixtures for Portugal and T h a il a n d ........................................ 117

3.16 Time series of mixtures for Turkey and Venezuela ............................................ 118

3.17 Time series of mixtures for Zimbabwe and Fra nce ............................................ 119

3.18 Time series of mixtures for Germany and Ja p a n ............................................... 120

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3.19 Tim e series of mixtures for UK and US 121

C .l Total return index and total return: Argentina. Brazile. Chile. Colombia.

Greece, and India ..................................................................................................... 178

C.2 Total return index and tota l return: Indonesia, Jordan. Korea. Malaysia.

Mexico, and N ig e ri a ............................................................................................... 179

C.3 Total return index and total return: Pakistan. Philippines. Portugal. Thai-

land. Turkey, and Venezuela............................ 180

C.4 Total return index and total return: Z im ba bw e ............................................. 181

C.5 1-month statistics, trailing 36-month window: Argentina and Brazil . . . . 182

C.6 1-month statistics, trailing 36-inonth window: Chile and Colombia . . . . 183

C.7 1-month statistics, trailing 36-month window: Greece and I n d ia ................ 184

C.8 1-month statistics, trailing 36-month window: Indonesia and Jord an . . . . L85

C.9 1-month statistics, trailing 36-inonth window: Korea and Malaysia . . . . 186

C.10 1-month statistics, trailing 36-month window: Mexico and Nigeria . . . . 187

C .l l 1-month statistics , trailing 36-month window: Pakistan and Philippines . 188

C.12 1-month statistics, trailing 36-month window: Portugal and Thailand . . . 189

C.13 1-month statistics, trailing 36-month window: Turkey and Venezuela . . . . 190

C.14 1-month statistics, trailing 36-month window: Zim bab we ............................. 191



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List of Abbreviations

A R ............................................................................................................................. Autoregression

ARMA ........................................................................................ Autoregressive Moving Average

AIC Akaike Information Criterion

ARCH Autoregressive Conditional Heteroskedasticity

CAPM ...............................................................................................Capital Asset Pricing Model

E A F E ...................................................................................... Europe. Australasia, and Far East

EM ...................................................................................................... Expectations-Maxirnization

G A RC H Generalized Autoregressive Conditional Heteroskedasticity

IFC ......................................................................................... International Finance Corporation

IFCG ......................................................................... International Finance Corporation Global

MAE .............................................................................................................. Mean Absolute Error

MLE Maximum Likelihood Estimation

MSCI ................................................................................ Morgan Stanley Capital International

RMSE ................................................................................................... Root Mean Squared Error

SVVARCH ....................................... Switching Autoregressive Conditional Heteroskedasticity

TVP ........................................................................................................ Time-varying probability

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Chapter 1

Introduction and Literature

Review

1.1 Introduction

This dissertation presents a framework for the reevaluation of emerging equity markets.

Emerging markets have been presented as an attractive investment from a portfolio stand

point in both the academic and practitioner literature because of their distribut ion of

returns, or more specifically their joint distribution of returns w ith the world market. The

framework that I propose indicates that the standard characterization of emerging market

returns is an artifact of the process of emergenceof the markets. Th is is significant because

it means that expectations of returns that are based on the period of emergence will be

biased unless the peculiarit ies of the emergence are taken into account. In general, they

have not been.

In this introductory chapter. I go over the intuition of the problem of including emerging

markets in a global portfolio. I review the simplest one-factor asset pricing model tha t serves

as a basis for much of the analysis. I then show how the analysis can be severely affected

by outliers that are frequently seen in emerging markets, and I present some evidence that

the observed return distributions are dominated by outliers in many emerging markets. I

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review the literature on emerging markets that both shows that this bias was ignored in

much of the early research on emerging markets and that the framework proposed here ties

together many of the strands of the later literature. Finally, I briefly introduce the next

three chapters of the dissertation:

Chapter 2: I show that there are two problems with using the unconditional CAPM to

form expectations of excess returns:

1. there is time-variation in the estimated coefficients over the period, and

2. outlier returns associated with liberalization episodes disrupt the CAPM pricing

relationship.

Chapter 3: Using a time-varying probability switching model, I decompose the uncondi

tional univariate distribution of the returns into a mixture of normal distributions. I

show that this decomposition explains the time-variation of the moments, and that

the switching process is largely explained by liberalization periods in many of the

emerging markets.

Chapter 4: I model the volatility of returns in emerging markets using a time-varying

probability switching ARCH model that accommodates the large outlie r returns. This

model is shown to outperform the standard GARCH(1,1) model and the fixed switch

ing probability model in out-of-sample forecasting simulations.

1.2 Em erging M arkets and Portfolio D ecisions

Portfolio theory plays a major role in the flow of institutional money to emerging markets

because it provides an important role for emerging markets in a global portfolio. However,

several aspects of how emerging markets fit into a global portfolio have been misunder

stood by research on emerging markets. I tie the return distribution directly to the actual

emergence of the market, which results in an intuitive explanation for the change of the

distribution over time and for the difference between emerging and developed markets.



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3

This framework provides a better explanation for the pattern of returns and points

out some possible biases in emerging market data that have been ignored by much of the

existing research. Fundamental to this framework is an emphasis on outliers in the return

distribution . By associating these large price movements with changes in the underlying

investment environment brought on by changes in government policy, I show why we ob

served important non-normal and time-varying aspects of the distributions that are likely

to occur in all markets th at a re emerging. This simple und erstan ding of how emerging

markets are different from developed markets helps explain why formulaic application of

standard mean-variance portfolios techniques is not optimal.

Investors invest in emerging markets because of expectations that the emerging market

assets will enhance their portfolios. Generally, these expec tations are based on historical

da ta for tha t country and can be based on regression analysis or on simple characterizations

of the return distribution. Regression analysis relates the re turns to othe r variables, whereas

using the moments of the unconditional distribution is the simplest model that assumes

no other variable has information related to returns. In either case, oddities in the return

distr ibu tion itself will have importan t effects on the portfolio analysis. For this reason. I

focus on the distribution and only make tangential comments on the regression effects.

This focus on the re turn distr ibution is not too limiting, however. Much portfolio theory

uses only the join t distribution of asset retu rns to make decisions about asset allocation. In

this dissertation. I look at univariate distributions of emerging marke t returns in chapters 3

and 4, and the joint distribution of emerging market returns with the world market returns

in chapter 2.

1.2.1 Intern ational investing

The appearance of emerging equity markets as investment opportunities in the late 1980s

and early 1990s came at a time when portfolio investing was becoming more international in

its orientation. Investors had already broadened their investment horizon from the domestic

stock market to include stocks in othe r developed markets. In this case, the diversification



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away from the domestic markets reduced the risk of portfolios by adding assets with low

covariances with the rest of the portfolio.

When emerging markets appeared, the typical question asked was. "are these jus t like

the other international stocks or are they fundamentally different?" In other words, did they

form a different asset class that could not be compared directly to the developed market,

or could they be compared? Did "emerging just mean "newor was there something

that set them apart from developed markets? These questions were important for both

the global investors and the emerging market economies themselves. Some preliminary

analysis1 suggested th at rebalancing global portfolios should result in the transfer of large

amounts of investment capital into emerging markets, with potentially large effects on the

developing economies.

There was some research in this early period that pointed out some of the differentiating

characteristics of emerging markets such as low liquidity, high transaction costs, and restric

tions on foreign ownership. However, much of the analysis focused on whether investing in

emerging equity markets was beneficial in a standard portfolio framework that implicitly

assumed that emerging markets could be compared directly to the existing markets. Given

tha t the appropriateness of portfolio analysis depends on the comparability of the markets,

a closer examination is warranted.

1.2 .2 The problem of portfol io theory

The portfolio problem is often expressed in a simple form

. ,3_ _ cv(r i,r rn)_ri Pir m (1-U

uar(rm)

where r, is the return to asset i and rm is the return to the market, and where both

are measured in excess of some risk-free rate of return. The /3 (or beta) notation is used

frequently to indicate the covariance risk of a particular asset. If asset i is a "high-beta

asset, then it will be highly correlated with the market and so will be a risky asset,

requiring a high ra te o f return to compensate the investor for holding the asset. Conversely,

lSee section 1.3.1 in the literature review below.



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a low beta asset is a good diversifier and so will have a low expected return in equilibrium.

To take into account possible deviations from this equilibrium relationship, an intercept

term is often included.

r i = a ,+ l3 lr m (1.2)

Here a (or alpha) refers to the return to the asset in excess of what is warranted by

its beta. If alpha is significantly positive, then the asset is a significant addition to the

portfolio because it increases the portfolio return above what is warranted by its effect on

the portfolio risk. In other words, an alpha significantly different from zero indicates tha t

the specified equilibrium relationship is not holding.

Herein lay the problem presented by emerging markets. The betas of many emerging

markets were low. indicating that these markets were not correlated with the world market,

and yet their returns were high, generating a high alpha. If this were true, emerging

markets would have presented something of a free lunch to international investors. There

are probably other explanations for the boom in emerging equity market investment in

the early 1990s. but this is the one frequently seen in the academic research on emerging

markets. Similarly, there are probably many contributing reasons for the emerging market

crash in 1997-1998; however, over-investment probably contributed to the crash and its

severity. From this point of view, an explanation of problems in the emerging market

analysis may shed light on these contributing factors. Furthermore, an understanding of

the first wave of emerging markets may help with interpreting the behavior of markets that

emerge in the future.

The first part of understanding the emerging market enigma is understanding the stan

dard equilibrium relationship given above. I need to make several preliminary caveats:

1. It is important to remember that a test of the relationship is a test of the jo in t

hypothesis of the pricing relationship and a number of assumptions, including that

the asset pricing relationship is correct, the market represents the correct portfolio,

and tha t expectations are formed rationally. For that reason, tests of asset pricing



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relationships are rarely conclusive. I am not particu larly interested in testing this

pricing relationship but ra th er I am interested in understanding how emerging markets

fit into a global portfolio framework. Because the simple portfolio relationship is a

reasonable approximation of equilibrium portfolio relationships. I will use it as an

indication that we do or do not understand emerging markets rather than conclusive

proof.

2. One critique of the simple framework is that it does not sufficiently take into account

exposure to other risk factors. A standard extension is a multifactor model written

as

r; = a, + ... + PktFk- (1.3)

Since a simple one-factor model illustrates my point. I will limit myself to a single

factor model.

My analysis of the portfolio relationship rests on expectat ions formation. Because we

do not know what the joint distribution of returns will be. we generally write

= < + f t t[rmt+1]. (1.4)

where Et indicates expectations take at time t.The expected return to the asset is a linear

function of the expected market retu rn. However, even this presupposes some knowledge

of at and Writing it out in full, we have

t[rit+i] = E t[ait+l] 4- Et[CaV u ^ [ r ^ ] . (1.5)oar(rm+i)

While expectations of the world return characteristics are certainly important. I will

focus on expectations of the emerging market returns and assume that expectations of the

world market are relatively accurate . Making this leap of faith, we can rewrite the equation

as

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7

t [n t+ i] = + i?t[/?+i]rmt+l. (1.6)

The key is understanding how investors expect emerging market returns to behave in

the future. In a standard framework, unconditional covariance with the world market are

used to form expectations. However, if there is something abou t tha t historical da ta th at we

do not expect to see repeated in the future, then expectations using unconditional moments

may be biased. A more reasonable approach is to take into account time-variation in the

return process.

1.2.3 Th e problem of time>variation

Much of the early research tha t found a high expected alpha simply used the historical mean,

standard deviation, and correlations in their calculations. For example in the World Bank

Econom ic Review special issue on emerging markets in 1995. the four papers dealing with

asset pricing issues show return statistics for the entire period from 1976 to the present, with

two of the four showing statistics on a subperiod beginning in 1985/6. While there is nothing

wrong with providing descriptive statistics, most of these papers assume that the return

process is stable over this period and so the historical statistic s are good approximations

of the expected returns at any point in time. While this may seem like an innocuous

assumption given our relative ignorance of returns in emerging markets, it is a misleading

assumption, as will be explained below.

Using the historical statistics in this context implicitly assumes that the re turn process

is stable in the sense that the expected distribution at every point is the same and equal to

the unconditional distr ibution , as is generally assumed in developed markets. Clearly this

is a courageous assumption, and I contend that it should be checked. In fact there have

been a number of papers th at have looked at the time variation of some of these statistics,

some of which are discussed in the literature review in section 1.3.1 below.

In general, these papers have not, however, attempted to relate the time variation to

expectations of excess retu rn (alpha). Furthermore, these papers generally have assumed



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that there has been some change over the period but have not looked specifically at the

process of emergence. There have been a few papers that have looked broadly at the

process of emergence bu t have not gone back to look at the effect that it has on how

emerging markets have changed our expectations of excess retu rn. This d issertation looks

at this process of emergence.

The emergence of emerging equity markets can be described as the process through

which investing in the market by global investors becomes possible. For emergence, then,

at least two things need to happen:

1. the development of the equity market institu tions that allow for the trading of shares.

2. the opening of the market to foreigners, which includes not only pe rmitting the ac

quisition of stocks, but the sale and repatriation of returns in the investors preferred

currency.

By and large these things happen as a result of government action. These actions usually

have by-products such as changes in macroeconomic conditions, tax rates, and economic

growth that also affect the stock market if the financial system becomes more efficient.

Some of the results of emergence, then, for which we should look are the following:

The development of the stock market may make the financial system more efficient

which should contribute to economic growth. There is some evidence that the emer

gence does lower the cost of capital in the countries but the specific topic is beyond

the scope of this paper. See Bekaert and Harvey (2000) for a discussion.

The opening of the stock market has been shown by Suret and L'Her (1997) and Henry

(2000b) to be directly related to a one time appreciation of stock values as would be

expected if the local stocks are more highly valued by international investors than by

local investors due to their diversification characteristics.

The effect on the volatility of the market may be mixed. On one hand, the growth

of the market should result in the deepening of the market which should reduce the



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9

volatility often seen in thinly traded markets. On the other hand, the opening of the

market to potentially large inflows and outflows of interna tional capital could increase

volatility. In fact, in Bekaert and Harvey (1997), the authors find that volatility is

lower after liberalization, while in a later paper (Bekaert et al. (1998b)), the authors

find that returns are more volatile after integration.

The expected effect on the correlations is also mixed. Related economic liberalization

may open up the economy to global shocks which may increase correlations. Fur

thermore, to the degree that stock market correlations are driven by the common

reaction of global investors to impor tant shocks, correlations may increase. However,

to the extent that these economies are structurally different from developed market

economies, the exposure to shocks should be different and so correlations of emerg

ing markets with the world markets should still be lower than the correlation of the

developed markets with the world market.

The finding has generally been that while correlations with developed markets have

risen, they are still fairly low. See Bekaert and Urias (1999) and Bekaert et al. (1998b)

for example.

In this dissertation. I look at the last three of these and four points discuss how we may

expect the emergence to lead to different mean returns, volatilities, and correlations with

the world market. These changes could affect the retu rn process in different ways. If the

change is an evolutionary change, it may be that the process changes slowly from a pre

emergence patte rn to a post-emergence pa ttern. It could also be that there is a rapid change

at the time of emergence which appears as a struc tural break in the time series. In either

case, it could be that the emergence is characterized by large outliers as the market adapts

to importan t changes. Because this dissertation emphasizes outliers that appear during

the period of emergence, it is worth considering the intuition of outliers in the context of

portfolio decisions.

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1.2 .4 Th e problem of out l iers - an i llustrat ion

To appreciate the significance of outliers in the context of portfolio analysis, it is important

to understand how outliers affect the measured statistics of returns in a small sample since

these statis tics can drive asset allocation. In some empirical studies, the tail observations

are not very important and can be dropped or winsorized. However, in a retu rn series, as

long as the returns are measured correctly, all of the observations are important for the

investor's overall return, so the tail observations cannot be ignored.

The effect of outliers on investors' portfolio decisions can be seen in the simple CAPM

model described above. The effect of a single outlier depends on its magnitude, its direction,

and whether it has the same sign as that period's world return . If an outlier is large, positive,

and has the same sign as the world return, then it will increase the measured beta of the

model, whereas if it has a sign opposite of the world return, it will reduce the measured

beta.

This effect is largely a small sample effect, but in terms of emerging markets, that is

still importan t since emerging markets have only a short history of returns.2 In Table (1.1).

I show the results of a simple simulation. Two return series are randomly generated using

the covariance structure of the Morgan Stanley Capital International (MSCI) world index

and France - as a representative market, using data from 1988:1-1993:12. The series are 72

observations long which is six years using monthly returns.

I change one of the country's observations by setting it equal to the mean of the series

plus or minus some multiple of the standa rd deviation of the series. I then estim ate alpha

and beta . As shown in Table (1.1), the beta increases when the shock has the same same

sign as the world return , and decreases otherwise. It is interesting to note how much the

shock affects the measured beta. With a three standard deviation shock, the measured beta

varies between 0.489 and 0.659. and with a six standard deviation shock, the measured beta

varies between 0.407 and 0.736.

2 Actually, the discussion of outliers probably has wider relevance given th at beta is often measured over

a moving window of 5 years or so, which ensures a fairly small sample.



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Also, notice that the alpha varies between -0.184 and 0.464. which indicates that the

monthlyreturn for the country unexplained by its comovement with the world return ranged

from -18.4% to 46.6%.

Shock has same sign as world return

shock crs a 0 P mean a skewness kurtosis

0 0.140 0.571 -0.012 0.753 5.921 0.089 2.410

1 0.194 0.599 -0.008 0.836 5.969 0.063 2.3472 0.248 0.626 -0.004 0.919 6.099 0.119 2.368

3 0.302 0.653 -0.001 1.003 6.304 0.305 2.845

4 0.355 0.681 0.001 1.086 6.578 0.629 4.105

5 0.409 0.708 0.003 1.169 6.913 1.066 6.269

6 0.463 0.736 0.004 1.252 7.300 1.575 9.246

Shock has sign opposite the world return

shock


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erally anticipated, with the effect of liberalization spread out over several months before

the official liberalization. Empirically, we see several emerging markets with consecutive

shocks, with the most notable example being Colombia. We would expect this to give these

countries an unusually high measured autocorre lation. There are also a few countries with

consecutive outliers of opposite sign, with the best example being Taiwan. We would expect

these to have lower measured autocorrelations than they would have otherwise.

Two consecutive identical shocks

shock cts Q 13 P mean a skewness kurtosis

0 0.262 0.544 0.007 0.845 5.876 0.060 2.462

1 0.426 0.546 0.057 1.011 5.969 0.008 2.3432 0.590 0.548 0.123 1.178 6.220 0.110 2.360

3 0.754 0.550 0.193 1.345 6.609 0.414 3.047

4 0.918 0.553 0.258 1.511 7.114 0.872 4.6065 1.083 0.555 0.313 1.678 7.713 1.401 6.850

6 1.247 0.557 0.356 1.844 8.385 1.932 9.440

Table 1.2: Outlier example 2. Alpha, beta, autocorrelation (p). and moments when adding

shocks to two consecutive observations. 72 observations.

1.2.5 Outl iers

Th e stand ard finding is tha t stock market returns are not normally distributed. I test for

normality using monthly return da ta for 20 emerging markets and 5 developed markets. The

emerging market data is from the International Finance Corporation's Emerging Market

Database, while the developed market data is from Morgan Stanley Capital International.

Tests of normality show that emerging market retu rns are not normally distributed. The

results for percent returns and log returns are show in Table (1.3). Normality is rejected

even when using log returns, although less so because it reduces the influence of the large

positive returns that are seen in the emerging market data . I will use percentage returnsrather than log returns in what follows and provide some evidence that the large positive

returns are interesting in their own right.



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Returns Log Returns

Country JB p-value JB p-value

Argentina 2004.9664 0.0000 291.1381 0.0000

Brazil 14.8491 0.0006 74.2198 0.0000

Chile 0.1316 0.9363 6.3144 0.0425

Colombia 152.8409 0.0000 71.2686 0.0000

Greece 488.2039 0.0000 173.7814 0.0000

India 29.6886 0.0000 13.8808 0.0010

Indonesia 0.1712 0.9180 0.3763 0.8285

Jordan 12.6885 0.0018 8.9265 0.0115

Korea 22.6990 0.0000 9.3407 0.0094

Malaysia 22.1099 0.0000 84.6089 0.0000

Mexico 130.1182 0.0000 1042.0557 0.0000

Nigeria 2121.0984 0.0000 4211.4708 0.0000

Pakistan 198.7556 0.0000 87.2724 0.0000

Philippines 57.4821 0.0000 37.3506 0.0000

Portugal 531.6033 0.0000 156.7812 0.0000

Taiwan 32.2870 0.0000 21.0684 0.0000

Thailand 62.5499 0.0000 163.5785 0.0000

Turkey 23.7839 0.0000 3.3469 0.1876Venezuela 45.1489 0.0000 252.0623 0.0000

Zimbabwe 40.7063 0.0000 26.5231 0.0000

Japan 4.9478 0.0843 11.2286 0.0036

France 42.8557 0.0000 116.7490 0.0000

Germany 11.7606 0.0028 20.5508 0.0000

UK 15.1477 0.0005 41.4398 0.0000

USA 191.6236 0.0000 421.1429 0.0000

Table 1.3: Jarqu e-Berra test for normality statis tic and p-value. for return s and log returns.



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Th e non-normality raises the possibility of outliers. Fi rs t, let me begin with an informal

definition of outlie r.3 An outlier is an observation that is located far away from the main

grouping of da ta. This definition is somewhat vague in that it does not mention conditioning

information. In the case of a simple univariate distribution, it means that the outliers lie

far away from mean of the d istribution. When there is oth er conditioning information, as

in the case of an estimated model, it must be that we have an outlier when the observation

does not follow the model well, meaning that it has a large forecast erro r.1 Clearly, this

raises the issue of specification. If there are outliers, should we try to build a be tte r model

that better forecasts outliers, or should we ignore the outliers in both the estimation and

the forecasting?

The treatm ent o f outliers is an important topic in all estimation, bu t I contend th at it

is especially interesting in financial markets. In many cases, outliers are of interes t because

they can have an unwanted effect on the estimation. In that case, once the outliers are

identified, they can be removed from the da ta set and th e estimation can proceed. In

finance, motives are less clear. If we know tha t there were very large negative or positive

returns in the data, tha t are not due to measurement or recording error,wewould somehow

like to include th at in our information set. It is not clear that investorswould take much

solace from being told that analysis was done only after removing all large gains and losses

from the da ta set. Basically, we do want to understand outliers in finance.

The handling of outliers in finance is somewhat confused. In developed markets, there is

evidence tha t there are sporad ic outliers in the return distribution , as shown by Table (1.5)

below. These are often stock marke t crashes, and their causes are not well understood .5

Indeed, so little is understood about them, that practitioners tend not to try to model

them, which can lead to somewhat of a schizophrenic approach in that they allow the

observation to influence the estimation but they do not try to differentiate the outliers

from the base observations in any way. The way that this is generally handled is to assume

3For more formed treatment o f outliers, see Gather and Becker (1997) and Barnett and Lewis (1984).

4 See West and Harrison (1997), p. 347 and following.

5See Kleidon (1995) for a discussion of the 1987 crash.



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that returns are drawn from a fat-tailed distribution, which means that we expect to see

outliers periodically, but are not able to explain why.

Emerging markets are somewhat different in that there are so many outliers, that there

is good reason to try to model them. Table (1.5) below shows tha t for many emerging

markets, roughly five percent of the observations are outliers. More importantly, I give

evidence below that these outliers are driving much of both the "stylized facts'' as well as

the ano malies seen in emerging market returns. It seems tha t th is gives sufficient reason

to try to unders tand the outliers. Below, I present a framework that attem pts to model

that outliers in a way that is both intuitive and informative.

1.2.6 R ob ust ou tl ier identi f ication

The identification of outliers is a topic with a long history. See Barnett and Lewis (1984)

and Gather and Becker (1997) for overviews. There are two main problems with identifying

outliers: maskingand swamping. In the case of masking, outliers disto rt the criteria used for

identifying outliers to such an extent tha t they are not identified as outliers. For example,

in the case of a univariate normal distribution, a reasonable statistic would be to look at

the t-statistic of the observation furthest from the mean and see if that could be considered

an outlier with a reasonable level of confidence:

j sr

wherex is the measured mean of the distribution and s x is the measure standard deviation.

The problem here is that if x} is indeed an outlier, it has an affect not only on x but

on sx . and it may well be that x3 is not signif icantly different from the mean x. A quick

fix is to drop observation j and look at the extreme studentized deviate statistic:

ESDm = max J ~J 3 - j

where x_j and s__, do not use observation Xj in their calculation. Obviously, the problem

here is that there may be other, related, outliers used in the calculations that prevent

Xj from being identified as an outlier. This problem is known as the masking problem



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because the outliers mask their identi ties as outliers. This problem is especially important

with large outliers, as is seen in emerging markets returns. The swamping problem is the

reverse problem in which the presence of the outliers causes an observation x, that is not an

outlier to be identified as an outlier. This problem is less importan t in the case of emerging

markets.

While there are several methods for identifying outliers. I will use the method found to

be very robust in bo th Gather and Becker (1997) and Davies and Gather (1993). namely

the inward iterative procedure that uses the Hampel statistic, described briefly below. See

a short appendix beginning on page 42 for details. This method uses a robust measure of

location and spread:

, \xj - rued mlH A m= max =t

j MADm

where medm is the median of the distribu tion, and MADm is the median absolute deviation

from the median:

MADm = med{|xj - medm|}.

The algorithm is iterative in that it tests the largest outlier, and if that observation is

outside the confidence interval, it is dropped from the observation sample and the procedure

is repeated for the next largest outlier. The m subscript refers to the reduced sample. This

continues until the observation is not significantly far from the median. The test statistic

details are given in Davies and Gather (1993).

In Table (1.4), I show the measured return moments with and without the outliers

identified using the robust outlier identification algorithm discussed above.6 Note the large

difference in the moments for most of the emerging markets. Table (1.5) shows the number

of observations in the full data set, the number of identified observations, the number of

outliers as a percentage of the total observations, and the arithmetic and geometric means

of the outliers. Several results stand out:

6N'ote that I am assu ming norm ality and using percent returns for doing the tests. Using log returns

does not change the qualitative results of the tests.

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for many of the emerging markets, there is a marked difference between the moments

measured over the entire data set and those on the subsample that drops the outliers.

for some of the emerging markets, outliers make up a large percentage of the tota l

number of observations.

for most of the emerging markets, especially those with multiple outliers, the mean

of the outliers is very large and positive.

there is considerable difference not only between the emerging and developed markets,

but also among emerging markets.

This last poin t warrants some expansion. We can make some broad characterizations

of the differences:

Developed markets: These tend to have fewer outliers, and the outliers tend to be

negative. This results in the unconditional distr ibution having a negative skewness.

Emerging markets similar to developed markets: There are a few emerging mar

kets tha t have a pa ttern similar to the developed markets. Specifically. Chile. In

donesia, and Malaysia have few outliers and they tend to be negative. Mexico and

Venezuela are the only other emerging markets with a negative outlier mean.

Prototypical emerging markets: Most emerging markets do have many outliers that

have a positive mean. The best examples of this group are Argentina. Greece, and

Portugal.

High variance emerging markets: Other emerging markets do not have clear outliers

but do have a high variance. The best examples are Brazil and Turkey.

Clearly, for some of the emerging markets, outliers do notseem to be much of a problem.

Much of the rest of this dissertation focuses on the outlier problem, so the results for some

of the emerging markets are weak precisely because there was not much of a problem from

the beginning.



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Out l i e rs dropp ed Full da ta se t

Count ry m ean


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C ountry to ta l obs o utlie r obs o u tlie rs as % of obs ari th. mean geo. mean

Argentina 209 11 5.26 53.37 27.94

Brazil 209 4 1.91 25.27 11.25

Chile 209 I 0.48 -28.03 -28.03

Colombia 150 5 3.33 33.06 32.95

Greece 209 S 3.83 30.32 27.16

India 209 5 2.39 8.70 5.02

Indonesia 90 0 0.00

Jo rd an 209 10 4.78 3.17 2.34

Korea 209 4 1.91 26.20 26.17

M alaysia 150 1 0.67 -30.59 -30.59

Mexico 209 6 2.87 -17.44 -26.51

Niger ia 150 19 12.67 0.09 -8.15

Pakistan 150 s 5.33 14.19 12.53

Phil ippines 150 6 4.00 15.35 t0.83

Portugal 137 12 8.76 19.45 15.67

Taiwan 150 7 4.67 20.18 13.65

Thai land 209 i 3.35 3.55 0.12

Thrkey 126 3 2.38 65.18 65.08

Venezuela 150 4 2.67 -0.39 -12.54

Zim babwe 209 4 1.91 5.78 0.64

Avg E merging 175 6.25 3.66 13.02 7.66

Ja p an 209 1 0.48 -24.51 -24.51

France 209 6 2.87 -12.55 -13.69

Germanv 209 5 2.39 -2.87 -4.40

UK 209 2 0.96 -19.16 -19.23

USA 209 2 0.96 -3.83 -5.49

Avg D eveloped 209 3.2 1.53 -12.58 -13.46

Table 1.5: Total number of observations, number of identified outliers, outliers as per

centage of total observations, arithmetic mean, and geometric mean of identified outliers.

Averages for emerging markets and developed markets are a rithmetic means of the num bers

presented.



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In Table (1.6) I show some detail on the specific outliers that are found. Notice tha t

while there are many examples of consecutive outliers, it is interesting to note that for many

countries the outliers are clustered in a few periods, even if they are not in consecutive

months. The effect on the statistics can also be seen on time-series graphs in an appendix

beginning on page 177.

Identified outlier returnsArgentina (y =0.53 . ct =14.78 )

Month Return cts from y

1980:2 59.08 3.96

1981:4 -45.02 3.08

1985:6 91.55 6.16

1985:8 128.11 8.64

1989:6 178.11 12.021989:7 -64.95 4.43

1989:8 45.92 3.07

1989:9 95.15 6.40

1990:1 -53.97 3.69

1991:3 57.08 3.83

1991:8 95.96 6.46

Brazil (y =2.47 a =15.86 )

Month Return as from y

1986:3 57.53 3.47

1988:3 52.62 3.16

1989:4 47.82 2.86

1990:3 -56.89 3.74

Chile {y =1.93 . a =8.54 )


1983:1 -28.03 3.51

Colombia (y =1.77 . a =6.56 )


1987:9 22.89 3.22

1991:10 34.56 5.00

1991:11 37.08 5.38

1991:12 37.34 5.42

1992:1 33.42 4.83

Greece (y =-0.27 . a =7.37 )Month Return cts from y

1987:1 30.94 4.23

1987:3 26.34 3.61

1987:9 38.06 5.20

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continued from previous page

1988:1 -30.81 4.14

1989:9 42.87 5.85

1990:4 58.58 7.98

1990:6 43.67 5.961991:2 32.89 4.50

India (p =1.18 . o =7.94 )

Month Return os from p

1990:7 26.30 3.16

1992:3 35.27 4.29

1992:5 -24.38 3.22

1996:5 -24.29 3.21

1996:6 30.59 3.70

Indonesia (p =0.78 . o =8.45 )


NoneJordan (p =0.51 . o =3.91 )


1981:1 -12.14 3.24

1981:7 13.18 3.24

1981:11 14.43 3.57

1985:6 12.18 2.99

1985:7 13.26 3.26

1989:2 -12.81 3.41

1989:7 16.15 4.01

1989:8 -12.23 3.26

1990:1 12.56 3.081990:8 -12.85 3.42

Korea {p =0.66 , o =7.74 )

Month Return C7S from p

1980:4 22.03 2.76

1981:1 25.57 3.22

1981:6 30.62 3.87

1992:10 26.58 3.35

Malaysia (p =1.43 . o =7.01 )


1987:10 -30.59 4.57

Mexico (p =2.49 . o =10.53 )Month Return os from p

1982:12 -46.61 4.66

1987:10 -42.47 4.27

1987:11 -59.32 5.87

1988:1 39.60 3.52

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continuec from previous page

1988:2 39.33 3.50

1994:12 -35.21 3.58

Nigeria (p =2.11 , a =4.49 )

Month Return crs from p.1986:9 -15.52 3.93

1986:10 -56.04 12.96

1987:1 24.48 4.99

1987:2 -11.42 3.02

1987:3 -12.60 3.28

1987:4 -18.20 4.53

1987:6 38.89 8.20

1989:1 -19.86 4.90

1991:9 18.83 3.73

1992:3 -42.26 9.89

1992:12 -13.04 3.381993:2 -28.57 6.84

1993:4 57.55 12.36

1993:9 -15.02 3.82

1994:1 99.47 21.70

1995:3 -70.25 16.13

1995:5 17.03 3.33

1995:6 22.97 4.65

1995:7 25.26 5.16

Pakistan {p =0.50 . a =5.08 )

Month Return crs fromp

1991:7 19.68 3.77

1991:11 35.27 6.84

1991:12 31.42 6.08

1992:4 16.41 3.13

1992:7 -15.82 3.21

1993:12 25.76 4.97

1996:7 -16.06 3.26

1997:1 16.85 3.22

Philippines {p =2.52 , a =8.00 )

Month Return crs fromp

1986:2 31.42 3.62

1986:9 33.92 3.93

1987:6 42.41 4.991987:9 -24.03 3.32

1990:9 -29.30 3.98

1993:12 37.67 4.40

Portugal (p =0.91 , cr =6.42 )

continued on next page



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Month Return crs from p

1986:3 27.67 4.17

1987:1 34.91 5.29

1987:3 24.96 3.751987:4 36.99 5.62

1987:5 24.72 3.71

1987:7 22.72 3.40

1987:8 35.78 5.43

1987:9 70.84 10.89

1987:10 -20.67 3.36

1987:11 -29.30 4.70

1987:12 -24.26 3.92

1989:9 29.02 4.38

Taiwan (p.=1.81 . a =11.04 )

Month Return as from p

1987:4 38.87 3.36

1987:8 34.71 2.98

1987:9 53.34 4.67

1987:10 -35.52 3.38

1990:8 -34.14 3.25

1993:2 34.29 2.94

1993:12 49.69 4.33

Thailand (p =1.28 , a =6.49 )


1982:9 19.66 2.83

1987:9 22.99 3.35

1987:10 -33.82 5.411990:9 -22.50 3.66

1993:10 32.24 4.77

1993:12 26.24 3.85

1996:10 -19.96 3.27

Turkey (p =2.09 . a =17.65 )


1987:7 69.31 3.81

1989:9 69.13 3.80

1997:1 57.11 3.12

Venezue a (p =2.48 . a =11.29 )

Month Return as from p1985:12 -49.79 4.63

1990:3 48.55 4.08

1990:8 45.88 3.85

1995:11 -46.22 4.31

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Zimbabwe (p =1.34 , ct =8.79 )


1981:9 -27.91 3.33

1984:6 -25.15 3.011985:5 45.98 5.08

1994:2 30.21 3.29

Japan (p =1.38 . ct =7.10 )

Month Return cts from p

1990:3 -24.51 3.65

France (p =1.41 . ct =5.47 )


1980:3 -17.38 3.44

1981:5 -26.92 5.18

1982:6 -18.37 3.62

1987:10 -17.73 3.501988:2 20.48 3.49

1990:8 -15.40 3.08

Germany {p =1.14 . ct =5.42 )


1980:3 -16.14 3.19

1985:12 18.18 3.14

1986:5 -16.02 3.16

1986:8 19.03 3.30

1987:10 -19.41 3.79

UK {p =1.27 . ct =5.31 )

Month Returncts

from p1981:9 -15.93 3.24

1987:10 -22.40 4.46

USA {p=1.12 . ct =3.62 )


1987:1 13.96 3.55

1987:10 -21.62 6.28

Table 1.6: All month returns found to be outliers using the

inward iterative algorithm using the Hampel statistics, with

a significance level of 0.01 (a = a.v = 0.01). Data are from

1980:1-1997:6. where available. The mean {p) and standard

deviation (cr) are calculated after dropping outliers.

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1 .2 .7 M ode l ing out l iers

There are two fundamental issues involved when modeling outliers: bias and efficiency. If

there is some way to model outliers and it is not done, it can affect the results differentlydepending on the natu re of the outliers. Consider the case in which a single variable could

be used to explain the outliers, bu t the variable is not used. Th is omission produces an

omitted variable bias. If any of the explanatory variables are correlated with the omitted

variable, their coefficients will be biased. If the outliers are not correlated with any of the

included explanatory variables, the effect of the outliers will not affect any of the estimated

explanatory variable coefficients. However, the intercept will pick up the effect of the

outliers and so may be "biased in the sense that the "unexplainable part of the variance

is not measured correctly. Estimation will be less efficient, of course, relative to the correct

explanatory variable being included.

Again, relating this result to financial outliers, in the case of developed markets, we

do not know what causes the outliers and so we do not include any explanatory variables.

We may suppose that this does not bias estimation (much) but we know that it decreases

efficiency relative to including a good explan atory variable. Unfortunately, we do not have a

good explana tory variable. In the case of emerging m arkets, it may well be that we do have

a reasonably good explanatory variable. Not including it not only decreases the efficiency

of the estim ation bu t can have a bias on the estim ated coefficients. In the simple case in

which only the intercept is affected, it is affected depending on the relation of the outliers

to the estimated intercept. Note in Table (1.6) tha t the average outlier return for most

emerging markets is positive and generally large. This indicates tha t these outliers bias

the estimated base retu rn of emerging markets in the da ta set upward if the outliers can

be explained. The point here is that failing to expla in the outliers can have an important

impact on our understanding of emerging market returns.

This dissertation focuses on using the history of the emergence to help explain the

retu rn distr ibu tion , including the outliers. Since what follows uses the term "liberalization

throughout, I will first clarify how I use it.



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There are many different types of liberalizations that are seen in emerging markets.

Since I am focusing on equity markets, I will try to limit my discussion of liberalization to

those that have a fairly direct impact on the equity markets. However, this is easier said than

done. While some liberalization policies, such as removing restrictions on foreign ownership

of domestic stocks or reducing capital gains taxes, are directly related to the equity market,

others a re not as clear. For example, liberalization of the exchange rate system may not

have a direct effect on the stock market, but does have an effect on foreigners?being able

to transfer their stock market returns back to their home currency, and so affects the value

of stocks for foreign investors. Therefore, I do look at changes in exchange rate regime.

Similarly, privatiza tion of government-owned companies may not have a direct impact on

the functioning of the stock market, but can have a large effect on the stock market being

an integral part of the financial system because these privatized stocks make up the core

of the stock market in many emerging markets.

On the other hand, some liberalization policies, such as trade liberalization, may have

some indirect affects on the equity market either through effects on economic growth or

through the effect of share prices affected by the trade liberalization, b ut I do not include

such liberalization episodes in my discussion.

Another aspect of liberalization that needs explanation is the timing. In the context

of equity markets, liberalization is the set of policies that reduce frictions in the equity

market. Not only may this include many different types of policies, as discussed above, but

the tim ing can vary significantly. We can consider the levelof liberalization to be between

limits of zero and one, with zero being a level that precludes the functioning of the market,

and one being a theoretical absence of frictions. Liberalization, then , is the sequence of

policies th at increase the liberalization level.

Two aspects of these liberalization policies need addressing. Firstly, the policies do not

all have the same importance, as measured by the effect on the stock market. Specifically,

there is one very important liberalization episode that I refer to as the "opening that allows

foreigners to invest in the stock market. I consider this to be th e most importan t single



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liberalization policy, although its analysis is complicated by the fact that in some countries

it is accomplished all at once, and in other countries (e.g. Korea) it is accomplished step

by step.

Secondly, it is not the case that the policy changes must always increase the level of

liberalization, meaning that the history of the liberalization level is not necessarily non

decreasing. Periodically, we may see policies that reduce the level of liberalization, such as

an increase in the restrictions on share-holders.

There is an important assumption implicit in my dissertation: that in the countries in

my dataset, there is a tendency to liberalize. I can speculate that this is generally true,

but it is probably unprovable. and also is not something I can address with my da taset.

My dataset, however, has the characteristic that it is made up of all the markets that were

deemed to be emerging markets by the IFC at the end of the data period (June 1997).

That in itself implies that there was some degree of liberalization in the history because

the 'emergence refers to the market going from submerged or closed, to being open. This

emergence was largely a result of changes in government policies that have increased the

liberalization level of the market.

The result of these two aspects of liberalization (the primacy of 'opening and the

tendency to liberalization) imply that if countries have just begun liberalizing, they are

more likely to liberalize more in the future: and that if countries have liberalized to the

point of having very developed markets, they are less likely to have important liberalization

policies in the future. In the simple case of opening, if markets have not opened, they are

likely to open in the future, whereas if they have already opened, they are not likely to open

again in the future. While it is possible that a country closes and therefore can re-open

in the future, we will know that this is the case and can condition on the knowledge.



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1.3 Literature review

The simulation and statistics presented in the previous section indicate that outliers are

prevalent in emerging markets and that this likely has an important effect on portfolio

analysis. In this section. I present a review of literature on emerging markets to give

context for this focus on outliers.

In the ten years or so in which significant research on emerging markets has been done,

there have been several phases through which the research has gone. They can be grouped

in the following categories:

Static characterization and portfolio consequences: Preliminary research merely char

acterized the historical (unconditional) returns of emerging markets. Closely related

were papers tha t used these static characterizations to sup por t th at emerging markets

would significantly enhance a global portfolio.

Integration and asset pricing: The promise of enhanced portfolios raised the important

question of whether emerging markets were integrated with the world market. If they

were not. then it may not even be possible to invest in the markets. Furthermore, if

standard asset pricing models did not hold then investors lacked an understanding of

how to value emerging market assets. These papers raised the possibility of important

changes in the return process.

Time-varying integration: Because many of the tests for integration were rejected, re

search was done that tested for time-varying integration.

Tim e-varying characteristics: Related to modeling time-varying integration but less

ambitious were papers that merely looked at time-variation in the return character

istics of emerging markets, although they did not focus on outliers.

Single break integration: One strand of research to emerge assumed that there was a

quick switch from being to segmented to being integrated. Realizing that the early

data would be characterized by a segmented return process, the researchers sought to



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find the date of the integration and then look at the emerging market characteristics

since then.

Characterization o f the emergence process: This research finds that there is evidence

that the emergence of emerging markets was over a long period of time and so was

not a sharp break between being segmented and being integrated. By studying the

emergence process in detail, the researchers hope to understand how expectations of

current and future emerging markets should be formed.

These categories are in rough chronological order, although there are some papers that

do not easily fit into one or more of the categories. Below. I give a quick review of some of

the more relevant papers.

1.3.1 Sta tic characterization and portfol io con sequ ences

The early articles looked at the characteristics of emerging market returns without dealing

with the influence of outliers. Much of the early work cited below is by practitioners who

were excited about the portfolio enhancing possibilities of emerging markets.

In Claessens et al. (1993), the authors look at a number of return anomalies and at

retu rn predictability in emerging markets. Using IFC data through 1992. they find that

anomalies that are often seen in developed markets are not present in emerging markets

bu t they do find indications of predictability in emerging markets. The most important

source of predictability is simply first-order autocorrelation, which is significantly positive

in a number of markets.

In Speidell and Sappenfeld (1992). the authors point out the importance of global

diversification. They then show that given the apparent rising correlation between the

EAFE (the Morgan Stanley Capital International's Europe, Australasia, and Far East

index) markets and the U.S. market, emerging markets should play a larger role in a global

portfolio because of their low correlations with the developed markets.

Wilcox (1992) gives intuitive explanations for his optim istic view of emerging market

investing. While he does discuss the standard risk-reward reasons, he also looks at three



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other reasons:

1. that global value investors will benefit from the relatively new and inefficient markets

that are dominated by momentum traders.

2. that an u nderstandin g of economic development can give global investors bette r fore

casts due to the importance of economic growth on emerging markets.

3. tha t the secular move away from managed economies toward market-oriented economies

will present more opportunities for global investors knowledgeable about emerging

markets.

In an early practitioners ' article. Divecha et al. (1992) expresses the basic argument for

including emerging markets in a global portfolio:

Even though emerging markets are risky individually. low correlations be

tween them and with developed markets lead to risk reduction for modest in

vestments. As these markets develop greater links (financial and trade) with

the developed markets, they will undoubtedly become more highly correlated.

Thus, there is a "diversification free lunch" currently available - one should

indulge while the opportunity exists, (p. 50)

They also point out that the stock level correlations within countries are very high, in

dicating that country selection is much more important than stock selection in emerging

markets.

In Errunza (1994). the author makes a general case for the inclusion of emerging markets

in a global portfolio and also looks at how their inclusion should be implemented. He points

to a number of studies that support this view and sums up the results:

All of these studies use efficient frontiers, factor analysis, or asset pricing

models. Thus, there is robust evidence of the benefits of EM [emerging market]

diversification over the last two decades for different sets of markets, varying

pr odu ced with permission of the copyright owner. Further reproduction prohibited without permission.


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time periods, many types of data and sources, and different methodologies. The

major findings over the period 1960-1990 are:

1. EM diversification would have been beneficial in terms of both increased

returns and reduced risk.

2. The domestic system atic risk has been higher than ma jor developed mar

kets (DMs) but not necessarily the smaller developed markets.

3. The return correlations vis-a-vis developed markets have been low. and at

time negative. Among themselves, the EMs are essentially uncorrelated.

1.3.2 Integration and asset pricing

Early excitement in emerging market portfolio possibilities was tempered by a wave of

more academic research on whether emerging markets were " integrated- with world equity

marke ts. While the precise definition of 'integra tion ' varied from paper to paper, the

idea is that if markets were integrated with the world market, then frictions would be low

enough that investors could include these assets in their portfolio. Conversely, if they were

segmented, then frictions existed that prevented their inclusion and they could not be used

by global investors to improve thei r portfolios.

The results of this research were somewhat mixed. The majority of evidence seemed to

indicate th at the emerging markets were not integrated with the world market. However,

many of the authors began to point out that there seemed to be some time-variation in the

process, so it may have been the case that emerging marke ts were becoming more integrated.

Nevertheless, most of the analysis took a static view of emerging market characteristics .

Two very important collections of this research came out of the World Bank. In 1993.

a collection of working papers edited by Claessens and Gooptu was published (Claessens

and Gooptu (1993)). In 1995, a issue of the World Bank Economic Review was dedicated

to eme

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