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How Do Crises Spread?

Evidence from Investable and Non-investable Stock Indices

Brian H. Boyer, Tomomi Kumagai, and Kathy Yuan∗

This version: September 2004

abstract

This paper provides empirical evidence that international investor asset holdings have

an effect on global transmission of stock market crisis. By separating stocks into two

categories, those eligible for purchase by foreigners (investable) and those that are

not (non-investable), we estimate and compare the degree to which investable and

non-investable stock index returns co-move with the crisis country index returns.

Our results indicate greater co-movement during high volatility periods, especially

for investable stock index returns. The more pronounced impact on investable stocks

than non-investable stocks provides evidence that crisis spreads through international

investors’ asset holdings rather than through changes in countries’ market fundamen-

∗Brian H. Boyer (bhb@byu.edu) is at Brigham Young University; Tomomi Kumagai (tkuma-

gai@wayne.edu) is at Wayne State University and Kathy Yuan (kyuan@umich.edu) is at Stephen

M. Ross Business School at the University of Michigan. Earlier versions of this paper circulated

with the title “Are Investors Responsible for Stock Market Contagion?” The authors wish to thank

Xiangming Li at the International Monetary Fund for her assistance, Mark Aguiar, Campbell Har-

vey, Jerry Hausman, Guido Kuersteiner, Matt Pritsker, and Ruy Ribeiro for valuable comments.

The authors also would like to thank participants at the University of Michigan finance lunch, the

University of Wisconsin-Madison finance seminar, the Wayne State University economics seminar,

the UCLA Brainstorming Conference on International Capital Flows, and the AFA Winter 2003

meetings in Washington, DC, for helpful comments. Kumagai gratefully acknowledges the financial

support from an University Research Grant from Wayne State University.

A series of emerging market stock market crises interspersed the decade of the 1990s:

the Mexican peso collapse in 1994, the East Asian crisis in 1997, and the Russian

default in 1998. One striking feature of these crises is how an initially country-

specific event seemed to rapidly transmit to markets around the globe. These events

have prompted a surge of theoretical and empirical interest in “contagion” and in

the determinants of a country’s vulnerability to crises that originate elsewhere.1

On the empirical side, Karolyi and Stulz (1996) and Connolly and Wang (1998a)

find that macroeconomic announcements and other public information do not affect

co-movements of Japanese and American stock markets. King, Sentana, and Wad-

hani (1994) find that observable economic variables explain only a small fraction of

international stock market co-movements. Forbes (2002) finds some evidence of trade

linkage: Countries that compete with exports from a crisis country and those that

export to the crisis country have significantly lower stock market returns. However,

these trade effects can only partially explain the sizably lower stock market returns

observed during crisis periods. In addition, the correlations between market returns

computed by Longin and Solnik (2001), Connolly and Wang (1998b), and Ang and

Chen (2002) are especially large in market downturns, suggesting asymmetry in the

contagion for market downturns and upturns.

Motivated by the lack of evidence that macroeconomic fundamentals serve as

determinants of contagion, researchers have sought for alternative explanations. In

particular, restrictions imposed on international investors’ asset holdings may lead

to limited arbitrage in the international asset market.2 Kodres and Pritsker (2002)

1Contagion, in this paper, is defined as a significant increase in cross-market link-

ages during periods of high volatility, similar to other work in this literature (for ex-

ample, Forbes and Rigobon (2002)). This is a more restrictive definition than conta-

gion defined as spillover effect from one country to another. For a detailed discussion of

the definition, go to “Definition and Causes of Contagion” at the World Bank Website,

http://www1.worldbank.org/economicpolicy/managing%20volatility/contagion/index.html.2Our paper focuses on contagion among equity markets. Other contagion literature explores

contagion due to currency crises, as in Corsetti, Pesenti, and Roubini (1999), Goldfajn and Valdes

(1997), Chan-Lau and Chen (1998), and Kaminsky and Reinhart (2000). Another branch explores

contagion due to linkages among financial intermediaries, as in Allen and Gale (2000), Lagunoff

and Schreft (2001), and Van Rijckeghen and Weder (2000).

develop a theoretical model of financial contagion through cross-market portfolio re-

balancing. One implication of their model is that market co-movements should be

symmetrical in market upturns and downturns. Kyle and Xiong (2001) suggest that

contagion occurs through the wealth effects of investors. When investors suffer a large

loss in investment in the crisis country, they may have to liquidate their positions

in other countries, thus causing equity prices to depreciate elsewhere. Moreover,

Calvo (1999) and Yuan (2004) find that wealth effects persist even when only a

small fraction of investors are wealth-constrained, as long as they are relatively more

informed. They argue that uninformed rational investors are not able to distinguish

between selling based on liquidity shocks and selling based on fundamental shocks. In

the presence of margin-constrained informed investors, it is possible for contagion to

result from confused uninformed investors. Kyle and Xiong (2001), Calvo (1999), and

Yuan (2004) predict that crises spread to stock markets by their wealth-constrained

investors, and that correlations among markets are greater in market downturns.

Although theoretically convincing, there is little empirical evidence for the investor-

induced contagion hypothesis.

To test whether crises spread across countries by asset holding patterns or, in-

stead, by correlated country fundamentals, we utilize a unique attribute of emerging

stock markets. In emerging market countries, not all publicly listed stocks are eligible

for purchase by foreigners. By differentiating between those stocks that are readily

accessible to foreign investors (investable) and those that are accessible primarily to

local investors (non-investable), we form testable implications that allow us to distin-

guish between the investor-induced contagion hypothesis and the fundamental-based

contagion hypothesis. In order to illustrate these testable implications, we describe

a simple thought experiment.3

Consider an economy with two emerging markets (A and B) that have inde-

pendent economic fundamentals. There are investable and non-investable stocks in

each country. Suppose there are three types of investors. First, there is a group

of investors, called global investors, who can invest in investable stocks in countries

3This simple thought experiment can be thought as a reduced form of models in Kyle and Xiong

(2001), Calvo (1999), and Yuan (2004).

A and B. For concreteness, one might interpret these investors as institutional in-

vestors from developed countries. Second, there is a group of investors from emerging

market country A, called local investors in country A, who only invest in country

A’s stocks (investable and non-investable). Similarly, there is a third group of in-

vestors from emerging market country B, called local investors in country B, who

only invest in country B’s stocks (investable and non-investable). One might think

of these last two groups of investors as local investors in emerging market countries.

We first assume that investors from country A (B) invest locally. We motivate this

assumption by pointing out that theoretically local investors face large transaction

costs when investing in assets in other countries. These costs may be a reduced

form of institutional constraints4 or information asymmetries.5 Moreover, we mo-

tivate this assumption by referring to the extensive empirical evidence established

by the “home bias” literature.6 We further assume that all investors face trading

constraints.7 One form of trading constraints is the borrowing constraint (i.e., in-

vestors have to deposit collateral in margin accounts). In this case, when country

4For example, most governments in emerging market countries impose capital outflow restrictions

on their citizens.5One simple story in the spirit of information asymmetry could be as follows. Suppose there is

a language barrier between the two countries, A and B. Then Country A investors will be reluctant

to hold an asset with information written in country B’s language for fear of holding a worthless

piece of paper. The reverse is true for investors in country B.6French and Poterba (1991) are the first to observe the home bias among equity investors in

developed countries, that is, they invest mostly in domestic markets. Recently this bias is shown

to be much stronger among equity investors in developing countries (Bertaut and Cole (2004)).

Furthermore, for emerging market countries, studies have shown that the net capital flows are

inflows rather than outflows (Lane and Milesi-Ferretti (2001)).7We motivate this assumption on trading constraints using the findings on international mu-

tual fund behavior by Kaminsky, Lyons and Schmukler (2001), Kaminsky, Lyons and Schmukler

(2003), Kaminsky and Reinhart (2002), Broner, Gelos, and Reinhart (2003), and Kallberg, Liu,

and Pasquariello (2003), where investor constraints are shown to affect investment decisions and

stock prices. For example, in their study of net monthly flow of equity funds in Indonesia, Malaysia,

the Philippines, South Korea, Taiwan, and Thailand during the Asian crisis, Kallberg, Liu, and

Pasquariello (2003) find that information spillover and investor constraints such as portfolio rebal-

ancing, rather than common information shocks, represent major channels for the transmission of

crisis across countries. We will relate our findings with this literature in details.

A is struck by a country-specific crisis, foreign investors suffer losses in country A’s

investable stock investment and have to withdraw country B’s investable investment

in order to meet margin calls. This selling of country B’s investable stocks is unre-

lated to country B’s economic fundamentals but, nevertheless, leads to a price drop

in country B’s investable stocks. If the price drop in country B’s investable stocks is

severe enough, it may affect country B investors’ wealth constraints and force them

to liquidate their holdings in non-investable stocks. Hence, the idiosyncratic shock

in country A, if severe enough, may spread to country B’s non-investable stocks.

We draw three testable implications from this thought experiment. First, if conta-

gion is investor-induced (either through portfolio re-balancing or wealth constraints),

stocks that are owned by international investors, i.e., investable stocks, should have

a higher than normal level of co-movement with the crisis country stock market

than non-investable stocks during the turmoil period. Alternatively, if contagion is

fundamental-based, the increase in co-movement between investable stocks with the

crisis country stock market during the turmoil period should not exceed the increase

in co-movement between non-investable stocks with the crisis country stock market.

Second, the investor-induced contagion hypothesis also predicts that within a coun-

try, movements of investable stock returns should lead movements of non-investable

stock returns through local investors’ wealth constraints during the turmoil periods.

Finally, if crises spread through investors’ wealth constraints, correlations should be

asymmetrically higher in market downturns than in market upturns due to wealth

constraints.

To formally test these implications, we estimate and compare the degree to which

investable and non-investable stock returns co-move with the stock market returns

of the crisis country. If the investable stocks and non-investable stocks have no fun-

damental differences in cash flow, the differences in co-movement can be attributed

to differences in investor activity, with investable stocks more susceptible to outside

shocks through the need of portfolio re-balancing or wealth constraints. Furthermore,

because both exchange rate and local stock return shocks affect investor constraints

and may be transmitted through investor holding, we decompose co-movements to

assess the relevant importance of the two.8

Although seemingly straightforward to implement, calculating return correlations

during the crisis period (in volatile times) is not a trivial statistical exercise. Some

misleading results have been reported in the past that ignore the relationship between

correlation and volatility. Stambaugh (1995), Boyer, Gibson, and Loretan (1999),

and Forbes and Rigobon (2002) note that calculating correlations conditional on high

(low) returns, or on high (low) volatility, induces a conditional bias in the correlation

estimate. To correct for the conditional bias, Forbes and Rigobon (2002) propose a

bias correction methodology based on the assumption of independent and identically

distributed (i.i.d) returns. However, Corsetti, Pericoli, and Sbracia (2002) show that

if the returns are not i.i.d. (for example, if variances increase during crisis periods)

the bias correction errs in the direction of not finding contagion. They modify the

Forbes-Rigobon (2002) test to account for changes in volatility. In this paper, we use

two different methodologies to calculate correlations. First, we estimate a regime-

switching model, in which return variances are allowed to change across regimes.

Second, we estimate correlations of tail observations using extreme value theory,

which is robust under any distributional assumption of returns.9

To determine the beginnings and ends of crisis and non-crisis regimes, we use

a dynamic model of stock returns that allows for endogenous structural breaks. In

particular, we estimate a regime-switching model where the unobserved state variable

is assumed to follow a two-state Markov process. Regime-switching models have

been found to successfully exhibit important features of the correlation dynamics of

financial times series (Ang and Chen (2002), Gibson and Boyer (1997)). We find that

(1) an overwhelming portion of emerging markets in our sample have an increased

8We thank an anonymous referee in pointing out that co-movement due to changes in currency

is of a great interest. Index returns measured in U.S. dollars is a sum of two components: index

return measured in local currency and exchange rate return. Therefore, by decomposing the co-

movement, we can measure the change in co-movement due to change in the equity market value

and change in the currency.9Earlier versions of this paper included another measure of correlation, following the bias cor-

rection suggested by Forbes and Rigobon (2002). The results are similar, and therefore omitted for

the clarity of exposition.

correlation in their investable returns with the crisis country during the volatile

regime; and (2) the correlation increase during the crisis time is more pronounced

with investable returns than with non-investable returns. These findings support the

investor-induced contagion hypothesis.

We also consider the time dimension in the transmission of crises between coun-

tries. As a preliminary measure, we test the lead and lag relationship between

investable and non-investable stock index returns for each country. If financial crisis

shocks spread through investors, the more accessible investable stocks should lead

the non-investable stocks during the crisis period. Specifically, we compare the sizes

of the cross-autocorrelations between investable and non-investable stock index re-

turns. Campbell, Lo, and MacKinlay (1997) use this methodology to determine if

large stocks lead small stocks. Our analysis shows that investable stock returns lead

non-investable stock returns during the crisis period. This suggests that crises are

absorbed into investable stocks first, thus providing additional support that interna-

tional investors are responsible for spreading the crises.

After finding empirical evidence supporting the investor-induced contagion hy-

pothesis, we conduct further tests to determine how crises spread through investors.

We test whether market co-movements are symmetric in extreme market upturns

and downturns. If crises are transmitted due to investor constraints, such as investor

wealth constraints, market co-movements should be greater in market downturns

than upturns. Instead, if crises spread due to investor constraints such as the need

for portfolio re-balancing, co-movements should be symmetric. A test of symmetry

at extreme tails helps to distinguish between these two hypotheses. To do so, we

estimate exceedance correlation, implemented by Ledford and Tawn (1997) and Lon-

gin and Solnik (2001). This methodology is based upon the non-degenerate limiting

distribution of the extreme tails, and is free of any assumptions on the underlying

joint distribution. In estimating exceedance correlation, we find that correlations in

negative tails are higher than correlations in positive tails, and that this effect is

more pronounced for investable returns than non-investable returns. This indicates

that some constraint, which causes investors to respond more to negative external

shocks than to positive external shocks, may be responsible for transmitting crises.

Investor wealth constraints could be one such constraint. Alternatively, we estimate

the correlations of index returns on stocks and government bonds during the crisis

and stable periods to shed light on the mechanism of the crisis transmission chan-

nel.10 This is to determine whether we observe portfolio re-balancing activities such

as “flight to quality” during the crisis period, where the correlation between stock

and government bond index returns decreases (and may become negative), or we

observe that investor are pulling out of the asset markets all across the board due

to wealth constraints, where the correlation between stock and bond index returns

increases.

These tests of the investor-induced contagion hypothesis against the correlated

country fundamental hypothesis hinge on two crucial assumptions. The first assump-

tion is on the feasibility of foreign investors to move the market.11 Empirically, it

has been shown that foreign investors are an important class of shareholders given

their observed information superiority and its impact on prices.12 Furthermore, in

emerging markets. foreign investors are extremely important due to their substan-

tial asset holdings.13 The second assumption is that investable firms share similar

cash-flow fundamentals with non-investable firms, and different only in investor own-

ership. Otherwise it is difficult to attribute the difference in co-movements strictly

to investor ownership. We establish the validity of these assumptions prior to our

analysis of co-movements.

Our paper is closely related to the literature on limited arbitrage (Shleifer and

10We thank an anonymous referee for this suggestion.11We thank an associate editor for pointing this out.12Clark and Berko (1996) find that unexpected foreign capital inflows of 1% of the market capi-

talization drive prices up by 13% in Mexico during the late 1980s through the crisis in 1993. Using

international portfolio flow data to 44 counties (both emerging and developed countries) from 1994

to 1998, Froot, O’Connell, and Seasholes (2001) have shown that foreign equity inflows have posi-

tive forecasting powers for future equity returns, statistically significant in emerging markets. They

interpret their evidence to be consistent with the view that foreign investors are better informed

than local investors. Seasholes (2004) and Froot and Ramadorai (2001) provide evidence support-

ive of this assessment of the information content for foreign equity flows. This finding is plausible

considering most foreign inflow comes from institutional investors.13Choe, Kho, and Stulz (2003) find for the Korean market as a whole, foreigner investors own

about 14.7% in 1997.

Vishny 1997). The literature on limited arbitrage emphasizes that market frictions

or noise trader risks break the link between asset price movements and economic fun-

damentals. This is in contrast to the traditional asset pricing theory where the view

is that co-movement in prices reflects co-movement in fundamentals in an economy

with rational investors. For example, in a recent paper, Barberis, Shleifer, and Wur-

gler (2003) find evidence for investor-induced co-movements by showing that some

co-movements of stock returns can be attributed to asset re-classification. Our paper

is complementary to this line of research since it provides cross-country evidence for

non-fundamental-based, investor-induced theory of co-movements.

Our paper also is related to the literature on measuring contagion across mar-

kets. In the literature on measuring contagion across markets, contagion is defined

as correlation between markets in excess of what would be implied by economic

fundamentals. Different approaches have been adopted to model the linkage be-

tween economic fundamentals and asset market co-movements. For example, Bae,

Karolyi, and Stulz (2003) estimate the joint occurrences of large absolute returns us-

ing multinomial logistic regression and define contagion within regions as the fraction

of exceedance events that is not explained by fundamentals (exchange rates, interest

rates, and market volatility). Instead of studying correlations of returns, they focus

on analyzing the joint occurrences of extreme events. Another approach is to use

a factor model. For example, Bekaert, Harvey, and Ng (2003) apply a two-factor

model with time-varying betas and measure contagion as correlation among model

residuals. By defining the contagion as excess correlation over and above what one

would expect, they measure the extent to which the crisis spreads due to reasons

other than changes in economic fundamentals. Tang (2001) uses a similar approach

but restricts the factor model to a world CAPM. In our analysis, by demonstrating

that investable and non-investable stocks share similar economic fundamentals, we

can measure the excess correlation over and above beyond what one would expect

from economic fundamentals as the difference-in-difference of co-movement with the

crisis country for investable and non-investable stock returns. Hence, we circumvent

the need of specifying and estimating a factor model.14

14The theoretical literature on international asset pricing under mild segmentation (e.g., unequal

Our paper is also closely related to the studies on equity fund portfolio holdings

by Kaminsky, Lyons and Schmukler (2001), Kaminsky, Lyons and Schmukler (2003),

Kaminsky and Reinhart (2002), and Broner, Gelos, and Reinhart (2003). Similar to

our findings, these studies find that investor asset holdings is a mechanism through

which crisis shocks propagate. Kaminsky, Lyons and Schmukler (2001) demonstrate

that the Mexican, Asian, and Russian crises triggered withdrawals by mutual funds

from other countries. In their study of mutual funds specializing in Latin American

countries, Kaminsky, Lyons and Schmukler (2003) also find mutual funds withdrew

money from other Latin American countries, following the initial shock in Mexico

during the Mexican crisis of 1994. Moreover, they find this contributed to the conta-

gion in the region. Kaminsky and Reinhart (2002) mention some anecdotal evidence

which shows that countries with negligible representations in the portfolios of mutual

funds were hardly affected by regional crises (for example, Colombia and Venezuela

during the Mexican crisis). Broner, Gelos, and Reinhart (2003) find that when some

funds are over-exposed to the crisis source, the extent to which other countries share

these over-exposed mutual funds, i.e. interdependence through mutual funds, pre-

dicts poor stock market performances in these countries, indicating crises spread

through mutual fund asset holdings. An analogue of our finding of asymmetric co-

movements between investable and the crisis country in the country fund research is

that the interdependence though mutual fund asset holdings demonstrated in these

studies would not have a price impact during the stable period, a hypothesis that

access to certain asset markets) predicts that restricted securities command a “super risk premium”

over unrestricted securities (Errunza and Losq, (1985) and (1989)), where the “super risk premium”

is related to three elements: (1) the risk aversion coefficients of two types of investors (those who

have restricted and those who have unrestricted access to the market), (2) the market capitalization

of all ineligible stocks (available only to those with unrestricted access to the market), and (3) the

return covariance of the ineligible stock with the portfolio of all ineligible stocks. This risk premium

does not vary with returns of eligible stocks. This theoretical finding lends support to the formation

of our testable hypotheses, since it predicts the “super risk premium” is the same over the volatile

and stable regimes. By examining the difference in pricing between investable (eligible) and non-

investable (ineligible) stocks (see Test 2A in Section II), we test two views of how crises spread:

fundamental-based versus investor-induced. We thank one of the participants at the AFA 2003

winter meetings in Washington, DC, for bringing this to our attention.

would be interesting to test.

The remainder of the paper is organized as follows. Section I presents the hy-

potheses we consider and the methodologies we use to construct our tests. Section

II describes the data and the summary statistics of the index returns (separately for

investable and non-investable stock index returns). This section also compares the

industry and market microstructure characteristics of investable and non-investable

stocks. Section III discusses tests results and the hypotheses laid out in Section I.

Section IV concludes.

I. Methodology

To gauge the cross-market transmission mechanism of financial crises, we employ

two different methodologies: regime-switching model and exceedance correlation.

These methodologies are used to calculate the co-movements of index returns and to

test our hypotheses on the transmission mechanism of crises.

A. Test Hypotheses and Statistics

We first describe our hypotheses on the transmission mechanism of crises as a

series of tests and spell out the corresponding test statistics.

Our first test is a test for contagion. We estimate co-movement measures (ρ)

between the crisis country’s total market index returns with another country’s total

market index returns (indexed by T ), investable index returns (indexed by I), and

non-investable index returns (indexed by N) during the stable and crisis periods. We

compare correlation differences between stable and crisis periods. If contagion exists,

no matter what the transmission mechanism is (investor-induced or fundamental-

based), then the investable stock index returns should have a greater co-movement

with the crisis country market index returns during the turmoil period. Therefore,

the relevant test statistic for existence is the difference in correlations across peri-

ods between the crisis country’s total market index returns and another country’s

investable index returns (indexed by I), both in U.S. dollars. We also perform this

test using index returns denominated in local currencies. The co-movement of index

returns in local currencies represent the portion of co-movement that excludes effects

from currency fluctuation.

Test 1 (Existence of stock market contagion) If stock market contagion exists, co-

movement is higher during the turmoil period for investable index returns (I).

H0 : ρI, turmoil − ρI, stable ≤ 0

H1 : ρI, turmoil − ρI, stable > 0

For the implementation of this test and those below, we use one-sided tests where

the alternatives support our hypothesis. To summarize the results across countries,

we use the nonparametric sign test to compare the number of positive and negative

differences to determine which sign is more likely overall.

Our second set of tests is a test of how crises spread: through changes in coun-

try fundamentals or through investor constraints (both portfolio re-balancing and

wealth constraints). For this test, we first compare how the crisis affects the index

of investable stocks to how the crisis affects the index of non-investable stocks. By

the argument from the simple thought experiment described earlier, if crises spread

through correlated fundamentals, the effect on investable returns should be the same

as the effect on non-investable returns. In other words, investable co-movements

should not be significantly higher than non-investable co-movements. A finding of

significantly higher co-movements for investable returns during the turmoil period

will reject the fundamental-based hypothesis and indicate that contagion is investor-

induced. Similar to the first test, we also perform this same test with index returns

in local currencies.

Test 2 A (Fundamentals vs. Investor-Induced Hypothesis) If stock market crises

spread due to investor constraints rather than fundamentals, the correlation in-

crease during turmoil periods is more pronounced for investable returns than for

non-investable returns.

T2− A :

H0 : (ρI, turmoil − ρI, stable)− (ρN, turmoil − ρN, stable) ≤ 0

H1 : (ρI, turmoil − ρI, stable)− (ρN, turmoil − ρN, stable) > 0

Furthermore, the investor-induced contagion hypothesis also predicts that within

a country, movements of investable stock returns should lead movements of non-

investable stock returns through local investors’ constraints (either due to portfolio

re-balancing needs or wealth constraints) during market downturns. In other words,

if external shocks spread through investors, then investable stocks should respond

first and lead the non-investable stocks. In order to test the lead and lag relation-

ship between investable and non-investable returns for each country, we compare

the cross-autocorrelation between investable and non-investable returns as in Camp-

bell, Lo, and MacKinlay (1997) in their comparison between large and small size

stocks. Higher correlation between non-investable returns and lagged investable re-

turns (than correlation between investable returns and lagged non-investable returns)

suggests that investable returns lead non-investable returns.

Test 2 B (Fundamentals vs. Investor-Induced: Lead-Lag) If stock market crises

spread due to investor constraints rather than fundamentals, the effect first appears

in the investable stock index, followed by the non-investable stock index.

T2 − B :

H0 : ρIt−1,Nt − ρIt,Nt−1 ≤ 0

H1 : ρIt−1,Nt − ρIt,Nt−1 > 0where It denotes the investable stock

index return at time t, and Nt denotes the non-investable stock index return at time

Our third test is a further test of how crises spread: due to either portfolio re-

balancing or wealth constraints. We first test to see whether market co-movements

are symmetric in extreme market upturns and downturns. If crises are transmit-

ted due to investors’ portfolio re-balancing needs, market co-movements should be

symmetric in extreme market upturns and downturns. On the other hand, if crises

are transmitted due to investor wealth constraints, market co-movements should be

greater in extreme market downturns than market upturns. A test of whether mar-

ket co-movements are symmetric or not at extreme tails allows us to distinguish

these two hypotheses. More specifically, we estimate and compare co-movements at

two extreme tails: jointly positive, denoted by (+), and jointly negative, denoted by

(−). If the cross-market linkage is through investor stock ownership, the relevant

test statistic is the co-movement difference for investable index returns. Despite the

similarity of this set to Test 1, there is one important difference: Instead of com-

paring co-movement between the turmoil period and the stable period, we compare

correlations at two extreme tails: extreme market upturns (+) and downturns (−).

Test 3 A (Portfolio Re-Balancing vs. Wealth Constraints) If stock market crises

are spread by constraints such as investor wealth constraints rather than the need for

portfolio re-balancing, co-movement is higher during extreme market downturns than

upturns, especially for investable stock index returns.

T3− A :

H0 : ρI, (−) − ρI, (+) ≤ 0

H1 : ρI, (−) − ρI, (+) > 0

Alternatively, we test this by analyzing the co-movement between the stock and

the government bond markets during crisis and non-crisis periods. If there are in-

creased portfolio re-balancing activities during the crisis period, the correlation be-

tween risky (i.e. stock) and safe (i.e. government bond) should be smaller (and

may even be negative) than in the stable period. This is due to “flight to quality”:

investors re-allocate investment to safer assets and cause the prices of safe assets

such as government bonds to increase. On the other hand, if during crisis peri-

ods, investors are wealth constrained, we would observe depressed prices in the risky

and the corresponding safe asset in each country, i.e., increased correlations between

safe and risky assets during crisis periods. This is because foreign investors have to

liquidate safe assets to cover their loss in risky assets. We estimate co-movement

measures (ρ) between the country’s stock market index returns (either investable

or non-investable) with the country’s government bond market index returns (in-

dexed by B) during the stable and crisis periods. We compare correlation differences

between stable and crisis periods.

Test 3 B (Portfolio Re-Balancing vs. Wealth Constraints: Bonds) If stock market

crises are spread by constraints such as investor wealth constraints rather than the

need for portfolio re-balancing, co-movement between equity market and bond market

is higher during crisis.

T3−B :

H0 : ρB, turmoil − ρB, stable > 0

H1 : ρB, turmoil − ρB, stable ≤ 0

The rest of this section describes in detail the methodologies we employ to esti-

mate the co-movement (ρ).

B. A Dynamic Model of Correlation: Regime-Switching Model

Our first methodology of estimating the correlation between asset returns is to

specify a dynamic model of stock returns that captures the potential differences in

the co-movements of asset returns. We estimate a parametric model of the joint

data-generating process that allows for endogenous structural breaks, but also nests

a simple model with fixed moments. We then determine if the joint distribution

changes between regimes by testing the differences in estimated parameters.

The model is the regime-switching model where the unobserved state variable

is assumed to follow a Markov process. Regime-switching models have been found

to successfully exhibit important features of the correlation dynamics of financial

time series (Ang and Chen (2002), Gibson and Boyer (1997)). We model the data

generating process of four time-series: the total returns of the crisis source coun-

try, investable and non-investable returns of another country, and the world index

returns.

The unobserved state variable in our model, st, is allowed to take on one of two

values, st ∈ 1, 2. Conditional on being in state s, returns are assumed normally

distributed,

(rt|st = s) ∼ N (αs,Σs) (1)

where rt is a 4× 1 vector of index returns realized at time t and t ranges from 0 to

T , αs is a vector of expected returns, and Σs is a 4 × 4 covariance matrix. Let ψt

incorporate all information at time t. The unobserved state variable st defines the

regime at time t and is assumed to follow a two-state Markov process:

P (st = b|ψt−1) = P (st = b|st−1 = a) = pab (2)

where a, b = 1, 2 thus resulting in a 2 × 2 transition matrix. For any two indices

out of four (crisis, world, investable, and non-investable), let σji,s be the covariance

between index i and index j given st = s (an element in Σs):

σij,s = Cov(rit, rjt|st = s) (3)

where rit is the return for index i. In addition, to clear up ambiguity in the definition

of states, let st = 1 be the regime in which estimated volatility for the crisis index

is higher. The correlation estimate ρij,s between rit and rjt given state st = s is as

follows:

ρij,s =σij,s√

σii,sσjj,s

Once we have estimates of Σs for each state, the test for contagion is a test for a

significant increase in the correlation in the crisis period.

B.1. Two-Step Estimation

The parameters of the regime-switching model are estimated using a two-step

estimation process.15 In the first step, we define the two regimes (volatile and stable)

by estimating a regime-switching model using two index returns: the crisis country

index return and the world index return.16 The crisis country index helps associate

the regimes with a particular financial crisis; while the world index allows for a clearer

distinction between the regimes by helping to define the high volatility regime as

a regime of high global volatility rather than country-specific volatility. Maximum

likelihood estimates of the model parameters, including the elements of the transition

matrix, are obtained using the approach discussed in Hamilton (1989), and robust

standard errors are generated using the method of White (1980). This involves

estimating a total of 13 parameters: two vectors of means (each with two elements),

two covariance matrices (each with three unique elements), the two diagonal elements

of the transition matrix, and the initial state probability.

Once we obtain the parameters that define the two regimes, we estimate the

probability of being in the high volatility (correlation) regimes using an algorithm

developed by Kim (1994) and discussed in Hamilton (1989). This algorithm provides

an estimate of P (st = a|ψT ) under the model assumption described above, where ψT

is all information at last period, T. That is, we are able to estimate the probability

15We have revised the estimation method to maintain the same endogenously specified regimes

across all countries.16Adding U.S. index return to define the regimes yields similar results. We thank our anonymous

referee for this suggestion.

of being in a state a given all information of the entire sample. Hamilton (1989)

estimates smoothed inferences using data on GNP growth to determine time periods

of economic recession. Hence, the regime switching model estimated in this paper

not only provides us with a basis to test the existence of two separate regimes,

but also allows us to objectively distinguish the time periods that are more likely

representative of a crisis (or stable) regime.17

In the second step, we estimate parameters of a regime-switching model of the

five index returns: the crisis country index return, the investable and non-investable

index returns of another country, the world index return, and the U.S. index return

conditioned on the estimated first-step parameters18 The second step parameters are

estimated using conditional maximum likelihood. The asymptotic variance of the

second-step parameter (Murphy and Topel, (1985)) is:

Σ2 = V2 + V2 [CV1C′ −RV1C

′ − CV1R′] V ′

and the asymptotic covariance between the first-step parameters (θ1) and second-step

parameters (θ2) is:

Σ1,2 = V1RV2 − V1CV2 (6)

where V1 is the asymptotic variance of the first-step parameters based on the first

likelihood function, ln(L1); V2 is the asymptotic variance of the second-step parame-

ters (θ2) based on the second likelihood function conditioned on first-step parameters

ln(L2|θ1), and C and R are expected cross derivatives. For details, see Appendix A.

In addition, it is of particular interest to determine the extent to which the

dollar-denominated correlations among the international indices studied in this paper

are driven by correlations in exchange rates. To control for currency fluctuations

(in other words, increase or decrease in co-movement due to changes in exchange

rates), we use local currency denominated returns in our estimation. We re-estimate

the second step, a regime-switching model of five index returns: the crisis country

17In the earlier versions of this paper, we estimated a separate four-variable regime-switching

model for each country, with the crisis country, as a one-step process. The average regime proba-

bilities obtained from that approach are very similar to the regime probabilities of this approach.18The second stage estimation using four index returns, excluding the U.S. index return, yields

similar results.

index return, the investable and non-investable index returns of another country, the

world index return, and the U.S. index return conditioned on the estimated first-step

parameters. The difference in this model is that the crisis country, investable and

non-investable index returns are all denominated in their local currencies.

We also observe the effect the crisis have on the government bond market. With

portfolio re-balancing, investors would re-allocate between risky assets (investable

and non-investable stocks) and safer asset (government bonds). Here, we re-estimate

the second step to be a regime-switching model of three index returns: the investable

and non-investable returns and the government bond index return of that country.

If investors tend to re-balance their portfolios during crisis, by shifting from risky

assets to safer ones, prices of bonds would increase and bond index return would

increase. If investors tend to be wealth-constrained during crisis, so that they shift

from safer asset to cover their positions in risky assets, prices of bonds would decrease

and bond index return would decrease. Therefore, portfolio re-balancing would be a

decrease in positive correlation or more negative correlation, while wealth constraint

would be an increase in positive correlation or less negative correlation.

B.2. Currency Decomposition

In our analysis, we estimate co-movement with index returns denominated in

U.S. dollars and separately in local currencies. We show in this section that co-

movement of index returns in local currencies represent the portion of co-movement

that excludes effects from currency fluctuation.

For any two countries, the log US dollar return can be written as

rA,t+1 = r∗A,t+1 −∆qA,t+1 (7)

rB,t+1 = r∗B,t+1 −∆qB,t+1

where rt+1 is the log US dollar return, r∗t+1 is the log return in terms of foreign

currency, qt+1 is the log exchange value of US dollars in terms of the foreign currency,

and ∆ denotes a first difference. The covariance between rA,t+1 and rB,t+1 is therefore

Cov[rA, rB] = Cov[r∗A, r∗B]− Cov[r∗A, qB]− Cov[r∗B, qA] + Cov[qA, qB].

Therefore, co-movements in terms of correlations of returns denominated in dollars

can be decomposed into the following four components:

ρ[rA, rB] =Cov[r∗A, r∗B]

σAσB

− Cov[r∗A, qB]

σAσB

− Cov[r∗B, qA]

σAσB

+Cov[qA, qB]

σAσB

= ρ[r∗A, r∗B]σ∗Aσ∗BσAσB︸︷︷︸

Local Stock Market Shock

(−ρ[r∗A, qB]

σ∗AσqA

σAσB

− ρ[r∗B, qA]σ∗BσqA

σAσB

+ ρ[qA, qB]σAσqB

σAσB

︸︷︷︸Exchange Rate Shock

where the first term represents the effect on the co-movement due to local stock

market shock and the remaining terms represent the effect on the co-movement due

to exchange rate shock. The objective has been to estimate the first term:

ρ[r∗A, r∗B]σ∗Aσ∗BσAσB

since this is the only term in the above correlation equation that is not influenced

by any exchange rate. By decomposing the co-movements, we assess the relative

importance of local stock market shocks and exchange rate shocks in influencing the

co-movements.

It is of particular interest to determine the extent to which the dollar-denominated

correlations among the international indices studied in this paper are driven by

correlations in exchange rates. As such, we re-estimate our models (second step)

using index returns defined in terms of the local currency.19

C. Exceedance Correlation

An alternative estimate of correlation between returns is obtained from the ex-

treme tails of the joint distribution. We estimate the extreme tails of the joint

distribution using extreme value distribution methodology as in Longin and Solnik

(2001). This method is attractive in that we do not need to specify a joint distri-

bution function and do not need to define a crisis period. Instead, we rely strictly

on the tail distribution, which is a non-degenerate limit distribution that converges

to a generalized Pareto distribution, regardless of the underlying joint distribution.

19Work is in progress.

This enables us to estimate the correlation without enforcing the underlying joint

distribution of returns to be normal. The result is also robust for non-i.i.d. returns.

In addition, this methodology allows us to distinguish the differences in correlation

during market upturns and downturns within periods of high volatility.

We estimate this limiting distribution to characterize the exceedance correlation

between returns of the crisis country and another country for the two tail ends of the

joint distribution function: positive tail (for jointly positive returns or bull market)

and negative tail (for jointly negative returns or bear market). If the joint distri-

bution is indeed a bivariate normal, the exceedance correlation is asymptotically

independent, which means that for high values of threshold (away from the mean),

the exceedance correlation will tend to zero, symmetrically in both tails. In their

study of correlations between equity markets, Longin and Solnik (2001) find decreas-

ing exceedance correlation in the bull market (positive tail) and increasing or flat

exceedance correlation in the bear market (negative tail), thus providing evidence

for asymmetry. Here, in addition to estimating the exceedance correlations of inter-

national equity markets at both tails, we estimate separate correlation measures for

the investable and non-investable portions of the markets, to see if the asymmetry

also appears at the disaggregate level.

Following Ledford and Tawn (1997) and Tawn (1988), the joint tail distribution

is as follows:

F ∗(x1, x2 . . . xq) = exp

( −1

logFm1 (x1)

logFm2 (x2)

, . . .−1

logFmq (xq)

where D() is some selected dependence function and Fmj () is the marginal distribu-

tion of the jth variable:

Fm(xj) = (1− λj) + λj

ξj(xj − θj)

)−1/ξj

with λj as the tail probability. The marginal distribution combines two parts: prob-

ability (1 − λj) that the observation is not in the tail and probability λj that the

observation is in the tail and represented by the limiting tail distribution. In the mul-

tivariate case, although the limiting marginal distributions are known to be Pareto,

the dependence function is not known and has to be chosen. We use the most widely

used dependence function, which is the equal-weighted (symmetric) logistic function:

D(z1, z2 . . . zq) = (z−1/α1 + z

−1/α2 + · · ·+ z−1/α

q )α (11)

where the dependence parameter is α, 0 < α ≤ 1. With only one parameter to

estimate, this dependence function is simple and tractable. Furthermore, with this

dependence function for q = 2, the measure of exceedance correlation is 1 − α2,

which is also very simple to compute. We proceed with this dependence function to

estimate the correlation between markets. With the symmetric logistic dependence

function, the bivariate tail distribution has seven parameters: two tail probabilities

(λ), two tail indices (ξ), and two dispersion parameters (σ) and parameters from the

dependence function, which in this case is one (α). See Appendix B for details.

We estimate parameters of the bivariate extreme value distribution by maximum

likelihood, assuming independent observations. The likelihood function for each ob-

servation is one of the four functions, determined by whether the pair of observations

each satisfy the threshold condition, x > θU for upper tail exceedance (and x < θL

for lower tail exceedance). The details on the construction of the likelihood function

can be found in Prescott and Walden (1980), Ledford and Tawn (1997), and Lon-

gin and Solnik (2001) and it is straightforward to implement. We use asymptotic

properties of the maximum likelihood estimator to obtain our parameter covariance

matrix, and perform inference based on asymptotic normality.

II. Data

This section offers a discussion of the data. Our analysis requires two types

of stock index returns for each country: returns for investable stocks and for non-

investable stocks. In developed financial markets, such as the U.S., practically all

stocks are accessible to both foreign and local investors, so that the entire market

is investable. However, in many emerging markets, some stocks are only accessible

to local investors. Section A discusses the size of the market and the corresponding

foreign ownership. Section B explains the construction of the index returns for

these two categories. Section C explains the bond data. Section D compares the

characteristics of the firms that are designated investable and non-investable. Section

E describes the overall sample correlation between investable and non-investable

stock index returns for each country.

We use two sources of financial market data for this paper: International Finance

Corporation’s (IFC) Emerging Markets Data Base (EMDB) and Datastream. Table

1 reports the size of the markets for the 31 emerging market countries and the

16 developed countries, measured in total market capitalization in millions of U.S.

dollars at year-end, 1997.

A. Foreign Ownership

Table 1 also reports the size of U.S. ownership both in millions of U.S. dollars,

from U.S. Treasury.20 By computing the U.S. ownership as a percent of total market

capitalization, we find that although only a small fraction of U.S. investment capital

is invested in a foreign stock market, this small fraction account for a large fraction

of market capitalization in this foreign market. In any market, especially in emerging

markets, the fraction of market held by foreign investors is substantial. To get an

idea of the magnitude, in 1996, if U.S. equity investors invest 0.9% of their equity

market capitalization in Korea, they would take over the Korean stock market; if

they put this 0.9% in Thailand, they would be more than taking over the market

there; (Korea equity market capitalization is only 0.9% of the U.S. equity market

capitalization, Thailand is 0.7%, and Argentina is 0.9%.)

B. Investable and Non-Investable Stock Index Returns

In the EMDB, IFC provides two stock market index series for each emerging

market: IFC Global (IFCG), representing the total market,21 and IFC Investable

(IFCI), consisting of firms that are designated to be investable. Both IFC index

series are dividend-inclusive and available in U.S. dollars or in local currency. The

IFC selects stocks for inclusion in its IFCG index by reviewing the trading activities

20U.S. Treasury Department, Table 1 of http://www.ustreas.gov/tic/flts.html21The totals reported for the emerging market countries are the totals in IFC Global index and

are not totals of all stocks.

and targets market coverage of 60 percent to 75 percent in market capitalization.22

The stock returns included in the IFCI index consist of a subset of firms that are in

IFCG, and selection for this subset is a three-step process. First, the IFC determines

which securities may be legally held by foreigners.23 Next, the IFC applies two

further screening criteria for practicality of investment. The first criterion screens

for a minimum investable market capitalization of $50 million or more over the 12

months prior to a stock’s addition to an IFCI index. The second criterion screens

firms for liquidity. A stock must trade at least $20 million over the prior year for

inclusion in an IFCI index, and also must have traded on at least half the local

exchange’s trading days. Thus, the IFCI index is designed to measure the composite

stock market index of what foreign investors might receive from investing in emerging

market securities that are legally and practically available to them (IFC 1999).

Since IFC does not provide an explicit series for non-investable stocks (IFCN),

we construct the index return using the global (IFCG) and the investable (IFCI)

series of index return (R) and market capitalization (M) as follows:

RIFCN =MIFCG ×RIFCG −MIFCI ×RIFCI

(MIFCG −MIFCI)(12)

The stocks in the global index that are not in the investable stock index are desig-

nated to be non-investable. We henceforth refer to the two stock index returns as

investable return and non-investable return.24

22The IFC trading criteria are as follows: Any share selected must be among the most actively

traded shares in terms of value traded; must have traded frequently during the annual review

period (i.e., one large block trade might skew the value traded statistics); and must have reasonable

prospects for a continued trading presence in the stock exchange without imminent danger of being

suspended or de-listed. Stocks are selected in the order of the trading criteria until the market

capitalization coverage target of 60 percent to 75 percent of total market capitalization is met.23The first legal test of a stock’s investability is to determine whether the market is open to

foreign institutions. IFC determines the extent to which foreign institutions can (1) buy and sell

shares on local exchanges, and (2) repatriate capital, capital gains, and dividend income without

undue constraint. The second legal test is to determine whether there are any corporate bylaw,

corporate charter, or industry limitations on foreign ownership of the stock.24For precision, the non-investable index returns are calculated for observations for which the

non-investable portion of the total market capitalization is greater than 0.01%. When the fraction

For the developed countries, we treat all stocks as investable. We use the U.S.

dollar-denominated country index returns from Datastream (Datastream Total Mar-

ket Index) as the investable index for these countries. The returns are calculated as

the first difference of the natural logarithm of the prices. Friday prices are used for

the weekly returns.25 We also use the Datastream World Market Index in weekly

frequency as a measure for the overall world market in our estimation of the regime

switching model.

We have 730 observations of weekly (Friday-to-Friday U.S. dollar returns) data

for the period January 1989 to December 2002, for both EMDB and Datastream

data, with the summary statistics in Table 2A.

The percent of market capitalization attributed to non-investable firms varies

greatly across countries. The average, weighted by total market capitalization, is 45%

in 1994 and declines to 20% in 1999 as shown in Table 3. From 1994 to 1999, some of

the countries with consistently high non-investable percentages are in Asia: China,

Sri Lanka, India, Taiwan, and Thailand. Outside Asia, other countries with high non-

investable percentages are Czech Republic, Jordan, and Zimbabwe. The emerging

market countries with low non-investable percentages are Argentina, Mexico, Peru,

Malaysia, Greece, South Africa, and Turkey.

We also use the stock level data from EMDB, in monthly frequency, from 1989

to 2002. IFC assigns a value ranging from 0 to 1 to each stock, representing how

accessible a firm is to foreign investors (“degree of open factor”). When this degree

of open factor is 1, the stock of the firm is completely investable (accessible to

foreigners); at 0, the stock of the firm is completely non-investable (inaccessible) to

foreigners. At the end of 1994, we have data on 1505 stocks across all emerging

market countries, with 480 completely investable, 534 completely non-investable and

491 in between. At the end of 1997, there are 2005 stocks, with 535 completely

investable, 581 completely non-investable and 889 in between.

of non-investable is extremely small, the precisions of the global and investable returns are not

sufficient for accuracy. This criterion was sufficient in deleting most of the very large returns for

weekly frequency data.25If Friday observations are not available due to closings or insufficient data, we use Thursday

observations.

Note that the degree of investor accessibility may not be a good proxy for the

degree of actual foreign ownership - a stock that is designated as investable may or

may not be owned by foreign investors. If the measure of investability overstates for-

eign ownership, there will be a bias against finding any systematic difference between

investable and non-investable stocks. In other words, our estimated differences in

co-movements are conservative measures of the more pronounced actual differences

that would be found if exact investor ownership were known.

C. Bond Index Returns

Ideally to test for “flight to quality,” we need returns on the safest asset in a

country. In most cases, the safest asset in a country is the government bonds with the

shortest maturity. An example is the 3-mont T-bill in the United States. However,

bond index data for emerging market countries are notoriously difficult to obtain.

For 21 of our emerging market countries, we are able to obtain the weekly returns

for the bond index, EMBI (Emerging Market Bond Index), from JPMorgan-Chase.

The duration for most EMBI index, however, is about five years. To maintain the

consistency of our analysis, we also choose to use 5-year bond index returns for 13

developed countries. The source for developed country bond data is Datastream.

Summary statistics for the weekly (Friday-to-Friday returns) data for the period

January 1994 to December 2002 is in Table 2B.

D. Characteristics of Investable and Non-Investable Firms

In order to compare the impact of crises on the emerging market’s investable index

and non-investable index, it is necessary to understand the differences in the sets of

firms that comprise the indices. Our test of the investor-induced contagion hypoth-

esis against the correlated country fundamental hypothesis requires the assumption

that investable firms share similar cash-flow fundamentals with non-investable firms.

That is, investable and non-investable firms differ primarily in investor holdings. To

explore structural differences that may affect our results, we examine the character-

istics of investable and non-investable firms. In these comparisons, investable firms

are those with a degree of open factor equal to one, while non-investable firms are

those with a degree of open factor equal to zero.26

One concern in our comparison is that the difference in co-movement may be due

to the fact that non-investable firms are in different industries from investable firms.

In particular, tradable sectors are more vulnerable to exogenous shocks originated

elsewhere in the world. Table 4A compares the distribution of investable firms with

non-investable firms among industries during the sample period (1989-2002).27 It

shows that investable firms feature more in the Finance, Insurance, and Real Estate

sector (a predominantly non-tradable sector), while the non-investable firms feature

more in the Manufacturing sectors (a predominantly tradable sector). If investable

firms are more concentrated in the tradable sectors (such as in the manufacturing

sector), the higher correlation can be caused by external shocks to tradable firms’

balance sheets (since these firms have a higher β with global portfolio). This is

disputed by the data because non-investable firms are more concentrated in the

manufacturing sector.

As an additional check, we consider the change in distribution of investable and

non-investable firms over time. In Figure 1, we compare the distribution of investable

and non-investable firms at beginning of years 1994, 1996 and 1997, using firm-level

data for December of the previous year (e.g. December 1993 data for 1994).28 We

find that the distribution are similar across years. For example, if we compare the

manufacturing sector, the percentage of firms in that sector is 39.0% in 1994, 39.1%

in 1997, and 39.5% in 1998 for investable firms, and it is 50.4% in 1994, 49.0% in 1997,

and 49.5% in 1998 for investable firms. In Table 4B, we compare the distribution

of firms that switch accessibility during the sample period (1989-2002), either from

accessible to inaccessible or from inaccessible to accessible. These two distributions

are both very similar to the overall distribution in Table 4A, implying that the

changes in the accessibility occurs similarly across the sectors.

26For the comparisons in this section, we do not include firms with the degree open factor between

zero and one.27We get similar distributions using any subset of data, for example 1994-2001, as was in our

previous version of this paper.28Other years are not shown but the figures are similar for all of the years.

Overall, there is no significant difference in the distribution of investable firms

with non-investable firms among industries. Bae, Chan, and Ng (2002) also find no

pronounced variation in the investability across industries. Therefore, any differences

we find in correlations cannot be attributed to sector differences in investable and

non-investable firms.

To determine if there are any other differences between the two sets of firms,

investable vs. non-investable, we compare two market microstructure characteristics

of the stocks: size (market capitalization as a fraction of total market capitalization)

and liquidity (ratio of value-traded to market capitalization). Table 5 reports the

one-sided test of the means to see if the average size or liquidity is significantly higher

for completely investable firms than completely non-investable firms. We conduct

this test using January data for 1994 (the year of Mexican crisis), 1997 (the year of

Asian crisis), and 1998 (the year of Russian crisis) respectively. In 1994, out of 16

countries for which comparison was possible, we find that average liquidity for in-

vestable firms is significantly greater in four countries (Mexico, Venezuela, Pakistan,

and South Africa) and average size is significantly greater in seven countries (Colom-

bia, Peru, Pakistan, Sri Lanka, Czech Republic, Greece, and Hungary). In 1997, out

of 16 countries, average liquidity for investable firms is significantly greater in four

countries (Mexico, Venezuela, Pakistan, and Czech Republic) and average size is sig-

nificantly greater in six countries (Colombia, Peru, Venezuela, Pakistan, Sri Lanka,

and Hungary). In 1998, out of 19 countries, average liquidity for investable firms is

significantly greater in three countries (Colombia, Mexico, and Czech Republic) and

average size is significantly greater in seven countries (Mexico, Venezuela, Pakistan,

Sri Lanka, Hungary, Slovakia, and Egypt).29 Our findings of average liquidity across

two types of firms are similar to those of Bae, Chan, and Ng (2002): Average liquid-

ity of investable stocks is not significantly greater than that of non-investable stocks;

29The monthly EMDB stock-level data used in this analysis contain more stocks than are con-

tained in the Global index. At the index level, the difference in liquidity and size between investable

and non-investable may be even less pronounced, because liquidity and size are in the criteria for

selecting stocks into the indexes (which includes both investable and non-investable stocks). Given

that our hypothesis tests are conducted at the index level, the less pronounced differences in market

microstructure characteristics lead to a lesser concern.

but they found that the average size of investable stocks is significantly higher than

that of non-investable stocks.30 Large exogenous shocks generally have a smaller

impact on large stocks. From this perspective, investable stocks are less susceptible

to external shocks, and, hence, we may expect their co-movements to change less

during crises. Therefore, any finding of increased co-movements for investable firms

during crises cannot be due to size. Furthermore, since cash flows of investable firms

are similar to cash flows of non-investable firms, a finding of higher cross-country

co-movements for investable stocks during crises would reject the correlated country

fundamental hypothesis.

E. Sample Correlations: Investable vs. Non-Investable

Our methodology tests whether the co-movements of investable returns and cri-

sis country index returns are significantly higher than the co-movements of non-

investable returns and crisis country index returns. For this methodology to be

meaningful, investable and non-investable returns should be different and not highly

correlated. In Table 2A, we show that investable returns are not identical to non-

investable returns. The sample correlations between investable and non-investable

returns for each country also are reported in the right-most column. The average

sample (contemporaneous) correlation between investable and non-investable returns

is 0.651 and all of the sample correlations are positive. The countries with especially

high positive correlations (over 0.9) are India, Korea, Malaysia, Taiwan, Thailand,

and Jordan. We are cautious in interpreting the results for these high correlation

countries. Other moderately high correlations are in Brazil and Philippines. The

low correlation countries are China, Slovakia, and Turkey. We conduct all analyses

at the weekly frequency to avoid market microstructure complications that appear

30Chari and Henry (2003) found the opposite results (they found higher liquidity for investable

but no difference in size between investable and non-investable stocks) using data before stock

market liberalization dates. Bae, Chan, and Ng (2002) conduct the analysis on liquidity and size

using the whole EMDB data sample (1994-2001), and we conduct the analysis using January of

crisis years: Mexico (1994), Asia (1997), and Russia (1998).

in daily level data.31

III. Contagion in the 1997 Asian Crisis

We consider the 1997 Asian crisis as our empirical analysis of transmission mech-

anisms of crises. As stated in Forbes and Rigobon (2002), there were multiple events

behind this Asian financial crisis, beginning with the Thailand stock market crash in

June, the Indonesian market crash in August, and then the Hong Kong market crash

in mid-October. As a benchmark case, we first adopt Forbes and Rigobon’s definition

of the Asian crisis, using the Hong Kong equity market as the source of contagion.

We use the measures of co-movement described in the previous section: from the

regime-switching model estimation and from exceedance correlation estimation. For

comparison, we then consider Thailand as the source of crisis.

A. Test 1: Existence of Stock Market Contagion

To test for the existence of stock market contagion, we examine whether there are

any increases in correlation from the stable regime to the crisis regime. We measure

the increase in correlation between all three index returns (investable index returns,

total market index returns, and non-investable index returns) and the crisis country

returns. If the cross-market linkage is through investor ownership, the correlation

change between investable index returns with the crisis country index returns is the

most relevant.

We analyze a regime-switching model where volatile and stable periods are es-

timated rather than exogenously specified. Note that high volatility regimes may

not be limited to the periods surrounding the Asian crisis. For emerging market

economies, we report test results with Hong Kong as the crisis source country in Ta-

ble 6A, and with Thailand as the source country in Table 6B. Table 6C reports test

31In addition to positive contemporaneous correlation with weekly data, we also find but do not

report that contemporaneous correlations are positive and negative with daily data. This pattern

is recognized as a market microstructure effect in the literature (Campbell, Lo, and MacKinlay

(1997)). Moreover, another market microstructure issue is that daily market closing prices are

non-synchronous across the globe.

results for developed countries. For each country in the tables we separately estimate

the parameters of the regime switching model outlined in Section I.B. Since we are

mainly interested in the change in volatilities and correlations across regimes and

disaggregate index returns, we report only differences in these estimated moments

and do not report levels. In what follows, we first discuss results using Hong Kong

as the crisis country, and then discuss results using Thailand as the crisis country.32

Changes in volatility have been annualized by multiplying by√

The crisis regime is defined as the state with higher volatility in the crisis country

index returns. In Table 6A, among the 30 emerging market countries, the differences

in volatility (column Volatility) are positive in 28 countries for investable returns (9

statistically significant at five percent) and in 23 countries for non-investable returns

(5 statistically significant at five percent). In Table 6C, the differences in volatility

when Hong Kong is used as the source country are positive and significantly positive

at five percent for all but Japan and U.S. This positive increase in volatility with

investable returns and non-investable returns is evidence for the existence of a crisis

regime during the sample estimation period.indices: crisis country, world market,

investable and non-investable in this regime.

We test for the existence of contagion by determining if the correlation with the

crisis country increases during the crisis period. In Table 6A for investable returns,

26 of 30 countries show increases in correlation with the Hong Kong index returns

during the crisis period (column Corr C). Of these increases, 7 are significant at

the five percent level. For non-investable returns, 19 countries show increases in

correlation with Hong Kong stock market index returns, and of these increases, 4

are significant at the five percent level. The sign test rejects the null that there are

no increases in correlation with the crisis country for investable returns but not for

non-investable returns at five percent significance level.

It is also interesting to look at increases in correlation with the world index and

the US index. In Table 6A for investable returns, 28 of 30 countries show increases

in correlation with the world index returns during the crisis period (column Corr

32Full set of parameter results, as well as our original results from estimating a regime switching

model for each country paired with the crisis country, are available from the authors upon request.

W). Of these increases, 7 are significant at the five percent level. For non-investable

returns, 26 countries show increases in correlation with the world index returns,

and of these increases, 5 are significant at the five percent level. Similar results are

obtained for correlations with the US index. The sign test rejects the null that there

are no increases in correlation with the world index for both investable returns and

non-investable returns. The slightly stronger evidence for increases in correlation

with the world index returns seems to suggest that the increase in correlation with

Hong Kong index returns is due to the increased correlation with the world index

returns. Alternatively, we consider Thailand as the possible source of the contagion,

with the direction of contagion being from Thailand to Hong Kong, and not vice

versa.

Figure 2 is a graph of time-series of the stock index returns for some Asian

countries from January 1997 to December 1997. It shows that the volatile period

started much earlier than October 1997. According to Nouriel Roubini’s account of

the Asian Crisis and its global contagion,

July 2 - The Bank of Thailand announces a managed float of the Baht

and calls on the International Monetary Fund for “technical assistance.”

The announcement effectively devalues the Baht by about 15-20 percent.

It ends at a record low of 28.80 to the dollar. This is a trigger for the

Asian crisis.33

Radelet and Sachs (1998) also document that the Thailand crisis started with this

devaluation: “... the Thai Baht devaluation triggered the capital outflows from the

rest of East Asia.” Given the consensus in the literature that the triggering event

started on July 2, we modify the regime switching model by redefining the crisis

country to be Thailand.34

Results from estimating the regime-switching model using Thailand as the crisis

country are reported in Table 6B. Again we find that volatilities of investable and

non-investable returns are significantly higher in the crisis regime, the state in which

33http://www.stern.nyu.edu/nroubini./asia/AsiaChronology1.html34Baur (2003) also shows evidence of Thailand shock transmitting volatility to other countries in

between July and September, prior to the Hong Kong shock in October.

estimated index return volatility of the crisis country, Thailand, is higher at five

percent significance level. The pattern of increased correlations with the Thailand

stock market index returns and the world index returns during the turmoil regime

is also similar to what we find when we use Hong Kong as the crisis source country.

The statistically significant increase in correlation with Thailand stock market re-

turns during the volatile regime is more pronounced in investable index returns (14

countries) than in non-investable index returns (5 countries). For investable returns,

a total of 11 countries show a statistically significant increase in correlation with the

world index returns (column Corr W). For non-investable returns, 5 countries show

statistically significant increase in correlation with the world index (column Corr

The above results indicate that, in general, periods of high volatility are associ-

ated with periods of high correlation. Moreover, in the case of emerging markets,

high correlation is especially characteristic of the investable indices. In Figure 3, we

plot the probability of being in the high volatility (correlation) regime across time.

These are estimates of smoothed inferences (Kim 1994) using Hong Kong as the crisis

country in one and using Thailand as the crisis country in another for a sub-sample

period from January 1996. In Hong Kong (Figure 3A), the probability of being in

the high volatility (correlation) regime jumps from near zero to near unity during

1997. The probability increases from sub-ten percent in May and June of 1997 to

100 percent in November 1997, in three jumps: first in July, then September, and

then finally in November. The downward spikes during 1997 measure at near zero

percent in August and three to four percent in October. From November, 1997, the

probability of being in the crisis regime is between 70 and 100 percent until Febru-

ary 1998. In Thailand (Figure 3B), the probability of being in the high volatility

(correlation) regime jumps dramatically from near zero to near unity slightly earlier

in 1997, in May. Then, it stays near 100 percent level for a longer duration, only

dipping down to 80 percent in April 1999. In comparing Figures 3A and 3B, we note

that the crisis regime begins much earlier for Thailand, and the regime probabilities

graph is much smoother (hence the two regimes are more clearly defined) for Thai-

land. In contrast to these results, Forbes and Rigobon (2002) narrowly define their

crisis period from October 17, to November 16, 1997. Hence, our results indicate the

Asian crises period could have started much earlier and lasted much longer than one

month. This also points out the difficulties in correctly specifying the regimes with

exogenously defined periods.

The persistence in the regimes is evident in our estimates of the transition prob-

abilities. For Hong Kong, the volatile regime probability is 0.685, with a standard

error of 0.134 and the stable regime probability is 0.923, with a standard error of

0.032. For Thailand, the volatile regime probability is 0.924, with a standard error

of 0.136 and the stable regime probability is 0.969, with a standard error of 0.026.

In summary, when we disaggregate the total market return index into investable

and non-investable stock return index, we find that (1) there are more emerging

market countries that have significant increases in correlation with the crisis country

market returns during the turmoil period (or the volatile regime) and this pattern

is more pronounced for investable returns than for non-investable returns; and (2)

the sign test indicates that correlations generally increase for investable returns, but

not for non-investable returns. These results provide supporting evidence for the

existence of stock market contagion during the 1997 Asian crisis period.

B. Test 2: Fundamental or Investor-Induced?

In this section, we report results from our second set of tests, which are tests of

how contagion spreads: either through correlated country fundamentals or through

investor constraints such as portfolio re-balancing and wealth constraints. We exam-

ine whether the increase in co-movement is more pronounced for investable stocks

than for non-investable stocks during the turmoil period (T2-A), and whether in-

vestable index returns lead non-investable index returns during the turmoil period

(T2-B). We report results based on correlations estimated from the regime-switching

model in Table 8A and summarize the results on the lead-lag comparison between

investable and non-investable stocks during the crisis period in Table 9.

With Hong Kong as the crisis country, out of 30 emerging markets, 26 countries

show positive difference-in-difference statistics for correlation with the Hong Kong

stock market index returns, although not all are significantly positive. With Thailand

as the crisis country, 19 countries show positive (but not all significant) difference-

in-difference statistics for correlation. The sign test rejects the null hypothesis of

equal probability of the difference-in-difference statistics being negative or positive,

favoring the positive, for Hong Kong, but not for Thailand. The increases in correla-

tion with Hong Kong stock market index returns affect investable and non-investable

similarly, as indicated by the fact that the sign test cannot reject the equality of cor-

relations at the five percent significance level. Whereas the increases in correlation

with Thailand stock market index returns are higher for investable, particularly in

the non-Asian countries. This finding corresponds with the results shown in Figure

3A and 3B, where the high volatility regime in Hong Kong is found to start much

later in September-November 1997 compared with May 1997 in Thailand. Our find-

ing here may reflect that crisis is originated in Thailand and spread to other Asian

countries through investable and non-investable stocks and to non-Asian countries

mostly through investable stocks, and by the time crisis reaches Hong Kong, the

crisis has affected both investable and non-investable stocks global-wide.

We report the results on lead-lag comparisons as specified in Test 2-B in Table 9.

If investable stocks are a larger channel to transmit external shocks, investable stocks

should lead non-investable stocks during the crisis period. In Table 9, the crisis period

is defined by the regime-switching model using Thailand as the source country.35 We

find that the correlation between lagged investable returns and non-investable returns

(ρ(It−1,Nt), column 2) is higher than correlation between lagged non-investable returns

and investable returns (ρ(It,Nt−1), column 4) for most countries: 21 out of 31 countries

for one-week lag and 22 countries for two-week lag. The differences (columns 6 and

7) are small, although positive, relative to the contemporaneous correlation (column

1). This suggests that investable stocks lead non-investable stocks, but the effect is

small.

To summarize, we find strong statistical evidence that during the turmoil period,

increase in co-movement is more pronounced with investable than non-investable

returns; and investable returns lead non-investable returns during the crisis period.

These findings indicate that investable returns act as a larger channel for crisis

35We obtain similar results by defining the crisis period to be from July 2 to November 16, 1997.

transmission and provide supporting evidence for the investor-induced contagion

hypothesis.

C. Test 3: Portfolio Re-balancing or Wealth Constraint

In this section, we conduct a further test to determine whether crises spread

through investors, by examining market co-movements in market downturns (ex-

treme tails of negative returns) and in market upturns (extreme tails of positive re-

turns). Instead of comparing correlations between the turmoil period and the stable

period as in previous sections, we compare exceedance correlations between negative

and positive tails, which are extreme events. Although this set of comparisons is

similar to those in the previous sections, the interpretations are slightly different.

If crises are transmitted by investor restrictions such as wealth constraints, market

co-movements should be asymmetric for upturns and downturns; in contrast, if crises

spread through the need for portfolio re-balancing, market co-movements should be

symmetric.

We use the extreme value estimation approach to estimate exceedance correlation.

The advantage of this method is that it requires neither distributional assumption

nor exogenously defined turmoil period. We follow Ang and Chen (2002) in set-

ting the threshold values to be multiples of sample deviations away from the sample

mean.36 We estimate exceedance correlations both for the positive tail and the neg-

ative tail at weekly frequency using the whole sample. We then check for asymmetry

in correlations in the positive and the negative tail for total market, investable and

non-investable returns.

C.1. Exceedance Correlation with Hong Kong

First, we estimate the positive and negative tails of the joint distribution of

the total market index returns with Hong Kong as the crisis country. Table 10A

contains correlation estimates at 1.5 deviations away from the mean, the difference

36A tradeoff exits in selecting the threshold values: The sample distribution converges toward

the true extreme tail distribution as threshold increases, but the precisions of the estimates suffer

from smaller number of observation satisfying the tail criteria.

between negative and positive tail correlations, and the corresponding t-statistics.37

At the total market index level, the comparison of the exceedance correlation across

tails, positive vs. negative, yields evidence of asymmetry. Overwhelmingly, most of

the differences show higher correlations in market downturns. Out of 44 countries

(including both developed and emerging markets), 35 have higher correlations during

market downturns, and the sign test clearly rejects the null hypothesis of equal

probability of positive and negative differences. The test favors the alternative of

increased correlations in the market downturns. In general, the correlations in the

positive tail are smaller than the correlations in the negative tail. This indicates

that it is a global phenomenon that stock index returns co-move more with the

crisis country stock index returns during market downturns. The countries with

statistically higher correlations with Hong Kong during the market downturns, at

five percent significance level, are Germany, Switzerland, Argentina, Chile, Mexico,

Malaysia, Pakistan, Philippines, and Greece.

At the disaggregate level (i.e., measuring co-movements of Hong Kong with other

country’s investable returns and non-investable returns), once again, most of the dif-

ferences show higher correlations in market downturns. Out of 31 countries for

investable index returns of emerging markets, 28 have higher correlations during

market downturns turns, and the sign test clearly rejects the null hypothesis of

equal probability of positive and negative differences. The countries with statis-

tically higher correlations with Hong Kong during the market downturns, at five

percent significance level, are Brazil, Chile, Mexico, Indonesia, Malaysia, Greece

and Turkey for investable returns, and Chile, Korea, Malaysia, and Philippines. In

general, the correlation increases in the market downturns are somewhat smaller in

non-investable returns, especially for the non-Asian countries. In fact, the Asian

countries’ correlations are very similar across investable returns and non-investable

returns, expect for China.

37We also estimate exceedance correlations at 1 and 2 deviations away from the mean. The

results are similar and therefore are not reported. We do report, however, the shape of asymmetry

whether exceedance correlations are increasing (denoted as +) as the thresholds are moved away

from the mean. An increase in the exceedance correlation away from the mean suggests that the

bivariate distribution is not normal.

These results on asymmetric exceedance correlation are consistent with hypothe-

ses that predict asymmetry in co-movements. One such hypothesis is that crises

spread through investor wealth constraints. On the other hand, these results are

not consistent with hypotheses such as portfolio re-balancing needs which predict

symmetric co-movements in market upturns and downturns.38

C.2. Exceedance Correlation with Thailand

We also estimate the exceedance correlation using Thailand as the crisis country.

Once again, we find some evidences of asymmetry between the positive and negative

tails, as reported in Table 10B at the total index returns level. Moreover, when we

disaggregate country’s total index returns into investable and non-investable returns,

the pattern is clearer. In general, the correlations in the negative tails are higher than

the correlations in the positive tails, more so for investable than for non-investable.

Similar to the findings in the Hong Kong case, across the board, the correlation

of crisis country returns with investable returns is significantly higher than that

with non-investable returns. In the case of China, the increased correlation in the

market downturns appears only in investable index, not for non-investable index nor

even total index. Also, the pattern of correlations for negative and positive tails

are remarkably similar for both investable and non-investable index returns in most

Asian countries, thus indicating the severity of regional contagion.

Hence, we again find contrasting contagion patterns when we define Thailand as

the crisis source country versus when we define Hong Kong as the source country.

38Moreover, the shape of the positive and negative tails implied by the exceedance estimates

could further our understanding of the distributional properties of underlying stochastic processes

and assist us in building a structural empirical model of correlations. For example, Longin and

Solnik (2001) document a particular shape of asymmetry in their sample – namely, asymptotic

independence in the positive tail and flat or increasing dependence in the negative tail, and conclude

that the data process cannot be described by a bivariate normal distribution. We find similar shape

for some countries, but for most countries, the correlation estimates decrease further out into the

tails, with some more substantially than the others. Hence, he shape implied by most is a tent-

shape, as could be described by two different bivariate normal distributions: one for positive and

another for negative tails.

Using Thailand as the source country, we find that Asian countries have higher

exceedance correlations with the crisis country, and larger increases in exceedance

correlations during market downturns. However, using Hong Kong as the source

country, we find larger overall correlations. This is consistent with the idea that the

Asian crisis spread regionally from Thailand primarily to Asian countries.39

In summary, the results on asymmetric exceedance correlation are robust to alter-

native crisis country specifications and hence provide further support for the investor-

induced contagion hypotheses that predict asymmetric co-movements in market up-

turns and downturns.

C.3. Government Bonds

We also consider the government bonds in determining whether the stock market

crisis spread to other countries due to portfolio re-balancing or wealth constraint.

We estimate and compare the bond market index volatility and correlation between

bond and stock market for both stable and turmoil periods. Increase in volatility

during crisis indicates increase in trading activities. If investors tend to re-balance

their portfolios during crisis, by shifting from risky assets to safer ones, prices of

bonds would increase and bond index return would increase. If investors tend to

be wealth-constrained during crisis, so that they shift from safer asset to cover their

positions in risky assets, prices of bonds would decrease and bond index return would

decrease. Therefore, portfolio re-balancing would be a decrease in positive correlation

or more negative correlation, while wealth constraint would be an increase in positive

correlation or less negative correlation.

39Technically, the primary difference in these results from those with Hong Kong as the source

country is in the shape of the exceedance correlation. We observe more cases of increased exceedance

correlation, implying that the asymptotic independence no longer holds and bivariate normal distri-

bution may not describe the data generating process. Whereas the predominant shape with Hong

Kong as the crisis country is tent-shape, this is not so with Thailand. With the total index return,

increasing exceedance correlation is found in the positive tails for a handful of countries and in

the negative tails for most countries. Although the correlations are larger with Hong Kong as the

source country, evidence of increasing dependence in the negative tail is much more prevalent using

Thailand as the crisis country.

Table 11A summarizes the results for emerging markets with Hong Kong as the

crisis country. Out of 21 countries that have estimates, 16 countries show an in-

crease in volatility (with six countries statistically significant at five percent signifi-

cance level: Korea, Pakistan, Thailand, Egypt, Morocco and South Africa) and five

countries show a decrease in volatility (with only one country statistically significant

at five percent significance level: Slovakia). The sign test indicates that increase

in volatility is more likely. No countries have investable or non-investable stock in-

dex returns with statistically significant increase or decrease in correlation with the

bond market at the five percent significance level. Out of 21 countries, 13 countries

show an increase and 8 countries show a decrease in correlation between investable

stock index return and bond index return. And 17 countries show an increase and

only four show a decrease in correlation between non-investable stock index return

and bond index return. The sign test show that increases in correlation between

non-investable stock index return and bond index return are likely. However, for

non-investable stock index returns, the sign test cannot reject the null hypothesis

that increase or decrease in correlation with the bond index return is equally likely.

The increases in correlations suggests that crisis spread from Hong Kong to other

emerging market countries due to wealth constraints.

Table 11B summarizes the results for emerging markets with Thailand as the crisis

country. Out of 21 countries that have estimates, 16 countries40 show an increase

in volatility (with nine countries statistically significant at five percent significance

level: Korea, Pakistan, Malaysia, Thailand, Colombia, Russia, Egypt, Morocco, and

South Africa) and five countreis show a decrease in volatility (with only one country

statistically significant at five percent significance level: Portugal). the sign test

indicates that increase in volatility is more likely. Out of 21 countries, 14 countries

show an increase and 7 countries show a decrease in correlation between investable

stock index return and bond index return. And 12 countries show an increase and 9

show a decrease in correlation between non-investable stock index return and bond

index return. The sign test show that increases in correlation between investable

stock index return and bond index return are likely. However, for investable stock

40same number as Table 11A results, but different countries.

index returns, the sign test cannot reject the null hypothesis that increase or decrease

in correlation with the bond index return is equally likely. These results are stronger

than those with Hong Kong as the crisis country. The increases in correlations

suggests that crisis spread from Thailand to other emerging market countries due to

wealth constraints.

Table 11C summarizes the results for developed markets for both Hong Kong

and Thailand as the crisis countries. With Hong Kong as crisis country, out of

13 countries, 8 show a decrease in volatility (none statistically significant) while 5

show an increase in volatility (only one, Japan, is statistically significant at five

percent significance level). All 13 countries show an increase in correlation between

stock market index return and government bond market index return, although not

all are statistically significant at five percent significance level. With Thailand as

crisis country, out of 13 countries, 12 show a decrease in volatility (four: Italy, Spain

Sweden, and Canada are statistically significant) while 1 show an increase in volatility

(none statistically significant). Similarly to the Hong Kong results, all 13 countries

show an increase in correlation between stock market index return and government

bond market index return, although not all are statistically significant at five percent

significance level. These results suggest that crisis spread to developed markets due

to portfolio re-balancing.

IV. Conclusion

We set out to empirically test the hypothesis that crises spread through investors’

asset holdings. We estimate and compare the degree to which investable and non-

investable stock index returns co-move with the crisis country returns in the case of

the 1997 Asian crisis. Our analysis using Thailand as the crisis country reveals that

the increase in correlation is more prevalent in investable stock index returns rather

than non-investable stock index returns, especially in Asian countries, indicating that

the crisis was transmitted through investable stocks. Our analysis using Hong Kong

as the crisis country finds that the increase in correlation is also more pronounced in

investable stocks than non-investable stocks and more so among non-Asian countries.

Since the Thailand stock market crash precedes the Hong Kong stock market crisis,

we interpret the finding as the crisis was first transmitted regionally to Asian coun-

tries and then globally to non-Asian countries. We also find that investable stocks are

not fundamentally different from non-investable stocks in terms of industry distribu-

tions, so the cash flow fundamentals cannot explain the increase in correlations. We

also compare cross-autocorrelations between investable and non-investable returns

for each country during the turmoil period, and evidence that investable stocks lead

non-investable stocks, further suggesting that crises spread first to investable stocks

through investors’ international holdings. Furthermore, we test whether market co-

movements are symmetric in extreme market upturns and downturns. Our finding

of high levels of asymmetry in the co-movements between investable and the crisis

country is consistent with the behavioral theory of investors who are constrained

by their wealth. Finally, we compare each country’s stock market with its govern-

ment bond market and find that crises spread to developed markets due to portfolio

re-balancing and to emerging markets due to investors’ wealth constraints.

REFERENCES

Allen, Franklin, and Douglas Gale, 2000, Financial contagion, Journal of Political

Economy 108, 1–33.

Ang, Andrew, and Joseph Chen, 2002, Asymmetric correlations of equity portfolios,

Journal of Financial Economics 63, 443–494.

Bae, Keehong, Kalok Chan, and Angela Ng, 2002, Investibility and return volatility

in emerging equity markets, Working Paper, Hong Kong University of Science and

Techology.

Bae, Kee Hong, Andrew Karolyi, and Rene Stulz, 2003, A new approach to measuring

financial market contagion, Review of Financial Studies.

Barberis, Nicholas, Andrei Shleifer, and Jeffrey Wurgler, 2003, Comovement, Journal

of Financial Economics Forthcoming.

Baur, Dirk, 2003, Testing for contagion: Mean and volatility contagion, Journal of

Multi-national Financial Management 13, 405–422.

Bekaert, Geert, Campbell R. Harvey, and Angela Ng, 2003, Market integration and

contagion, Journal of Business forthcoming.

Bertaut, Carol C., and Linda S. Kole, 2004, What makes investors over or under-

weight? explaining international appetites for foreign equities, Working Paper.

Boyer, Brian H., Michael. S. Gibson, and Mico Loretan, 1999, Pitfalls in tests for

changes in correlations, Working Paper.

Broner, Fernando A., R. Gaston Gelos, and Carmen M. Reinhart, 2003, When in

peril, retrench: International investors and crises, Working paper University of

Maryland.

Calvo, Guilermo A., 1999, Contagion in emerging markets: When wall street is a

carrier, AEA (1999) Conference Working Paper.

Campbell, John Y., Andrew W. Lo, and A. Craig Mackinlay, 1997, The Econometrics

of Financial Markets (Princeton University Press: New Jersey).

Chan-Lau, Jorge A., and Chen Zhaohui, 1998, Financial crisis and credit crunch as

a result of ineffecient financial intermediation – with reference to asian financial

crisis, IMF Working Paper.

Chari, Anusha, and Peter Blair Henry, 2003, Risk sharing and asset prices: Evidence

from a natural experiment, Journal of Finance Forthcoming.

Choe, Hyuk, Bong-Chan Kho, and Rene M. Stulz, 2003, Do foreign investors destabi-

lize stock markets? the korean experience in 1997, Journal of Financial Economics

54, 227–264.

Clark, John, and Elizabeth Berko, 1996, Foreign investment fluctuations and emerg-

ing market stock returns: the case of mexico, Working paper Federal Reserve Bank

of New York 9635.

Connoly, Robert, and Albert Wang, 1998a, International equity market co-

movements: Economic fundamentals or contagion?, Mimeo.

, 1998b, On stock market return co-movements: Macroeconomic news, dis-

persion of beliefs, and contagion, Mimeo.

Corsetti, Giancarlo, Marcello Pericoli, and Massimo Sbracia, 2002, Some contagion,

some interdependence: More pitfalls in tests of financial contagion, Working Paper.

Corsetti, Giancarlo, Paolo Pesenti, and Nouriel Roubini, 1999, Paper tigers? a model

of the asian crisis, European Economic Review 43, 1211–1236.

DeHaan, L., I.S. Resnick, H. Rootzen, and C.G. de Vries, 1989, Extremal behavior

of solutions to a stochastic difference equations with applications to arch process,

Stochastic Processes and Their Applications 32, 213–224.

Embrechts, P., C. Kluppelberg, and T. Mikosch, 1997, Modeling Extremal Events:

For Insurance and Finance (Springer: Berlin, Heidelberg).

Errunza, Vihang, and Etienne Losq, 1985, International asset pricing under mild

segmentation: Theory and test, Journal of Finance 40, 105–124.

, 1989, Capital flow controls, international asset pricing, and investor’s wel-

fare: A multi-country framework, Journal of Finance 44, 1025–1037.

Forbes, Kristin J., 2002, Are trade linkages important determinants of country vul-

nerability to crises, in Sebastian Edwards, and Jeffrey Frankel, ed.: Preventing

Currency Crises in Emerging Markets . pp. 77–124 (University of Chicago Press:

Chicago).

, and Roberto Rigobon, 2002, No contagion, only interdependence: Measur-

ing stock market co-movements, Journal of Finance 56, 2223–2261.

French, Kenneth R., and James Poterba, 1991, Investor diversification and interna-

tional equity markets,, American Economic Review 81, 222–226.

Froot, Kenneth A., Paul G.J. O’Connell, and Mark S. Seasholes, 2001, The portfolio

flows of international investors, Journal of Financial Economics 59, 151–193.

Froot, Kenneth A., and Tarun Ramadorai, 2001, The information content of inter-

national portfolio flows, Working paper NBER.

Gibson, M. S., and B. H. Boyer, 1997, Evaluating forecasts of correlation using option

pricing, Working Paper.

Goldfajn, Ilan, and Rodrigo O. Valdes, 1997, Capital flows and the twin crises: the

role of liquidity, IMF Working Paper.

IFC, 1999, The ifc indexes: Methodology, definitions, and practices, User Guide.

Kallberg, Jarl G., K. Crocker Liu, and Paolo Pasquariello, 2003, An examination of

the asian crisis: Regime shifts in currency and equity markets, Journal of Business

forthcoming.

Kaminsky, Graciela, Richard K. Lyons, and Sergio Schmukler, 2001, Mutual fund

investment in emerging markets: An overview, World Bank Economic Review 15,

315–340.

, 2003, Managers, investors, and crises: Mutual fund strategies in emerging

markets, Journal of International Economics forthcoming.

Kaminsky, Graciela, and Carmen Reinhart, 2002, On crises, contagion, and confu-

sion, Journal of International Economics 51, 145–168.

Kaminsky, Gaciela L., and Carmen M. Reinhart, 2000, On crises, contagion, and

confusion, Journal of International Economics 51, 145–168.

Karolyi, Andrew, and Rene Stulz, 1996, Why do markets move together? an investi-

gation of u.s.-japan stock return co-movements, Journal of Finance 51, 951–986.

Kim, Chang-Jin, 1994, Dynamic linear models with markov-switching, Journal of

Econometrics 60, 1–22.

King, Mervyn, Enrique Sentana, and Sushil Wadhwani, 1994, Volatility and links

between national stock markets, Econometrica 62, 901–933.

Kodres, Laura E., and Matthew Pritsker, 2002, A rational expectations model of

financial contagion, Journal of Finance 57, 769–799.

Kyle, Albert S., and Wei Xiong, 2001, Contagion as a wealth effect, Journal of

Finance 56, 1410–1440.

Lagunoff, R.D., and S.L. Schreft, 2001, A model of financial fragility, Journal of

Economic Theory 99, 220–264.

Lane, Philip R., and Gian Maria Milesi-Ferretti, 2001, The external wealth of nations:

Measures of foreign assets and liabilities for industrial and developing countries,

Journal of International Economics 55, 263–294.

Leadbetter, M.R., G. Lindgren, and H. Rootzen, 1983, Extremes and Related Prop-

erties of Random Sequences and Processes (Springer Verlag: New York).

Ledford, M. R., and J.A. Tawn, 1997, Statistics for near independence in multivariate

extreme values, Biometrika pp. 169–187.

Longin, Franois, and Bruno Solnik, 2001, Extreme correlation of international equity

markets, Journal of Finance 56, 649–676.

Murphy, Kevin M., and Robert H. Topel, 1985, Estimation and inference in two-step

econometric models, Journal of Business and Economic Statistics 3, 370–379.

Prescott, P., and A. T. Walden, 1980, Maximum likelihood estimation of the param-

eters of the generalized extreme-value distribution, Biometrika 67, 723–724.

Radelet, S., and J. Sachs, 1998, The onset of asian crisis, Working Paper.

Rijckeghem, C. Van, and B. Weder, 2000, Spillovers through banking centers: A

panel data analysis, Working Paper.

Seasholes, Mark, 2004, Re-examining information asymmetries in emerging stock

markets, Working Paper.

Shleifer, Andrew, and Robert W. Vishny, 1997, The limit of arbitrage, Journal of

Finance 52, 35–55.

Stambaugh, Robert F., 1995, Unpublished discussion of karolyi and stulz (1996),

National Bureau of Economic Research Universities Research Conference on Risk

Management.

Tang, Jon Wongswan, 2001, Contagion: An empirical test, Working Paper, Duke

University.

Tawn, Jonathan, 1988, Bivariate extreme value theory; models and estimation,

Biometrika 75, 397–415.

Yuan, Kathy, 2004, Asymmetric price movements and borrowing constraints: A ra-

tional expectations equilibrium model of crisis, contagion, and confusion, Journal

of Finance Forthcoming.

V. Appendix

A. Second Stage Covariance Matrix

The asymptotic variance of the second-step parameter (Murphy and Topel, (1985))

Σ2 = V2 + V2 [CV1C′ −RV1C

′ − CV1R′] V ′

2 (13)

and the asymptotic covariance between the first-step parameters (θ1) and second-step

parameters (θ2) is:

Σ1,2 = V1RV2 − V1CV2 (14)

where V1 is the asymptotic variance of the first-step parameters based on the first

likelihood function, ln(L1); V2 is the asymptotic variance of the second-step parame-

ters (θ2) based on the second likelihood function conditioned on first-step parameters

ln(L2|θ1), and C and R are expected cross derivatives. More specifically,

V1 = E

[∂L1

∂θ1

(∂L1

∂θ1

)′]−1

V2 = E

[∂L2

∂θ2

(∂L2

∂θ2

)′]−1

[∂L2

∂θ1

(∂L2

∂θ2

[∂L1

∂θ1

(∂L2

∂θ2

Murphy and Topel (1985) show that the parameters are expressed as results of ap-

plying law of large numbers:

√n(θ1 − θ∗1) = −V1

n∑i=1

∂L1(y2i, θ∗1)

∂θ1

√n(θ2 − θ∗2) = −V2

n∑i=1

∂L2(y2i; θ∗1, θ

∂θ2

+ V2C′V1

n∑i=1

∂L1(y2i, θ∗1)

∂θ1

where the joint distribution of the first partials of the likelihood functions is asymp-

totically normal, as a result of applying central limit theorem.

[1√n

∑ni=1

∂L1(y1i,θ∗1)

∂θ1

∑ni=1

∂L2(y2i,θ∗1 ,θ∗2)

∂θ2

]∼ N(0, Ω) (17)

[V −1

R′ V −12

Then, asymptotic variance and covariance of all parameters are derived from calcu-

lating the variances of from the two equations (15), (16), by using equation (17).

Volatilities, correlations, changes in volatilities, and changes in correlations are all

functions of the estimated parameters, so that standard errors of these functions are

calculated using the delta method.

B. Extreme Value Distribution

For a univariate random variable (X), the tail probability beyond the threshold

level (θ) is obtained from the generalized Pareto distribution:

Pr (X > x|X > θ) =

ξ(x− θ)

)−1/ξ

for x > θ,41 where ξ and σ are shape parameters, also called the tail index and

dispersion parameters, respectively. The tail index characterizes the tail distribution:

ξ > 0 corresponds to fat-tailed distributions, ξ < 0 to thin-tailed distributions, and

ξ = 0 to no-tail or finite-distributions.42 Therefore, the univariate tail distribution

(F ∗(x)), conditional on the observation being in the tail, is fully characterized by

two parameters: the tail index (ξ) and the dispersion parameter (σ):

F ∗(x) = 1−(

1 +ξ(x− θ)

)−1/ξ

For the bivariate (or more generally, multivariate) case, the resulting multivariate

distribution of extreme returns is a function of the univariate generalized Pareto

distributions. As stated before, this distribution does not depend on the underlying

true distribution. Let (X1, X2, . . . Xq)′ be a q-dimensional vector of random variables,

and (θ1, θ2, . . . θq)′ denote the vector of threshold level. Following Ledford and Tawn

(1997) and Tawn (1988), the joint tail distribution is as follows:

F ∗(x1, x2 . . . xq) = exp

( −1

logFm1 (x1)

logFm2 (x2)

, . . .−1

logFmq (xq)

)](21)

where D() is some dependence function and Fmj () is the marginal distribution of the

jth variable:

Fm(xj) = (1− λj) + λj

ξj(xj − θj)

)−1/ξj

41The plus-sign notation is defined as s+ = max(s, 0).42Longin and Solnik (2001) cite some known tail index values for various distributions. ξ = 0

for normal and log-normal distributions, ξ > 0 for student-t distribution, and ξ < 0 for uniform

distribution (Embrechts, Kluppelberg, and Mikosch (1997)). Also, various processes based on the

normal distribution - autocorrelated processes, discrete mixtures of normal distributions and mixed

diffusion jump process – have thin tails, so that ξ = 0 (Leadbetter, Lindgren, and Rootzen, (1983)).

GARCH process have ξ < 0.5. (De Haan, Resnick, Rootzen, and DeVries, (1989)).

with λj as the tail probability. The marginal distribution combines two parts: prob-

ability (1 − λj) that the observation is not in the tail and probability λj that the

observation is in the tail and represented by the Pareto distribution of the tail. In the

multivariate case, although the limiting marginal distributions are known, the depen-

dence function is not known. Therefore, the estimation of the joint tail distribution

requires choosing a functional form for the dependence function.

The models of the dependence function can be either nonparametric or paramet-

ric. In the class of parametric functions, Tawn (1988) describes two distinct models:

mixed and logistic. These mixed and logistic models have equal-weight (symmet-

ric) and weighted (asymmetric) versions where equal weight means simply that the

weight given to each of the variables are all the same.43

The most widely used dependence function is the equal-weighted (symmetric)

logistic function:

D(z1, z2 . . . zq) = (z−1/α1 + z

−1/α2 + · · ·+ z−1/α

q )α (23)

where the dependence parameter is α, 0 < α ≤ 1. With only one parameter to

estimate, this dependence function is simple and tractable. Furthermore, with this

dependence function for q = 2, the measure of exceedance correlation is 1 − α2,

which is also very simple to compute. We proceed with this dependence function to

estimate the correlation between markets. With the symmetric logistic dependence

function, the bivariate tail distribution has seven parameters: two tail probabilities

(λ), two tail indices (ξ), and two dispersion parameters (σ) and parameters from the

dependence function, which in this case is one (α).

43The use of the word “symmetry” here refers to the weights given to each variable (in this case,

asset returns) in the model. This “symmetry” is not the same as it appears elsewhere in this paper,

where it is used to describe the difference in the tail distribution between market upturns and

downturns.

This table reports the total market capitalization and U.S. Equity ownership in each stock market.All statistics are reported as millions of U.S. dollars as of year-end 1996. For emerging market countries,statistics are reported based on IFCG index in emerging market database (EMDB) from the InternationalFinance Corporation (2000). For developed countries, data source is Datastream.

Region Country Total Market Capitalization

(millions) (millions) (percent mcap)Emerging Markets

Latin Argentina 35,142 12,892 36.7%America Brazil 102,965 31,338 30.4%

Chile 44,498 4,555 10.2%Colombia 11,452 704 6.1%Mexico 108,941 34,965 32.1%Peru 9,657 2,341 24.2%Venezuela 9,138 1,975 21.6%

Asia-Pacific China 49,981 2,256 4.5%South Asia India 50,856 6,176 12.1%

Indonesia 13,553 2,488 18.4%Korea 25,157 4,428 17.6%Malaysia 45,911 4,713 10.3%Pakistan 5,979 1,180 19.7%Philippines 18,650 2,848 15.3%Sri Lanka 1,293 133 10.3%Taiwan, China 153,176 4,939 3.2%Thailand 10,921 2,158 19.8%

Emerging Czech Rep. 4,847 763 15.7%Europe Greece 16,255 1,513 9.3%

Hungary 11,175 3,483 31.2%Poland 6,234 1,618 26.0%Portugal 24,745 6,993 28.3%Russia 54,744 8,457 15.4%Slovakia 1,320 87 6.6%

Others Egypt 8,141 763 9.4%Israel 18,812 7,036 37.4%Jordan 3,261 40 1.2%Morocco 7,562 217 2.9%South Africa 90,297 9,937 11.0%Turkey 33,732 6,005 17.8%Zimbabwe 1,123 133 11.8%

Developed Markets Asia-Pacific Australia 548,380 31,120 5.7%

Hong Kong 205,200 28,102 13.7%Japan 2,690,000 136,404 5.1%Singapore 141,100 10,185 7.2%

Europe Belgium 787,920 6,099 0.8%France 1,320,150 85,019 6.4%Germany 608,510 64,965 10.7%Italy 1,871,040 41,547 2.2%Netherlands 1,055,560 106,984 10.1%Spain 302,010 25,223 8.4%Sweden 181,110 38,783 21.4%SwitzerlandUnited Kingdom 5,407,200 217,525 4.0%

North America Canada 461,120 70,798 15.4%United States 13,292,800 952,900 92.8%

Table 1Market Capitalization and U.S. Equity Ownership

US Equity Ownership

This table contains summary statistics for weekly data from EMDB and Datastream. Full 730 observations are from January 1989 to December 2002.For the emerging markets, the overall and investable returns are as reported in the EMDB database from IFC. Non-investable returns are calculated.The last column is the contemporaneous correlation between investable (IR) and non-investable (NR) weekly returns.

Start Standard Start Standard Start StandardEmerging Markets Obs Date Mean Deviations Obs Date Mean Deviations Obs Date Mean Deviations Obs CorrelationArgentina 730 Jan-89 0.0019 0.0806 730 Jan-89 0.0020 0.0826 730 Jan-89 -0.0028 0.1041 730 0.505Brazil 730 Jan-89 0.0017 0.0715 730 Jan-89 0.0018 0.0790 730 Jan-89 0.0012 0.0712 730 0.880Chile 730 Jan-89 0.0027 0.0308 730 Jan-89 0.0028 0.0324 730 Jan-89 0.0024 0.0288 730 0.775Colombia 730 Jan-89 0.0023 0.0361 560 Feb-91 0.0019 0.0440 560 Feb-91 0.0006 0.0388 560 0.618Mexico 730 Jan-89 0.0024 0.0424 730 Jan-89 0.0029 0.0441 730 Jan-89 0.0015 0.0454 730 0.611Peru 521 Jan-93 0.0018 0.0342 521 Jan-93 0.0015 0.0356 521 Jan-93 0.0018 0.0398 521 0.492Venezuela 730 Jan-89 0.0012 0.0586 616 Jan-89 0.0031 0.0701 616 Jan-89 0.0002 0.0655 616 0.575

China 521 Jan-93 0.0004 0.0528 521 Jan-93 -0.0017 0.0520 521 Jan-93 0.0001 0.0574 521 0.186India 730 Jan-89 0.0006 0.0398 529 Jan-89 -0.0004 0.0385 529 Jan-89 -0.0004 0.0369 529 0.988Indonesia 639 Oct-90 -0.0023 0.0724 639 Oct-90 -0.0022 0.0732 639 Oct-90 -0.0039 0.0839 639 0.830Korea 730 Jan-89 -0.0006 0.0559 573 Jan-89 -0.0001 0.0610 573 Jan-89 -0.0011 0.0585 573 0.916Malaysia 730 Jan-89 0.0004 0.0442 730 Jan-89 0.0005 0.0447 730 Jan-89 0.0009 0.0446 730 0.904Pakistan 613 Apr-91 0.0014 0.0424 556 Apr-91 0.0006 0.0480 552 Apr-91 0.0002 0.0367 552 0.760Philippines 730 Jan-89 -0.0007 0.0434 730 Jan-89 -0.0011 0.0462 730 Jan-89 -0.0005 0.0442 730 0.846Sri Lanka 521 Jan-93 0.0002 0.0368 460 Jan-93 -0.0009 0.0431 460 Jan-93 -0.0009 0.0330 460 0.816Taiwan 730 May-95 -0.0003 0.0505 625 May-95 -0.0002 0.0449 625 May-95 -0.0003 0.0450 625 0.995Thailand 730 Jan-89 -0.0004 0.0547 730 Jan-89 -0.0005 0.0536 730 Jan-89 -0.0002 0.0563 730 0.983

Czech Republic 469 Jan-94 -0.0010 0.0354 469 Jan-94 -0.0007 0.0420 469 Jan-94 -0.0024 0.0451 469 0.422Greece 721 Jan-89 0.0019 0.0461 721 Jan-89 0.0022 0.0482 565 Jan-89 0.0048 0.0551 565 0.462Hungary 521 Jan-93 0.0016 0.0454 520 Jan-93 0.0023 0.0472 520 Jan-93 0.0007 0.0453 520 0.407Poland 521 Jan-93 0.0033 0.0634 521 Jan-93 0.0033 0.0634 455 Jan-93 0.0030 0.0679 455 0.756Portugal* 534 Jan-89 0.0021 0.0290 534 Jan-89 0.0023 0.0295 534 Jan-89 0.0022 0.0322 534 0.808Russia 365 Jan-96 0.0042 0.0852 307 Feb-97 0.0008 0.0919 307 Feb-97 -0.0016 0.0964 307 0.647Slovakia 365 Jan-96 -0.0009 0.0373 247 Feb-97 -0.0031 0.0423 247 Feb-97 -0.0034 0.0437 247 0.216

Egypt 365 Jan-96 -0.0012 0.0313 308 Feb-97 -0.0038 0.0343 308 Feb-97 -0.0031 0.0232 308 0.516Israel 312 Jan-97 0.0008 0.0354 312 Jan-97 0.0008 0.0354 312 Jan-97 0.0028 0.0630 312 0.597Jordan 730 Jan-89 0.0013 0.0214 669 Jan-89 0.0016 0.0237 669 Jan-89 0.0012 0.0212 669 0.933Morocco 365 Jan-96 0.0015 0.0198 307 Feb-97 0.0004 0.0214 307 Feb-97 -0.0004 0.0227 307 0.629South Africa 521 Jan-93 0.0018 0.0377 521 Jan-93 0.0017 0.0378 243 Jan-93 0.0011 0.0617 243 0.575Turkey 730 Jan-89 0.0018 0.0861 699 Jan-89 0.0009 0.0871 395 Jan-89 -0.0440 0.7591 395 0.042Zimbabwe 495 Jul-93 0.0020 0.0856 434 Jul-93 0.0034 0.0594 434 Jul-93 0.0001 0.0996 434 0.490

Emerging Market Average 0.0010 0.0486 -0.0013 0.0750 0.651

Developed MarketsAustralia 730 Jan-89 0.0008 0.0233Hong Kong 730 Jan-89 0.0016 0.0368Japan 730 Jan-89 -0.0012 0.0327Singapore 730 Jan-89 0.0004 0.0307

Belgium 730 Jan-89 0.0007 0.0228France 730 Jan-89 0.0011 0.0250Germany 730 Jan-89 0.0007 0.0270Italy 730 Jan-89 0.0002 0.0316Netherlands 730 Jan-89 0.0014 0.0230Spain 730 Jan-89 0.0008 0.0271Sweden 730 Jan-89 0.0009 0.0353Switzerland 730 Jan-89 0.0018 0.0237United Kingdom 730 Jan-89 0.0010 0.0219

Canada 730 Jan-89 0.0010 0.0225United States 730 Jan-89 0.0017 0.0222

Developed Market Average 0.0009 0.0270

World Index 730 Jan-89 0.0006 0.0195Note: (*) Portugal data ends in Mar-99.

Table 2ASummary Statistics of Stock Index Returns

IR and NROverall Return Investable Return Non-Investable Return

This table contains summary statistics for the government bond index, weekly data from Datastream.Full 469 observations are from January 1994 to December 2002.

Start StandardEmerging Markets Obs Date Mean Deviations Min MaxArgentina 469 Jan-94 -0.0012 0.0340 -0.2564 0.0922Brazil 469 Jan-94 0.0018 0.0312 -0.1750 0.1267Chile 187 Jun-99 0.0022 0.0134 -0.0897 0.0968Colombia 304 Mar-97 0.0017 0.0220 -0.1243 0.1086Mexico 469 Jan-94 0.0020 0.0188 -0.1134 0.0659Peru 469 Jan-94 0.0026 0.0361 -0.2360 0.1572Venezuela 469 Jan-94 0.0022 0.0313 -0.3010 0.1356

China -- -- -- -- -- --India -- -- -- -- -- --Indonesia -- -- -- -- -- --Korea 469 Jan-94 0.0016 0.0116 -0.1153 0.0686Malaysia 468 Jan-94 0.0013 0.0067 -0.0275 0.0828Pakistan 78 Jul-01 0.0054 0.0229 -0.0905 0.0827Philippines -- -- -- -- -- --Sri Lanka -- -- -- -- -- --Taiwan -- -- -- -- -- --Thailand 291 Jun-97 0.0019 0.0210 -0.1366 0.0831

Czech Republic 139 May-00 0.0016 0.0044 -0.0140 0.0125Greece -- -- -- -- -- --Hungary 204 Feb-99 0.0015 0.0039 -0.0139 0.0123Poland 469 Jan-94 0.0022 0.0205 -0.1261 0.1102Portugal* 519 Jan-93 0.0019 0.0049 -0.0309 0.0367Russia 469 Jan-94 0.0027 0.0605 -0.5404 0.3726Slovakia 325 Oct-96 0.0023 0.0436 -0.5493 0.5548

Egypt 73 Aug-01 0.0028 0.0184 -0.0800 0.0663Israel -- -- -- -- -- --Jordan -- -- -- -- -- --Morocco 469 Jan-94 0.0018 0.0242 -0.2082 0.1158South Africa 417 Jan-95 0.0024 0.0156 -0.1050 0.1422Turkey 339 Jul-96 0.0022 0.0233 -0.1850 0.1592Zimbabwe -- -- -- -- -- --

Emerging Market Average 0.0020 0.0223

Developed MarketsAustralia 521 Jan-93 0.0016 0.0063 -0.0223 0.0240Hong Kong -- -- -- -- -- --Japan 521 Jan-93 0.0007 0.0033 -0.0121 0.0118Singapore -- -- -- -- -- --

Belgium 521 Jan-93 0.0014 0.0048 -0.0191 0.0197France 521 Jan-93 0.0013 0.0039 -0.0130 0.0150Germany 521 Jan-93 0.0012 0.0045 -0.0176 0.0129Italy 521 Jan-93 0.0020 0.0055 -0.0239 0.0226Netherlands 521 Jan-93 0.0013 0.0036 -0.0125 0.0122Spain 521 Jan-93 0.0018 0.0048 -0.0193 0.0204Sweden 521 Jan-93 0.0015 0.0055 -0.0315 0.0164Switzerland 521 Jan-93 0.0010 0.0028 -0.0119 0.0097United Kingdom 521 Jan-93 0.0014 0.0043 -0.0137 0.0158

Canada 521 Jan-93 0.0014 0.0054 -0.0225 0.0207United States 521 Jan-93 0.0013 0.0050 -0.0208 0.0180

Developed Market Average 0.0014 0.0046

Table 2BSummary Statistics of Bond Index Returns

This table reports percentages of year-end market capitalization that are investable and non-investable according to the IFC index, for the emerging market countries. For the developedcountries, the investable are 100%. The market capitalizations are year-end values in millions of U.S. dollars.

Region Country 1994 1996 1997 1998 1999 1994 1996 1997 1998 1999 1994 1996 1997 1998 1999Latin Argentina 98% 99% 100% 100% 100% 2% 1% 0.4% 0.1% 0.5% 18,751 26,564 35,142 24,894 23,319 America Brazil 63% 82% 87% 92% 94% 37% 18% 13% 8% 6% 111,906 94,879 102,965 65,871 107,803

Chile 22% 99% 99% 95% 95% 78% 0.8% 1.1% 5% 5% 45,058 35,780 44,498 31,837 42,639 Colombia 78% 76% 80% 78% 72% 22% 24% 20% 22% 28% 11,437 9,287 11,452 6,337 5,371 Mexico 89% 93% 95% 92% 99% 11% 7% 5% 8% 1% 83,279 72,197 108,941 67,174 118,418 Peru 81% 93% 94% 88% 97% 19% 7% 6% 12% 3% 5,271 7,605 9,657 6,151 7,583 Venezuela 78% 89% 91% 98% 98% 22% 11% 9% 2% 2% 3,352 7,153 9,138 4,689 4,237

Asia-Pacific China 10% 12% 10% 25% 37% 90% 88% 90% 75% 63% 19,307 38,754 49,981 52,452 78,532 India 22% 26% 29% 26% 28% 78% 74% 71% 74% 72% 65,358 50,802 50,856 49,797 92,526 Indonesia 50% 62% 98% 98% 96% 50% 38% 2% 2% 4% 22,199 49,638 13,553 10,682 23,260 Korea 11% 22% 54% 91% 94% 89% 78% 46% 9% 6% 125,065 73,108 25,157 61,203 181,989 Malaysia 83% 92% 94% 94% 96% 17% 8% 6% 6% 4% 125,883 169,941 45,911 44,849 68,937 Pakistan 53% 80% 89% 84% 81% 47% 20% 11% 16% 19% 7,668 4,630 5,979 2,400 3,115 Philippines 54% 46% 48% 48% 47% 46% 54% 52% 52% 53% 30,222 48,633 18,650 21,969 25,953 Sri Lanka 30% 32% 34% 33% 53% 70% 68% 66% 67% 47% 1,710 1,126 1,293 1,036 29,232 Taiwan, China 8% 33% 40% 35% 55% 92% 67% 60% 65% 45% 160,212 151,413 153,176 138,369 230,955 Thailand 28% 34% 47% 54% 53% 72% 66% 53% 46% 47% 79,660 57,750 10,921 16,735 29,232

Emerging Czech Rep. 35% 35% 38% 71% 80% 65% 65% 62% 29% 20% 8,735 9,558 4,847 4,204 4,205 Europe Greece 93% 99% 100% 100% 100% 7% 1.3% 0.2% 0.2% 0.0% 8,021 10,210 16,255 40,891 90,076

Hungary 63% 90% 100% 99% 99% 37% 10% 0.5% 0.6% 0.5% 747 3,569 11,175 10,933 13,076 Poland 100% 100% 100% 100% 100% 0.0% 0.2% 0.0% 0.1% 0.1% 1,478 5,730 6,234 8,134 11,770 Portugal 64% 77% 90% 91% - 36% 23% 10% 9% - 11,185 16,437 24,745 34,574 -Russia - 0% 63% 67% 77% - 100% 37% 33% 23% - 19,273 54,744 7,558 25,252 Slovakia - 0% 86% 85% 91% - 100% 14% 15% 9% - 1,311 1,320 600 433

Others Egypt - 0% 79% 82% 93% - 100% 21% 18% 7% - 1,884 8,141 5,848 11,433 Israel - 94% 99% 99% 100% - 6% 0.6% 0.6% 0.3% - 12,609 18,812 16,373 26,527 Jordan 35% 26% 34% 34% 51% 65% 74% 66% 66% 49% 2,787 2,890 3,261 4,277 4,109 Morocco - 0% 86% 81% 95% - 100% 14% 19% 5% - 4,551 7,562 9,674 8,141 South Africa 96% 100% 99% 100% 100% 4% 0.0% 0.5% 0.0% 0.0% 137,866 86,122 90,297 68,672 110,672 Turkey 100% 100% 100% 100% 99% 0.0% 0.0% 0.0% 0.0% 1.3% 15,106 16,276 33,732 16,947 58,708 Zimbabwe 12% 26% 40% 30% 32% 88% 74% 60% 70% 68% 1,341 2,386 1,123 653 1,613

Total 55% 61% 73% 77% 80% 45% 39% 27% 23% 20% 946,506 965,348 816,590 713,342 1,217,913

Investable Non-Investable Market Capitalization

Table 3Percentage of Investable and Non-Investable by Country

This table reports percentage distributions of investable and non-investable firms in each industry (defined bythe SIC code in parentheses) for all firms in the EMDB sample across countries, using monthly stock-level datafrom January 1989 to December 2002. Industry groupings are similar to those found in Bae, Chan, Ng (2001).

Industry (SIC codes) Investable % Non-Investable % Difference

Agriculture, Forestry and Fishing 2.29% 3.18% -0.89(1,2,7,8,9)

Mining 5.65% 4.48% 1.18(10,12,13,14)

Construction 4.43% 2.94% 1.49(15,16,17)

Manufacturing 40.95% 46.85% -5.90(20-36)

Transportation and Public Utilities 12.61% 13.63% -1.02(38-49)

Wholesale Trade 0.61% 0.91% -0.30(50, 52)

Retail Trade 4.13% 2.60% 1.53(53,54,55,58,59)

Finance, Insurance and Real Estate 15.05% 16.75% -1.71(51,60,61,62,63,65)

Services 7.10% 5.54% 1.57(67,70,73,75,78,79,89)

Others 7.18% 3.13% 4.05(99)

Median Difference 0.44

Table 4ADistribution of Investable and Non-Investable Firms by Industry

This table reports percentage distributions in each industry by firms that switch from completely investableto completely non-investable and vice versa in the EMDB sample across countries, using monthly stock-leveldata from January 1989 to December 2002. Industry groupings are similar to those found in Bae, Chan, Ng (2001).

Investable to Non-Investable toIndustry (SIC codes) Non-Investable Investable Difference

Agriculture, Forestry and Fishing 2.89% 1.55% 1.34(1,2,7,8,9)

Mining 5.60% 6.21% -0.62(10,12,13,14)

Construction 5.23% 2.17% 3.06(15,16,17)

Manufacturing 44.40% 50.31% -5.91(20-36)

Transportation and Public Utilities 9.75% 9.01% 0.74(38-49)

Wholesale Trade 0.90% 0.62% 0.28(50, 52)

Retail Trade 3.25% 2.80% 0.45(53,54,55,58,59)

Finance, Insurance and Real Estate 13.36% 17.08% -3.72(51,60,61,62,63,65)

Services 7.76% 4.97% 2.79(67,70,73,75,78,79,89)

Others 6.86% 5.28% 1.58(99)

Median Difference 0.60

Table 4BDistributions of Firms with Changing Investability by Industry

This table compares investable firms and non-investable firms for each country, by comparing the averages of two measures of firm characteristics: (1) Liquidity: turnover rate ofstock holdings as represented by the ratio of value-traded to total market capitalization, and (2) size: ratio of the firm market capitalization to total country market capitalization.A firm is defined to be investable if the investable fraction reported in the EMDB database is 1 and non-investable if the fraction is 0. All observations with interim values are deletedfrom this analysis. The test of comparison is a one-sided t-test to see if liquidy or size of investable firms is significantly larger than that of the non-investable firms, at 95% significancelevel. All values are taken at the beginning of the year.

Region Country Inv. Non-Inv. Liquidity Size Inv. Non-Inv. Liquidity Size Inv. Non-Inv. Liquidity Size

Latin Argentina 24 6 N N 25 3 N N 26 1 - -America Brazil 44 26 N N 25 12 N N 44 10 Y N

Chile 0 15 N N 35 3 N N 30 11 N YColombia 10 14 N N 4 12 N Y 4 10 N YMexico 63 13 Y N 5 6 Y Y 51 13 Y NPeru 11 24 N Y 16 13 N Y 13 14 N YVenezuela 12 5 N N 10 7 N Y 13 5 N Y

Asia-Pacific China 18 99 N Y 43 152 N N 47 156 N NIndia 0 43 - - 0 61 - - 0 56 - -Indonesia 0 9 - - 45 1 - - 31 17 N NKorea 0 6 - - 57 13 N N 52 27 N YMalaysia 99 0 - - 88 0 - - 33 18 N YPakistan 15 56 Y Y 10 31 N Y 0 36 - -Phillipines 16 22 Y N 3 10 N N 1 21 - -Sri Lanka 4 27 N Y 4 45 N Y 0 46 - -Taiwan 0 2 - - 5 0 - - 0 0 - -Thailand 0 6 - - 0 9 - - 0 21 - -

Emerging Czech Rep 5 55 N Y 3 35 Y N 5 24 N NEurope Greece 25 11 N Y 43 2 N N 43 2 N N

Hungary 5 8 N Y 8 2 N Y 9 2 N YPoland 12 0 - - 13 0 - - 12 1 - -Portugal 19 7 N N 10 0 - - 10 0 - -Russia -- -- -- -- 11 7 N N 5 19 Y NSlovakia -- -- -- -- 3 15 N Y 2 14 N Y

Others Egypt -- -- -- -- 11 26 N Y 5 32 N YIsrael -- -- -- -- 10 3 N N 13 3 N NJordan 0 29 - - 1 38 - - 2 35 N YMorocco -- -- -- -- 5 6 N N 5 8 Y NSouth Africa 58 4 N N 17 1 - - 32 0 - -Turkey 40 0 - - 30 0 - - 31 0 - -Zimbabwe 0 19 - - 0 12 - - 0 9 - -

Significantly Greater?1994 1997

Observations

Table 5

Observations Observations1998

Firm-Level Comparison of Investable vs. Non-Investable

Significantly Greater? Significantly Greater?

This table reports levels in estimated moments across two regimes. The T-test statistics reported are of tests of equivalence in volatility (Vol), correlation with crisis country (Corr C), andcorrelation with world index (Corr W) between the turmoil regime and stable regime separately for investable and non-investable stock index returns. The rejections of the null, against the one-sided alternativethat the turmoil regime volatility or correlation is greater, at the 10%, 5% and 1% significance level rejections are denoted by (*) , (**) and (***), respectively. N is the number of observations used in estimation.

Emerging Markets: N Diff T-Stat Diff T-Stat Diff T-Stat Diff T-Stat Diff Vol. Diff T-Stat Diff T-Stat Diff T-StatArgentina 730 0.078 0.222 0.119 0.220 0.094 0.182 0.084 0.088 0.016 0.103 -0.037 -0.072 -0.013 -0.012 -0.069 -0.127Brazil 730 0.201 0.006 0.080 0.009 0.209 0.520 0.132 0.016 0.083 0.002 0.121 0.010 0.168 0.020 0.122 0.007Chile 730 0.049 0.801 0.113 1.027 0.137 0.429 0.160 1.317 * 0.028 0.262 0.079 0.576 0.033 0.066 0.042 0.256Colombia 560 -0.011 -0.058 -0.051 -0.472 0.150 0.857 0.134 1.146 -0.006 -0.098 -0.056 -0.541 0.023 0.229 0.029 0.184Mexico 730 0.114 3.016 *** 0.176 1.998 ** 0.319 3.085 *** 0.257 2.849 *** 0.060 0.667 -0.027 -0.336 0.003 0.030 0.013 0.143Peru 521 0.022 0.140 0.207 0.323 0.189 0.657 0.169 1.044 0.008 0.040 0.072 0.261 0.080 0.107 0.091 0.123Venezuela 616 0.020 0.164 0.121 1.014 -0.006 -0.025 -0.026 -0.121 -0.007 -0.024 -0.021 -0.282 -0.055 -0.733 -0.016 -0.270

China 521 0.219 4.709 *** 0.220 2.453 *** 0.135 1.234 0.091 0.352 -0.135 -0.905 -0.092 -0.530 -0.036 -0.270 -0.008 -0.022India 529 0.064 0.587 0.205 0.639 0.197 1.045 0.125 0.863 0.061 0.560 0.225 0.696 0.216 1.091 0.149 0.966Indonesia 639 0.429 5.285 *** 0.283 2.863 *** 0.171 1.215 0.158 0.771 0.410 3.211 *** 0.250 2.908 *** 0.194 1.861 ** 0.199 1.009Korea 573 0.266 1.707 ** 0.089 0.994 0.107 0.902 0.098 0.787 0.270 2.063 ** 0.095 1.095 0.135 1.290 * 0.115 0.969Malaysia 730 0.249 3.930 *** 0.211 2.063 ** 0.167 1.656 ** 0.185 1.526 * 0.250 4.351 *** 0.237 2.361 *** 0.167 1.773 ** 0.166 1.433 *

Pakistan 552 0.071 1.403 * -0.095 -1.499 -0.126 -2.070 -0.109 -2.094 0.071 1.488 * -0.082 -0.876 -0.099 -0.852 -0.097 -0.893Philippines 730 0.135 1.773 ** 0.273 2.967 *** 0.215 3.153 *** 0.203 2.120 ** 0.167 1.993 ** 0.312 3.667 *** 0.268 3.392 *** 0.249 2.818 ***

Sri Lanka 460 0.055 0.623 0.022 0.216 0.111 0.876 0.047 0.184 0.028 0.532 0.037 0.246 0.165 0.926 0.112 0.423Taiwan 625 0.081 0.488 0.206 1.237 0.116 0.133 0.089 0.081 0.079 0.449 0.209 1.323 * 0.122 0.130 0.094 0.081Thailand 730 0.229 0.465 0.253 2.326 ** 0.206 1.020 0.166 1.005 0.257 0.466 0.261 2.279 ** 0.219 1.376 * 0.179 1.340 *

Czech Republic 469 0.092 0.242 0.144 0.318 0.301 2.615 *** 0.252 1.508 * 0.020 0.155 0.081 1.318 * 0.226 0.535 0.199 1.052Greece 565 0.125 0.715 0.021 0.125 0.218 1.554 * 0.348 1.558 * 0.061 1.104 -0.020 -0.047 0.134 0.540 0.202 0.627Hungary 520 0.159 0.852 0.102 0.771 0.238 2.703 *** 0.166 1.543 * 0.065 0.410 0.076 0.480 0.145 0.130 0.118 0.180Poland 455 0.000 0.000 0.128 0.110 0.222 0.160 0.148 0.209 0.040 0.029 -0.087 -0.556 0.115 0.165 0.146 0.433Portugal 534 0.104 3.217 *** 0.217 1.873 ** 0.289 2.491 *** 0.377 2.262 ** 0.116 4.235 *** 0.193 1.242 0.276 1.909 ** 0.341 2.159 **

Russia 307 0.272 0.542 0.087 0.461 0.141 0.671 0.080 0.288 0.321 0.794 -0.120 -0.714 -0.029 -0.086 -0.049 -0.117Slovakia 247 -0.007 -0.074 0.042 0.297 0.132 1.101 0.130 0.974 -0.042 -0.201 -0.078 -0.245 0.068 0.297 0.087 0.329

Egypt 308 0.053 0.114 0.021 0.047 0.099 0.121 0.082 0.120 -0.008 -0.029 -0.021 -0.067 0.095 0.103 0.090 0.105Israel 312 0.098 1.924 ** -0.032 -0.223 0.018 0.149 -0.005 -0.029 0.102 0.276 0.072 0.498 0.094 0.665 0.054 0.301Jordan 669 0.029 1.899 ** 0.196 1.438 * 0.237 3.331 *** 0.208 4.833 *** 0.029 0.598 0.209 1.614 * 0.293 4.458 *** 0.238 4.390 ***

Morocco 307 -0.033 -0.425 0.169 0.746 0.134 0.324 0.178 0.365 -0.039 -1.234 0.052 0.021 0.134 0.169 0.154 0.119South Africa 243 0.169 0.536 0.325 0.672 0.409 0.231 0.380 0.522 0.247 0.257 0.032 0.054 0.357 0.126 0.323 0.229Turkey 395 0.289 0.236 -0.011 -0.037 0.063 0.598 0.010 0.016 -0.016 -0.002 0.022 0.046 0.060 0.515 0.052 0.062Zimbabwe 434 0.168 0.363 -0.076 -0.067 -0.007 -0.007 0.018 0.107 -0.263 -0.556 -0.091 -1.589 0.007 0.025 0.027 0.047

Countries with Higher Moment in Turmoil: 28 26 28 28 23 19 26 26Sign Test (P-value) 0.000 0.000 0.000 0.000 0.002 0.075 0.000 0.000

Corr USCorr CVolatility Volatility Corr CCorr USCorr W Corr W

Table 6A

Non-Investable Stock Index ReturnsInvestable Stock Index Returns

Test 1: Existence of Contagion (Regime-Switching Model)Crisis Country: Hong Kong

This table reports levels in estimated moments across two regimes. The T-test statistics reported are of tests of equivalence in volatility (Vol), correlation with crisis country (Corr C), andcorrelation with world index (Corr W) between the turmoil regime and stable regime separately for investable and non-investable stock index returns. The rejections of the null, against the one-sided alternativethat the turmoil regime volatility or correlation is greater, at the 10%, 5% and 1% significance level rejections are denoted by (*) , (**) and (***), respectively. N is the number of observations used in estimation.

Emerging Markets: N Diff T-Stat Diff T-Stat Diff T-Stat Diff T-Stat Diff Vol. Diff T-Stat Diff T-Stat Diff T-StatArgentina 730 -0.210 -2.125 0.286 1.944 ** 0.236 0.510 0.207 0.569 0.087 0.233 0.047 0.058 0.007 0.014 0.019 0.070Brazil 730 -0.054 -0.562 0.341 4.521 *** 0.258 2.309 ** 0.248 2.340 *** -0.019 -0.213 0.283 3.555 *** 0.176 1.552 * 0.153 1.357 *

Chile 730 0.044 1.566 * 0.235 0.362 0.304 0.433 0.326 2.332 *** 0.009 0.187 0.113 0.120 0.117 0.147 0.105 0.670Colombia 560 0.010 0.167 0.096 0.517 0.198 1.535 * 0.199 1.884 ** -0.067 -2.229 -0.003 -0.068 0.114 2.018 ** 0.116 1.775 **

Mexico 730 0.065 1.054 0.262 2.907 *** 0.365 1.759 ** 0.296 1.595 * 0.034 0.125 0.130 0.399 0.097 0.767 0.089 0.912Peru 521 -0.032 -0.313 0.143 0.973 0.206 0.539 0.195 0.373 0.000 0.002 -0.007 -0.041 -0.032 -0.175 -0.012 -0.074Venezuela 616 -0.057 -0.742 0.258 2.961 *** 0.016 0.120 -0.011 -0.137 0.028 0.192 0.025 0.538 -0.069 -1.817 -0.058 -1.606

China 521 0.172 4.081 *** 0.089 0.842 0.108 0.833 0.104 0.543 -0.226 -2.535 -0.117 -0.554 -0.025 -0.807 0.023 0.131India 529 0.042 1.063 0.230 1.316 * 0.191 1.113 0.121 0.665 0.039 1.008 0.239 1.360 * 0.207 1.190 0.144 0.785Indonesia 639 0.534 5.375 *** 0.244 2.037 ** 0.246 3.509 *** 0.200 3.637 *** 0.669 5.404 *** 0.137 1.239 0.160 2.021 ** 0.170 3.169 ***

Korea 573 0.347 5.403 *** 0.307 3.534 *** 0.085 0.588 0.083 0.536 0.332 4.667 *** 0.337 3.876 *** 0.092 0.666 0.079 0.537Malaysia 730 0.287 3.243 *** 0.214 1.714 ** 0.078 0.591 0.096 0.282 0.289 4.654 *** 0.256 3.271 *** 0.086 0.568 0.070 0.394Pakistan 552 0.098 0.477 0.030 0.223 -0.025 -0.199 -0.038 -0.281 0.079 0.439 0.039 0.326 -0.057 -0.320 -0.039 -0.295Philippines 730 0.162 2.988 *** 0.315 3.148 *** 0.300 3.146 *** 0.203 0.984 0.201 4.986 *** 0.439 3.598 *** 0.321 2.867 *** 0.242 1.042Sri Lanka 460 0.039 0.680 0.219 2.719 *** 0.172 0.901 0.179 0.963 -0.014 -0.476 0.161 1.506 * 0.228 0.896 0.227 1.119Taiwan 625 0.040 0.235 0.258 0.412 0.174 0.360 0.152 0.794 0.039 0.215 0.240 0.380 0.169 0.345 0.148 0.704

Czech Republic 469 0.042 0.351 0.254 0.694 0.302 3.737 *** 0.264 3.125 *** 0.147 0.983 0.125 1.069 0.049 1.112 0.062 0.766Greece 565 0.122 0.081 0.341 0.245 0.367 0.245 0.371 0.285 0.215 0.044 0.165 0.357 0.028 0.019 0.103 0.105Hungary 520 0.133 1.498 * 0.211 2.377 *** 0.325 4.097 *** 0.277 2.668 *** 0.139 2.075 ** -0.045 -0.452 -0.002 -0.014 -0.005 -0.054Poland 455 -0.109 -2.102 0.286 1.895 ** 0.266 2.671 *** 0.181 1.974 ** -0.061 -0.779 0.073 0.485 0.109 0.874 0.108 1.063Portugal 534 0.118 4.643 *** 0.347 4.494 *** 0.384 4.130 *** 0.422 2.808 *** 0.123 5.035 *** 0.392 6.910 *** 0.360 3.143 *** 0.368 2.071 **

Russia 307 0.284 0.171 0.155 0.157 0.120 0.537 0.009 0.050 0.343 0.403 0.067 0.114 0.016 0.065 -0.078 -0.365Slovakia 247 -0.030 -0.297 -0.076 -0.396 0.039 0.105 0.042 0.096 -0.034 -0.153 -0.086 -0.323 0.054 0.088 0.069 0.057

Egypt 308 0.011 0.145 0.064 0.186 0.079 0.308 0.074 0.130 -0.020 -0.441 0.031 0.288 0.066 0.286 0.084 0.340Israel 312 0.062 1.312 * 0.035 0.013 -0.026 -0.052 -0.062 -0.386 0.074 0.090 0.066 0.038 0.077 0.088 0.072 0.108Jordan 669 0.003 0.063 0.110 0.775 0.181 2.050 ** 0.154 1.089 0.006 0.150 0.123 0.754 0.243 2.169 ** 0.184 1.007Morocco 307 -0.032 -0.490 -0.030 -0.200 0.083 0.547 0.095 0.631 -0.031 -0.633 -0.107 -0.740 0.083 0.914 0.066 0.751South Africa 243 0.183 2.654 *** 0.432 4.054 *** 0.352 2.585 *** 0.361 2.672 *** 0.412 4.955 *** 0.043 0.291 0.271 0.692 0.274 0.847Turkey 395 0.231 0.704 0.244 1.622 * 0.157 1.810 ** 0.055 0.395 6.943 1.905 ** -0.025 -0.303 -0.084 -0.785 -0.079 -0.577Zimbabwe 434 0.166 0.113 0.039 0.038 0.027 0.263 -0.003 -0.006 -0.331 -0.587 0.119 0.359 0.026 0.136 0.008 0.078

Table 6B

Non-Investable Stock Index ReturnsInvestable Stock Index Returns

Test 1: Existence of Contagion (Regime-Switching Model)Crisis Country: Thailand

This table reports levels in estimated moments across two regimes. The T-test statistics reported are of tests of equivalence in volatility (Vol), correlation with crisis country (Corr C), andcorrelation with world index (Corr W) between the turmoil regime and stable regime separately for investable and non-investable stock index returns. The rejections of the null, against the one-sided alternative of that the turmoil regime volatility or correlation is greater, at the 10%, 5% and 1% significance level rejections are denoted by (*) , (**) and (***), respectively.N is the number of observations used in estimation.

Developed Markets N Diff T-Stat Diff T-Stat Diff T-Stat Diff T-Stat Diff Vol. Diff T-Stat Diff T-Stat Diff T-Stat

Australia 730 0.048 1.921 ** 0.092 1.081 0.212 2.307 ** 0.229 2.339 *** 0.043 2.488 *** 0.301 4.101 *** 0.215 2.752 *** 0.220 2.266 **

Hong Kong 730 -- -- -- -- -- -- -- -- 0.116 1.921 ** 0.265 3.281 *** 0.269 2.959 *** 0.227 2.142 **

Japan 730 0.099 1.559 * 0.075 1.013 0.023 0.068 0.157 0.783 0.079 2.738 *** 0.359 0.829 -0.097 -0.633 0.082 0.444Singapore 730 0.172 5.755 *** 0.240 3.584 *** 0.173 1.660 ** 0.197 1.479 * 0.176 7.660 *** 0.347 6.002 *** 0.237 4.277 *** 0.240 3.356 ***

Belgium 730 0.082 4.291 *** 0.126 1.863 ** 0.220 2.564 *** 0.265 2.272 ** 0.073 4.502 *** 0.217 3.139 *** 0.193 2.767 *** 0.227 2.574 ***

France 730 0.092 4.978 *** 0.088 1.538 * 0.153 2.175 ** 0.224 1.731 ** 0.082 5.493 *** 0.298 3.842 *** 0.182 3.703 *** 0.217 3.066 ***

Germany 730 0.112 4.226 *** 0.051 0.923 0.161 1.892 ** 0.228 1.097 0.097 6.029 *** 0.245 4.427 *** 0.228 5.215 *** 0.266 3.819 ***

Italy 730 0.105 1.857 ** 0.213 1.386 * 0.272 1.181 0.307 0.993 0.075 3.008 *** 0.212 2.910 *** 0.358 5.036 *** 0.327 3.851 ***

Netherlands 730 0.106 4.339 *** 0.049 0.996 0.131 2.540 *** 0.209 2.269 ** 0.100 6.028 *** 0.230 4.022 *** 0.115 2.643 *** 0.182 2.718 ***

Spain 730 0.090 3.763 *** 0.087 1.158 0.213 2.801 *** 0.242 1.498 * 0.084 4.293 *** 0.297 2.120 ** 0.240 4.633 *** 0.259 2.945 ***

Sweden 730 0.147 4.673 *** 0.018 0.272 0.206 1.960 ** 0.271 2.437 *** 0.127 4.539 *** 0.236 3.116 *** 0.251 2.967 *** 0.282 3.433 ***

Switzerland 730 0.103 5.415 *** 0.035 0.467 0.126 1.954 ** 0.183 1.423 * 0.075 4.696 *** 0.203 2.628 *** 0.144 2.760 *** 0.206 2.621 ***

UK 730 0.079 4.474 *** 0.023 0.429 0.138 2.726 *** 0.218 2.130 ** 0.054 3.383 *** 0.258 4.224 *** 0.171 1.613 * 0.251 2.091 **

Canada 730 0.090 3.657 *** -0.030 -0.490 0.109 1.089 0.094 1.613 * 0.099 5.760 *** 0.219 3.333 *** 0.172 2.766 *** 0.085 1.547 *

US 730 0.102 5.134 -0.011 -0.219 0.110 0.792 -- -- 0.103 7.758 *** 0.222 4.294 *** 0.157 3.087 *** -- --

Table 6C

Crisis Country: ThailandCrisis Country: Hong Kong

Test 1: Existence of Contagion (Regime-Switching Model)Developed Markets

This table reports levels in estimated moments across two regimes. The T-test statistics reported are of tests of equivalence in volatility (Vol), correlation with crisis country (Corr C), and correlation with world index (Corr W) between the turmoil regime and stable regime separately for investable and non-investable stock index returns. The rejections of the null, against the one-sided alternativethat the turmoil regime volatility or correlation is greater, at the 10%, 5% and 1% significance level rejections are denoted by (*) , (**) and (***), respectively. N is the number of observations used in estimation.

Emerging Markets: N Diff T-Stat Diff T-Stat Diff T-Stat Diff T-Stat Diff Vol. Diff T-Stat Diff T-Stat Diff T-StatArgentina 730 -0.175 -4.249 0.185 1.708 ** 0.236 2.596 *** 0.163 1.515 * 0.081 1.662 ** 0.005 0.040 0.043 0.317 0.011 0.093Brazil 730 -1.783 -7.128 0.034 0.053 0.075 0.118 0.081 0.125 -1.783 -7.940 0.015 0.023 0.053 0.083 0.056 0.086Chile 730 0.038 2.765 *** 0.190 2.092 ** 0.314 3.563 *** 0.335 3.653 *** 0.007 0.570 0.044 0.474 0.099 1.046 0.082 0.777Colombia 560 -0.006 -0.332 0.126 1.150 0.179 1.298 * 0.190 1.473 * -0.072 -3.637 -0.021 -0.134 0.071 0.423 0.084 0.523Mexico 730 0.074 4.189 *** 0.228 2.700 *** 0.364 6.205 *** 0.302 4.860 *** 0.064 5.672 *** 0.086 0.810 0.056 0.621 0.068 0.643Peru 521 -0.032 -1.996 0.138 1.225 0.197 1.379 * 0.196 1.302 * 0.007 0.416 0.004 0.035 -0.048 -0.374 -0.017 -0.134Venezuela 616 0.011 0.382 0.261 2.671 *** -0.007 -0.065 -0.006 -0.050 0.127 4.908 *** -0.001 -0.006 -0.083 -0.689 -0.057 -0.457

China 521 0.161 5.945 *** 0.021 0.189 0.127 1.231 0.107 1.026 -0.224 -9.560 -0.120 -0.955 -0.020 -0.122 0.020 0.125India 529 0.040 1.865 ** 0.183 1.771 ** 0.185 1.602 * 0.111 0.971 0.037 1.734 ** 0.190 1.822 ** 0.202 1.753 ** 0.134 1.169Indonesia 639 0.267 11.152 *** 0.219 2.648 *** 0.256 2.744 *** 0.175 1.764 ** 0.398 14.351 *** 0.064 0.607 0.123 0.819 0.119 0.778Korea 573 0.205 6.912 *** 0.323 3.631 *** 0.122 1.226 0.111 1.059 0.190 7.489 *** 0.336 3.466 *** 0.125 1.124 0.101 0.881Malaysia 730 0.192 11.993 *** 0.141 1.791 ** 0.132 1.573 * 0.124 1.332 * 0.190 11.069 *** 0.176 2.030 ** 0.141 1.716 ** 0.095 1.052Pakistan 552 0.096 3.643 *** 0.017 0.174 -0.046 -0.340 -0.060 -0.462 0.076 3.963 *** 0.026 0.248 -0.086 -0.609 -0.068 -0.536Philippines 730 0.091 4.790 *** 0.242 2.935 *** 0.287 3.002 *** 0.180 1.791 ** 0.129 7.912 *** 0.368 4.964 *** 0.318 3.876 *** 0.222 2.510 ***

Sri Lanka 460 0.036 1.595 * 0.243 1.814 ** 0.167 1.148 0.176 1.342 * -0.018 -1.078 0.142 1.066 0.222 1.329 * 0.225 1.443 *

Taiwan 625 0.025 1.359 * 0.171 1.758 ** 0.169 1.733 ** 0.161 1.631 * 0.023 1.295 * 0.160 1.650 ** 0.164 1.689 ** 0.157 1.614 *

Czech Republic 469 0.014 0.766 0.193 1.659 ** 0.281 2.081 ** 0.264 1.924 ** 0.148 6.455 *** 0.045 0.193 -0.014 -0.059 0.014 0.057Greece 565 0.140 5.840 *** 0.288 2.818 *** 0.426 4.629 *** 0.388 3.693 *** 0.212 8.763 *** 0.092 0.621 0.094 0.595 0.117 0.992Hungary 520 0.126 6.410 *** 0.217 2.003 ** 0.299 2.809 *** 0.250 2.292 ** 0.142 7.454 *** -0.067 -0.524 -0.043 -0.264 -0.045 -0.278Poland 455 -0.145 -5.685 0.286 2.642 *** 0.273 1.939 ** 0.190 1.369 * -0.099 -2.992 0.021 0.172 0.087 0.517 0.097 0.570Portugal 534 0.153 8.194 *** 0.320 3.232 *** 0.522 6.703 *** 0.480 5.507 *** 0.157 8.355 *** 0.385 3.164 *** 0.497 5.588 *** 0.427 4.718 ***

Russia 307 0.258 4.228 *** 0.113 0.826 0.074 0.557 -0.035 -0.219 0.316 5.934 *** 0.025 0.181 -0.037 -0.251 -0.130 -0.811Slovakia 247 -0.021 -0.612 -0.078 -0.465 -0.047 -0.254 -0.034 -0.168 -0.030 -0.797 -0.099 -0.484 -0.034 -0.152 -0.012 -0.049

Egypt 308 0.006 0.325 0.085 0.484 0.127 0.853 0.100 0.610 -0.019 -1.385 0.098 0.698 0.134 0.705 0.123 0.587Israel 312 0.044 1.842 ** 0.022 0.170 0.013 0.110 -0.032 -0.234 0.045 1.220 0.005 0.028 0.112 0.734 0.096 0.608Jordan 669 0.015 2.203 ** 0.105 0.946 0.178 1.315 * 0.157 1.146 0.020 3.738 *** 0.110 0.991 0.242 1.919 ** 0.189 1.484 *

Morocco 307 -0.026 -1.761 -0.077 -0.454 0.055 0.268 0.080 0.353 -0.030 -1.839 -0.124 -0.683 0.057 0.318 0.049 0.271South Africa 243 0.182 6.824 *** 0.393 2.610 *** 0.363 2.894 *** 0.451 2.580 *** 0.459 5.466 *** -0.042 -0.192 0.350 1.683 ** 0.391 1.990 **

Turkey 395 0.077 0.765 0.375 1.390 * 0.199 1.442 * 0.053 0.338 6.928 9.502 *** -0.020 -0.009 -0.080 -0.136 -0.077 -0.080Zimbabwe 433 0.073 4.086 *** 0.049 0.344 -0.074 -0.488 -0.057 -0.368 0.098 4.600 *** 0.084 0.593 -0.150 -0.994 -0.123 -0.776

Table 7LOCAL CURRENCY WITH LOCAL CURRENCY

Investable Stock Index Returns Non-Investable Stock Index ReturnsVolatility Corr C Corr W Corr US Volatility Corr C Corr W Corr US

This table reports the difference between turmoil and stable regime correlations in the investable returns with the crisis countryindex returns and difference-in-difference (difference between investable and non-investable in the difference between turmoil andstable) corresponding to test 2. The rejections of the null, against the one-sided alternative that turmoil period correlation is greater,at the 10%, 5% and 1% significance levels are denoted by (*), (**), and (***), respectively.

Emerging Markets: Investable Diff-in-Diff T-Stat Investable Diff-in-Diff T-StatArgentina 0.119 0.156 1.761 ** 0.286 ** 0.239 0.347Brazil 0.080 -0.041 -0.011 0.341 *** 0.057 1.001Chile 0.113 0.034 0.371 0.235 0.122 0.400Colombia -0.051 0.005 0.041 0.096 0.099 0.608Mexico 0.176 ** 0.203 2.595 *** 0.262 *** 0.132 0.486Peru 0.207 0.134 0.313 0.143 0.150 1.175Venezuela 0.121 0.142 0.986 0.258 *** 0.233 1.892 **

China 0.220 *** 0.312 1.487 * 0.089 0.205 0.823India 0.205 -0.020 -0.935 0.230 * -0.009 -0.541Indonesia 0.283 *** 0.033 0.671 0.244 ** 0.107 1.231Korea 0.089 -0.007 -0.177 0.307 *** -0.029 -0.689Malaysia 0.211 ** -0.026 -0.519 0.214 ** -0.043 -0.498Pakistan -0.095 -0.013 -0.178 0.030 -0.009 -0.122Philippines 0.273 *** -0.039 -0.783 0.315 *** -0.124 -1.867Sri Lanka 0.022 -0.015 -0.100 0.219 *** 0.058 0.562Taiwan 0.206 -0.003 -0.117 0.258 0.018 1.644 *Thailand 0.253 ** -0.008 -0.395 --

Czech Republic 0.144 0.063 0.142 0.254 0.129 0.285Greece 0.021 0.041 0.085 0.341 0.176 0.185Hungary 0.102 0.026 0.120 0.211 *** 0.255 2.583 ***Poland 0.128 0.216 0.185 0.286 ** 0.213 2.063 **Portugal 0.217 ** 0.023 0.291 0.347 *** -0.045 -0.842Russia 0.087 0.207 1.520 * 0.155 0.088 0.204Slovakia 0.042 0.121 0.398 -0.076 0.011 0.026

Egypt 0.021 0.041 0.060 0.064 0.033 0.134Israel -0.032 -0.104 -0.465 0.035 -0.032 -0.033Jordan 0.196 * -0.013 -0.308 0.110 -0.013 -0.245Morocco 0.169 0.117 0.051 -0.030 0.077 0.361South Africa 0.325 0.294 1.152 0.432 *** 0.389 2.603 ***Turkey -0.011 -0.033 -0.045 0.244 * 0.269 1.732 **Zimbabwe -0.076 0.016 0.014 0.039 -0.079 -0.108

Countries with Higher Corr. in Turmoil 26 19 19 21Sign Test (P-value) 0.000 0.075 0.075 0.008

Crisis Country: ThailandCrisis Country: Hong Kong

Table 8A

Crisis Country: Hong Kong and ThailandTest 2: Fundamentals vs. Investor-Induced Contagion (Regime-Switching Model)

This table exhibits the contemporaneous correlations and lag correlations between investable and non-investablereturns for weekly data, during the crisis period, as defined by the regime-switching model using Thailand as thecrisis period. Contemporaneous correlations between investable and non-investable are reported in (1), differences between the correlations of non-investable with lagged investable (2, 3), and of investable with laggednon-investable (4, 5) are reported in (6, 7).

ContemporaneousCorrelation 1 week 2 weeks 1 week 2 weeks 1 week 2 weeks

Emerging Markets: (1) (2) (3) (4) (5) (6) (7)Argentina 0.206 0.150 0.039 -0.091 -0.002 0.111 -0.089Brazil 0.868 0.153 0.078 0.193 0.182 0.076 0.012Chile 0.302 0.107 0.144 0.120 -0.023 -0.037 0.143Colombia 0.531 0.116 0.187 0.204 0.095 -0.071 0.110Mexico 0.479 0.248 -0.001 0.192 0.079 0.249 0.114Peru 0.423 0.033 0.149 0.015 -0.098 -0.116 0.113Venezuela 0.467 0.100 0.063 0.072 0.115 0.037 -0.043

China 0.127 0.113 0.010 -0.071 0.125 0.103 -0.196India 0.991 0.100 0.087 0.010 0.006 0.013 0.004Indonesia 0.791 -0.041 -0.154 0.164 0.084 0.112 0.079Korea 0.909 -0.105 -0.120 0.123 0.130 0.015 -0.007Malaysia 0.952 -0.061 -0.030 0.086 0.080 -0.031 0.006Pakistan 0.794 0.301 0.184 0.061 0.042 0.118 0.019Philippines 0.904 0.119 0.008 0.184 0.256 0.110 -0.072Sri Lanka 0.799 0.214 0.188 0.105 0.015 0.026 0.091Taiwan 0.990 -0.007 0.001 0.088 0.081 -0.008 0.007Thailand 0.985 0.014 -0.023 0.218 0.193 0.037 0.025

Czech Rep. 0.454 0.076 0.046 0.018 0.081 0.030 -0.063Greece 0.313 0.027 -0.049 -0.097 0.009 0.075 -0.106Hungary 0.347 0.043 0.022 0.372 0.041 0.022 0.331Poland 0.494 -0.079 -0.096 0.134 0.014 0.017 0.120Portugal 0.875 0.112 -0.033 -0.067 0.036 0.145 -0.103Russia 0.644 0.120 0.152 0.271 0.111 -0.032 0.160Slovakia 0.199 0.129 0.105 0.045 0.092 0.024 -0.047

Egypt 0.528 -0.083 0.134 0.081 -0.130 -0.217 0.210Israel 0.598 -0.082 -0.064 0.131 0.125 -0.018 0.007Jordan 0.922 0.056 0.030 0.026 -0.048 0.026 0.074Morocco 0.628 0.209 0.139 0.054 -0.014 0.070 0.068South Africa 0.602 0.407 0.287 0.264 0.097 0.119 0.168Turkey 0.506 -0.114 -0.078 0.020 -0.044 -0.036 0.064Zimbabwe 0.947 0.070 0.074 0.104 0.081 -0.004 0.023Average 0.631 0.079 0.048 0.098 0.058 0.031 0.039

Number of Positive Differences 21 22Sign Test (P-value) 0.0041 0.0012

Table 9Lead and Lag Relationship

Lag Investable Lag Non-Investable Difference

Crisis Period

This table reports the correlation estimates and the difference between negative and positive tail, at 1.5 standard deviations away from the mean. The rejection of the null, againstthe one-sided alternative that the negative tail correlation is greater, at the 10%, 5% and 1% significance level are denoted by (*), (**), (***). A plus sign (+) denotes tails thatincrease in correlation as the thresholds are moved away from the mean.

Positive Negative Difference T-stat Positive Negative Difference T-stat Positive Negative Difference T-statDeveloped Markets

Australia 0.222 0.327 0.105 0.872Japan 0.145 0.274 0.129 1.150Singapore 0.562 0.579 0.016 0.143

Belgium 0.288 0.245 -0.043 -0.337France 0.322 0.319 -0.004 -0.029Germany 0.241 0.290 0.048 0.378Italy 0.300 + 0.317 0.017 0.130Netherlands 0.264 0.387 0.123 0.954Spain 0.279 0.303 0.023 0.185Sweden 0.253 0.211 -0.042 -0.339Switzerland 0.268 0.384 0.116 0.894UK 0.252 0.320 0.068 0.576

Canada 0.328 0.159 -0.169 -1.370US 0.243 0.131 -0.112 -0.937

Emerging Markets:Argentina 0.000 0.135 0.135 1.650 ** 0.000 0.135 0.135 1.590 * 0.000 0.000 0.000 -5.190Brazil 0.000 0.143 0.143 1.920 ** 0.001 0.191 0.190 2.460 *** 0.001 0.069 0.068 --Chile 0.002 0.221 0.219 2.670 *** 0.000 0.283 0.283 3.430 *** 0.001 0.180 0.178 2.300 **Colombia 0.004 0.072 0.068 0.908 0.002 0.014 0.012 0.097 0.001 0.000 -0.001 -1.950Mexico 0.073 0.436 0.364 3.710 *** 0.068 0.424 0.356 3.820 *** 0.000 0.102 0.102 1.470 *Peru 0.072 0.254 + 0.182 1.420 * 0.051 0.212 0.161 1.260 0.001 0.071 + 0.070 0.784Venezuela 0.000 0.091 0.091 1.280 0.002 0.049 0.047 0.665 0.001 0.041 0.040 0.561

China 0.013 0.001 -0.012 -0.155 0.548 0.571 0.023 0.162 0.006 0.000 -0.006 -1.100India 0.124 + 0.179 0.055 0.473 0.112 0.237 0.125 0.936 0.123 0.253 0.130 0.976Indonesia 0.178 0.439 0.260 1.900 ** 0.161 0.437 + 0.276 2.130 ** 0.167 0.368 + 0.202 1.470 *Korea 0.216 0.330 0.114 0.887 0.207 0.366 0.159 1.170 0.146 0.391 + 0.245 1.810 **Malaysia 0.155 0.486 0.332 2.700 *** 0.198 0.463 0.266 2.030 ** 0.150 0.374 0.224 1.680 **Pakistan 0.003 0.109 + 0.106 1.330 * 0.013 0.115 + 0.102 1.120 0.000 0.103 0.103 1.260Philippines 0.187 0.417 + 0.230 1.880 ** 0.261 0.348 0.087 0.662 0.171 0.419 + 0.248 1.990 **Sri Lanka 0.002 0.011 0.009 0.067 0.002 + 0.085 + 0.083 0.866 0.002 0.000 -0.002 -0.796Taiwan 0.159 0.305 + 0.146 1.280 0.248 0.289 0.041 0.318 0.241 0.289 0.048 0.373Thailand 0.307 0.471 + 0.163 1.410 * 0.328 0.483 0.155 1.340 * 0.312 0.464 0.151 1.250

Czech Republic 0.163 + 0.083 -0.080 -0.568 0.104 0.177 0.072 0.541 0.045 + 0.000 -0.044 --Greece 0.001 0.148 0.148 1.920 ** 0.001 0.145 0.145 1.890 ** 0.001 0.019 0.018 0.235Hungary 0.232 0.355 + 0.123 0.879 0.166 0.319 0.153 1.180 0.002 0.184 0.182 1.570 *Poland 0.000 0.146 0.146 1.500 * 0.000 0.147 0.146 1.540 * 0.000 0.036 0.036 0.408Portugal 0.298 + 0.253 -0.046 -0.302 0.300 + 0.270 -0.029 -0.194 0.212 + 0.162 -0.050 -0.348Russia 0.146 0.000 -0.145 -1.130 0.163 0.146 -0.017 -0.088 0.000 0.130 0.130 1.040Slovakia 0.001 0.053 0.052 0.559 0.000 0.071 + 0.071 0.599 0.001 0.000 -0.001 -3.940

Egypt 0.001 0.275 + 0.275 2.240 ** 0.000 + 0.107 0.107 0.971 0.002 0.068 0.066 0.599Israel 0.159 0.194 0.035 0.204 0.150 0.189 0.039 0.227 0.000 0.056 0.056 0.662Jordan 0.000 0.068 + 0.068 1.000 0.000 0.050 + 0.049 0.768 0.000 0.061 0.060 0.847Morocco 0.003 + 0.000 -0.003 -0.741 0.018 + 0.000 -0.018 -0.220 0.001 0.000 + -0.001 -4.260South Africa 0.212 0.362 0.150 1.040 0.212 0.362 0.150 1.010 0.169 0.172 0.002 0.012Turkey 0.002 0.103 0.100 1.270 0.000 0.133 0.133 1.690 ** 0.011 0.002 -0.009 --Zimbabwe 0.006 0.115 0.109 -- 0.001 0.094 0.093 1.120 0.028 0.158 0.130 1.100

Total Number of Countries Estimated: 44 31 28Countries with Positive Difference: 35 28 24Sign Test (P-value) 0.000 0.000 0.000

Correlation

Table 10ATest 3: Portfolio Balancing vs. Wealth Constraint (Exceedance Correlation)

Crisis Country: Hong Kong

Investable Index Return Non-Investable Index ReturnNegative-PositiveNegative-Positive

Total Index ReturnNegative-PositiveCorrelation Correlation

This table reports the correlation estimates and the difference between negative and positive tail, at 1.5 standard deviations away from the mean. The rejection of the null, againstthe one-sided alternative that the negative tail correlation is greater, at the 10%, 5% and 1% significance level are denoted by (*), (**), (***). A plus sign (+) denotes tails thatincrease in correlation as the thresholds are moved away from the mean.

Positive Negative Difference T-stat Positive Negative Difference T-stat Positive Negative Difference T-statDeveloped Markets

Australia 0.213 0.312 0.098 0.859Japan 0.307 0.471 + 0.164 1.380 *Hong Kong 0.164 0.292 + 0.128 1.100Singapore 0.493 0.629 + 0.135 1.160

Belgium 0.161 0.165 0.003 0.029France 0.282 + 0.190 + -0.092 -0.792Germany 0.204 0.198 -0.007 -0.056Italy 0.200 0.223 0.023 0.193Netherlands 0.201 0.234 0.033 0.280Spain 0.287 0.218 -0.069 -0.597Sweden 0.233 0.134 -0.099 -0.887Switzerland 0.198 + 0.223 + 0.024 0.203UK 0.194 0.084 -0.110 -1.040

Canada 0.220 0.271 + 0.051 0.421US 0.196 + 0.166 -0.031 -0.272

Emerging Markets:Argentina 0.000 0.106 0.105 1.520 * 0.000 0.102 0.102 1.450 * 0.000 0.000 0.000 1.080Brazil 0.001 0.181 + 0.181 2.420 *** 0.002 0.191 + 0.189 2.400 *** 0.009 0.128 0.119 1.700 **Chile 0.102 0.168 0.066 0.619 0.086 0.153 0.066 0.661 0.033 0.146 + 0.113 1.260Colombia 0.048 0.068 0.020 0.200 0.011 0.006 -0.005 -0.051 0.000 0.000 0.000 -0.029Mexico 0.075 0.293 + 0.218 1.910 ** 0.033 0.282 0.250 2.230 ** 0.030 0.110 0.080 0.791Peru 0.066 0.261 0.195 1.540 * 0.056 0.225 0.170 1.430 * 0.001 0.094 0.094 1.140Venezuela 0.161 + 0.221 + 0.060 0.523 0.073 0.085 + 0.011 0.102 0.002 + 0.170 + 0.168 1.940 **

China 0.000 0.000 0.000 -3.700 0.088 0.406 0.318 2.120 ** 0.001 0.000 -0.001 -3.790India 0.084 + 0.260 + 0.175 1.660 ** 0.060 0.292 + 0.232 1.840 ** 0.066 0.282 + 0.216 1.720 **Indonesia 0.668 + 0.414 + -0.254 -1.560 0.615 + 0.413 + -0.202 -1.410 0.547 + 0.381 -0.166 -1.070Korea 0.347 0.481 + 0.134 1.060 0.404 + 0.490 + 0.086 0.606 0.371 + 0.541 + 0.170 1.220Malaysia 0.405 0.521 + 0.116 0.874 0.394 0.521 + 0.127 0.986 0.394 0.439 + 0.046 0.341Pakistan 0.024 0.180 + 0.156 1.590 * 0.028 0.203 + 0.175 1.610 * 0.000 0.184 + 0.184 2.120 **Philippines 0.342 0.481 0.139 1.100 0.284 0.390 0.106 0.835 0.381 0.516 + 0.135 1.120Sri Lanka 0.026 0.024 -0.002 -0.019 0.071 0.085 0.014 0.113 0.054 0.060 0.006 0.047Taiwan 0.192 0.297 0.105 0.857 0.185 0.320 0.135 1.080 0.148 0.292 0.144 1.150

Czech Republic 0.090 0.128 0.039 0.296 0.078 0.190 0.112 0.841 0.263 + 0.059 -0.204 -1.610Greece 0.144 + 0.288 + 0.143 1.220 0.141 + 0.284 + 0.143 1.180 0.018 0.207 + 0.189 1.640 *Hungary 0.166 0.355 + 0.189 1.390 * 0.124 0.358 + 0.235 1.680 ** 0.001 0.153 0.152 1.460 *Poland 0.000 0.110 0.110 1.320 * 0.000 0.110 0.110 1.280 0.009 0.032 0.023 0.298Portugal 0.316 + 0.289 -0.028 -0.186 0.326 + 0.268 -0.058 -0.392 0.223 + 0.316 + 0.093 0.629Russia 0.266 0.344 0.077 0.501 0.286 0.325 0.039 0.241 0.280 0.304 0.025 0.140Slovakia 0.002 0.091 0.090 0.868 0.002 0.066 0.064 0.521 0.002 0.000 -0.002 -1.870

Egypt 0.009 0.000 -0.009 -0.097 0.002 0.000 -0.001 -0.278 0.012 0.000 -0.012 -0.034Israel 0.216 0.202 -0.014 -0.083 0.360 0.197 -0.164 -0.621 0.009 0.068 0.059 0.637Jordan 0.045 0.057 0.013 0.153 0.024 0.036 0.012 0.163 0.035 0.025 -0.009 -0.126Morocco 0.007 0.000 -0.006 -0.009 0.001 0.000 0.000 -0.683 0.000 0.000 0.000 -0.712South Africa 0.311 0.399 + 0.087 0.624 0.310 0.398 + 0.089 0.637 0.175 0.192 0.018 0.083Turkey 0.002 0.206 0.204 2.460 *** 0.000 0.211 0.211 2.510 *** 0.011 0.009 -0.002 --Zimbabwe 0.212 0.298 + 0.085 0.104 0.012 0.233 + 0.221 0.520 0.005 0.370 + 0.365 --

Total Number of Countries Estimated: 45 30 28Countries with Positive Difference: 32 24 20Sign Test (P-value) 0.001 0.000 0.006

DifferenceDifferenceTotal Index Return

DifferenceCorrelation Correlation Correlation

Table 10BTest 3: Portfolio Balancing vs. Wealth Constraint (Exceedance Correlation)

Crisis Country: Thailand

Investable Index Return Non-Investable Index Return

This table reports correlations between bond and stock market in two regimes. The T-test statistics reported are of tests of equivalence in volatility (Vcorrelation between bond and stock markets. The rejection of the null against the two-sided alternative that the turmoil regime volatility or correlationat the 10%, 5% and 1% significance level are denoted by (*) , (**) and (***), respectively. N is the number of observations used in estimation.

Emerging Markets: N Diff T-Stat Crisis Stable Diff T-Stat Crisis Stable Diff T-StatArgentina 469 -0.025 -0.663 0.612 0.452 0.160 0.845 0.290 0.165 0.124 0.002Brazil 469 0.073 1.587 0.722 0.667 0.055 0.322 0.651 0.626 0.025 0.000Chile 187 -0.011 -0.333 0.004 0.020 -0.016 -0.093 -0.108 -0.119 0.011 0.000Colombia 243 0.016 0.431 0.374 0.250 0.123 0.056 0.269 0.204 0.065 0.001Mexico 469 0.011 0.193 0.635 0.623 0.011 0.068 0.575 0.513 0.062 0.000Peru 469 0.058 0.472 0.540 0.310 0.230 0.384 0.262 0.170 0.091 0.000Venezuela 408 0.081 0.752 0.570 0.248 0.322 0.333 0.275 0.098 0.177 0.002

China -- -- -- -- -- -- -- -- -- -- --India -- -- -- -- -- -- -- -- -- -- --Indonesia -- -- -- -- -- -- -- -- -- -- --Korea 469 0.065 3.087 *** 0.496 0.178 0.319 0.182 0.522 0.184 0.338 0.000Malaysia 468 0.012 1.340 0.207 0.180 0.027 0.060 0.208 0.204 0.004 0.000Pakistan 17 0.298 5.847 *** 0.060 0.112 -0.052 -0.088 0.497 0.129 0.368 0.001Philippines -- -- -- -- -- -- -- -- -- -- --Sri Lanka -- -- -- -- -- -- -- -- -- -- --Taiwan -- -- -- -- -- -- -- -- -- -- --Thailand 291 0.103 4.237 *** 0.438 0.254 0.184 0.341 0.443 0.253 0.190 0.001

Czech Republic 139 0.004 0.625 -0.004 0.168 -0.172 -1.097 -0.027 0.041 -0.069 0.000Greece -- -- -- -- -- -- -- -- -- -- --Hungary 204 0.002 0.246 -0.198 0.028 -0.225 -1.813 * -0.045 -0.111 0.066 0.001Poland 407 -0.003 -0.187 0.281 0.127 0.154 0.483 0.183 0.051 0.132 0.001Portugal 323 -0.008 -0.745 -0.062 0.139 -0.201 -1.913 * -0.031 0.083 -0.114 -0.008Russia 307 0.253 1.218 0.645 0.426 0.220 0.089 0.646 0.432 0.213 0.001Slovakia 247 -0.165 -5.379 *** 0.008 0.033 -0.025 -0.166 -0.025 -0.039 0.014 0.000

Egypt 73 0.164 3.606 *** 0.062 0.140 -0.078 -0.128 0.174 0.235 -0.061 -0.001Israel -- -- -- -- -- -- -- -- -- -- --Jordan -- -- -- -- -- -- -- -- -- -- --Morocco 307 0.104 2.232 ** -0.153 -0.013 -0.140 -0.503 -0.118 0.025 -0.143 -0.001South Africa 139 0.075 2.020 ** 0.506 0.178 0.328 0.680 0.300 0.020 0.279 0.001Turkey 165 0.044 1.221 0.794 0.622 0.172 0.090 0.087 -0.045 0.131 0.000Zimbabwe -- -- -- -- -- -- -- -- -- -- --

Countries with Lower Moment in Turmoil: 5 8 4Sign Test (P-value) 0.987 0.808 0.996

Countries with Higher Moment in Turmoil: 16 13 17Sign Test (P-value) 0.004 0.095 0.001

Bond Volatility Corr Bond Corr Bond

Table 11ACorrelation with the Bond Market

Crisis Country: Hong Kong

Investable Stock Index Returns Non-Investable Stock Index Returns

This table reports correlations between bond and stock market in two regimes. The T-test statistics reported are of tests of equivalence in volatility (Vol), and correlation between bond and stock markets. The rejection of the null against the two-sided alternative that the turmoil regime volatility or correlation is greater,at the 10%, 5% and 1% significance level are denoted by (*) , (**) and (***), respectively. N is the number of observations used in estimation.

Emerging Markets: N Diff T-Stat Crisis Stable Diff T-Stat Crisis Stable Diff T-StatArgentina 469 -0.071 0.000 0.594 0.446 0.147 0.000 0.222 0.177 0.044 0.000Brazil 469 0.058 0.066 0.771 0.618 0.153 0.026 0.678 0.610 0.069 0.000Chile 187 0.000 0.025 0.039 0.005 0.034 0.269 -0.084 -0.132 0.047 0.001Colombia 243 0.068 2.974 *** 0.320 0.249 0.072 0.115 0.234 0.241 -0.006 0.000Mexico 469 -0.011 -0.191 0.611 0.644 -0.034 -0.230 0.493 0.557 -0.063 -0.001Peru 469 -0.037 -0.540 0.495 0.322 0.173 0.670 0.190 0.206 -0.016 0.000Venezuela 408 0.064 0.935 0.604 0.161 0.444 1.596 0.209 0.108 0.101 0.001

China -- -- -- -- -- -- -- -- -- -- --India -- -- -- -- -- -- -- -- -- -- --Indonesia -- -- -- -- -- -- -- -- -- -- --Korea 469 0.073 3.929 *** 0.424 0.080 0.345 0.442 0.439 0.101 0.337 0.000Malaysia 468 0.031 2.986 *** 0.261 0.013 0.248 0.907 0.259 0.057 0.201 0.000Pakistan 17 0.310 4.263 *** 0.122 0.137 -0.015 -0.031 0.506 0.133 0.373 0.001Philippines -- -- -- -- -- -- -- -- -- -- --Sri Lanka -- -- -- -- -- -- -- -- -- -- --Taiwan -- -- -- -- -- -- -- -- -- -- --Thailand 291 0.130 5.650 *** 0.392 0.052 0.340 3.744 *** 0.398 0.042 0.356 0.001

Czech Republic 139 0.000 0.021 0.014 0.176 -0.163 -0.745 0.007 0.056 -0.050 0.000Greece -- -- -- -- -- -- -- -- -- -- --Hungary 204 0.002 0.406 -0.157 0.061 -0.218 -0.914 -0.075 -0.106 0.031 0.000Poland 407 -0.087 -1.470 0.381 0.124 0.256 0.193 0.212 0.057 0.155 0.007Portugal 323 -0.017 -3.380 *** -0.049 0.177 -0.226 -2.105 ** -0.010 0.103 -0.113 -0.002Russia 307 0.326 3.280 *** 0.559 0.478 0.081 0.088 0.571 0.441 0.130 0.000Slovakia 247 -0.053 -1.810 * 0.033 0.023 0.010 0.044 -0.033 -0.040 0.007 0.000

Egypt 73 0.129 2.938 *** 0.073 0.139 -0.066 -0.260 0.200 0.212 -0.012 0.000Israel -- -- -- -- -- -- -- -- -- -- --Jordan -- -- -- -- -- -- -- -- -- -- --Morocco 307 0.123 3.443 *** -0.101 0.014 -0.115 -0.842 -0.033 0.001 -0.034 -0.001South Africa 139 0.154 3.062 *** 0.395 0.172 0.224 0.197 0.150 0.199 -0.049 0.000Turkey 165 0.012 0.090 0.753 0.643 0.110 0.038 -0.069 0.097 -0.166 0.000Zimbabwe -- -- -- -- -- -- -- -- -- -- --

Countries with Lower Moment in Turmoil: 5 7 9Sign Test (P-value) 0.987 0.905 0.668

Countries with Higher Moment in Turmoil: 16 14 12Sign Test (P-value) 0.004 0.039 0.192

Bond Volatility Corr Bond Corr Bond

Table 11BCorrelation with the Bond Market

Crisis Country: Thailand

Investable Stock Index Returns Non-Investable Stock Index Returns

This table reports correlations between bond and stock market in two regimes. The T-test statistics reported are of tests of equivalence in volatility (Vol), and correlation between bond and stock markets. The rejection of the null against the two-sided alternative that the turmoil regime volatility or correlation is greater,at the 10%, 5% and 1% significance level are denoted by (*) , (**) and (***), respectively. N is the number of observations used in estimation.

Developed Markets N Diff T-Stat Crisis Stable Diff T-Stat Diff T-Stat Crisis Stable Diff T-Stat

Australia 520 0.005 0.053 0.114 0.141 -0.028 -0.036 -0.002 -0.108 0.048 0.194 -0.146 -0.942Hong Kong -- -- -- -- -- -- -- -- -- -- -- -- --Japan 520 -0.004 -2.314 ** -0.296 -0.148 -0.148 -0.637 -0.008 -0.536 -0.228 -0.165 -0.063 -0.321Singapore -- -- -- -- -- -- -- -- -- -- -- -- --

Belgium 520 -0.003 -0.156 0.002 0.188 -0.186 -2.302 ** -0.009 -1.686 * 0.026 0.202 -0.176 -2.159 **France 520 0.000 -0.002 -0.054 0.171 -0.225 -0.274 -0.004 -1.002 -0.078 0.224 -0.302 -0.560Germany 520 0.003 0.274 -0.109 0.088 -0.197 -0.387 -0.002 -0.186 -0.103 0.118 -0.220 -1.666 *Italy 520 -0.010 -0.857 0.143 0.444 -0.301 -1.064 -0.020 -4.214 *** 0.100 0.492 -0.392 -0.702Netherlands 520 0.002 0.543 -0.082 0.068 -0.151 -0.586 -0.003 -0.555 -0.118 0.121 -0.239 -1.463Spain 520 -0.007 -0.965 0.013 0.308 -0.295 -6.228 *** -0.018 -5.006 *** 0.000 0.359 -0.360 -5.811 ***Sweden 520 -0.007 -0.803 0.104 0.243 -0.140 -1.051 -0.017 -2.643 *** 0.085 0.278 -0.193 -1.439Switzerland 520 0.000 -0.060 -0.173 0.035 -0.208 -2.204 ** 0.000 0.022 -0.128 0.044 -0.172 -0.571UK 520 0.002 0.644 -0.091 0.182 -0.274 -1.440 -0.002 -0.536 -0.067 0.208 -0.275 -1.891 *

Canada 520 -0.006 -0.402 -0.037 0.225 -0.263 -3.259 *** -0.010 -2.684 *** -0.025 0.294 -0.319 -3.898 ***US 520 0.001 0.078 -0.120 0.077 -0.197 -0.904 -0.002 -0.326 -0.119 0.116 -0.235 -1.471

Countries with Lower Moment in Turmoil: 8 13 12 13Sign Test (P-value) 0.133 0.000 0.000 0.000

Countries with Higher Moment in Turmoil: 5 0 1 0Sign Test (P-value) 0.709 1.000 0.998 1.000

Table 11CCorrelation with the Bond Market

Developed Markets

Crisis Country: Hong Kong Crisis Country: ThailandBond Volatility Corr Bond Corr BondBond Volatility

Based on the number of firms that are investable (investability = 1) and non-investable (investability=0), using firm-level data for December of the previous year.

Figure 1Percentage of Investable and Non-Investable by Industry

0102030405060

Investable Non-Investable

This figure shows weekly index returns for Hong Kong, Thailand, Indonesia and Malaysia from January to December 1997.The total index return data is from Datastream.

Figure 2

Hong KongThailandIndonesiaMalaysia

The figures show the time-series probabilities of the volatile regime, from the estimated regime-switching model, for the periodJanuary 1996 to January 2000. Figure 2A shows probabilities from the regime-switching model estimated using Hong Kong stockindex and the world index. Figure 2B shows probabilities from the regime-switching model estimated using Thailand stock indexand the world index.

Regime Probabilities (January 1996 to January 2000)Figure 3

Figure 3A: Hong Kong and World Index

Jan-96

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Figure 3B: Thailand and World Index

Jan-96

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