Are Investors Responsible for Stock Market

Are Investors Responsible for Stock Market Contagion? Brian H. Boyer, Tomomi Kumagai, and Kathy Yuan∗ Preliminary and Incomplete September 3, 2002

Abstract This paper provides empirical evidence that stock market crises are spread between countries through changes in international investors’ asset holdings rather than changes in each country’s market fundamentals. By separating firms into two categories, those eligible for purchase by foreigners (investable) and those that are not (non-investable), we show that international transmissions of crises are more pronounced in investable stocks than non-investable stocks. To do so, we use a variety of measures for computing and comparing the co-movement between crisis country stock index returns, and the investable and non-investable index returns of other markets. Our results show an increase in correlation during crisis periods for the investable stock index returns, and thus provide evidence for contagion induced by international investors.

∗

Brian H. Boyer ([email protected]) and Kathy Yuan ([email protected]) are at the University of Michigan Business School, 701 Tappan Street, Ann Arbor, MI 48109-1234. Tomomi Kumagai ([email protected]) is at Department of Economics, Wayne State University, 2097 FAB Detroit, MI 48202. The authors wish to thank Xiangming Li at the International Monetary Fund for her assistance, Jerry Hausman, Mark Aguiar, and Campbell Harvey for helpful comments.

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Introduction

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 was how an initial country-specific event seem 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 Wadhani (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: the 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 on country’s economic fundamentals as determinants of contagion, researchers sought explanations in investment holding patterns.2 Kodres and Prisker (2002) develop a theoretical model of financial contagion through cross-market hedging. This hedging model predicts market co-movements should be symmetrical in market upturns and downturns. Kyle and Xiong (2001) suggest that contagion occurs through 1

Contagion, in this paper, is defined as a significant increase in cross-market linkages after a shock to one country (or group of countries), similarly to the literature (Forbes and Rigobon 2002). 2 Our 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). A similar argument for investor-induced co-movement appears in the research on domestic stock returns within a country. In a recent paper, Barberis, Schleifer, and Wurgler (2002) show the co-movement of stock returns can be attributed to asset reclassification.

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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 (2000) 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 would not be able to discern the purpose of informed investors, and would not be 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 (2000) predict that crises are 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 investors are responsible for stock market contagion against the alternative hypothesis that crises are spread through correlated country fundamentals, we utilize a unique phenomenon in emerging stock markets. In emerging market countries, not all publicly listed firms are eligible for purchase by foreigners. By distinguishing between those stocks that are readily accessible to foreign investors (investable) and those that are accessible primarily to the local investors (non-investable), we form testable implications for the investor-induced contagion hypothesis versus fundamentalbased contagion hypothesis. A simple thought experiment will help us to spell out testable implications. Consider an economy with two emerging markets (A and B) that have independent economic fundamentals. There are investable and noninvestable stocks in each country. Suppose there are three types of investors. First, there is a group of investors who can only invest in investable stocks in country A and B. For concreteness, one might interpret these investors as institutional investors from developed countries. Second, there is a group of investors from emerging-market country A who only invest in country A’s two stock market indexes. Similarly, there is a third group of investors from emerging-market country B who only invest in country B’s two stock market indexes. One might think of these last two groups of investors as investors from emerging-market countries. We simply assume that investors from country A face large transaction costs when investing in country B’s asset, and similarly for investors in country B for assets in country A. These costs may 2

be a reduced form for institutional constraints or information asymmetries.3 For example, most governments in emerging market countries impose capital outflow restrictions on their citizens. All investors face borrowing constraints (i.e., they have to deposit collateral in margin accounts). When country A is struck by a country-specific crisis, foreign investors suffer losses in country A investable stock investment and have to withdraw country B investable investment in order to meet margin calls. This selling of country B investable stocks is unrelated 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 liquid their holdings in noninvestable stocks. Hence, the idiosyncratic shock in country A may even spread to country B’s non-investable stocks. This simple thought experiment illustrates three testable implications. First, if contagion is investor-induced (either through portfolio-balancing or wealth constraints), investable stocks should have a higher level of comovements with the crisis country stock market than non-investable stocks during the turmoil period, while the fundamental-based contagion hypothesis predicts that there are no differences between investable stocks’ and non-investable stocks’ correlations with external shocks. Second, if crises are spread through investors’ wealth constraints, correlation should be asymmetrical – higher in the market downturns due to wealth constraints. Finally, the investor-induced contagion hypothesis also predicts that within a country, return movements of investable stocks should lead return movements of non-investable stocks through local investors’ wealth constraints. We begin with a detailed explanation of the data and some summary statistics. We describe how the returns of investable stock index and noninvestable stock index are constructed for the emerging market countries. The summary statistics include the contemporaneous correlation between the investable and the non-investable returns for each country. We also test the lead and lag relationship between the investable and non-investable stock index returns for each country. If financial crisis shocks are spread through investors, the more accessible investable stocks should lead the non-investable ones. We compare the sizes of the cross-autocorrelations between investable 3

One simple story in the spirit of information asymmetry could be as follows. Country A investors doe not understand country B’s language and hence will not 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.

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and non-investable stock index returns in the same way that Campbell, Lo, and MacKinlay (1997) determine if large size stocks lead small stocks. Evidence of investable stocks leading the non-investable stocks lies in the larger correlation (1) between the lag of investable stock index return and the current non-investable stock index return, as compared with the correlation (2) between the lag of non-investable stock index return and the current investable stock index returns. This leading by investable stock index returns suggests that crises are absorbed into investable stocks first, thus providing support that investors are responsible for spreading the crises. For more structural tests of the investor-induced contagion hypothesis, we calculate the impact of the crisis country’s stock market return separately on investable and on non-investable stock index returns. We compare this impact, measured as the contemporaneous correlation of each of the returns with the crisis country stock market returns. If the bundle of investable and non-investable stocks have no intrinsically fundamental differences – in terms of export-orientation, size, and liquidity – the differences in the impact can be attributed to investor behavior. However, calculating the equity market correlation in volatile times is not a trivial statistic exercise. Some misleading results have been reported in the past that ignore the relationship between correlation and volatility. Boyer, Gibson, and Loretan (1999), Forbes and Rigobon (2002), and Stambaugh (1995) note that calculating correlations conditional on high (low) returns, or on high (low) volatility, induces a conditioning bias in the correlation estimates.4 We first construct unconditional correlation measures that correct for conditional bias, assuming asymptotic bivariate normality and stationarity. However, these bias-corrected statistics may be restrictive in the sense that it requires defining an exogenously determined crisis period, and assuming a bivariate normal distribution. Alternatively, we adopt the exceedance (or extreme value) correlation estimation implemented by Ledford and Tawn (1997) and Longin and Solnik (2001). This methodology is based upon the non-degenerate limiting distribution of the extreme tails, and is free of any 4

Bekaert, Harvey, and Ng (2002) propose a factor-model approach to measure contagion and do not suffer from the conditional bias problem reported in Forbes and Rigobon (2002). They apply a two-factor model with time-varying betas and measure contagion as correlation among the model residuals. They assume that crises are not spread through economic fundamentals as they define contagion as excess correlation over and above what one would expect from economic fundamentals. Tang (2001) uses a similar approach but restricts the factor model to a world CAPM.

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underlying distributional assumptions. Hence, the correlation estimates using this methodology are appropriate for any bivariate distribution. Similar to findings by Longin and Solnik (2001) and Hartmann et al (2001), we find asymmetry in the correlation that would not be explained by bivariate normal or simple ARCH models. We then further characterize stock market co-movements by specifying an empirical dynamic model of stock returns that is compatible with the exhibited asymmetric characteristics. The regime-switching model is chosen as a candidate model based on the findings of Ang and Chen (2002) regarding its success in replicating the empirical correlation asymmetry. We specify a tworegime switching model to test for a structural break in the joint distribution of asset returns. In this paper, we discuss our findings for the testing of investor-induced contagion hypothesis for the 1997 East Asian Crisis. Interestingly, using the above methods, we find that the investable stock index returns have higher correlations with the crisis country (either Hong Kong or Thailand).5 Using Hong Kong as the crisis country, we find some evidence of global contagion, as developed countries and Latin American emerging market countries have higher correlation with Hong Kong during the crisis period than during the stable period. Using Thailand as the crisis country, we find evidences of regional contagion, as only the Asian countries have higher correlation with Thailand during the crisis period. We also perform these tests for the Mexican peso collapse and the Russian crisis, and find additional evidence for the investor-induced contagion hypothesis. Tables and discussion of Mexican and Russian crises are available upon request. The remainder of the paper is organized as follows. Section 2 describes the data and the summary statistics of the index returns (both separately for the investable and non-investable stock index returns) This section also includes the results of testing the lead-lag relationship between investable and non-investable stock index returns. Section 3 discusses in detail the models considered in our calculation of the impact of the crisis on other countries. Section 4 estimates the models and compares the impact on the investable versus the non-investable. Section 5 concludes. 5

We will discuss the selection of crisis source countries in a later section.

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2

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., most stocks are accessible to both foreign and local investors, so the entire market is considered investable. However, in many emerging markets, some stocks are only accessible to local investors. Section 2.1 explains the construction of the index returns for these two categories. Section 2.2 compares the characteristics of the firms that are designated investable and non-investable. The characteristic differences introduce potential bias in comparing the effects on the investable and the non-investable stock index returns. Section 2.3 describes the overall sample correlation between investable and non-investable stock index returns for each country. Finally, section 2.4 discusses the lead and lag relationship between investable and non-investable stock index returns.

2.1

Investable and Non-Investable Stock Index Returns

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, measured in total market capitalization and total value traded (both year-end U.S. dollars), for the 31 emerging market countries and the 16 developed countries. In the EMDB, IFC provides two stock market index series for each of the emerging markets in the database: IFC Global (IFCG), representing the total market,6 and IFC Investable (IFCI), consisting of firms that are designated to be investable. Both IFC index series are dividend-inclusive and U.S. dollar-denominated. The IFC selects stocks for inclusion in its IFCG index by reviewing the trading activities and targets market coverage of 60 to 75 percent in market capitalization.7 The stock returns included in 6

The totals reported for the emerging market countries are the totals in IFC Global index and are not totals of all stocks. 7 The 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 (i.e. one large block trade might skew the value traded statistics) during the annual review period; and must have reasonable prospects for a continued trading presence in the stock exchange

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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. 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 must also 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: RIF CN =

MIF CG × RIF CG − MIF CI × RIF CI (MIF CG − MIF CI )

(1)

The stocks in the global index that are not in the investable stock index are designated to be non-investable. We henceforth refer to the two stock index returns as investable return and non-investable return.8 For the developed countries, we treat all stocks as investable. We use the U.S. dollar-denominated country index returns calculated by Datastream (from the Datastream Total Market 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.9 We also have the Datastream Total Market Index for the world as a measure for the overall world market, in weekly frequency. 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. 8 For 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 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. 9 If Friday observations are not available due to closings or insufficient data, we use Thursday observations.

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We have 1623 observations of daily data for the period from July 1995 to September 2001 and 665 observations of weekly (Friday-to-Friday returns) data for the period January 1989 to September 2001, for both EMDB and Datastream data. Tables 2A and 2B contain summary statistics for the weekly and daily frequency data.10

2.2

Characteristics of Investable and Non-Investable Firms

In order to compare the impact of crises on the emerging market’s investable and non-investable indices, it is necessary to understand the differences in the sets of firms that comprise the indices. Investable firms may be systematically different from non-investable firms, aside from the restrictions on accessibility. To explore structural differences that may affect our results, we examine the characteristics of investable and non-investable firms in detail. Percent of market capitalization attributed to non-investable firms varies greatly across countries. The average percentage is 42% in 1994 and declines to 21% 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. To determine if there are any systematic differences between the two sets of firms, investable vs. non-investable, we compare two characteristics of the firm: size – market capitalization as a fraction of total market capitalization – and liquidity – ratio of value-traded to market capitalization. Table 4 reports the one-sided test of significance to see if the size or liquidity is significantly higher for investable firms. In 1994, out of the sixteen countries for which comparison was possible, we find that average liquidity for investable firms is significantly greater in three countries (Mexico, Pakistan, and Philippines) and average size is significantly greater in six countries (China, Czech Republic, Greece, Hungary, Pakistan, Peru, and Sri Lanka). In 1997, out 10

We observe some number of abnormally large returns in the daily frequency data for Greece, Poland, South Africa, and Turkey. In addition to the restriction on the fraction of non-investable to be greater than 0.01%, we also cap the returns at absolute value of 10.0 (or 1000%).

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of twenty-one countries, average liquidity for investable firms is significantly greater in only two countries (Czech Republic and Mexico) and average size is significantly greater in nine countries (Colombia, Egypt, Hungary, Mexico, Pakistan, Peru, Slovakia, Sri Lanka, and Venezuela). In 1998, out of twentytwo countries, average liquidity for investable firms is significantly greater in four countries (Brazil, Mexico, Russia, and Philippines) and average size is significantly greater in six countries (Chile, Colombia, Egypt, Greece, Jordan, Korea, Malaysia, Slovakia, and Venezuela).11 In most countries, there exist more stocks than are contained in the EMDB database. Selecting only those stocks that are in the Global index may make the difference in liquidity and size between the two categories even less pronounced, because liquidity and size are in the criteria for selecting stocks into the Global index (which includes both investable and non-investable stocks). Another concern is that investable firms are more export or import oriented, and therefore may be more vulnerable to exogenous shocks originated elsewhere in the world. Table 5 compares the distribution of investable and non-investable firms among industries. It shows that investable firms appear more often in sectors such as banking, communications, petroleum refining & related products, security & commodity brokers, diversified holding companies, and food & kindred products; while non-investable firms tend to be concentrated in sectors of apparel & other textile products, machinery (except electrical), transportation equipment, chemical & allied products, credit agencies other than banks, and real estate. Overall, investable firms are distributed into more industries, but appear to be no more or less export- or import-oriented than non-investable firms. Therefore, if the correlations between investable return and crisis country index return are systematically higher than the correlations between non-investable return and crisis country index return, the higher correlation cannot be caused by external shocks to export firms’ balance sheets, because investable firms are as likely to be export-oriented as non-investable firms. 11 More liquid or larger-sized stocks are less susceptible to exogenous shocks, because high turnovers have little impact on prices. Therefore, liquidity will bias downward the co-movements of investable stocks across countries, and will have an effect contrary to the investor-induced contagion hypothesis.

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2.3

Sample Correlations: Investable vs. Non-Investable

Our methodology tests whether the co-movements of investable returns and crisis 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 Tables 2A and 2B, we show that investable returns are not identical to non-investable returns. The sample correlations between investable and non-investable returns for each country are also reported in the right-most column in Tables 2A, 2B, for EMDB daily, EMDB weekly.12 The average sample (contemporaneous) correlation between investable and non-investable returns is 0.647 for the EMDB weekly data and is 0.047 for the EMDB daily data. In the weekly data, all of the sample correlations are positive, with the average at 0.647. The countries with especially high positive correlations (over 0.9) are India, Jordan, Korea, Malaysia, Taiwan, and Thailand. 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, Hungary, Slovakia, and Turkey. In the daily data, more than half of the countries (18 out of 31) have correlations that are negative. The countries with highly positive correlation are India, Taiwan, and Thailand; the countries with highly negative correlations are Mexico, Venezuela, Malaysia, and Indonesia. The countries with low positive correlations are Egypt, Korea, and Portugal. Out of the countries that have highly positive weekly correlations, India, Jordan, Taiwan, and Thailand also have highly positive daily correlations; but Brazil, Korea, Malaysia, and Philippines have low or negative daily correlations.

2.4

Lead and Lag Relationships between Investable Stocks and Non-Investable Stocks

If external shocks are spread through investors, investable stocks should respond first and lead the non-investable stocks. In order to test the lead 12

We also compute the sample correlations for overlapping two-day average returns. We find the values to be very similar to those for daily returns.

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and lag relationship between investable and non-investable returns for each country, we compare the cross-autocorrelation between investable and noninvestable returns as in Campbell, Lo, and MacKinlay (1997) in their comparison between large and small size stocks. Higher correlation between lag of investable return and non-investable return (than the correlation between investable return and lag of non-investable return) suggests that investable stocks lead the non-investable stocks. In other words, if we let It denote the investable stock index return at time t, and Nt denote non-investable stock index return at time t, higher correlation is expressed as follows: Corr(It−1 , Nt ) > Corr(Nt−1 , It )

(2)

We summarize the lead-lag comparisons in Table 6. Indeed, the correlation between one-period lag investable return with non-investable return (Corr(It−1 , Nt ), column 2) is higher than correlation between lag noninvestable return with investable return (Corr(It , Nt−1 ), column 3) for most countries, except Egypt, Greece, Israel, Malaysia, Russia, Sri Lanka, Taiwan, and Thailand. In most countries, the difference (column 4) is small, although positive, relative to the contemporaneous correlation (column 1). This suggests that the investable stocks lead the non-investable stocks at an one-week lag, but the effect is small. The countries with relatively high positive difference are Pakistan and some Latin American countries: Argentina, Mexico, Venezuela, and Colombia. We also find that the correlations with lags more than one week do not have lingering effects on the returns, which implies that the lead-lag effect is no more than one-week.

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Methodology

In order to calculate the cross-market transmission of financial crises, we employ several measures of correlation between crisis countries and each of the other countries. We compute separate correlation measures, one with investable returns and another with non-investable returns. By comparing the two correlations, we analyze the transmission of the financial crisis.

3.1

Conditional Correlation Correction

The first approach is the bias-adjusted correlation calculation used by Forbes and Rigobon (2002). Here, we study the correlation of different sample11

periods: full-sample, turmoil, and stable period. Forbes and Rigobon (2002) introduce a bias-correction formula, based on assumptions of bivariate normality and of constant variance during the full sample period. From equation (11) of their paper, the unconditional correlation coefficient (ρ), based on conditional correlation (ρc ) is as follows: ρc 1 + δ [1 − ρ2c ]

ρ= p

(3)

where (δ) is the relative increase in the variance: δ=

σ −1 σF

and (σ 2 ) is the measure of conditional (or sub-sample: either turmoil or stable period) variance for the sub-sample and (σF2 ) is the measure of unconditional (or full-sample) variance. In the bias-adjustment for the unconditional correlation, the full sample period variance is assumed to be the true variance for the crisis country. We follow Forbes and Rigobon (2002) in using the VAR framework to correct for serial correlation ” c • ” c • Xt Xt−1 (4) + t = Φ(L) o o Xt Xt−1 where XtC is the return for the crisis country and Xto is the return for other country, either investable or non-investable.13 If we denote the variance of t by a 2x2 matrix: • ” 2 σc σoc V ar() = σoc σo2 the asymptotic variance of the (Hamilton (1989)):  2   ˆc − σc2 σ √ ˆoc − σoc  ∼ N  T σ σ ˆc2 − σc2 13

estimated variance elements is as follows:

  4  2 0 2σoc 2σc2 σoc 2σc 2 0  ,  2σc2 σoc σc2 σo2 + σoc 2σoc σo2  2 2 0 2σoc 2σo σoc 2σo4

Forbes and Rigobon (2002) also have short-term interest rates and the lags in their model, to control for macro-aggregate shocks and monetary policy coordination. But as their sensitivity test results show, neither the number of lags nor the inclusion of interest rates seem to matter.

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The variance matrix for the returns (X) are solved using the stationarity assumption. The test for contagion is a test for a significant increase in the correlation coefficient in the period after the financial crisis (during the turmoil period). We use simple t-tests to compare unconditional correlation computed from the conditional correlation in the crisis period to the unconditional correlation computed from the conditional correlation in the stable period. This is an extension of the correlation calculation in Forbes and Rigobon(2002). We use their methodology to construct the correlation of returns between the crisis country and other countries, and to compare the correlation between stable and turmoil periods. Our contribution is that we allow correlations to differ for investable and non-investable segments of the market, in order to observe the differences. If contagion is spread through investment holdings, the correlation of the crisis country with investable returns should be significantly higher than correlation between non-investable returns.

3.2

Exceedance Correlation

An alternative estimate of correlation between returns is obtained from the extreme tails of the joint distribution. We estimate the parameters of the dependence function, a function that measures the relationship between markets, 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 distribution function. Instead, we rely on the tail distribution, which is a non-degenerate limit distribution that converges to a generalized Pareto distribution, regardless of the underlying joint distribution. 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. We estimate this limiting distribution to characterize the exceedance correlation between returns of any unknown multivariate distributions. If the joint distribution is indeed a bivariate normal, the exceedance joint distribution 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 measuring the correlations between equity markets, Longin and Solnik (2001) find decreasing exceedance correlation in the bull market (right or positive tail) and increasing or flat exceedance correlation in the bear market (left or negative tail), thus providing evidence for asymmetry. Here, in addition to estimating the correlations of international 13

equity markets, we estimate separate correlation measures for the investable and non-investable portions of the markets. 3.2.1

Tail Distribution: Generalized Pareto Distribution

For the univariate random variable (X), the tail probability beyond the threshold level (θ) is obtained from the generalized Pareto distribution: “−1/ξ ’ ξ(x − θ) P r (X > x|X > θ) = 1 + σ +

(5)

for x > θ.14 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 thintailed distributions, and ξ = 0 to no-tail or finite-distributions.15 Therefore, 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): ’ “−1/ξ ξ(x − θ) F (x) = 1 − 1 + σ + ∗

(6)

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 is not dependent on the underlying true distribution. Let (X1 , X2 , . . . Xq )0 be a qdimensional vector of random variables, and (θ1 , θ2 , . . . θq )0 denote the vector of threshold level. Following Ledford and Tawn (1997) and Tawn (1988), the joint tail distribution is as follows: ” ’ “• −1 −1 −1 ∗ F (x1 , x2 . . . xq ) = exp −D , ,... (7) logF1m (x1 ) logF2m (x2 ) logFqm (xq ) 14

The plus-sign notation is defined as s+ = max(s, 0). Longin 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)). 15

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where D() is some dependence function and Fjm () is the marginal distribution of the jth variable: ’ “−1/ξj ξj (xj − θj ) F (xj ) = (1 − λj ) + λj 1 + σj + m

(8)

with λj as the tail probability. The marginal distribution combines two parts: probability (1 − λj ) that the observation is not in the tail and probability λj 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 dependence function is not known. Therefore, the estimation of the joint tail distribution requires choosing a functional form for the dependence function. 3.2.2

Dependence Function and Estimation

The models of the dependence function can be either non-parametric or parametric. In the class of parametric functions, Tawn (1988) describes two distinct models: mixed and logistic models. These mixed and logistic models have equal-weight (symmetric) and weighted (asymmetric) versions16 where equal-weight means simply that the weight given to each of the variables are all the same. The most widely used dependence function for q = 2 is the equal-weighted (symmetric) logistic function: −1/α

D(z1 , z2 . . . zq ) = (z1

−1/α

+ z2

+ · · · + zq−1/α )α

(9)

where the dependence parameter is α, 0 < α ≤ 1. With only one parameter to estimate, this dependence function is simple and tractable. Furthermore, from this dependence function, 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 16

The 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 in the later section, to describe the difference in the tail distribution, between the left tail and the right tail.

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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).17 We estimate parameters of the bivariate extreme value distribution with the maximum likelihood estimation, assuming independent observations. The likelihood function for each observation 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 Longin and Solnik (2001) and it is straightforward to implement. We use asymptotic properties of maximum likelihood estimator to obtain our parameter covariance matrix, and perform inference based on asymptotic normality.

3.3

A Dynamic Model of Correlation: Regime-Switching Model

Another methodology to estimate the correlation between asset returns is to specify a richer dynamic model of stock returns that captures the asymmetry in the co-movements of asset returns. We estimate a parametric model of the joint data-generating process that allows for endogenized structural breaks, but also nests a simple model with fixed moments. We then determine if the joint distribution is changing over time between regimes, by merely testing for differences in the estimated parameters. Our model of choice 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)). In this approach we model the data generating process of four time-series: the general return of the crisis source country, the investable and non-investable returns of another country, and the world index return. Inclusion of the crisis country index helps associate the regimes with a particular financial crises. Inclusion of the world index allows for a clearer distinction between the two regimes by helping to define the high volatility regime as 17

A future extension to this study is to consider nonparametric dependence functions, to see if the choice of the symmetric logistic dependence functions is appropriate for the data.

16

a regime of high global volatility rather than country-specific volatility. In addition, it allows us to test for the presence of global contagion, i.e. the presence of higher correlation with the world index when world markets are more volatile. 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 j, returns are assumed normally distributed, (rt |st = j) ∼ N (αj , Σj )

(10)

where rt is a 4 × 1 vector of index returns realized at time t and t ranges from 0 to T , αj is a vector of expected returns, and Σj 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 = j|ψt−1 ) = P (st = j|st−1 = i) = pij

(11)

where i, j = 1, 2 thus resulting in a 2 × 2 transition matrix. 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). One advantage of this approach over the conditional correlation correction approach is that we are not required to exogenously specify time periods of high volatility (crisis) and low volatility (stability). Clearly any such time specification is somewhat subjective and restrictive. Instead, we estimate the parameters of a model of the joint data-generating process from which we can objectively reject the null of a single volatility (correlation) regime. In addition, once we have established the characteristics of the two regimes, we can estimate the probability of being in the high volatility (correlation) regime for each period in the sample. This is done by estimating smoothed inferences using an algorithm developed by Kim (1994) and discussed in Hamilton (1989). This algorithm provides an estimate of P (st = j|ψT ) under the model assumptions described above. That is, we are able to estimate the probability of being in state j 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 17

existence of two separate regimes, but also allows us to objectively distinguish the time periods that are more likely to be in crisis (or stable) regime. More specifically, for each country (other than the crisis country), we separately estimate the parameters of the regime switching model outlined above using weekly data. This involves estimating a total of 31 parameters: two vectors of means (each with four elements), two covariance matrices (each with ten unique elements), the two diagonal elements of the transition matrix, and the initial state probability. Let C, W , I and N respectively denote the crisis, world, investable-target and non-investable-target indices and let σj,ik be the covariance between index i and index k given st = j σj,ik = Cov(rit , rkt |st = j)

(12)

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 found to be highest. For each country in our data, we test the following three restrictions: σ1,ii − σ2,ii = 0 for i = C, W, I, N ρ1,ik − ρ2,ik = 0 for i = I, N, and k = C, W (ρ1,Ik − ρ2,Ik ) − (ρ1,N k − ρ2,N k ) = 0 for k = C, W

(13) (14) (15)

where ρj,ik is the correlation coefficient between rit and rkt given st = j ρj,ik =

σj,ik . σj,ii σj,ij

The first restriction is of course, a test for the existence of a crisis regime (a regime with higher volatility) while the second restriction is a test for the existence of contagion (higher correlations) during the crisis regime. We can also use the second restriction to help us determine which (if any) investor class is responsible for the cross-market transmission of financial crises by comparing the number of rejections across countries with i = I to the number of rejections with i = N . The last restriction is an additional test to help us sort out which investor class transmits financial crises. Since it is possible that both investor classes act as a channel for contagion, this restriction helps us determine which may act as the larger channel. For example, if the third restriction is positively rejected, then the change in correlation across states for investable securities is larger than the change across states for the noninvestables, implying that contagion is largely carried out by the investable 18

class. For lack of a better term, we will refer to the left side of Equation (15) as the “difference-in-difference.”

4

Contagion in the 1997 East Asia Crisis

We consider the 1997 East Asian Crisis as our first empirical analysis of transmission mechanisms of contagion. 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, and use the October decline in the Hong Kong equity market as the source of contagion. We use the measures of co-movement described in the previous section: the conditional correlation correction, exceedance correlation, and the regime-switching model estimation. For comparison, we then consider Thailand as the source of crisis.

4.1

Conditional Correlation Correction Tests

We first measure the cross-market linkage as unconditional contemporaneous correlations that correct for upward conditional bias using the methods outlined in Boyer, Gibson, and Loretan (1999), Forbes and Rigobon (2002), and Stambaugh (1995), assuming bivariate normality and stationarity. We replicate Forbes and Rigobon (2002) results using daily overall market returns for each country.18 Table 7 shows that our conditional and unconditional estimates closely resemble those reported in Tables III and IV of Forbes and Rigobon (2002). We perform two sets of comparisons in this section. First, we observe any asymmetry between the correlations in the turmoil period and in the stable period, to determine if the unconditional contemporaneous correlation is different during market stable periods and turmoil period, at the overall market return (investable and non-investable combined) level, and at the disaggregated investable and non-investable return level. We compute 18

Except in section 4.1, we conduct most analysis at the weekly frequency to avoid market microstructure complications. (One market microstructure issue is that these markets are in difference time zones and daily open and close times are different.) We use daily frequency for conditional correlation correction tests mainly to be comparable to Forbes and Rigobon (2002).

19

a simple t-test for the comparison of the correlation for the two periods. Second, we observe the differences in the unconditional contemporaneous correlations with investable returns and non-investable returns during each of the two periods. 4.1.1

Hong Kong during October 17, 1997 to November 16, 1997

We use Forbes and Rigobon’s definition of the “turmoil” period to be the period of one month starting on October 17, 1997 and the “stable” period to be from January 1, 1996 to the start of this turmoil period. Since Hong Kong is completely investable, what we have called the crisis source country index will be the Hong Kong investable stock index. First we calculate the unconditional correlation estimate between each of 30 emerging markets’ and 14 developed markets’ overall stock market index returns with Hong Kong investable returns. Consistent with Forbes and Rigobon’s findings, we cannot reject the null hypothesis of no increase in the unconditional estimates during the turmoil period at 95% significant level using one-sided t-test except for two countries: Italy and Egypt (Table 8A). Here we conduct one set of t-tests comparing the turmoil period to the fullsample period, as in Forbes and Rigobon (2002) and another set of t-tests comparing the turmoil period to the stable period.19 However, the sign test of equal probability of higher and lower correlation, against the alternative of higher correlation during the turmoil period, has a p-value less than 0.01, indicating the existence of contagion. We extend the analysis by estimating the correlations between the Hong Kong market return, and investable and non-investable returns of emerging markets. The unconditional correlations between Hong Kong market and both investable and non-investable returns of each country and the t-tests comparing the turmoil period to the stable period are reported in Table 8B. The t-tests comparing the correlation with investable returns to the correlation with non-investable returns for each period (turmoil, stable, and full period) are reported in Table 8C. Interesting patterns emerge at the investable and the non-investable level.20 19

We compare the turmoil period to the stable period because it correctly satisfies the assumption of independence between the two samples for the construction of t-statistics. For this reason, we will focus on the comparison of the turmoil period to stable period, although both statistics are reported. 20 We use the daily data in our model with no lags and non interest rates, while Forbes

20

For emerging-market countries, at the overall stock market index level, the ttests reject the null hypothesis of no increase in correlation during the turmoil period for only one emerging-market country: Egypt (Table 8A). However, at the disaggregate level, once we separate overall market index returns into investable and non-investable returns, the results are somewhat different (Table 8B). The table shows significant increases in the unconditional correlations between Hong Kong index return and investable return during the turmoil period, as compared to the stable period, for two Latin American countries: Colombia and Mexico. We find significant increases in the unconditional correlations between Hong Kong index return and non-investable return during the turmoil period for one Asian country: Pakistan.21 For both investable and non-investable returns, the sign test indicates that the correlation with Hong Kong investable returns is higher during the turmoil period, although higher correlations with Hong Kong investable returns appear in more countries’ investable index returns (19 countries) than in non-investable index returns (14 countries). This provides some suggestive evidence that investable stock index returns are more correlated with external shocks during the turmoil period. Furthermore, in Table 8C, we note that there are several countries for which t-tests reject the null hypothesis that the correlations for investable and non-investable are the same: Malaysia, Indonesia, Chile, Colombia, Mexico, Peru, Israel, Poland, and South Africa. For these countries, the correlations between their investable returns with the Hong Kong market are higher than the correlation between their non-investable index returns with the Hong Kong market. Therefore, we find some statistically significant evidence that the Hong Kong crisis is contagious to the investable returns for a few EMDB markets through investable returns. Table 8C also reports difference in difference statistics, i.e., (ρI,turmoil − ρI,stable ) − (ρN,turmoil − ρN,stable ), where ρ denotes the contemporaneous correlation between investable or noninvestable returns with crisis country investable index returns. The sign test and Rigobon (2002) in their paper report results for two-day rolling average data with five lags and five lags of interest rates in their model. We also estimate models with five lags, no interest rates, for both daily data and the two-day rolling average data and find similar results as those reported in this section. We also estimate the model using weekly data, but because of the short duration of the “turmoil” period (five weekly observations), the estimates are not precise enough for comparison. 21 We get similar but slightly weaker results by comparing the unconditional correlations of the turmoil period to the full period, as Forbes and Rigobon (2002).

21

positively rejects the null hypothesis of equal probability of difference in difference being negative or positive, which indicates that investable returns act as a larger channel for contagion. One peculiar observation to note from Tables 8A and 8B is that one of the most-affected countries, Thailand, which co-moves positively with the Hong Kong market during the full-sample period, correlates negatively with the Hong Kong market during the turmoil period. This is puzzling because although we need not find signs of contagion in the form of statistically significant increases in correlations, we did not expect to see negative correlations. One potential reason for this peculiarity is that because the Thailand market crisis precedes the Hong Kong market crash, the direction of contagion is from Thailand to Hong Kong and not vice versa. Then it is possible that negative correlation captures the post-crisis adjustment of the Thailand market. Figure 1 is a graph of time-series of the stock index returns for Asian countries from January 1997 to November 1997. It shows that the volatile period started much earlier than October of 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 East Asian crisis.22 Radelet and Sachs (1998) also document that 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 our conditional correlation correction test by defining the turmoil period to start on July 2. 4.1.2

Hong Kong during July 2, 1997 to November 16, 1997

We redefine the “turmoil” period to begin on July 2, 1997, maintaining the same ending date of November 16, 1997, from the previous section. The “stable” period is from January 1, 1996 to July 1, 1997 and the full sample remains the same. The summary of our t-statistics, for testing the increase in the unconditional estimates of turmoil period correlation, is in Tables 9A9C. The tests comparing the turmoil to the stable period are much stronger 22

http://www.stern.nyu.edu/nroubini./asia/AsiaChronology1.html

22

for this new definition of the turmoil period at the total stock index level (Table 9A). Here, we reject the null hypothesis for no correlation increase in the turmoil period for 8 of 14 developed markets (Canada, France, Italy, Netherlands, Spain, Sweden, United States, and Belgium), 10 of 30 emerging markets (Chile, Colombia, Pakistan, Hungary, Poland, Portugal, Egypt, Israel, South Africa, and Turkey). In the testing of the unconditional correlation for the investable returns in Table 9B, we reject the null hypothesis of no increase in correlation during the turmoil period for 11 of 30 emerging markets (Chile, Colombia, Egypt, Hungary, Jordan, Peru, Poland, Portugal, South Africa, and Turkey). And just as expected, we do not see as many rejections with the non-investable returns. The sign test indicates that for investable returns, correlations are more likely to be higher in turmoil period, but it cannot reject the null hypothesis of equality probability of higher or lower correlations in the turmoil period than in the stable period for non-investable returns. Moreover, when we compare investable returns’ correlations with Hong Kong market return and non-investable index returns’ correlations with Hong Kong in Table 9C, we see stronger rejections of the null hypothesis of equal in correlation (for investable and non-investable) in the turmoil period (13 rejections) than in the stable period (8 rejections). In the turmoil period, Chile, China, Colombia, Hungary, Indonesia, Poland, Israel, Malaysia, Mexico, Pakistan, Peru, South Africa, and Zimbabwe all show significantly higher correlations with their investable returns than with the non-investable returns, which indicates that investable returns co-move more with the crisis country return and hence provides some evidence for the investor-induced contagion hypothesis. There are 11 cases where difference in difference statistic is statistically positive at 95% confidence level using one-sided t-tests. The sign test on difference in difference also indicates that investable returns act as a larger channel for contagion. Simply by re-defining the crisis period, we get a much stronger result on increased correlations during the crisis period and higher correlations between investable returns with crisis country returns than that between non-investable returns with crisis country returns. 4.1.3

Thailand during July 2, 1997 to November 16, 1997

In this section, we report our results from assuming Thailand to be the crisis source country, instead of Hong Kong (Tables 10A-10C). Using Thailand 23

overall market returns as the source country returns, we find that the correlations between Thailand stock index returns with non-Asian stock market returns are very small (Table 10A). However, the correlations are high between Thailand and Asian countries. Three of the six countries, where we observe a statistically significant increase during the turmoil period in correlations during the turmoil period (at 95% significant level using one-sided t-tests), are Asian countries. When we conduct unconditional correlation analysis at the investable and non-investable return level (Tables 10B-10C), we observe similar findings to Section 4.1.1 and 4.1.2: (1) there more significant increases during the turmoil period in correlations for investable returns than for non-investable returns; (2) the sign test rejects equal probability of higher or lower correlations for investable returns but not for non-investable returns; (3) the sign test indicates that there is a higher probability that investable returns have higher correlation with crisis country returns than non-investable returns; (4) and finally, the sign test on difference in difference statistics suggests that investable returns act as a larger contagion channel. Additionally, by using Thailand’s investable returns as the crisis country returns, we observe consistently that correlations are high between Thailand and Asian countries. For investable returns, we observe a statistically significant increase during the turmoil period for Korea, Sri Lanka, Taiwan, and Greece, and a marginally significant increase for India, Pakistan, and Philippines (Table 10B). Almost all Asian countries show statistically significant (or near-significant) increases in unconditional correlations with investable returns during the turmoil period. As for non-investable returns, we observe a statistically significant increase during the turmoil period for Korea, Sri Lanka, Taiwan, and South Africa. In the t-test comparison of investable to non-investable, we find a statistically significant increase in correlation of investable for Chile, Mexico, Malaysia, Philippines, Greece, Morocco, and Poland (Table 10C). In our comparison for investable in non-investable during the turmoil period, we find significant differences in Greece, Malaysia, Philippines, and Poland (Table 10C).23 Since we assume that the Thailand crisis precedes the Hong Kong crisis, the high correlations between Thailand and Asian countries investable indices 23

Similar results are obtained using Thailand’s investable returns instead of overall returns - investable and non-investable combined- as the crisis country index return.

24

are indicative that initially, the Asian crisis was a regional phenomenon. In comparison, Hong Kong shows significantly large correlations with countries outside Asia (these countries include developed countries such as United States, Canada, France, Italy, Netherlands, Spain, Sweden and some emerging market countries such as Mexico, Chile, and South Africa), which is suggestive of a global contagion. Therefore, our analysis seems to show that the Asian crisis started out as a regional phenomenon – with the effect of Thailand spreading through the Asian countries – and then later became a global phenomenon. 24

4.2

Exceedance Correlation

The method of correction for conditional correlation imposes asymptotic normality and the results are sensitive to the exogenously determined turmoil and stable periods. Another approach we take in comparing the correlations is to use the extreme value estimation approach, to estimate exceedance correlation. This approach does not require any distributional assumption or specific definition of the turmoil period. We follow Ang and Chen (2002) in setting the threshold values to be multiples of sample deviations away from the sample mean. We estimate the exceedance correlations for 0, 1, and 1.5 standard deviations away from the mean, both for the positive tail and the negative tail at weekly frequency using the whole sample. We perform two sets of comparisons. First, we observe any asymmetry between the correlations in the positive tail and in the negative tail, to determine if the exceedance correlation is different during market upturns and downturns, at the overall market return level, and at the disaggregated investable and non-investable return level. We compute a simple t-test for the comparison of the correlation for positive and negative tails, at each number of standard deviations from the mean.25 Second, we observe the differences in the exceedance correlations with investable returns and 24

An alternative interpretation is that Thailand in the usual stable period has unusually low correlation with the non-Asian markets, whereas Hong Kong is generally more highly correlated with non-Asian markets. During the crisis period, the heightened correlation may seem more pronounced in Thailand than in the relatively highly correlated Hong Kong index returns. 25 This comparison is not very accurate in that it disregards the possible correlation in the parameter estimates across tails. We are also considering an alternative test with the flavor of a likelihood ratio test, by jointly estimating both tails of the extreme value distribution with symmetry imposed on the tail dependence parameters.

25

non-investable returns. Although the set of comparisons is similar to those in Section 4.1, the interpretation is slightly different. Instead of comparing contemporaneous correlations between the turmoil period and the stable period as in Section 4.1, we compare exceedance correlations between negative and positive tails which are extreme events (market upturns and downturns). 4.2.1

Exceedance Correlation with Hong Kong

First, using Hong Kong as the crisis country and at the overall market index level, the comparison of the exceedance correlation across tails, positive vs. negative, yields evidence of asymmetry. The t-statistics are reported in Table 11A. 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 results from separating investable returns from non-investable returns are in Tables 11B-11C. The sign test rejects the null hypothesis of equal probability of higher or lower correlations against the alternative of higher correlations in the negative tail for all thresholds (0, 1, and 1.5 standard deviations away from the mean) except for non-investable returns at 1.5 standard deviations away from the mean. This indicates that non-investable returns are not more correlated with external shocks in market downturns than in upturns. In comparing the exceedance correlation of Hong Kong with investable index returns vs. non-investable index returns, at each deviation from the mean, we find that correlation with the investable returns is significantly larger across the board. The t-statistics are also reported in Table 11C. This result indicates that investable stock returns co-move more with the crisis country index returns, thus provides empirical evidence for investor-induced contagion hypothesis. The sign test on difference in difference, however, cannot reject that the null hypothesis that the increase in exceedance correlations of Hong Kong with investable index returns between market downturns and upturns is as large as in that of Hong Kong with non-investable index returns. Moreover, 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, asymp26

totic 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 this shape for Switzerland, Mexico, Indonesia, Slovakia, and Zimbabwe. However, we do find that for most countries, the correlation estimates decrease further out into the tails, with some more substantially than the others. The shape implied by these exceedance correlations is a tent-shape, as could be described by a bivariate normal distribution that has slightly different properties in its positive and negative tails.26 4.2.2

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 12A, although it is not clear whether exceedance correlations in the negative tails are uniformly higher across all countries. When we disaggregate country’s index returns into investable and noninvestable returns, the pattern is clear. 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, and the crisis country return correlation with the investable return is significantly higher across the board than that with the non-investable return, as in the previous section with Hong Kong as the crisis country. 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 may no longer hold and bivariate normal distribution 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 market index return, increasing exceedance correlation is found in the positive tails for six countries - France, Spain, Sweden, United King26

With investable returns, only five countries (Chile, Mexico, Korea, Thailand, and Portugal) exhibit the Longin and Solnik (2001) asymmetry– an increased dependence in the negative tail with decreased dependence in the positive tail. Of the five countries, Chile, Korea, and Portugal also exhibit the same pattern with the non-investable returns, although the correlation values are smaller. For India, the increasing dependence pattern is visible in all tails, and both investable and non-investable, both negative and positive.

27

dom, India, and Korea – and in the negative tails for most other 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.27 Again, we find contrasting contagion patterns when we define Thailand as the crisis source country versus when we define Hong Kong as the source country. With Thailand, the larger size (and larger increase) exceedance correlations appear in Asian countries, but all countries have larger overall correlations with Hong Kong. This is consistent with the idea that the Asian crisis spread regionally from Thailand primarily to Asian countries.

4.3

Regime-Switching Model

We next characterize the co-movement among markets by estimating a regimeswitching model. In this model, the index returns are conditionally distributed normal given the regime or state of the data generating process. The model is estimated using four time series: the index returns of a crisis source country, the investable and non-investable index returns of another country, and the world index. We report the model results in Table 13 with Thailand as the crisis source country. For each country in the table we separately estimate the parameters of the regime switching model outlined in Section 3.3. Since we are mainly interested in the change in volatilities and correlations across regimes and investor classes, we report only differences in these estimated moments and do not report levels.28 Changes in volatility have been annualized. Results for emerging market countries are given in panel A of Table 13 while results for developed countries are given in panel B. The columns to the left (1a-10a) of Table 13 report differences in estimated moments while the columns to the right (1b-10b) report the corresponding t-statistics to test the restrictions defined by Equations 13 to 15. The first 4 columns, columns 1a through 4a, report differences in volatility to test restriction 13, columns 5a through 8a report differences in correlations to test restriction 14, and columns 9 and 27 Interestingly, when we separate the investable returns from the non-investable returns (Table 12B), the increasing dependence pattern disappears for Czech Republic, Greece, and Hungary. The remaining countries are some Latin American countries (Chile, Mexico, Peru, Venezuela) and Asian countries, where the increased dependence pattern are in general larger for investable returns than for non-investable returns. 28 These results are available from the authors upon request.

28

10 report differences to test restriction 15. These columns are then followed by t-statistics (1b-10b). The last two columns (11 and 12) provide the mean value of the log-likelihood function and the number of data points available to estimate the parameters of the model. First note that volatility across all indices tends to be significantly higher in state 1, the state in which estimated volatility of the crisis country, Thailand, is higher (columns 1a-4a). Of the 44 countries in the table, the difference in volatility for the crisis country is found to be significantly positive in 43 cases (column 1b) while the difference in volatility for the world index returns is found to be significantly positive in 35 cases (column 2b). In addition, among the 30 emerging market countries, the differences in volatility for the investable returns are significantly positive in 23 cases (column 3b) while the differences in volatility for the non-investable returns are significantly positive in 21 cases (column 4b). This is strong evidence for the existence of a crises regime during the sample estimation period. Further, financial turmoil appears to have a similar impact on both investable and non-investable securities in terms of volatility. For two countries (Israel and Turkey) the differences in volatility for both the investable and non-investable returns are negative (columns 3a and 4a) while the corresponding differences in volatilities for the crisis country, Thailand, are positive (column 1a). For these countries, the differences in world volatility are also negative. Apparently the crisis regimes for these countries are not associated with economic turmoil in Thailand but may be associated with some global instability unrelated to the East Asian crisis. In the case of Turkey, the crisis regime does not appear to be associated with instabilities in either global economy or Thailand since the differences in volatility for both the world index return and Thailand are significantly positive. Hence, the country crisis regime for Turkey appears to be a period of isolated country-specific economic turmoil that occurs over periods when markets are relatively calm in the rest of the world. However, for a majority of the countries in Table 13, the crisis regimes appear to be associated with economic turmoil in Thailand. Of the 44 countries, 36 have a positive significant difference in volatility for the investable return and Thailand (columns 3a and 1a, respectively). We now look at evidence for contagion. Among the 30 emerging market countries, the differences in correlation are significantly positive for much more for investable returns than for non-investable returns (columns 5b through 8b). A total of 11 investable returns have a positive difference in 29

correlation with the world (column 6b) and the same number have a positive difference in correlation with Thailand (column 5b). Only 5 non-investable index have a positive difference in correlation with the world (column 8b) and the same number have a positive difference in correlation with Thailand (column 7b). Of the 14 developed countries, 11 have a positive difference in correlation for Thailand (column 6b) while 10 have a positive significant difference with the world (column 5b). Overall, this is strong evidence for the existence of economic contagion during the East Asian crisis. In addition, there is evidence that this contagion spread to emerging markets mainly through the investable class. It is also interesting to note that three out of the five non-investable returns with positive significant differences in correlation with Thailand (column 7b) are in Asia (Korea, Philippines, and Taiwan). Also, of the five non-investable returns with positive significant differences in correlation with the world, four are in Asia (Korea, Malaysia, Philippines, and Taiwan). This suggests, similar to our previous findings, that the Thailand shock is transmitted within Asia through both investable and noninvestable stocks, but is transmitted outside Asia only through investable stocks. Lastly, we examine the restriction given in Equation 15. Since it is possible that both investor classes act as a channel for contagion, this restriction helps us determine which may act as the larger channel. Out of 30 emerging markets, 15 countries show a positive difference-in-difference for correlation with the Thailand, although not all are significantly positive (columns 9a and 9b). Five countries (Chile, Mexico, Indonesia, Greece, and Hungary) show a positively significant difference-in-difference for correlation with Thailand and only one country (Argentina) shows a negatively significant differencein-difference. As discussed above, a positive difference-in-difference indicates that the change in correlation across the two regimes is more pronounced in investable returns than in non-investable returns and therefore is consistent with the investor-induced contagion hypothesis. We observe predominantly positive difference-in-difference in the non-Asian emerging markets, but observe a mixture of positive and negative (about half and half) difference-indifference in the Asian emerging markets. This is additional evidence that the Thailand shock is transmitted within Asia through both investable and non-investable stocks, but is transmitted outside Asia through investable stocks. In analyzing these differences across regimes and comparing investable and non-investable return correlations, we observe an interesting pattern. 30

For 21 emerging market countries (all except Argentina, Venezuela, China, Poland, Slovakia, Egypt, Israel, Morocco, Turkey, and Zimbabwe) and 13 developed market countries (all except Japan), we observe a regime with higher return volatilities in the crisis source country (positive in column 1a), the world index (positive in column 2a), and the investable index (positive in column 3a), and with higher correlation between the investable return and the crisis source country (positive in column 5a). Out of these 21 emerging market countries, in 19 countries, we also observe higher correlation between country’s non-investable return and crisis source country return (positive in column 7a). The fact remains that all over Asia (except China), the higher volatility regime corresponds to higher volatility in both the investable and non-investable returns (columns 3a and 4a) as well as higher correlations between both these returns and both the crisis source country and the world index (columns 5a and 6a). Once again, we interpret this as evidence that shocks in Thailand spread in Asia countries through both investable and noninvestable stocks, but transmit to other countries only through investable stocks. The above results indicate that, in general, periods of high volatility are associated with periods of high correlation. Moreover, in the case of emerging markets, high correlation is especially characteristic of the investable indices. In Figure 2, we plot the probability of being in the high volatility (correlation) regime across time. These are estimates of smoothed inferences (Kim 1994) using Thailand as the crisis country and Philippines as the affected country. In the figure, the probability of being in the high volatility (correlation) regime jumps dramatically from near zero to near unity in the middle of 1997. The probability increases from 10% during the middle of April, 1997 to over 99% at the beginning of May, 1997. There are then three downward spikes during 1997 which bottom out at 78% towards the end of May, 14% in August, and 71% in September. From that point the probability of being in the crisis regime is over 90% until April 1998. In contrast to these results, Forbes and Rigobon (2002) narrowly define their crises period from October 17, to November 17, 1997. Hence, our results indicate the Asian crises period could have started much earlier and lasted much longer than they assumed. This also points out the difficulties in and restriction of exogenously specifying regimes.

31

5

Conclusion

We set out to empirically test the hypothesis that crises are spread through investors’ asset holdings. Applying a variety of tests, we find evidence that investable stocks of each country co-move more with crisis country during the turmoil regime than non-investable stocks do, indicating that contagion is transmitted through investable stocks. We also find that investable stocks are not fundamentally different from non-investable stocks in terms of size, liquidity, export-orientation, and sector distribution, so those characteristics do not explain the increase in correlation. Furthermore, we find evidence that investable stocks lead non-investable stocks, thus suggesting that crises are spread first to investable stocks, through investors’ international holdings. The 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. These results are in contrast with the findings of Forbes and Rigobon (2002). They find that significant increases in correlation from stable to turmoil periods are mere statistical results of conditional correlations, conditional on observed conditional variances. Once the conditional correlations have been corrected to reflect unconditional correlation, they find no significant increases. Our study differs from theirs for three reasons. One is that we differentiate between investable and non-investable portions of the market, while the previous research has focused on the aggregate index. The second is that we find their approach of bias corrected correlation to be much too restrictive. The results are sensitive to the exogenously defined crisis period, and to the selection of the crisis country. Furthermore they impose normality in the bivariate distribution of asset returns. We verify that different definitions of the crisis period and crisis country greatly influence the results. Alternatively, we propose correlation calculations that are more robust in their construction and assumptions. First, we estimate exceedance correlation, which is free of distributional assumptions. Second, the regimeswitching model we use is a dynamic model that endogenizes the determination of the crisis and turmoil regimes and characterizes the asymmetric nature of the correlation exhibited in the data. Our analysis of the Asian Crisis in 1997 reveals that the crisis was transmitted regionally to Asian countries, with the increase in correlation being more prevalent in investable stocks rather than non-investable stocks. Our findings in this paper have interesting policy implications for countries 32

considering financial liberalization. By opening up their financial markets, countries may unnecessarily expose themselves to fundamentally unrelated external shocks and introduce more volatility to their economy.

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DeHaan, L., I. Resnick, H. Rootzen, and C. 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. Forbes, K. J. (2002): “Are Trade Linkages Important Determinants of Country Vulnerability to Crises?,” Preventing Currency Crises in Emerging Markets, p. forthcoming. Forbes, K. J., and R. Rigobon (2002): “No Contagion, Only Interdependence: Measuring Stock Market Co-Movements,” Journal of Finance, 56, forthcoming. Gibson, M. S., and B. H. Boyer (1997): “Evaluating Forecasts of Correlation Using Option Pricing,” Working Paper. Goldfajn, I., and R. O. Valdes (1997): “Capital Flows and the Twin Crises: the Role of Liquidity,” IMF Working Paper. Hamilton, J. D. (1989): “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrics, 57, 357–384. Hartmann, P., S. Straetmans, and C. G. deVries (2001): “Asset Market Linkages in Crisis Periods,” . IFC (1999): “The IFC Indexes: Methodology, Definitions, and Practices,” User Guide. Kaminsky, G. L., and C. M. Reinhart (2000): “On Crises, Contagion, and Confusion,” Journal of International Economics, 51, 145–168. Karolyi, A., and R. Stulz (1996): “Why Do Markets Move together? An Investigation of U.S.-Japan Stock Return Co-movements,” Journal of Finance, 51, 951–986. Kim, C.-J. (1994): “Dynamic Linear Models with Markov-Switching,” Journal of Econometrics, 60, 1–22. 34

King, M., E. Sentana, and S. Wadhwani (1994): “Volatility and Links between National Stock Markets,” Econometrica, 62, 901–933. Kodres, L. E., and M. Pritsker (2002): “A Rational Expectations Model of Financial Contagion,” Journal of Finance, 57(2), 769–799. Kyle, A. S., and W. Xiong (2001): “Contagion as a Wealth Effect,” Journal of Financial Economics, 62(2), 247–292. Lagunoff, R., and S. Schreft (2001): “A Model of Financial Fragility,” Journal of Economic Theory, 99, 220–264. Leadbetter, M., G. Lindgren, and H. Rootzen (1983): Extremes and Related Properties of Random Sequences and Processes. Springer Verlag, New York. Ledford, M. R., and J. Tawn (1997): “Statistics for Near Independence in Multivariate Extreme Values,” Biometrika, pp. 169–187. Longin, F., and B. Solnik (2001): “Extreme Correlation of International Equity Markets,” Journal of Finance, 56(2), 649–676. Prescott, P., and A. T. Walden (1980): “Maximum Likelihood Estimation of the parameters of the Generalized Extreme-Value Distribution,” Biometrika, 67(3), 723–724. Radelet, S., and J. Sachs (1998): “The Onset of Asian Crisis,” Working Paper. Rijckeghem, C. V., and B. Weder (2000): “Spillovers Through Banking Centers: A Panel Data Analysis,” Working Paper. Stambaugh, R. F. (1995): “Unpublished Discussion of Karolyi and Stulz (1996),” National Bureau of Economic Research Universities Research Conference on Risk Management. Tang, J. W. (2001): “Contagion: An Empirical Test,” Working paper, Duke University. Tawn, J. (1988): “Bivariate Extreme Value Theory; Models and Estimation,” Biometrika, 75(3), 397–415. 35

White, H. (1980): “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity,” Econometrica, 48, 817– 838. Yuan, K. (2000): “Asymmetric Price Movements and Borrowing Constraints: A REE Model of Crisis, Contagion, and Confusion,” PhD Dissertation.

36

De c

No v

Oc t

Se pt

Au g

Jul

Jun

Ma y

Ap r

Ma r

Fe b

Jan

Figure 1 1997 Stock Index Returns

0.3

0.2

0.1

0

Hong Kong Korea

-0.1

Thailand Indonesia Taiwan

-0.2

-0.3

-0.4

-0.5

Malaysia

Figure 2 Smoothed Inference of Being in Crisis Regime This figure uses data on Philippines and Thailand to plot the smoothed inference of being in the high volatility/high correlation regime. Smoothed inferences are defined by a two-state Markov regime-switching model as discussesd in Hamilton (1989). 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

2001 .07

2001 .01

2000 .07

2000 .01

1999 .07

1999 .01

1998 .07

1998 .01

1997 .07

1997 .01

1996 .07

1996 .01

1995 .07

1995 .01

1994 .07

1994 .01

1993 .07

1993 .01

1992 .07

1992 .01

1991 .07

1991 .01

1990 .07

1990 .01

1989 .07

1989 .01

0

Table 1 Market Capitalization and Value Traded, 1996 This table reports the total market capitalization and annual value-traded of each stock market in the sample. All statistics are reported as millions of U.S. dollars as of year-end of 1996. For emerging market countries, statistics are reported based on IFCG index in emerging market database (EMDB) from the International Finance Corporation (2000). For developed countries, data source is Datastream.

Region

Country

Total Market Capitalization Total Value Traded

Emerging Markets

Latin America

Argentina Brazil Chile Colombia Mexico Peru Venezuela

26,564 94,879 35,780 9,287 72,197 7,605 7,153

339 7,422 390 119 2,311 166 120

Asia-Pacific South Asia

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan, China Thailand

38,754 50,802 49,638 73,108 169,941 4,630 48,633 1,126 151,413 57,750

19,962 1,735 2,808 3,245 3,835 335 1,163 11 22,375 1,482

Emerging Europe

Czech Rep. Greece Hungary Poland Portugal Russia Slovakia

9,558 10,210 3,569 5,730 16,437 19,273 1,311

24 647 165 191 853 154 27

Others

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

1,884 12,609 2,890 4,551 86,122 16,276 2,386

322 44 45 1,362 1,744 33

Asia-Pacific

Australia Hong Kong Japan Singapore

311,988 449,381 3,088,850 150,215

145,482 166,419 1,251,998 42,739

Europe

Belgium France Germany Italy Netherlands Spain Sweden Switzerland United Kingdom

119,831 591,123 670,997 258,160 378,721 242,779 247,217 402,104 1,740,246

26,120 277,100 768,745 102,351 339,500 249,128 136,898 392,783 578,471

North America

Canada United States

486,268 8,484,433

265,360 7,121,487

Developed Markets

Table 2A Summary Statistics for Weekly Data This table contains summary statistics for weekly data from EMDB and Datastream. Full 665 observations are from January 1989 to September 2001. 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 and non-investable weekly returns. Overall Return Start Standard Date Mean Deviations

Obs Emerging Markets Argentina Brazil Chile Colombia Mexico Peru Venezuela

665 665 665 665 665 456 665

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand

456 665 574 665 665 548 665 456 333 665

Czech Republic Greece Hungary Poland Portugal* Russia Slovakia

404 665 456 456 534 300 300

Jan.94 Jan.89 Jan.93 Jan.93 Jan.89 Jan.96 Jan.96

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

300 247 665 300 456 665 430

Jan.96 Jan.97 Jan.89 Jan.96 Jan.93 Jan.89 Jul.93

Jan.89 Jan.89 Jan.89 Jan.89 Jan.89 Jan.93 Jan.89

IR and NR Obs Correlation

0.0800 0.0710 0.0314 0.0376 0.0429 0.0362 0.0576

665 665 665 556 665 456 612

Jan.89 Jan.89 Jan.89 Feb.91 Jan.89 Jan.93 Jan.89

0.0063 0.0052 0.0036 0.0031 0.0042 0.0017 0.0056

0.0820 0.0778 0.0330 0.0444 0.0450 0.0376 0.0692

665 665 665 556 665 456 612

Jan.89 Jan.89 Jan.89 Feb.91 Jan.89 Jan.93 Jan.89

0.0079 0.0047 0.0029 0.0015 0.0033 0.0035 0.0038

0.1085 0.0694 0.0326 0.0400 0.0441 0.0427 0.0639

665 665 665 556 665 456 612

0.464 0.890 0.646 0.623 0.621 0.510 0.542

Jan.93 0.0024 Jan.89 0.0011 Oct.90 -0.0002 Jan.89 0.0000 Jan.89 0.0013 Apr.91 0.0006 Jan.89 0.0003 Jan.93 -0.0011 May.95 -0.0011 Jan.89 0.0006

0.0622 0.0411 0.0739 0.0553 0.0466 0.0420 0.0440 0.0326 0.0433 0.0560

455 464 574 508 665 548 665 456 333 665

Jan.93 Jan.89 Oct.90 Jan.89 Jan.89 Apr.91 Jan.89 Jan.93 May.95 Jan.89

-0.0005 -0.0003 -0.0001 0.0006 0.0014 0.0011 0.0002 -0.0011 -0.0010 0.0005

0.0544 0.0398 0.0746 0.0608 0.0471 0.0474 0.0465 0.0403 0.0436 0.0549

455 464 574 508 665 548 665 456 333 665

Jan.93 Jan.89 Oct.90 Jan.89 Jan.89 Apr.91 Jan.89 Jan.93 May.95 Jan.89

0.0029 -0.0002 -0.0004 0.0005 0.0018 0.0008 0.0006 -0.0010 -0.0012 0.0007

0.0707 0.0383 0.0857 0.0595 0.0469 0.0365 0.0452 0.0310 0.0434 0.0575

455 464 574 508 665 548 665 456 333 665

0.158 0.989 0.807 0.920 0.910 0.751 0.842 0.785 0.992 0.984

-0.0020 0.0035 0.0019 0.0054 0.0025 0.0073 -0.0015

0.0361 0.0478 0.0466 0.0663 0.0292 0.0913 0.0365

404 665 455 455 534 242 242

Jan.94 Jan.89 Jan.93 Jan.93 Jan.89 Feb.97 Feb.97

-0.0014 0.0039 0.0028 0.0053 0.0027 0.0033 -0.0025

0.0438 0.0502 0.0486 0.0664 0.0297 0.1016 0.0426

404 584 455 401 534 242 242

Jan.94 Jan.89 Jan.93 Jan.93 Jan.89 Feb.97 Feb.97

-0.0030 0.0148 0.0019 0.0081 0.0027 0.0046 -0.0020

0.0371 0.1828 0.0481 0.0760 0.0326 0.1022 0.0422

404 584 455 401 534 242 242

0.561 0.226 0.405 0.775 0.804 0.628 0.216

-0.0007 0.0022 0.0014 0.0020 0.0021 0.0053 0.0037

0.0337 0.0357 0.0216 0.0199 0.0381 0.0853 0.0655

242 247 665 242 455 634 430

Feb.97 Jan.97 Jan.89 Feb.97 Jan.93 Jan.89 Jul.93

-0.0038 0.0022 0.0018 0.0010 0.0020 0.0043 0.0054

0.0369 0.0357 0.0236 0.0213 0.0382 0.0864 0.0590

242 247 665 242 229 406 430

Feb.97 Jan.97 Jan.89 Feb.97 Jan.93 Jan.89 Jul.93

-0.0045 0.0088 0.0013 -0.0006 0.0039 0.1833 0.0034

0.0226 0.0691 0.0211 0.0242 0.0607 1.5389 0.0692

242 247 665 242 229 408 430

0.523 0.570 0.930 0.634 0.598 0.062 0.703

0.0020

0.0486

0.0019

0.0510

0.0082

0.1046

Developed Markets Australia Hong Kong Japan Singapore

665 665 665 665

Jan.89 Jan.89 Jan.89 Jan.89

0.0009 0.0027 -0.0006 0.0010

0.0234 0.0378 0.0331 0.0309

Belgium France Germany Italy Netherlands Spain Sweden Switzerland United Kingdom

665 665 665 665 665 665 665 665 665

Jan.89 Jan.89 Jan.89 Jan.89 Jan.89 Jan.89 Jan.89 Jan.89 Jan.89

0.0011 0.0015 0.0019 0.0008 0.0020 0.0012 0.0017 0.0024 0.0015

0.0225 0.0269 0.0297 0.0323 0.0218 0.0273 0.0347 0.0237 0.0231

Canada United States

665 665

Jan.89 Jan.89

0.0015 0.0024

0.0225 0.0216

0.0015

0.0274

0.0008

0.0192

Developed Markets Average 665

Obs

Non-Investable Return Start Standard Date Mean Deviations

0.0060 0.0045 0.0034 0.0028 0.0036 0.0020 0.0037

Emerging Markets Average

World Index

Obs

Investable Return Start Standard Date Mean Deviations

Jan.89

Note: (*) Portugal data ends in Mar-99.

0.6474

Table 2B Summary Statistics for Daily Data This table contains summary statistics for daily returns from EMDB and Datastream. Full 1623 observations are from July 1995 to September 2001. For the emerging markets, 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 and non-investable daily returns.

Obs Emerging Markets Argentina Brazil Chile Colombia Mexico Peru Venezuela

1623 1623 1623 1623 1623 1623 1623

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand

1623

Czech Republic Greece** Hungary Poland** Portugal* Russia Slovakia

1623

Egypt Israel Jordan Morocco South Africa** Turkey** Zimbabwe

1494

1623 1623 1623 1623 1623 1623 1623 1623 1623

1623 1623 1623 1623 490 1214

1235 1623 1494 1623 1623 1623

Overall Return Start Standard Date Mean Deviations Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95

0.0001 0.0009 0.0001 0.0001 0.0007 0.0002 0.0014

0.0203 0.0212 0.0099 0.0103 0.0160 0.0113 0.0205

1623

Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95

0.0007 0.0001 0.0001 0.0001 -0.0001 0.0000 -0.0004 -0.0002 0.0000 -0.0008

0.0175 0.0163 0.0239 0.0254 0.0212 0.0201 0.0162 0.0097 0.0182 0.0237

1623

Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Nov.99 Jan.97

0.0001 0.0008 -0.0001 0.0010 0.0004 0.0015 -0.0002

0.0103 0.0191 0.0117 0.0208 0.0180 0.0306 0.0162

1623

Jan.96 Jan.97 Jul.95 Jan.96 Jul.95 Jul.95 Jul.95

0.0000 0.0007 0.0002 0.0006 0.0005 0.0025 0.0016

0.0130 0.0144 0.0068 0.0066 0.0131 0.0346 0.0161

1211

0.0004

0.0172

Jul.95 Jul.95 Jul.95 Jul.95

0.0002 0.0003 -0.0003 -0.0002

0.0111 0.0194 0.0149 0.0152

Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95

0.0002 0.0003 0.0003 0.0004 0.0003 0.0003 0.0004 0.0003 0.0002

0.0098 0.0128 0.0139 0.0139 0.0111 0.0126 0.0163 0.0104 0.0111

Jul.95 Jul.95

0.0004 0.0005

0.0110 0.0110

0.0002

0.0130

Emerging Markets Average Developed Markets Australia Hong Kong Japan Singapore

1623 1623 1623 1623

Belgium France Germany Italy Netherlands Spain Sweden Switzerland United Kingdom

1623

Canada United States

1623

1623 1623 1623 1623 1623 1623 1623 1623

1623

Obs

Developed Markets Average

1623 1623 1623 1623 1623 1623

1623 1623 1623 1623 1623 1623 1623 1623 1623

1623 1623 1623 1623 490 1211

1235 1623 1211 1623 1623 1623

Investable Return Start Standard Date Mean Deviations

Non-Investable Return Start Standard Obs Date Mean Deviations

Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95

0.0001 0.0003 -0.0003 -0.0005 0.0005 0.0000 0.0006

0.0204 0.0235 0.0113 0.0135 0.0193 0.0122 0.0257

1620

Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95

0.0000 -0.0002 -0.0004 -0.0001 -0.0004 -0.0003 -0.0008 -0.0006 -0.0002 -0.0011

0.0248 0.0172 0.0382 0.0315 0.0241 0.0224 0.0207 0.0141 0.0190 0.0267

1623

Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Nov.99 Jan.97

-0.0005 0.0005 -0.0001 0.0005 0.0001 0.0010 -0.0004

0.0135 0.0201 0.0164 0.0218 0.0195 0.0343 0.0195

1623

Jan.97 Jul.97 Jul.95 Jan.97 Jul.95 Jul.95 Jul.95

-0.0007 0.0004 0.0002 0.0002 -0.0001 0.0004 0.0006

0.0151 0.0158 0.0080 0.0083 0.0158 0.0373 0.0238

1211

0.0000

0.0204

Note: (*) Portugal data ends in Mar-99. (**) We delete some especially large returns (over 10) for Greece, Poland, South Africa, and Turkey.

1622 1618 1623 1615 1623 1623

1623 1540 1623 1622 1623 1623 1623 1623 1623

975 1526 1017 972 490 1211

1167 1623 1211 397 353 1623

IR and NR Obs Correlation

Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95

0.0027 0.0083 0.0149 0.0032 0.0102 0.0039 0.0173

0.1662 0.1100 0.1946 0.0308 0.2359 0.0494 0.2541

1620

Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Jul.95

0.0008 0.0003 0.1383 -0.0009 0.0035 0.0018 0.0000 0.0001 0.0000 -0.0006

0.0184 0.0162 0.7776 0.0466 0.0956 0.0274 0.0157 0.0096 0.0179 0.0236

1623

Jul.95 Jul.95 Jul.95 Jul.95 Jul.95 Nov.99 Jan.97

-0.0005 0.4263 0.1547 1.0939 0.0024 0.0031 0.0012

0.0408 1.0986 0.5346 2.0633 0.0385 0.0295 0.0750

1623

Jan.97 Jan.97 Jul.95 Jan.97 Jul.95 Jul.95 Jul.95

0.0006 0.1326 0.0002 0.0022 0.9974 0.9035 0.0022

0.0237 0.6425 0.0065 0.0507 2.1647 1.9738 0.0180

1211

0.1266

0.3500

1622 1618 1623 1615 1623 1623

1623 1540 1623 1622 1623 1623 1623 1623 1623

975 1526 1017 972 490 1211

1167 1623 1211 397 353 1623

-0.011 -0.219 -0.276 -0.193 -0.369 -0.132 -0.499 0.189 0.954 -0.611 0.015 -0.332 0.249 0.579 0.539 0.950 0.787 -0.133 -0.217 -0.155 -0.146 0.034 0.613 -0.283 0.076 -0.417 0.852 -0.375 -0.267 -0.034 0.296 0.0472

Table 3 Percentage of Investable and Non-Investable by Country This table reports percentages of market capitalization that are investable and non-investable according to IFC, for emerging market countries. For developed countries, the investables are 100% of market capitalization. The market capitalizations are year-end values in millions of U.S. dollars.

Region Latin America

Country Argentina Brazil Chile Colombia Mexico Peru Venezuela

Others

1999 100% 94% 95% 72% 99% 97% 98%

1994 2% 37% 78% 22% 11% 19% 22%

Non-Investable 1996 1997 1998 1% 0.4% 0.1% 18% 13% 8% 0.8% 1.1% 5% 24% 20% 22% 7% 5% 8% 7% 6% 12% 11% 9% 2%

1999 0.5% 6% 5% 28% 1% 3% 2%

1994 18,751 111,906 45,058 11,437 83,279 5,271 3,352

25% 26% 98% 91% 94% 84% 48% 33% 35% 54%

37% 28% 96% 94% 96% 81% 47% 53% 55% 53%

90% 78% 50% 89% 17% 47% 46% 70% 92% 72%

88% 74% 38% 78% 8% 20% 54% 68% 67% 66%

90% 71% 2% 46% 6% 11% 52% 66% 60% 53%

75% 74% 2% 9% 6% 16% 52% 67% 65% 46%

63% 72% 4% 6% 4% 19% 53% 47% 45% 47%

19,307 65,358 22,199 125,065 125,883 7,668 30,222 1,710 160,212 79,660

38,754 50,802 49,638 73,108 169,941 4,630 48,633 1,126 151,413 57,750

49,981 50,856 13,553 25,157 45,911 5,979 18,650 1,293 153,176 10,921

52,452 49,797 10,682 61,203 44,849 2,400 21,969 1,036 138,369 16,735

78,532 92,526 23,260 181,989 68,937 3,115 25,953 29,232 230,955 29,232

38% 100% 100% 100% 90% 63% 86%

71% 100% 99% 100% 91% 67% 85%

80% 100% 99% 100% 77% 91%

65% 7% 37% 0.0% 36% -

65% 1.3% 10% 0.2% 23% 100% 100%

62% 0.2% 0.5% 0.0% 10% 37% 14%

29% 0.2% 0.6% 0.1% 9% 33% 15%

20% 0.0% 0.5% 0.1% 23% 9%

8,735 8,021 747 1,478 11,185

9,558 10,210 3,569 5,730 16,437 19,273 1,311

4,847 16,255 11,175 6,234 24,745 54,744 1,320

4,204 40,891 10,933 8,134 34,574 7,558 600

4,205 90,076 13,076 11,770 25,252 433

0% 94% 26% 0% 100% 100% 26%

79% 99% 34% 86% 99% 100% 40%

82% 99% 34% 81% 100% 100% 30%

93% 100% 51% 95% 100% 99% 32%

65% 4% 0.0% 88%

100% 6% 74% 100% 0.0% 0.0% 74%

21% 0.6% 66% 14% 0.5% 0.0% 60%

18% 0.6% 66% 19% 0.0% 0.0% 70%

7% 0.3% 49% 5% 0.0% 1.3% 68%

137,866 15,106 1,341

1,884 12,609 2,890 4,551 86,122 16,276 2,386

8,141 18,812 3,261 7,562 90,297 33,732 1,123

5,848 16,373 4,277 9,674 68,672 16,947 653

11,433 26,527 4,109 8,141 110,672 58,708 1,613

61%

73%

77%

80%

45%

39%

27%

23%

20%

946,506

965,348

816,590

713,342

1,217,913

1996 99% 82% 99% 76% 93% 93% 89%

10% 22% 50% 11% 83% 53% 54% 30% 8% 28%

12% 26% 62% 22% 92% 80% 46% 32% 33% 34%

10% 29% 98% 54% 94% 89% 48% 34% 40% 47%

Czech Rep. Greece Hungary Poland Portugal Russia Slovakia

35% 93% 63% 100% 64% -

35% 99% 90% 100% 77% 0% 0%

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

35% 96% 100% 12% 55%

Asia-Pacific China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan, China Thailand Emerging Europe

Investable 1997 1998 100% 100% 87% 92% 99% 95% 80% 78% 95% 92% 94% 88% 91% 98%

1994 98% 63% 22% 78% 89% 81% 78%

Total

2,787 -

Market Capitalization 1996 1997 26,564 35,142 94,879 102,965 35,780 44,498 9,287 11,452 72,197 108,941 7,605 9,657 7,153 9,138

1998 24,894 65,871 31,837 6,337 67,174 6,151 4,689

1999 23,319 107,803 42,639 5,371 118,418 7,583 4,237

-

Table 4 Firm-Level Comparison of Investable vs. Non-Investable 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 o stock 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 delete from 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 the 95% significance level.

Region

Country

1994 Significantly Greater? Observations Inv. Non-Inv. Liquidity Size

1997 Observations Significantly Greater? Inv. Non-Inv. Liquidity Size

1998 Observations Significantly Greater? Inv. Non-Inv. Liquidity Size

Latin America

Argentina Brazil Chile Colombia Mexico Peru Venezuela

24 44 0 10 63 11 12

6 26 15 14 13 24 5

N N N N Y N N

N N N N N Y N

25 25 35 4 5 16 10

3 12 3 12 6 13 7

N N N N Y N N

N N N Y Y Y Y

26 44 30 4 51 13 13

1 10 11 10 13 14 5

Y N N Y N N

N Y Y N N Y

Asia-Pacific

China India Indonesia Korea Malaysia Pakistan Phillipines Sri Lanka Taiwan Thailand

18 0 0 0 99 15 16 4 0 0

99 43 9 6 0 56 22 27 2 6

N Y Y N -

Y Y N Y -

43 0 45 57 88 10 3 4 5 0

152 61 1 13 0 31 10 45 0 9

N N N N -

N Y N Y -

47 0 31 52 33 0 1 0 0 0

156 56 17 27 18 36 21 46 0 21

N N N N -

N N Y Y -

Emerging Europe

Czech Rep Greece Hungary Poland Portugal Russia Slovakia

5 25 5 12 19 ---

55 11 8 0 7 ---

N N N N ---

Y Y Y N ---

3 43 8 13 10 11 3

35 2 2 0 0 7 15

Y N N N N

N N Y N Y

5 43 9 12 10 5 2

24 2 2 1 0 19 14

N N N Y N

N N N N Y

Others

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

--0 -58 40 0

--29 -4 0 19

---N -

---N -

11 10 1 5 17 30 0

26 3 38 6 1 0 12

N N N -

Y N N -

5 13 2 5 32 31 0

32 3 35 8 0 0 19

N N N Y -

Y N Y N -

Table 5 Percentage of Investable and Non-Investable by Industry This table reports percentage distributions of investable and non-investable firms in each industry (by SIC code) for all firms in the EMDB sample across countries. Industries with larger than 1% absolute percentage difference between investable and non-investable are shaded. SIC code 1 2 7 8 9 10 12 13 14 15 16 17 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 41 43 44 45 47 48 49 50 51 52 53 54 55 58 59 60 61 62 63 65 67 70 73 75 78 79 89 99

Industry Agricultural Production-Crops, forestry and fishing Agricultural Production-Livestock Agricultural Services Forestry Fishing, hunting and trapping Metal Mining Bituminous Coal & Lignite Mining Oil & Gas Extraction Nonmetallic Minerals, Except Fuel General Building Contractors Construction - General Contractors General Building Contractors Food & Kindred Products Tobacco manufactures Textile Mill Products Apparel & Other Textile Products Lumber & Wood Products Furniture & Fixtures Paper & Allied Products Printing & Publishing Chemicals & Allied Products Petroleum Refining & Related Products Rubber & Misc. Plastics Products Leather Goods & Products Cement & Glass Products Primary Metal Industries Fabricated Metal Products Machinery Except Electrical Electric & Electronic Equipment Transportation Equipment Measuring & Precision Instruments Miscellaneous Manufacturing Transportation - Motor Passenger Electric, Gas or Sanitary Services Water Transportation Transportation by Air Transportation Services Communications Electric, Gas or Sanitary Services Wholesale Trade - Durable Goods Finance Wholesale Trade-Nondurable Goods General Merchandise Stores Food Stores Automotive Dealers & Gas Service Station Miscellaneous Retail Retail Banking Credit Agencies Other Than Banks Security & Commodity Brokers Insurance Real Estate Holding & Other Investment Services Hotels & Other Lodging Places Business Services Automotive Repair Service Motion Pictures Amusement & Recreation Services Misc Services Other, Diversified Holding Companies

Total (Median)

Investable % Non-Investable % Difference 1.19 1.46 -0.27 0.44 0.93 -0.49 0.13 0.27 -0.14 0.19 0.40 -0.21 0.25 0.40 -0.15 1.76 2.52 -0.76 0.13 0.53 -0.4 1.19 1.73 -0.54 0.38 0.38 2.33 2.52 -0.19 1.26 0.93 0.33 0.50 0.53 -0.03 6.73 5.31 1.42 0.94 0.40 0.54 2.96 3.45 -0.49 1.38 2.52 -1.14 0.57 0.13 0.44 0.19 0.13 0.06 2.20 2.26 -0.06 0.19 0.13 0.06 5.72 6.91 -1.19 2.52 0.93 1.59 1.89 2.26 -0.37 0.13 0.40 -0.27 4.97 5.84 -0.87 1.95 2.12 -0.17 2.83 3.19 -0.36 1.13 2.66 -1.53 6.29 6.64 -0.35 2.96 4.25 -1.29 0.13 0.40 -0.27 2.33 1.99 0.34 0.19 0.40 -0.21 0.13 0.40 -0.27 0.88 1.20 -0.32 0.63 0.40 0.23 0.25 0.53 -0.28 4.78 1.86 2.92 3.65 4.38 -0.73 0.63 1.06 -0.43 0.69 0.40 0.29 0.06 0.06 1.64 1.86 -0.22 0.38 0.40 -0.02 0.06 0.40 -0.34 0.06 0.06 0.50 0.66 -0.16 9.06 4.52 4.54 1.89 2.92 -1.03 2.39 1.33 1.06 1.64 0.80 0.84 2.26 3.45 -1.19 2.58 3.45 -0.87 1.45 1.06 0.39 0.13 0.40 -0.27 0.06 0.06 0.06 0.13 -0.07 0.44 0.27 0.17 0.44 0.13 0.31 5.35 3.45 1.90 1590

753

-0.18

Table 6 Lead and Lag Relationship This table exhibits contemporaneous correlations and lag correlations between investable and non-investable returns for weekly data. Contemporaneous correlations between investable and non-investable are in (1). The differences between correlations of non-investable with lag investable and of investable with lag non-investable are reported in (4). All positive differences (of larger correlation with lag-investable) are denoted in bold typescript Contemporaneous

1 week

1 week

1 week

Correlation

Lag Investable

Lag Non-Investable

Difference

(1)

(2)

(3)

(4)

Argentina Brazil Chile Colombia Mexico Peru Venezuela

0.464 0.890 0.646 0.623 0.621 0.510 0.542

0.119 0.115 0.150 0.238 0.283 0.151 0.120

-0.010 0.063 0.130 0.125 0.121 0.084 0.027

0.129 0.052 0.020 0.113 0.161 0.067 0.092

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan, China Thailand

0.158 0.989 0.807 0.920 0.910 0.751 0.842 0.785 0.992 0.984

0.115 0.078 -0.023 -0.070 -0.029 0.300 0.116 0.203 -0.008 0.041

0.090 0.052 -0.125 -0.081 0.003 0.175 0.059 0.218 -0.003 0.008

0.025 0.026 0.101 0.011 -0.032 0.126 0.057 -0.014 -0.005 0.032

Czech Rep. Greece Hungary Poland Portugal Russia Slovakia

0.561 0.226 0.405 0.775 0.804 0.628 0.216

0.148 -0.010 0.128 0.056 0.061 0.118 0.150

0.102 0.026 0.041 0.020 0.015 0.132 0.097

0.046 -0.036 0.086 0.036 0.046 -0.014 0.052

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

0.523 0.570 0.930 0.634 0.598 0.062 0.703

-0.081 -0.053 -0.026 0.258 0.293 -0.044 0.124

0.121 -0.047 -0.049 0.185 0.197 -0.021 0.068

-0.201 -0.005 0.023 0.073 0.095 -0.023 0.055

Average

0.647

0.097

0.059

0.039

Emerging Markets:

Number of positive differences Sign test for equal probability of negative/positive (p-value )

23 0.002

Table 7 Comparison of Correlation with Forbes/Rigobon Tables III and IV This table compares our correlation calculations using daily data, no interest rates, no lags, to those reported in Forbes and Rigobon (2002) tables III and IV, of correlation estimated using 2-day average daily data, interest rates, 5 lags. The crisis country is Hong Kong and the crisis period is from October 17 to November 16, 1997.

Developed Markets

Full Period FR ρ ρ

Conditional Correlation Turmoil Stable FR ρ ρ FR ρ

ρ

Unconditional Correlation Turmoil Stable FR ρ ρ FR ρ

ρ

Australia Japan Singapore

0.431 0.263 0.348

0.522 0.329 0.473

0.865 0.559 0.493

0.783 0.497 0.718

0.356 0.231 0.341

0.352 0.262 0.334

0.561 0.255 0.343

0.493 0.391 0.476

0.385 0.252 0.348

0.359 0.265 0.340

Belgium France Germany Italy Netherlands Spain Sweden Switzerland UK

0.178 0.299 0.450 0.236 0.347 0.269 0.298 0.232 0.280

0.237 0.324 0.494 0.269 0.396 0.284 0.365 0.258 0.268

0.714 0.886 0.902 0.896 0.742 0.878 0.796 0.842 0.615

0.536 0.778 0.810 0.785 0.790 0.634 0.755 0.670 0.643

0.140 0.227 0.383 0.175 0.319 0.191 0.233 0.183 0.255

0.152 0.149 0.328 0.133 0.266 0.136 0.170 0.168 0.145

0.371 0.596 0.642 0.818 0.397 0.584 0.454 0.591 0.292

0.410 0.492 0.500 0.494 0.495 0.449 0.486 0.461 0.452

0.153 0.248 0.412 0.191 0.346 0.209 0.255 0.200 0.278

0.153 0.150 0.333 0.133 0.269 0.136 0.170 0.168 0.146

Canada United States

0.170 -0.027

0.283 0.091

0.378 -0.390

0.416 0.090

0.145 0.021

0.201 0.091

0.156 -0.169

0.348 0.089

0.159 0.023

0.202 0.091

Argentina Brazil Chile Mexico

0.004 0.080 0.197 0.238

0.117 0.173 0.204 0.170

-0.144 -0.593 0.619 0.241

0.055 0.169 0.504 0.083

0.030 0.105 0.144 0.238

0.143 0.147 0.108 0.224

-0.059 -0.025 0.302 0.102

0.054 0.164 0.395 0.082

0.033 0.113 0.157 0.256

0.144 0.148 0.108 0.226

Indonesia Korea Malaysia Philippines Thailand Taiwan

0.381 0.173 0.288 0.323 0.028 0.082

0.411 0.157 0.399 0.378 0.088 0.102

0.749 0.683 0.465 0.705 0.149 0.402

0.554 0.224 0.604 0.722 -0.019 0.087

0.381 0.092 0.802 0.294 0.010 0.046

0.376 0.106 0.349 0.285 0.132 0.123

0.399 0.380 0.200 0.388 0.058 0.171

0.418 0.212 0.438 0.477 -0.019 0.086

0.413 0.098 0.305 0.315 0.010 0.051

0.385 0.106 0.356 0.289 0.132 0.124

Emerging Markets

Table 8A T-test of Turmoil Period vs. Sample or Stable Period Crisis: Hong Kong, October 17, 1997 to November 16, 1997 The T-test statistics reported are of tests of equivalence of correlation between the turmoil period and either the sample or stable period for stock market index returns. The bolded statistics indicate rejection of the null, at the 95% significance level, against the one-sided alternative that the turmoil period correlation is greater. 99%, 95%, 90% significance level rejections are denoted with (***, **, *). The last row provides p-value for the sign test of equal probability of higher and lower correlations, against the alternative of higher correlations during the turmoil period. The crisis country is Hong Kong and the crisis period is from October 17, 1997 to November 16, 1997.

Conditional Correlation Turmoil=Sample Stable T-stat df 0.352 1.670 ** 511 0.262 0.868 511 0.334 1.466 * 511

Sample 0.522 0.329 0.473

Turmoil 0.783 0.497 0.718

Belgium France Germany Italy Netherlands Spain Sweden Switzerland UK

0.237 0.324 0.494 0.269 0.396 0.284 0.365 0.258 0.268

0.536 0.778 0.810 0.785 0.790 0.634 0.755 0.670 0.643

0.152 0.149 0.328 0.133 0.266 0.136 0.170 0.168 0.145

1.466 2.368 1.954 2.596 2.184 1.771 2.095 2.050 1.876

Canada US

0.283 0.091

0.416 0.090

0.201 0.091

Argentina Brazil Chile Colombia Mexico Peru Venezuela

0.117 0.173 0.204 0.149 0.170 0.119 0.078

0.055 0.169 0.504 0.448 0.083 0.172 0.030

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand

0.139 0.053 0.411 0.157 0.399 0.180 0.378 0.012 0.102 0.088

Czech Republic Hungary Poland Portugal Slovakia Egypt Greece Israel Jordan Morocco South Africa Turkey Zimbabwe

Developed Markets:

Australia Japan Singapore

Turmoil=Stable T-stat df 2.296 ** 489 1.162 489 2.012 ** 489

Sample 0.522 0.329 0.473

Unconditional Correlation Turmoil=Sample Turmoil=Stable Turmoil Stable T-stat df T-stat df 0.493 0.359 -0.188 511 0.615 489 0.391 0.265 0.325 511 0.580 489 0.476 0.340 0.013 511 0.621 489

510 511 511 511 511

1.819 3.003 2.526 3.101 2.637 2.359 2.811 2.404 2.363

0.668 -0.003

511 510

1.035 -0.006

489 488

0.283 0.091

0.348 0.089

0.202 0.091

0.329 -0.007

511 510

0.674 -0.010

489 488

0.143 0.147 0.108 0.049 0.224 0.091 0.085

-0.288 -0.018 1.451 * 1.412 * -0.413 0.246 -0.222

508 508 508 508 508 508 508

-0.413 0.102 1.852 ** 1.842 ** -0.678 0.371 -0.252

489 489 489 489 489 489 489

0.117 0.173 0.204 0.149 0.170 0.119 0.078

0.054 0.164 0.395 0.366 0.082 0.166 0.030

0.144 0.148 0.108 0.049 0.226 0.092 0.085

-0.289 -0.045 0.932 1.035 -0.417 0.219 -0.222

508 508 508 508 508 508 508

-0.407 0.071 1.335 * 1.474 * -0.651 0.340 -0.250

489 489 489 489 489 489 489

0.440 0.142 0.554 0.224 0.604 0.507 0.722 0.034 0.087 -0.019

0.138 0.040 0.376 0.106 0.349 0.085 0.285 -0.021 0.123 0.132

1.418 * 0.410 0.795 0.319 1.126 1.564 * 1.868 ** 0.101 -0.069 -0.495

508 508 508 508 508 508 508 508 508 508

1.419 0.465 0.955 0.549 1.339 1.962 2.205 0.250 -0.168 -0.701

*

489 489 489 489 489 489 489 489 489 489

0.139 0.053 0.411 0.157 0.399 0.180 0.378 0.012 0.102 0.088

0.362 0.138 0.418 0.212 0.438 0.396 0.477 0.034 0.086 -0.019

0.138 0.040 0.385 0.106 0.356 0.085 0.289 -0.021 0.124 0.132

1.060 0.395 0.039 0.260 0.215 1.044 0.543 0.101 -0.073 -0.495

508 508 508 508 508 508 508 508 508 508

1.036 0.449 0.151 0.485 0.373 1.448 * 0.864 0.249 -0.170 -0.688

489 489 489 489 489 489 489 489 489 489

-0.007 0.398 0.314 0.483 0.103

-0.312 0.643 0.662 0.845 0.055

0.067 0.191 0.154 0.234 0.145

-1.405 1.349 * 1.791 ** 2.204 ** -0.215

508 508 508 508 228

-1.749 2.171 2.412 3.012 -0.269

*

489 489 489 489 489

-0.007 0.398 0.314 0.483 0.103

-0.280 0.452 0.459 0.508 0.055

0.067 0.193 0.154 0.236 0.146

-1.267 0.300 0.749 0.151 -0.215

508 508 508 508 228

-1.605 1.202 1.412 * 1.255 -0.264

489 489 489 489 489

0.093 0.069 0.539 0.039 -0.045 0.568 0.118 -0.012

0.644 -0.139 0.800 -0.186 0.104 0.782 0.346 -0.194

-0.011 0.134 0.240 0.073 -0.085 0.294 0.026 0.036

2.588 *** 508 -0.959 508 1.698 ** 249 -1.037 508 0.681 508 1.469 508 1.065 508 -0.835 508

3.041 -1.269 1.846 -1.193 0.868 2.484 1.472 -1.054

***

489 489 489 489 489 489 489 489

0.093 0.069 0.539 0.039 -0.045 0.568 0.118 -0.012

0.453 -0.136 0.561 -0.179 0.102 0.493 0.304 -0.186

-0.011 0.134 0.243 0.073 -0.085 0.298 0.026 0.036

1.700 ** -0.946 0.146 -1.006 0.676 -0.516 0.875 -0.799

508 508 249 508 508 508 508 508

2.165 ** -1.233 0.974 -1.156 0.856 0.897 1.288 -1.017

489 489 489 489 489 489 489 489

0.231

0.283

0.157

*

511

*** 511 **

511

*** 511 ** ** ** ** **

** *** *** *** *** *** *** *** ***

489 489 489 489 488 489 489 489 489

0.237 0.324 0.494 0.269 0.396 0.284 0.365 0.258 0.268

0.410 0.492 0.500 0.494 0.495 0.449 0.486 0.461 0.452

0.153 0.150 0.333 0.133 0.269 0.136 0.170 0.168 0.146

0.853 0.879 0.037 1.133 0.552 0.840 0.651 1.015 0.925

511 511 511 511 510 511 511 511 511

1.193 1.595 0.764 1.683 1.047 1.456 1.469 1.361 1.425

* ** * * * *

489 489 489 489 488 489 489 489 489

Emerging Markets:

Average and average difference between turmoil and stable period 32 0.001

Number of countries with higher correlation in Turmoil: Sign Test (p-value )

33 0.000

* ** **

** ** ***

**

*** *

30 0.005

0.126 33 0.000

Table 8B T-test of Turmoil Period vs. Sample or Stable Period Unconditional Correlations Crisis: Hong Kong, October 17, 1997 to November 16, 1997 The T-test statistics reported are of tests of equivalence of correlation between the turmoil period and either the sample or stable period for unconditional correlations separately for investable stock index returns and non-investable stock index returns. The bolded statistics indicate rejections of the null at the 95% significance level, against the one-sided alternative that the turmoil period correlation is greater. 99% , 95%, 90% significance level rejections are denoted with (***, **, *). The last row provides p-value for the sign test of equal of higher and lower correlations, against the alternative of higher correlation during the turmoil period.

Investable Stock Index Returns Turmoil=Sample Turmoil Stable T-stat df 508 0.055 0.144 -0.287 0.161 0.143 -0.034 508 0.446 0.090 0.920 508 0.417 0.058 1.091 508 0.343 0.240 0.110 508 0.248 0.100 0.415 508 0.004 0.114 -0.386 508

Turmoil=Stable T-stat df -0.403 489 0.078 489 1.658 ** 489 1.670 ** 489 0.475 489 0.681 489 -0.500 489

Argentina Brazil Chile Colombia Mexico Peru Venezuela

Sample 0.117 0.168 0.263 0.192 0.322 0.160 0.088

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand

0.539 0.036 0.416 0.154 0.373 0.192 0.331 -0.073 0.101 0.060

0.484 0.111 0.444 0.188 0.424 0.402 0.464 -0.268 0.089 -0.029

0.541 0.018 0.322 0.111 0.301 0.114 0.200 -0.056 0.111 0.096

-0.360 0.342 0.161 0.160 0.275 1.019 0.699 -0.912 -0.056 -0.412

508 508 508 508 508 508 508 508 508 508

-0.253 0.422 0.560 0.356 0.561 1.339 * 1.223 -0.979 -0.103 -0.570

489 489 489 489 489 489 489 489 489 489

0.089 0.057 -0.044 0.158 -0.045 -0.010 0.324 0.067 0.102 0.101

0.272 0.148 -0.099 0.223 -0.186 0.318 0.436 0.193 0.084 0.008

0.090 0.047 0.320 0.104 0.070 -0.077 0.296 0.012 0.129 0.142

0.014 0.017 0.380 0.329 0.351 0.064

-0.199 -0.232 0.451 0.467 0.494 -0.042

0.063 0.100 0.176 0.134 0.159 0.123

-0.986 -1.148 0.390 0.721 0.766 -0.475

508 508 508 508 508 225

-1.207 -1.527 1.277 1.545 * 1.556 * -0.476

489 489 489 489 489 489

-0.023 0.066 0.242 -0.102 0.360 0.068

-0.292 0.091 0.401 -0.108 0.477 0.300

0.047 0.061 0.120 0.032 0.156 0.007

0.121 0.540 0.017 -0.142 0.518 0.104 0.104

0.480 0.562 -0.361 -0.479 0.494 0.284 0.158

-0.026 0.247 0.066 -0.115 0.244 0.022 0.076

1.691 ** 0.143 -1.759 -1.595 -0.155 0.845 0.249

225 249 508 225 508 508 508

1.480 * 0.962 -1.979 -1.060 1.152 1.214 0.377

489 489 489 489 489 489 489

0.133 -0.073 0.049 0.150 -0.361 . -0.085

0.539 -0.281 -0.044 0.396 -0.327 . -0.232

0.010 -0.035 0.076 -0.014 0.000 . 0.013

0.195

0.202

0.131

0.018

0.031

0.057

Emerging Markets:

Czech Republic Greece Hungary Poland Portugal Slovakia Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe Average

Number of countries with higher correlation in Turmoil: Sign Test (p-value )

17 0.181

0.071 19 0.049

Sample -0.005 0.176 -0.153 -0.077 -0.490 -0.164 0.012

Non-Investable Stock Index Returns Turmoil=Sample Turmoil=Stable Turmoil Stable T-stat df T-stat df -0.107 0.014 -0.471 508 -0.555 489 0.159 0.152 -0.080 508 0.033 489 -0.331 0.052 -0.803 503 -1.685 487 -0.414 -0.006 -1.584 508 -1.903 489 -0.484 -0.114 0.042 508 -1.722 489 -0.456 -0.023 -1.401 508 -2.016 489 0.213 -0.034 0.928 508 1.135 489 0.853 0.423 -0.254 0.309 -0.651 1.525 * 0.587 0.587 -0.086 -0.432

508 508 508 508 508 508 508 508 508 508

0.838 0.467 -1.883 0.549 -1.178 1.828 ** 0.644 0.832 -0.205 -0.611

489 489 489 489 489 489 489 489 489 489

-1.246 0.108 0.791 -0.016 0.626 1.055

508 496 508 416 508 225

-1.566 0.125 1.304 * -0.363 1.491 * 0.853

489 486 489 475 489 489

1.928 ** -0.931 -0.426 1.159 0.085 . -0.686

225 243 508 225 19 . 508

1.549 * -0.737 -0.548 1.198 -7.755 . -1.130

489 488 489 489 477 . 489

15 0.356

-0.026 14 0.500

Table 8C T-test of Investable Stock Index Return Correlation vs. Non-Investable Stock Index Return Correlation Crisis: Hong Kong, October 17, 1997 to November 16, 1997 The T-test statistics reported are of tests of equivalence of correlations between Hong Kong market return and investable stock index return and between Hong Kong market return and non-investable stock index return within each defined period. The bolded statistics indicate rejections of the null at the 95% siginficance level, against the one-sided alternative that turmoil period corrleation is greater. 99%, 95%, 90% significance level rejections are denoted with (***, **, *). The last row provides p-values for the sign test of equal probability of higher and lower correlations, against the alternative of higher correlations during the turmoil period.

Emerging Markets:

Argentina Brazil Chile Colombia Mexico Peru Venezuela

Sample Period Correlation Investable Non-Investable obs ρ obs ρ T-stat 488 0.117 488 -0.005 1.91 488 0.168 488 0.176 -0.14 488 0.263 485 -0.153 6.81 488 0.192 488 -0.077 4.30 488 0.322 488 -0.490 15.28 488 0.160 488 -0.164 5.21 488 0.088 488 0.012 1.20

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand

488 488 488 488 488 488 488 488 488 488

0.539 0.036 0.416 0.154 0.373 0.192 0.331 -0.073 0.101 0.060

488 488 488 488 488 488 488 488 488 488

0.089 0.057 -0.044 0.158 -0.045 -0.010 0.324 0.067 0.102 0.101

8.14 -0.32 7.83 -0.06 7.02 3.21 0.12 -2.19 -0.02 -0.64

Czech Republic Greece Hungary Poland Portugal Slovakia

488 488 488 488 488 205

0.014 0.017 0.380 0.329 0.351 0.064

488 479 488 410 488 205

-0.023 0.066 0.242 -0.102 0.360 0.068

0.59 -0.76 2.40 6.87 -0.17 -0.05

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

205 229 488 205 488 488 488

0.121 0.540 0.017 -0.142 0.518 0.104 0.104

205 224 488 205 11 . 488

0.133 -0.073 0.049 0.150 -0.361 . -0.085

-0.12 7.58 -0.50 -3.01 3.92 . 2.98

0.018

0.177 17 0.132

Average

0.195

Number of countries with higher correlation in Turmoil: Sign Test (p-value )

df *** *** *** *** ***

*** *** *** ***

*** ***

***

*** ***

974 974 971 974 974 974 974

Turmoil Period/Unconditional Investable Non-Investable obs ρ obs ρ T-stat 22 0.055 22 -0.107 0.55 22 0.161 22 0.159 0.00 22 0.446 20 -0.331 5.14 *** 22 0.417 22 -0.414 8.19 *** 22 0.343 22 -0.484 7.00 *** 22 0.248 22 -0.456 4.20 *** 22 0.004 22 0.213 -0.76

974 974 974 974 974 974 974 974 974 974

22 22 22 22 22 22 22 22 22 22

0.484 0.111 0.444 0.188 0.424 0.402 0.464 -0.268 0.089 -0.029

22 22 22 22 22 22 22 22 22 22

0.272 0.148 -0.099 0.223 -0.186 0.318 0.436 0.193 0.084 0.008

1.39 -0.13 2.57 -0.14 3.12 0.55 0.39 -1.95 0.02 -0.12

974 965 974 896 974 408

22 22 22 22 22 22

-0.199 -0.232 0.451 0.467 0.494 -0.042

22 19 22 8 22 22

-0.292 0.091 0.401 -0.108 0.477 0.300

0.40 -1.18 0.53 2.78 0.48 -1.27

408 451 974 408 497 . 974

22 22 22 22 22 22 22

0.480 0.562 -0.361 -0.479 0.494 0.284 0.158

22 21 22 22 10 . 22

0.539 -0.281 -0.044 0.396 -0.327 . -0.232

-0.58 4.99 -1.34 -5.69 4.05 . 1.53

0.031

0.171 19 0.031

0.202

* *** ***

***

***

*** *

df 42 42 40 42 42 42 42

Stable Period/Unconditional Investable Non-Investable obs ρ obs ρ T-stat 469 0.144 469 0.014 1.98 469 0.143 469 0.152 -0.13 469 0.090 469 0.052 0.59 469 0.058 469 -0.006 0.98 469 0.240 469 -0.114 5.36 469 0.100 469 -0.023 1.88 469 0.114 469 -0.034 2.26

42 42 42 42 42 42 42 42 42 42

469 469 469 469 469 469 469 469 469 469

0.541 0.018 0.322 0.111 0.301 0.114 0.200 -0.056 0.111 0.096

469 469 469 469 469 469 469 469 469 469

0.090 0.047 0.320 0.104 0.070 -0.077 0.296 0.012 0.129 0.142

6.75 -0.43 0.04 0.11 3.50 2.90 -1.45 -1.05 -0.26 -0.71

42 39 42 28 42 42

469 469 469 469 469 469

0.063 0.100 0.176 0.134 0.159 0.123

469 469 469 469 469 469

0.047 0.061 0.120 0.032 0.156 0.007

0.25 0.60 0.85 1.51 0.05 1.11

42 41 42 42 30 . 42

469 469 469 469 469 469 469

-0.026 0.247 0.066 -0.115 0.244 0.022 0.076

469 469 469 469 469 . 469

0.010 -0.035 0.076 -0.014 0.000 . 0.013

-0.34 2.83 -0.16 -0.96 5.21 . 0.95

0.057

0.074 20 0.012

0.131

Diff. in Diff df **

*** ** ** ***

*** ***

*

***

***

936 936 936 936 936 936 936

Diff. 0.033 0.010 0.738 0.767 0.473 0.580 -0.357

T-stat 0.109 0.035 4.340 6.350 3.499 3.225 -1.258

936 936 936 936 936 936 936 936 936 936

-0.238 -0.010 0.541 -0.042 0.379 -0.107 0.124 -0.393 0.022 0.009

-1.423 -0.034 2.437 -0.162 1.838 -0.646 1.280 -1.598 0.075 0.030

936 936 936 936 936 936

0.076 -0.362 -0.006 0.472 0.014 -0.458

0.320 -1.266 -0.049 1.365 0.188 -1.589

936 936 936 936 936 . 936

-0.023 0.561 -0.306 -0.775 0.577 . 0.328

-0.157 2.810 -1.249 -4.163 1.872

0.091 19

0.012

.

1.245

*** *** *** ***

*** **

*

***

**

Table 9A T-test of Turmoil Period vs. Sample or Stable Period Crisis: Hong Kong, July 2, 1997 to November 16, 1997 The T-test statistics reported are of tests of equivalence of correlation between the turmoil period and either the sample or stable period for stock market index returns. The bolded statistics indicate rejections of the null, at the 95% significance level, against the one-sided alternative that the turmoil period correlation is greater. 99%, 95%, 90% significance level rejections are denoted with (***, **, *). The last row provides p-value for the sign test of equal probability of higher and lower correlations against the alternative of higher correlations during the turmoil period. The crisis country is Hong Kong and the crisis period is from July 2, 1997 to November 16, 1997.

Developed Markets:

Australia Japan Singapore

Sample Turmoil 0.522 0.654 0.329 0.426 0.473 0.551

Conditional Correlation Turmoil=Sample Stable T-stat df 0.377 1.694 ** 588 0.236 0.999 588 0.345 0.920 588

Belgium France Germany Italy Netherlands Spain Sweden Switzerland UK

0.237 0.324 0.494 0.269 0.396 0.284 0.365 0.258 0.268

0.340 0.519 0.577 0.438 0.515 0.419 0.594 0.353 0.398

0.135 0.130 0.395 0.165 0.254 0.147 0.161 0.169 0.163

1.001 2.033 1.016 1.686 1.307 1.358 2.490 0.927 1.288

Canada US

0.283 0.091

0.386 0.119

0.180 0.055

1.023 0.259

Argentina Brazil Chile Colombia Mexico Peru Venezuela

0.117 0.173 0.204 0.149 0.170 0.119 0.078

0.137 0.178 0.366 0.301 0.173 0.125 0.113

0.086 0.132 0.084 0.058 0.171 0.114 0.056

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand

0.139 0.053 0.411 0.157 0.399 0.180 0.378 0.012 0.102 0.088

0.255 0.166 0.438 0.209 0.396 0.322 0.443 0.022 0.105 0.023

0.100 -0.016 0.333 0.085 0.381 0.096 0.266 -0.020 0.104 0.199

Czech Republic Greece Hungary Poland Portugal Slovakia

-0.007 0.069 0.398 0.314 0.483 0.103

-0.058 0.064 0.560 0.513 0.686 0.102

0.032 0.073 0.198 0.151 0.141 0.146

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

0.093 0.539 0.039 -0.045 0.568 0.118 -0.012

0.303 0.680 0.074 0.032 0.709 0.265 -0.061

-0.058 0.201 0.037 -0.114 0.292 0.035 0.013

Turmoil=Stable T-stat df 3.044 * 489 1.832 ** 489 2.159 ** 489

Turmoil=Stable T-stat df 1.560 * 489 1.433 * 489 1.241 489

** * * * * * * ** **

489 489 489 489 488 489 489 489 489

0.237 0.324 0.494 0.269 0.396 0.284 0.365 0.258 0.268

0.324 0.466 0.507 0.405 0.463 0.390 0.518 0.335 0.373

0.135 0.130 0.408 0.166 0.258 0.148 0.161 0.170 0.164

0.851 588 1.507 ** 588 0.163 588 1.374 ** 588 0.755 587 1.078 588 1.694 ** 588 0.757 588 1.049 588

1.727 3.167 0.901 2.214 1.909 2.240 3.400 1.500 1.925

588 587

1.935 ** 0.568

489 488

0.283 0.091

0.362 0.118

0.181 0.055

0.801 0.253

588 587

1.664 ** 0.560

0.185 0.048 1.556 * 1.422 * 0.022 0.058 0.323

585 585 585 585 585 585 585

0.458 489 0.421 489 2.579 *** 489 2.198 ** 489 0.019 489 0.101 489 0.512 489

0.117 0.173 0.204 0.149 0.170 0.119 0.078

0.136 0.176 0.346 0.289 0.170 0.124 0.113

0.086 0.132 0.084 0.058 0.172 0.114 0.056

0.176 0.025 1.380 * 1.326 * 0.001 0.051 0.318

585 585 585 585 585 585 585

0.444 489 0.388 489 2.405 *** 489 2.106 ** 489 -0.011 489 0.089 489 0.505 489

1.086 1.033 0.300 0.488 -0.030 1.347 * 0.692 0.090 0.023 -0.596

585 585 585 585 585 585 585 585 585 585

1.406 1.616 1.070 1.115 0.161 2.059 1.728 0.370 0.007 -1.619

489 489 489 489 489 *** 489 ** 489 489 489 489

0.139 0.053 0.411 0.157 0.399 0.180 0.378 0.012 0.102 0.088

0.248 0.164 0.405 0.205 0.371 0.308 0.409 0.022 0.104 0.023

0.100 -0.016 0.340 0.085 0.393 0.096 0.270 -0.020 0.104 0.201

1.026 1.017 -0.062 0.453 -0.303 1.226 0.334 0.090 0.019 -0.596

585 585 585 585 585 585 585 585 585 585

1.335 1.603 0.583 1.075 -0.191 1.930 1.265 0.370 0.001 -1.565

-0.464 -0.050 1.794 ** 2.053 ** 2.510 *** -0.003

585 585 585 585 585 305

-0.803 489 -0.084 489 3.517 *** 489 3.433 *** 489 5.318 *** 489 -0.227 489

-0.007 0.069 0.398 0.314 0.483 0.103

-0.058 0.063 0.495 0.462 0.577 0.102

0.032 0.073 0.199 0.151 0.142 0.147

-0.463 -0.051 1.100 1.551 * 1.175 -0.005

585 585 585 585 585 305

-0.802 489 -0.085 489 2.785 *** 489 2.910 *** 489 4.192 *** 489 -0.225 489

1.948 1.767 0.322 0.694 1.968 1.367 -0.438

585 326 585 585 585 585 585

3.260 2.820 0.333 1.306 4.358 2.069 -0.654

0.093 0.539 0.039 -0.045 0.568 0.118 -0.012

0.291 0.623 0.074 0.032 0.590 0.257 -0.061

-0.058 0.204 0.037 -0.114 0.297 0.035 0.013

0.231

0.290

0.145

588 588 588 ** 588 * 587 * 588 *** 588 588 * 588 **

1.899 3.685 1.994 2.575 2.577 2.546 4.198 1.711 2.206

Sample Turmoil 0.522 0.556 0.329 0.395 0.473 0.489

Unconditional Correlation Turmoil=Sample Stable T-stat df 0.388 0.451 588 0.239 0.693 588 0.354 0.188 588

** 489 *** 489 489 ** 489 ** 488 ** 489 *** 489 * 489 ** 489 489 488

Emerging Markets:

** **

** *

Average and average difference between turmoil and stable period 38 0.000

Number of countries with higher correlation in Turmoil: Sign Test (p-value )

39 0.000

* *

*** 489 *** 489 489 * 489 *** 489 ** 489 489

1.855 ** 585 1.038 326 0.320 585 0.694 585 0.316 585 1.302 * 585 -0.437 585

36 0.000

3.173 2.321 0.331 1.290 2.768 2.009 -0.653 0.145 37 0.000

* **

** *

489 489 489 489 489 489 489 489 489 489

*** 489 ** 489 489 * 489 *** 489 ** 489 489

Table 9B T-test of Turmoil Period vs. Sample or Stable Period Unconditional Correlations Crisis: Hong Kong, July 2, 1997 to November 16, 1997 The T-test statistics reported are of tests of equivalence of correlation between the turmoil period and either the sample or stable period for unconditional correlations separately for investable stock index returns and non-investable stock index returns. The bolded statistics indicate rejections of the null at the 95% significance level, against the one-sided alternative that the turmoil period correlation is greater. 99%, 95%, 90% significance level rejections are denoted with (***, **, *). The last row provides p-values for the sign test of equal probability of higher and lower correlations against the alternative of higher correlations in the turmoil period.

Investible Stock Index Returns Turmoil=Sample Turmoil Stable T-stat df 585 0.136 0.085 0.179 0.172 0.125 0.039 585 0.443 0.052 1.831 ** 585 0.325 0.065 1.276 * 585 0.384 0.191 0.637 585 0.199 0.122 0.359 585 0.095 0.102 0.072 585

Turmoil=Stable T-stat df 0.453 489 0.419 489 3.674 *** 489 2.380 *** 489 1.761 ** 489 0.687 489 -0.058 489

Argentina Brazil Chile Colombia Mexico Peru Venezuela

Sample 0.117 0.168 0.263 0.192 0.322 0.160 0.088

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand

0.539 0.036 0.416 0.154 0.373 0.192 0.331 -0.073 0.101 0.060

0.528 0.103 0.402 0.195 0.348 0.332 0.341 -0.086 0.101 -0.010

0.401 -0.012 0.343 0.087 0.367 0.101 0.254 -0.088 0.100 0.179

-0.147 0.610 -0.160 0.383 -0.275 1.348 * 0.109 -0.120 0.001 -0.644

585 585 585 585 585 585 585 585 585 585

1.166 1.029 0.525 0.970 -0.175 2.107 ** 0.787 0.024 0.008 -1.663

489 489 489 489 489 489 489 489 489 489

0.089 0.057 -0.044 0.158 -0.045 -0.010 0.324 0.067 0.102 0.101

0.140 0.170 -0.070 0.211 -0.075 -0.016 0.333 0.095 0.104 0.048

0.079 -0.017 0.323 0.084 0.206 0.019 0.266 0.033 0.105 0.207

0.468 1.035 -0.236 0.495 -0.266 -0.057 0.090 0.257 0.016 -0.491

585 585 585 585 585 585 585 585 585 585

0.547 489 1.671 ** 489 -3.393 489 1.141 489 -2.459 489 -0.313 489 0.605 489 0.545 489 -0.013 489 -1.394 489

0.014 0.017 0.380 0.329 0.351 0.064

-0.004 -0.010 0.494 0.459 0.513 0.072

0.038 0.036 0.179 0.140 0.051 0.075

-0.164 -0.240 1.264 1.373 * 1.776 ** 0.064

585 585 585 585 585 302

-0.369 489 -0.409 489 2.960 *** 489 2.990 *** 489 4.420 *** 489 -0.018 489

-0.023 0.066 0.242 -0.102 0.360 0.068

-0.092 0.058 0.299 -0.054 0.442 0.081

0.017 0.094 0.152 0.002 0.159 0.078

-0.624 -0.063 0.564 0.346 0.884 0.106

585 568 585 466 585 302

-0.971 -0.301 1.333 * -0.377 2.638 *** 0.015

0.121 0.540 0.017 -0.142 0.518 0.104 0.104

0.283 0.624 0.018 -0.208 0.591 0.235 0.149

-0.155 0.207 0.027 -0.155 0.244 0.029 0.047

1.366 * 1.033 0.014 -0.555 0.962 1.218 0.411

302 326 585 302 585 585 585

2.172 2.305 -0.079 -0.261 3.310 1.860 0.914

0.133 -0.073 0.049 0.150 -0.361 . -0.085

0.288 -0.140 0.095 0.294 -0.327 . -0.156

-0.043 -0.026 0.042 -0.135 0.000 . -0.004

1.307 * -0.551 0.421 1.230 0.085 . -0.655

302 318 585 302 19 . 585

1.657 ** 481 -0.612 489 0.468 489 2.138 ** 448 -7.087 489 . . -1.355 477

0.195

0.241

0.108

0.018

0.019

0.060

Emerging Markets:

Czech Republic Greece Hungary Poland Portugal Slovakia Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe Average

Number of countries with higher correlation in Turmoil: Sign Test (p-value )

22 0.003

0.133 22 0.003

489 489 489 489 *** 489 ** 489 489 *** ***

Sample -0.005 0.176 -0.153 -0.077 -0.490 -0.164 0.012

Non-Investible Stock Index Returns Turmoil=Sample Turmoil=Stable Turmoil Stable T-stat df T-stat df -0.057 0.019 -0.470 585 -0.674 489 0.179 0.144 0.030 585 0.311 489 -0.360 0.111 -1.955 580 -4.279 487 -0.139 0.007 -0.565 585 -1.299 489 -0.551 -0.115 -0.767 585 -4.183 489 -0.399 -0.022 -2.270 585 -3.502 489 0.157 -0.060 1.327 * 585 1.935 ** 489

16 0.229

-0.040 13 0.644

489 489 486 489 489 400

Table 9C T-test of Investable Stock Index Return Correlation vs. Non-Investable Stock Index Return Correlation Crisis: Hong Kong, July 2, 1997 to November 16, 1997 The T-test statistics reported are of tests of equivalence of correlations between Hong Kong market return and investable stock index return and between Hong Kong market return and non-investable stock index return within each defined period. The bolded statistics indicate rejections of the null at the 95% siginficance level, against the one-sided alternative that turmoil period corrleation is greater. 99%, 95%, 90% significance level rejections are denoted with (***, **, *). The last row provides p-values for the sign test of equal probability of higher and lower correlations, against the alternative of higher correlations during the turmoil period.

Emerging Markets:

Argentina Brazil Chile Colombia Mexico Peru Venezuela

Investable obs ρ 488 0.117 488 0.168 488 0.263 488 0.192 488 0.322 488 0.160 488 0.088

Sample Period Correlation Non-Investable obs ρ T-stat 488 -0.005 1.913 488 0.176 -0.137 485 -0.153 6.811 488 -0.077 4.303 488 -0.490 15.277 488 -0.164 5.211 488 0.012 1.191

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand

488 488 488 488 488 488 488 488 488 488

0.539 0.036 0.416 0.154 0.373 0.192 0.331 -0.073 0.101 0.060

488 488 488 488 488 488 488 488 488 488

0.089 0.057 -0.044 0.158 -0.045 -0.010 0.324 0.067 0.102 0.101

8.145 -0.321 7.834 -0.058 7.019 3.207 0.116 -2.186 -0.021 -0.642

Czech Republic Greece Hungary Poland Portugal Slovakia

488 488 488 488 488 205

0.014 0.017 0.380 0.329 0.351 0.064

488 479 488 410 488 205

-0.023 0.066 0.242 -0.102 0.360 0.068

0.588 -0.763 2.401 6.870 -0.172 -0.046

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

205 229 488 205 488 488 488

0.121 0.540 0.017 -0.142 0.518 0.104 0.104

205 224 488 205 11 . 488

0.133 -0.073 0.049 0.150 -0.361 . -0.085

-0.124 7.576 -0.503 -3.014 3.925 . 2.983

0.018

0.177 17 0.132

Average

0.195

Number of countries with higher correlation in Turmoil: Sign Test (p-value )

df ** *** *** *** ***

*** *** *** ***

*** ***

***

*** ***

974 974 971 974 974 974 974

Turmoil Period/Unconditional Investable Non-Investable obs ρ obs ρ T-stat 99 99 -0.057 0.136 1.381 99 99 0.172 0.179 -0.056 99 97 -0.360 0.443 7.611 99 99 -0.139 0.325 3.626 99 99 -0.551 0.384 10.501 99 99 -0.399 0.199 5.007 99 99 0.095 0.157 -0.444

974 974 974 974 974 974 974 974 974 974

99 99 99 99 99 99 99 99 99 99

0.528 0.103 0.402 0.195 0.348 0.332 0.341 -0.086 0.101 -0.010

99 99 99 99 99 99 99 99 99 99

0.140 0.170 -0.070 0.211 -0.075 -0.016 0.333 0.095 0.104 0.048

3.519 -0.483 3.813 -0.118 3.301 2.680 0.073 -1.287 -0.022 -0.409

974 965 974 896 974 408

99 99 99 99 99 99

-0.004 -0.010 0.494 0.459 0.513 0.072

99 91 99 58 99 99

-0.092 0.058 0.299 -0.054 0.442 0.081

0.624 -0.471 1.878 3.930 0.824 -0.067

408 451 974 408 497 . 974

99 99 99 99 99 99 99

0.283 0.624 0.018 -0.208 0.591 0.235 0.149

99 96 99 99 10 . 99

0.288 -0.140 0.095 0.294 -0.327 . -0.156

-0.034 6.662 -0.545 -3.740 6.021 . 2.231

0.019

0.221 17 0.132

0.241

df * *** *** *** ***

*** *** *** ***

** ***

***

*** **

196 196 194 196 196 196 196

Stable Period/Unconditional Investable Non-Investable obs ρ obs ρ T-stat 392 392 0.085 0.019 0.918 392 392 0.125 0.144 -0.271 392 392 0.052 0.111 -0.827 392 392 0.065 0.007 0.806 392 392 -0.115 0.191 4.243 392 392 -0.022 0.122 2.007 392 392 -0.060 0.102 2.254

196 196 196 196 196 196 196 196 196 196

392 392 392 392 392 392 392 392 392 392

0.401 -0.012 0.343 0.087 0.367 0.101 0.254 -0.088 0.100 0.179

392 392 392 392 392 392 392 392 392 392

0.079 -0.017 0.323 0.084 0.206 0.019 0.266 0.033 0.105 0.207

4.371 0.065 0.270 0.045 2.190 1.141 -0.157 -1.698 -0.076 -0.383

196 188 196 155 196 196

392 392 392 392 392 392

0.038 0.036 0.179 0.140 0.051 0.075

392 392 392 392 392 392

0.017 0.094 0.152 0.002 0.159 0.078

0.287 -0.798 0.377 1.873 -1.508 -0.021

196 193 196 196 107 . 196

392 392 392 392 392 392 392

-0.155 0.207 0.027 -0.155 0.244 0.029 0.047

392 392 392 392 392 . 392

-0.043 -0.026 0.042 -0.135 0.000 . -0.004

-0.810 1.850 -0.213 -0.145 4.714 . 0.710

0.060

0.048 17 0.132

0.108

Diff. in Diff df

*** ** ** ***

**

**

**

***

782 782 782 782 782 782 782

Diff. 0.127 0.012 0.863 0.406 0.628 0.453 -0.223

T-stat 0.808 0.079 6.747 2.768 5.473 3.253 -1.434

782 782 782 782 782 782 782 782 782 782

0.066 -0.071 0.451 -0.019 0.260 0.266 0.020 -0.059 0.002 -0.030

0.497 -0.458 3.128 -0.125 1.764 1.792 0.146 -0.372 0.015 -0.190

782 782 782 782 782 782

0.068 -0.011 0.167 0.374 0.180 -0.007

0.427 -0.067 1.323 2.294 1.599 -0.034

782 782 782 782 782 . 782

0.108 0.530 -0.062 -0.482 0.674 . 0.254

0.562 3.104 -0.389 -2.495 2.170 . 1.646

0.171 20 0.012

*** *** *** ***

*** ** **

** *

***

** **

Table 10A T-test of Turmoil Period vs. Sample or Stable Period Crisis: Thailand, July 2, 1997 to November 16, 1997 The T-test statistics reported are of tests of equivalence of correlation between turmoil period and either sample or stable period for stock market index returns. The bolded statistics indicate rejections of the null, at the 95% significance level, against the one-sided alternative that the turmoil period correlation is greater. 99%, 95%, 90% significance level rejections are denoted with (***, **, *). The last row provides p-values for the sign test of equal probability of higher and lower correlations, against the alternative of higher correlations during the turmoil period. The crisis country is Thailand and the crisis period is from July 2, 1997 to November 16, 1997.

Developed Markets:

Australia Hong Kong Japan Singapore

Sample Turmoil 0.099 0.146 0.060 -0.010 0.001 0.001 0.201 0.185

Conditional Correlation Turmoil=Sample Stable T-stat df 585 0.043 0.439 585 0.178 -0.644 585 -0.008 -0.003 585 0.215 -0.150

Turmoil=Stable T-stat df 0.918 489 -1.711 489 0.076 489 -0.271 489

Sample Turmoil 0.099 0.145 0.060 -0.010 0.001 0.001 0.201 0.183

Unconditional Correlation Turmoil=Sample Stable T-stat df 0.043 0.430 585 0.178 -0.644 585 -0.008 -0.003 585 0.216 -0.170 585

Turmoil= T-stat 0.909 -1.670 0.076 -0.290

Belgium France Germany Italy Netherlands Spain Sweden Switzerland UK

0.013 0.062 0.041 0.044 -0.019 0.059 0.087 0.020 0.009

-0.007 0.095 0.069 0.060 -0.003 0.064 0.169 0.040 -0.012

0.027 0.032 0.008 0.037 -0.041 0.050 0.021 0.001 0.025

-0.186 0.298 0.249 0.148 0.143 0.047 0.756 0.178 -0.191

585 585 585 585 584 585 585 585 585

-0.304 0.557 0.540 0.204 0.333 0.128 1.323 * 0.345 -0.328

489 489 489 489 489 489 489 489 489

0.013 0.062 0.041 0.044 -0.019 0.059 0.087 0.020 0.009

-0.007 0.094 0.068 0.060 -0.003 0.064 0.168 0.040 -0.012

0.027 0.032 0.008 0.037 -0.041 0.050 0.021 0.001 0.025

-0.186 0.295 0.248 0.147 0.143 0.046 0.742 0.177 -0.191

585 585 585 585 584 585 585 585 585

-0.303 0.555 0.539 0.203 0.332 0.127 1.311 * 0.345 -0.328

Canada US

-0.012 -0.068

-0.061 -0.090

0.031 -0.055

-0.447 -0.194

585 585

-0.817 -0.310

489 489

-0.012 -0.068

-0.061 -0.090

0.031 -0.055

-0.447 -0.192

585 585

-0.816 -0.307

Argentina Brazil Chile Colombia Mexico Peru Venezuela

0.013 0.084 -0.007 0.026 0.021 0.100 0.002

-0.013 0.118 -0.028 0.114 -0.093 0.137 0.071

0.031 0.014 -0.003 -0.024 0.151 0.067 -0.046

-0.229 0.315 -0.197 0.799 -1.040 0.334 0.630

585 585 585 585 585 585 585

-0.390 0.932 -0.224 1.228 * -2.209 0.621 1.040

489 489 489 489 489 489 489

0.013 0.084 -0.007 0.026 0.021 0.100 0.002

-0.013 0.118 -0.028 0.114 -0.093 0.136 0.071

0.031 0.014 -0.003 -0.024 0.152 0.067 -0.046

-0.229 0.310 -0.197 0.795 -1.038 0.327 0.629

585 585 585 585 585 585 585

-0.390 0.928 -0.224 1.224 -2.167 0.612 1.037

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan

0.060 0.063 0.203 0.100 0.237 0.178 0.090 0.126 0.015

-0.048 0.198 0.231 0.221 0.270 0.293 0.093 0.320 0.070

0.115 0.006 0.151 -0.022 0.176 0.119 0.074 -0.010 -0.035

585 -0.982 585 1.229 0.261 585 585 1.116 0.310 585 585 1.090 0.029 585 1.815 ** 585 0.500 585

-1.461 1.716 0.726 2.180 0.860 1.588 0.167 2.984 0.930

489 489 489 489 489 489 489 489 489

0.060 0.063 0.203 0.100 0.237 0.178 0.090 0.126 0.015

-0.048 0.195 0.227 0.218 0.263 0.285 0.093 0.310 0.070

0.115 0.006 0.152 -0.022 0.177 0.119 0.074 -0.010 -0.035

-0.981 1.209 0.225 1.086 0.251 1.019 0.027 1.727 0.499

585 585 585 585 585 585 585 585 585

-1.445 1.697 0.674 2.153 0.777 1.501 0.163 2.905 0.928

Czech Republic Greece Hungary Poland Portugal Slovakia

0.103 0.156 0.091 0.114 0.104 -0.059

0.257 0.361 0.139 0.231 0.168 -0.166

0.011 0.000 0.036 0.027 0.018 0.171

1.420 * 1.940 ** 0.446 1.079 0.594 -0.890

585 585 585 585 585 305

2.206 3.274 0.917 1.827 1.342 -1.761

489 489 489 489 489 489

0.103 0.156 0.091 0.114 0.104 -0.059

0.251 0.346 0.139 0.227 0.167 -0.166

0.011 0.000 0.036 0.027 0.018 0.172

1.375 ** 1.813 ** 0.438 1.045 0.581 -0.883

585 585 585 585 585 305

2.165 3.160 0.909 1.796 1.330 -1.707

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

-0.060 0.082 0.021 0.057 0.109 0.168 0.147

-0.046 0.130 0.059 0.104 0.126 0.214 0.157

-0.082 -0.011 0.008 0.053 0.077 0.161 0.142

585 326 585 585 585 585 585

0.319 0.778 0.451 0.451 0.437 0.484 0.144

489 489 489 489 489 489 489

-0.060 0.082 0.021 0.057 0.109 0.168 0.147

-0.046 0.130 0.059 0.103 0.126 0.211 0.156

-0.082 -0.011 0.008 0.053 0.077 0.162 0.142

585 326 585 585 585 585 585

0.318 0.776 0.450 0.446 0.429 0.441 0.127

0.067

0.097

0.044

Emerging Markets:

0.130 0.400 0.346 0.423 0.158 0.433 0.093

Average and average difference between turmoil and stable period 32 0.001

Number of countries with higher correlation in Turmoil: Sign Test (p-value )

33 0.000

** ** * ***

*** ***

** *

0.130 0.397 0.345 0.420 0.152 0.405 0.082

36 0.000

0.053 33 0.000

** ** * ***

** *** ** *

Table 10B T-test of Turmoil Period vs. Sample or Stable Period Unconditional Correlations Crisis: Thailand, July 2, 1997 to November 16, 1997 The T-test statistics reported are of tests of equivalence of correlation between the turmoil period and either the sample or stable period for unconditional correlations separately for investable stock index returns and non-investable stock index returns. The bolded statistics indicate rejections of the null at the 95% significance level, against the one-sided alternative that the turmoil period correlation is greater. 99%, 95%, 90% significance level rejections are denoted with (***, **, *). The last row provides p-values for the sign test of equal probability of higher and lower correlations against the alternative of higher correlations in the turmoil period.

Investible Stock Index Returns Turmoil=Sample Turmoil Stable T-stat df -0.012 0.033 -0.234 585 0.118 0.014 0.316 585 0.001 0.016 -0.126 585 0.118 -0.022 0.718 585 -0.037 0.161 -0.853 585 0.122 0.073 0.224 585 0.070 0.003 0.400 585

Argentina Brazil Chile Colombia Mexico Peru Venezuela

Sample 0.014 0.084 0.015 0.039 0.057 0.098 0.026

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan

0.007 0.064 0.199 0.112 0.231 0.158 0.180 0.109 0.055

-0.047 0.187 0.222 0.240 0.260 0.262 0.246 0.259 0.164

0.082 0.007 0.151 -0.022 0.173 0.107 0.074 0.002 -0.036

-0.485 1.132 0.220 1.188 0.281 0.976 0.621 1.394 * 0.998

0.075 0.148 0.089 0.107 0.095 -0.059

0.156 0.343 0.145 0.195 0.190 -0.185

0.019 -0.002 0.032 0.028 -0.001 0.199

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

0.016 0.051 0.011 0.124 0.092 0.167 0.069

-0.032 0.112 0.050 0.126 0.101 0.223 0.004

0.078 -0.066 -0.007 0.149 0.075 0.156 0.121

Average

0.084

0.124

0.055

Emerging Markets:

Czech Republic Greece Hungary Poland Portugal Slovakia

Number of countries with higher correlation in Turmoil: Sign Test (p-value )

Turmoil=Stable T-stat df -0.400 489 0.928 489 -0.131 489 1.242 489 -1.791 489 0.438 489 0.599 489 489 489 489 489 489 489 489 489 489

0.062 0.061 0.130 0.094 -0.031 0.094 -0.025 0.108 -0.008

-0.040 0.180 0.167 0.208 -0.053 0.107 -0.089 0.291 0.015

0.114 0.004 0.147 -0.022 0.085 0.084 0.064 -0.015 -0.034

-0.924 1.092 0.336 1.054 -0.205 0.121 -0.583 1.711 ** 0.213

585 585 585 585 585 585 585 585 585

-1.359 1.571 * 0.171 2.067 ** -1.226 0.207 -1.353 2.773 *** 0.439

489 489 489 489 489 489 489 489 489

0.742 585 1.837 ** 585 0.513 585 0.812 585 0.872 585 -1.044 302

1.214 489 3.129 *** 489 1.007 489 1.499 * 489 1.705 ** 489 -1.999 489

0.089 -0.054 0.018 -0.064 0.017 0.052

0.250 -0.088 0.005 -0.104 -0.001 0.100

-0.002 -0.008 0.028 -0.013 0.032 -0.001

1.482 ** -0.295 -0.122 -0.284 -0.157 0.395

585 568 585 466 585 302

2.269 ** -0.679 -0.209 -0.619 -0.292 0.507

489 481 489 448 489 489

-0.387 0.510 0.353 0.016 0.085 0.524 -0.592

-0.555 0.983 0.500 -0.116 0.232 0.606 -1.045

-0.063 0.140 0.024 -0.084 0.684 . 0.165

-0.072 0.078 0.059 -0.153 0.690 . 0.178

-0.065 0.220 0.015 -0.013 0.000 . 0.142

-0.078 -0.519 0.319 -0.566 0.022 . 0.118

302 318 585 302 19 . 585

-0.038 -0.765 0.395 -0.697 19.842 *** . 0.318

489 486 489 489 400 . 489

0.042

0.058

0.016

22 0.001

585 585 585 585 585 585 585 585 585

302 326 585 302 585 585 585

-1.149 1.610 0.650 2.356 0.805 1.409 1.551 2.308 1.788

Sample -0.042 0.075 -0.104 -0.024 -0.089 0.021 -0.063

Non-Investible Stock Index Returns Turmoil=Sample Turmoil=Stable Turmoil Stable T-stat df T-stat df -0.062 -0.044 -0.177 585 -0.155 489 0.110 0.013 0.328 585 0.869 489 -0.143 -0.061 -0.362 580 -0.730 487 -0.028 -0.007 -0.035 585 -0.185 489 -0.092 -0.084 -0.033 585 -0.076 489 0.080 -0.012 0.539 585 0.821 489 0.038 -0.114 0.915 585 1.343 * 489

0.069 21 0.004

* *** * *

** **

489 489 489 489 489 489 489

14 0.425

0.042 14 0.425

Table 10C T-test of Investable Stock Index Return Correlation vs. Non-Investable Stock Index Return Correlation Crisis: Thailand, July 2, 1997 to November 16, 1997 The T-test statistics reported are of tests of equivalence of correlations between Thailand market return and investable stock index return and between Thailand market return and non-investable stock index return within each defined period. The bolded statistics indicate rejections of the null at the 95% siginficance level, against the one-sided alternative that turmoil period corrleation is greater. 99%, 95%, 90% significance level rejections are denoted with (***, **, *). The last row provides p-values for the sign test of equal probability of higher and lower correlations, against the alternative of higher correlations during the turmoil period.

Emerging Markets:

Argentina Brazil Chile Colombia Mexico Peru Venezuela

Sample Period Correlation Investable Non-Investable obs ρ obs ρ T-stat 488 0.014 488 -0.042 0.876 488 0.084 488 0.075 0.148 488 0.015 485 -0.104 1.861 ** 488 0.039 488 -0.024 0.982 488 0.057 488 -0.089 2.282 ** 488 0.098 488 0.021 1.204 488 0.026 488 -0.063 1.390 *

df 974 974 971 974 974 974 974

Turmoil Period/Unconditional Investable Non-Investable obs ρ obs ρ T-stat 99 -0.012 99 -0.062 0.351 99 0.118 99 0.110 0.054 99 0.001 97 -0.143 1.025 99 0.117 99 -0.028 1.029 99 -0.037 99 -0.092 0.390 99 0.122 99 0.080 0.294 99 0.070 99 0.038 0.227

df 196 196 194 196 196 196 196

Stable Period/Unconditional Investable Non-Investable obs ρ obs ρ T-stat 392 0.033 392 -0.044 1.079 392 0.014 392 0.013 0.019 392 0.016 392 -0.061 1.067 392 -0.022 392 -0.007 -0.210 392 0.161 392 -0.084 3.410 *** 392 0.073 392 -0.012 1.190 392 0.003 392 -0.114 1.622 **

Diff. in Diff df 782 782 782 782 782 782 782

Diff. -0.028 0.007 0.068 0.161 -0.189 -0.043 -0.084

T-stat -0.174 0.039 0.429 1.012 -1.198 -0.280 -0.531

China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan

488 488 488 488 488 488 488 488 488

0.007 0.064 0.199 0.112 0.231 0.158 0.180 0.109 0.055

488 488 488 488 488 488 488 488 488

0.062 0.061 0.130 0.094 -0.031 0.094 -0.025 0.108 -0.008

-0.861 0.046 1.104 0.281 4.202 *** 1.020 3.248 *** 0.012 0.985

974 974 974 974 974 974 974 974 974

99 99 99 99 99 99 99 99 99

-0.047 0.185 0.219 0.236 0.255 0.256 0.241 0.254 0.163

99 99 99 99 99 99 99 99 99

-0.040 0.180 0.167 0.208 -0.053 0.107 -0.089 0.291 0.015

-0.049 0.039 0.386 0.207 2.265 ** 1.106 2.423 *** -0.293 1.054

196 196 196 196 196 196 196 196 196

392 392 392 392 392 392 392 392 392

0.082 0.007 0.151 -0.022 0.174 0.107 0.074 0.002 -0.036

392 392 392 392 392 392 392 392 392

0.114 0.004 0.147 -0.022 0.085 0.084 0.064 -0.015 -0.034

-0.438 0.040 0.056 -0.003 1.232 0.322 0.141 0.238 -0.031

782 782 782 782 782 782 782 782 782

0.025 0.005 0.052 0.032 0.226 0.132 0.325 -0.049 0.151

0.154 0.016 0.314 0.183 1.428 0.826 2.078 ** -0.372 0.952

Czech Republic Greece Hungary Poland Portugal Slovakia

488 488 488 488 488 205

0.075 0.148 0.089 0.107 0.095 -0.059

488 479 488 410 488 205

0.089 -0.054 0.018 -0.064 0.017 0.052

-0.232 3.178 *** 1.105 2.583 *** 1.233 -1.132

974 965 974 896 974 408

99 99 99 99 99 99

0.155 0.330 0.144 0.193 0.188 -0.185

99 91 99 58 99 99

0.250 -0.088 0.005 -0.104 -0.001 0.100

-0.708 196 3.119 *** 188 0.990 196 1.862 ** 155 1.357 * 196 -2.034 196

392 392 392 392 392 392

0.019 -0.002 0.032 0.028 -0.001 0.200

392 392 392 392 392 392

-0.002 -0.008 0.028 -0.013 0.032 -0.001

0.305 0.083 0.046 0.550 -0.465 1.456 *

782 782 782 782 782 782

-0.116 0.424 0.137 0.259 0.224 -0.485

-0.768 2.697 *** 0.861 1.450 * 1.419 * -2.471

Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

205 229 488 205 488 488 488

0.016 0.051 0.011 0.124 0.092 0.167 0.069

205 224 488 205 11 0 488

-0.063 0.140 0.024 -0.084 0.684 . 0.165

0.793 -0.953 -0.208 2.130 ** -1.973 . -1.530

408 451 974 408 497 . 974

99 99 99 99 99 99 99

-0.032 0.112 0.050 0.126 0.101 0.219 0.004

99 96 99 99 10 0 99

-0.072 0.078 0.059 -0.153 0.690 . 0.178

0.285 0.243 -0.068 1.983 ** -1.835 . -1.250

392 392 392 392 392 392 392

0.078 -0.066 -0.007 0.149 0.076 0.157 0.121

392 392 392 392 392 0 392

-0.065 0.220 0.015 -0.013 0.000 . 0.142

1.041 -2.289 -0.298 1.182 1.488 * . -0.301

782 782 782 782 782 . 782

-0.103 0.321 0.012 0.116 -0.664 . -0.152

-0.520 1.692 ** 0.074 0.589 -2.610 .

0.016

0.039 20 0.006

Average

0.084

0.042

Number of countries with higher correlation in Turmoil: Sign Test (p-value )

0.041 21 0.002

0.122

0.058

0.064 21 0.002

196 193 196 196 107 . 196

0.055

0.027 18 0.044

-0.973

Table 11A Exceedance Correlation Estimates and Standard Errors This table reports the correlation estimates and its standard errors, for each threshold value. The crisis country is Hong Kong. The standard errors are in parentheses. Positive difference, denoting larger negative tail correlation is depicted in bold typeface. Increasing exceedance correlation is shaded The last row provides p-value for the sign test of equal probability of higher and lower correlations, against the alternative of higher correlation in the negative tail. Std Dev. Developed Markets Australia Japan Singapore Belgium France Germany Italy Netherlands Spain Sweden Switzerland UK Canada US Emerging Markets:

Argentina Brazil Chile Colombia Mexico Peru Venezuela China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand Czech Republic Greece Hungary Poland Portugal Russia Slovakia Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

0

Positive Tail 1

0

Negative Tail 1

1.5

0.376

0.205

0.214

0.418

0.318

0.342

(0.001)

(0.002)

(0.005)

(0.001)

(0.002)

(0.005)

(0.001)

(0.002)

(0.003)

(0.001)

(0.002)

(0.000)

(0.001)

(0.003)

(0.005)

(0.001)

(0.002)

(0.005)

0.202 0.454

0.115 0.360

0.036 0.394

0.286 0.534

0.185 0.489

0.118 0.485

0.191

0.110

0.146

0.247

0.235

0.243

(0.001)

(0.002)

(0.004)

(0.001)

(0.002)

(0.005)

(0.002)

(0.004)

(0.007)

(0.002)

(0.004)

(0.008)

(0.001)

(0.003)

(0.003)

(0.001)

(0.002)

(0.005)

(0.001)

(0.000)

(0.003)

(0.001)

(0.002)

(0.005)

(0.001)

(0.002)

(0.005)

(0.001)

(0.002)

(0.005)

(0.002)

(0.004)

(0.008)

(0.002)

(0.004)

(0.008)

(0.001)

(0.003)

(0.006)

(0.001)

(0.003)

(0.005)

(0.001)

(0.002)

(0.004)

(0.001)

(0.002)

(0.005)

(0.001)

(0.002)

(0.004)

(0.001)

(0.003)

(0.005)

0.345 0.327 0.180 0.338 0.370 0.316 0.292 0.326

0.262 0.205 0.182 0.234 0.269 0.238 0.172 0.134

0.228 0.126 0.106 0.234 0.317 0.248 0.154 0.166

0.428 0.366 0.223 0.401 0.388 0.332 0.328 0.396

0.375 0.299 0.172 0.338 0.319 0.237 0.274 0.288

0.398 0.276 0.162 0.305 0.353 0.222 0.346 0.241

0.267

0.147

0.184

0.302

0.200

0.246

(0.001)

(0.002)

(0.004)

(0.000)

(0.002)

(0.005)

(0.001)

(0.000)

(0.004)

(0.001)

(0.002)

(0.005)

0.263

0.142 (0.003)

0.147

(0.003)

0.185

(0.003)

0.196

0.203

0.304

0.275

0.291

--

0.002

0.239

0.155

0.172

--

(0.013)

(0.002)

(0.005)

(0.009)

0.070

--

--

0.216

0.208

0.116

0.147

0.044

--

(0.002)

(0.004)

(0.006)

0.277

0.297

0.248

0.030

--

--

(0.005)

(0.002)

(0.004)

(0.008)

(0.003)

--

--

(0.002)

(0.005)

(0.005)

0.000

0.123

0.084

(0.002)

(0.005)

(0.004)

(0.002)

(0.004)

(0.008)

(0.004)

(0.007)

(0.009)

(0.003)

(0.007)

(0.013)

(0.000)

(0.003)

--

(0.003)

(0.003)

(0.005)

0.001

0.147

--

0.000

0.052

0.229 0.010

0.112

--

0.240 0.170

0.358

0.014

0.236

0.185

0.374

0.104

0.312

0.262 0.075

0.403 0.246 0.080

0.192

0.070

(0.004)

(0.008)

0.064

0.120

--

(0.004)

0.136

0.173

0.151

(0.002)

(0.003)

(0.007)

(0.002)

(0.004)

(0.007)

(0.003)

(0.005)

(0.009)

(0.002)

(0.006)

(0.012)

(0.002)

(0.000)

(0.007)

(0.002)

(0.005)

(0.011)

(0.002)

(0.005)

(0.008)

(0.002)

(0.005)

(0.010)

(0.003)

(0.005)

0.249

--

0.175

(0.003)

(0.005)

0.401

--

0.386

(0.000)

(0.005)

(0.007)

(0.002)

(0.005)

(0.010)

(0.003)

(0.005)

(0.007)

(0.003)

(0.007)

(0.010)

(0.004)

(0.009)

(0.017)

(0.004)

(0.008)

(0.018)

(0.002)

(0.005)

(0.009)

(0.000)

(0.004)

(0.008)

0.061 0.291 0.273 0.379 0.063 0.338 0.037 0.326 0.393

0.173 0.238 0.229 0.044

0.056 0.289 0.304

0.206 0.179 0.150

--

0.035 0.343 0.300

0.395 0.304 0.446 0.090 0.392 0.053 0.370 0.433

--

0.373 0.356 0.426 0.110

0.133 0.368 0.484

--

0.386 0.296 0.403 0.045

0.036 0.361 0.454

0.152

0.206

0.209

0.168

0.175

0.032

(0.003)

(0.000)

(0.013)

(0.004)

(0.007)

(0.010)

(0.002)

(0.003)

(0.005)

(0.002)

(0.004)

(0.000)

(0.003)

(0.006)

(0.012)

(0.003)

(0.007)

(0.013)

(0.004)

(0.006)

(0.012)

(0.004)

(0.005)

(0.000)

(0.003)

(0.005)

(0.010)

(0.003)

(0.005)

(0.011)

(0.005)

(0.010)

(0.013)

(0.005)

(0.010)

(0.020)

--

--

--

(0.005)

(0.009)

(0.012)

--

0.001

0.001

0.119

0.199

0.060

--

(0.010)

0.310

--

0.344

(0.005)

(0.010)

(0.008)

(0.005)

(0.012)

(0.000)

(0.006)

(0.011)

(0.000)

(0.002)

(0.002)

0.084 0.268 0.208 0.213 0.317

--

0.377 0.006

(0.002)

---

0.084 0.348 0.167 0.194 0.273

--

0.000

(0.002)

-0.097

0.329 0.195 0.248 0.144

--

0.043

(0.005)

-0.097

0.281 0.188 0.214 0.339 0.035

0.321 0.031

-0.097

0.198 0.347 0.080 0.191 0.162 0.121

0.215 0.023 0.002

0.121 0.243

.

0.260 0.181 0.042

0.233 0.053

(0.005)

-0.097

0.285

0.357

--

(0.031)

0.375

0.358

(0.003)

(0.006)

(0.011)

(0.003)

(0.006)

(0.013)

(0.003)

(0.004)

--

0.001

--

(0.002)

(0.004)

(0.008)

(0.004)

--

--

(0.003)

(0.007)

(0.011)

0.006

0.027

--

0.121

0.291

0.071

--

0.104

0.336

Average Total number of countries, with estimates: Number of countries with positive difference: Sign Test (p-value )

1.5

0.000

0.166 0.096

0.137 0.096

--

0.127 0.143

Difference: Negative-Positive 0 1 1.5 0.042

0.113

0.128

0.084

0.070

0.082

0.080

0.129

0.091

0.056

0.125

0.097

0.083

0.113

0.170

0.039

0.094

0.150

0.043

-0.010

0.056

0.063

-0.104

-0.071

0.018

-0.050

-0.036

0.016

0.001

0.026

0.036

0.102

0.192

0.070

0.154

0.075

0.035

0.053

0.062

0.041

0.079

0.088

0.097

--

0.170

0.069

0.138

--

0.092

0.150

0.204

0.093

--

0.014

0.062

0.122

0.319

0.055

0.033

0.134

0.118

0.065

--

-0.045

--

-0.001

0.075

0.109

0.031

0.104

0.200

0.180

0.031

0.118

0.117

0.067

0.197

0.253

0.027

0.066

--

0.054

0.152

0.211

0.017

0.077

0.001

0.044

0.079

0.018

0.040

0.180

0.154

0.016

-0.031

-0.177

0.037

0.114

0.017 -0.086

0.013

-0.001

-0.020

-0.087

--

0.001

-0.003

0.012

0.022

-0.111

0.037

--

--

--

--

0.198

0.059

-0.056

-0.095

-0.111

0.025

0.023

0.010

--

0.099

0.000

0.021

0.084

0.073

0.096

0.110

0.127

0.090

--

0.142

0.046 42 39 0.000

0.071 40 32 0.000

0.075 40 34 0.000

Table 11B Exceedance Correlations: Difference between Negative Tail and Postive Tail This table reports the difference between negative tail and positive tail exceedance correlation estimates for investable and non-investable for each threshold parameter. Positive difference, denoting larger correlation for negative tail, is depicted in bold typeface. The last row provides p-value for the sign test of equal probability of higher and lower correlations against the alternative of higher correlation in negative tail. The crisis country is Hong Kong.

Investable EMDB Data Difference: Negative-Positive Std Dev. 0 1 1.5

Non-Investable EMDB Data Difference: Negative-Positive 0 1 1.5

Emerging Markets:

Argentina

0.097

--

0.166

-0.009

0.002

-0.001

Brazil

0.073

0.151

--

0.063

0.101

0.030

Chile

0.099

0.175

0.285

0.071

0.070

0.209

Colombia

0.083

--

--

--

--

--

Mexico

0.063

0.127

0.327

0.096

0.026

0.090

Peru

0.044

0.001

0.093

0.021

0.036

0.020

Venezuela

0.128

--

--

--

0.040

-0.030

China

0.058

0.236

-0.021

--

0.013

-0.013

India

0.084

0.125

0.099

0.084

0.169

0.096

Indonesia

0.097

0.201

0.154

0.062

0.068

0.152

Korea

0.048

0.156

0.162

0.063

0.132

0.128

Malaysia

0.062

0.149

0.257

0.077

0.170

0.167

Pakistan

0.019

0.097

--

0.023

--

0.032

Philippines

0.042

0.126

0.126

0.074

0.203

0.202

--

--

--

-0.002

0.006

-0.042

Taiwan

0.035

0.093

-0.001

0.052

0.077

0.025

Thailand

0.044

0.189

0.134

0.039

0.169

0.103

Czech Republic

0.042

0.044

-0.072

-0.056

--

-0.048

Greece

0.044

0.104

-0.036

-0.045

-0.132

0.054

Hungary

-0.002

0.009

-0.066

0.109

0.114

0.093

Poland

-0.023

-0.091

-0.194

-0.123

--

--

0.004

-0.011

0.034

0.006

0.001

-0.004

-0.048

-0.341

--

0.017

-0.025

-0.002

Slovakia

--

--

0.000

-0.018

0.018

--

Egypt

--

--

0.066

--

--

--

Israel

-0.060

-0.094

0.046

0.024

0.070

0.071

Jordan

0.006

0.011

-0.002

--

0.032

0.043

--

0.109

-0.131

-0.063

0.064

-0.063

South Africa

0.019

0.082

0.075

0.025

0.091

-0.004

Turkey

0.093

0.114

--

--

0.258

-0.069

--

0.144

0.124

0.112

--

0.118

0.044

0.076

0.068

0.028

0.071

0.050

26 22 0.000

25 21 0.000

24 16 0.032

25 18 0.007

25 23 0.000

27 17 0.061

Sri Lanka

Portugal Russia

Morocco

Zimbabwe Average Total number of countries with estimates: Number of countries with positive difference:

Sign Test (p-value )

Table 11C Investable Stock Index Return Exceedance Correlation vs. Non-Investable Stock Index Return Exceedance Correlation This table reports the correlation estimates and its standard errors, for each threshold parameter. The crisis country is Hong Kong. The standard errors are in parentheses. Positive differences, denoting higher correlations with investable, are depicted in bold typeface. Increasing exceedance correaltion is shaded. The last row provides p-value for the sign test of equal probability of higher and lower correlations, against the alternative of higher correlation (difference) with investable.

Std Dev. Argentina Brazil Chile Colombia Mexico Peru Venezuela China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Thailand Czech Republic Greece Hungary Poland Portugal Russia Slovakia Egypt Israel Jordan Morocco South Africa Turkey Zimbabwe

Investable EMDB Data Positive Tail Negative Tail 0 1 1.5 0 1 0.147 (0.003)

0.146

(0.003)

1.5

--

0.000

0.244

0.147

0.166

0.023

0.001

0.001

0.014

0.002

0.000

--

(0.017)

(0.002)

(0.005)

(0.009)

(0.003)

(0.000)

(0.000)

(0.003)

(0.006)

(0.004)

--

(0.002)

(0.004)

(0.007)

(0.003)

(0.004)

(0.000)

(0.002)

(0.004)

(0.005)

(0.009)

0.058

--

--

0.219

(0.005)

(0.002)

(0.004)

(0.009)

(0.002)

(0.000)

(0.005)

(0.002)

(0.004)

--

(0.003)

(0.005)

0.348

--

0.401

--

0.188

--

0.093

--

0.000

(0.003)

(0.006)

0.119

0.090

(0.005)

(0.000)

(0.002)

(0.004)

(0.006)

0.001

0.065

---

0.221

0.074

(0.002)

(0.005)

(0.004)

(0.002)

(0.004)

(0.008)

(0.003)

(0.004)

(0.006)

(0.009)

(0.003)

(0.006)

(0.011)

(0.003)

--

-0.030

(0.004)

(0.005)

0.000

-0.030

(0.003)

(0.005)

0.010

-0.030

(0.003)

(0.000)

(0.004)

(0.003)

(0.003)

(0.005)

--

--

--

--

(0.002)

--

--

-0.012

0.001

-0.012

0.001

-0.012

0.043

0.218 --

0.107 --

0.253 0.171

0.219 0.061

0.200 0.050

0.024

0.015

0.000

0.284

0.003

0.250

0.298 0.209

0.361

--

0.194

0.030

--

--

0.177

0.151

(0.004)

--

0.041

0.192

(0.003)

0.073

0.124

0.000

(0.003)

0.123

0.106

0.050

0.030 0.000

0.315

0.129

0.129

--

0.304

0.163

0.187 0.040

0.286

0.209

0.045

0.051

0.020

(0.007)

0.638

0.422

0.509

0.696

0.658

0.488

(0.001)

(0.006)

(0.011)

(0.001)

(0.003)

(0.013)

0.246

0.116

0.118

0.155

0.200

0.287

--

(0.000)

(0.003)

(0.005)

(0.009)

(0.003)

(0.006)

(0.012)

(0.003)

(0.005)

(0.010)

(0.003)

(0.006)

(0.012)

(0.003)

(0.005)

(0.008)

(0.002)

(0.006)

(0.011)

(0.003)

(0.006)

(0.000)

(0.002)

(0.007)

(0.011)

(0.003)

(0.006)

(0.010)

(0.003)

(0.006)

(0.013)

(0.003)

(0.006)

(0.010)

(0.003)

(0.007)

(0.014)

(0.002)

(0.005)

(0.008)

(0.002)

(0.005)

(0.010)

(0.002)

(0.005)

(0.008)

(0.002)

(0.005)

(0.000)

(0.003)

(0.004)

--

(0.003)

(0.005)

(0.007)

(0.003)

(0.000)

(0.000)

(0.003)

(0.005)

(0.006)

(0.002)

(0.005)

--

(0.002)

(0.005)

(0.010)

(0.002)

(0.004)

(0.007)

(0.002)

(0.005)

(0.009)

0.130 0.288 0.279 0.387 0.071 0.347 ---

0.130 0.170 0.221 0.253 0.025 0.259 --

0.155 0.192 0.150 --

0.238

--

0.214 0.385 0.327 0.449 0.090 0.389 0.535

0.255 0.371 0.377 0.402 0.122 0.385 0.111

0.309 0.354 0.407 0.044 0.364 0.058

--

0.264 0.248 0.332 0.029 0.276 0.058

--

0.260 0.250 0.232 --

0.193 0.100

--

0.161 0.178 0.147 0.002 0.161 0.042

--

0.326 0.311 0.409 0.051 0.350 0.056

0.328 0.382 0.402 0.093 0.396 0.106

0.251 0.313 0.306 0.314 0.034 0.363 0.000

0.337

0.265

0.350

--

(0.003)

(0.006)

(0.010)

(0.003)

(0.006)

(0.007)

(0.004)

(0.006)

(0.000)

(0.004)

(0.008)

(0.017)

(0.004)

(0.008)

(0.017)

(0.004)

(0.009)

(0.017)

(0.004)

(0.008)

(0.018)

(0.002)

(0.005)

(0.000)

(0.002)

(0.004)

(0.008)

(0.002)

(0.005)

(0.009)

(0.002)

(0.004)

(0.008)

0.399

--

0.147

0.305

0.324

0.372 0.443

0.358 0.494

0.349 0.458

0.324 0.387

0.277 0.305

0.334 0.305

0.376 0.426

0.354 0.474

0.359 0.408

0.178

0.203

0.138

0.220

0.247

0.066

0.053

-0.004

0.044

-0.004

--

-0.004

(0.004)

(0.007)

(0.009)

(0.004)

(0.000)

(0.011)

(0.002)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.002)

(0.003)

(0.005)

(0.002)

(0.004)

(0.005)

(0.002)

(0.008)

0.053

0.069

.

(0.003)

(0.000)

(0.000)

(0.003)

(0.006)

(0.010)

(0.000)

(0.006)

(0.012)

(0.004)

(0.005)

(0.009)

(0.004)

(0.008)

(0.018)

(0.003)

(0.006)

(0.012)

(0.004)

(0.006)

(0.008)

(0.004)

(0.008)

0.144

0.180

--

(0.004)

0.169

0.145

0.176

(0.003)

(0.005)

(0.010)

(0.003)

(0.005)

(0.011)

(0.003)

(0.005)

(0.009)

(0.003)

(0.005)

(0.009)

(0.005)

(0.012)

(0.023)

(0.000)

(0.013)

(0.000)

(0.007)

0.000

(0.013)

-0.018

(0.019)

--

(0.007)

-0.018

(0.012)

0.000

(0.021)

-0.018

--

--

--

(0.007)

(0.010)

(0.000)

--

--

--

--

--

--

--

--

0.000

0.123

0.196

0.067

--

--

0.002

0.017

0.013

--

--

--

--

(0.006)

(0.013)

(0.010)

0.234

--

0.162

--

0.049

--

0.000

(0.007)

(0.009)

(0.000)

.

(0.006)

(0.011)

(0.021)

(0.008)

(0.014)

(0.000)

(0.007)

(0.011)

(0.011)

(0.002)

(0.003)

(0.005)

(0.000)

(0.000)

(0.005)

(0.002)

(0.003)

0.078 0.261 0.209 0.227 0.419 --

0.077 0.302 0.172 0.213 0.436 --

0.378

0.309

(0.005)

(0.012)

0.019

(0.001)

---

0.098 0.226 0.198 0.248 0.204 0.000

0.188

0.034

0.052

(0.002)

(0.004)

-0.109

0.022

0.122 0.259 0.186 0.231 0.371 0.019

0.318 0.025

-0.109

0.181 0.311 0.081 0.202 0.095 0.070

0.215 0.045 0.000

0.061 0.160 0.004 0.282 .

0.000

0.050

-0.109

0.094 0.063 0.148 0.163 0.203

.

0.001

0.144

0.107

0.155

0.000

-0.062

--

0.070

0.018 0.001

0.172 0.025

0.220

0.186 0.030

-0.062

0.012 0.167 ---

0.130

0.119 0.032 0.002

0.133 0.162 ---

0.068

0.071 0.061

(0.006)

-0.062

--

(0.016)

0.284

0.356

--

(0.543)

0.374

0.359

--

(0.010)

0.159

0.180

0.195

0.184

--

(0.017)

0.271

0.191

(0.003)

(0.006)

(0.012)

(0.003)

(0.006)

(0.013)

(0.006)

(0.013)

(0.019)

(0.006)

(0.014)

(0.027)

--

0.179

0.139

0.000

-0.068

0.190

-0.068

-0.012

(0.004)

(0.000)

0.003

-0.012

(0.003)

(0.004)

(0.008)

0.127

0.008

--

--

(0.015)

(0.000)

(0.000)

(0.000)

--

--

(0.007)

--

(0.006)

(0.009)

(0.004)

--

--

(0.003)

(0.008)

(0.011)

0.132

0.133

---

-0.068

--

--

(0.003)

0.025

--

0.049

0.292

0.086

--

0.080

0.337

Average Total number of countries with estimates: Number of countries with positive difference (or difference in difference) Sign Test (p-value )

1.5

Non-Investable EMDB Data Positive Tail Negative Tail 0 1 1.5 0 1

0.001

0.120

0.145

0.119

Difference: Inv.-- Non-Inv Positive Tail Negative Tail 0 1 1.5 0 1

Diff in Diff 1.5

0

1

1.5 0.166

0.124

--

0.000

0.230

0.145

0.166

0.106

--

0.017

0.008

--

0.027

0.058

0.133

0.010

0.050

--

0.081

0.005

-0.011

0.109

0.110

0.065

0.028

0.105

0.076

--

--

-0.001

0.058

0.069

--

--

--

--

0.110

0.128

0.074

0.077

0.229

0.311

-0.033

0.101

0.236

0.185

0.203

0.107

0.208

0.168

0.180

0.023

-0.035

0.073

--

--

--

0.201

0.051

0.080

--

--

--

--

0.434

0.508

0.708

0.657

0.500

--

0.223

-0.008

0.014

0.012

-0.008

0.014

-0.032

-0.005

0.000

-0.044

0.003

0.024

-0.090

-0.006

0.059

0.043

-0.004

0.035

0.133

0.002

0.031

-0.029

0.014

0.016

-0.005

0.048

-0.015

0.024

0.034

0.055

0.021

0.003

0.040

0.000

0.093

-0.015

-0.021

0.090

0.043

--

--

0.038

0.029

0.010

-0.004

--

--

0.071

0.066

0.077

0.039

-0.011

0.001

-0.032

-0.077

-0.076

--

--

--

0.479

0.005

0.057

--

--

--

0.013

-0.012

0.016

-0.004

0.004

-0.010

-0.017

0.016

-0.026

0.012

0.000

0.019

0.017

0.020

0.050

0.005

0.020

0.031

0.125

0.207

0.094

0.224

--

0.069

0.098

--

-0.025

-0.016

-0.067

0.018

0.073

0.169

-0.072

0.089

0.237

-0.090

0.198

0.249

0.157

0.087

0.144

-0.002

-0.111

-0.105

-0.159

0.061

0.065

--

0.161

--

--

0.100

--

--

0.064

0.069

0.068

0.062

0.057

0.106

-0.002

-0.012

0.038

0.216

0.281

0.134

0.151

-0.035

--

-0.065

-0.316

--

--

--

--

0.037

0.069

0.018

--

--

--

--

--

-0.002

0.106

0.184

--

--

--

--

0.216

0.260

0.188

0.132

0.096

0.163

-0.084

-0.164

-0.025

--

0.034

0.034

-0.005

0.013

-0.011

--

-0.021

-0.045

--

-0.047

0.021

-0.047

-0.002

-0.047

--

0.046

-0.068

0.178

0.112

0.089

0.172

0.103

0.168

-0.006

-0.009

0.079

--

0.093

--

0.247

-0.051

0.201

--

-0.144

--

--

--

0.002

-0.132

-0.013

0.008

--

--

0.006

0.087 21 20 0.000

0.087 23 18 0.001

0.066 24 21 0.000

0.116 31 27 0.000

0.078 29 22 0.001

0.084 27 22 0.000

0.005 21 10 0.500

0.000 21 10 0.500

0.015 21 12 0.192

Table 12A Exceedance Correlation Estimates and Standard Errors This table reports the correlation estimates and its standard errors, for each threshold value. The crisis country is Hong Kong. The standard errors are in parentheses. Positive difference, denoting larger negative tail correlation is depicted in bold typeface. Increasing exceedance correlation is shaded The last row provides p-value for the sign test of equal probability of higher and lower correlations, against the alternative of higher correlation the negative tail. Difference: Negative -Positive Positive Tail Negative Tail Std Dev. 0 1 1.5 0 1 1.5 0 1 1.5 Developed Markets: Australia 0.310 0.248 0.259 0.295 0.325 0.309 -0.015 0.077 0.050 Japan Singapore Belgium France Germany Italy Netherlands Spain Sweden Switzerland UK Canada US Argentina Brazil Chile Colombia Mexico Peru Venezuela Emerging Markets: China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Czech Republic Greece Hungary Poland Portugal Russia Slovakia

(0.002)

(0.005)

(0.008)

(0.002)

(0.004)

(0.008)

(0.002)

(0.000)

(0.006)

(0.002)

(0.004)

(0.009)

(0.001)

(0.005)

(0.009)

(0.001)

(0.004)

(0.006)

0.245 0.536

Jordan Egypt South Africa Turkey Zimbabwe

0.487

0.176 0.514

0.227 0.561

0.277 0.627

0.212

0.259

0.209

0.271

0.164

0.135

(0.005)

(0.008)

(0.002)

(0.004)

(0.005)

(0.002)

(0.004)

(0.008)

(0.002)

(0.004)

(0.008)

(0.002)

(0.005)

(0.007)

(0.002)

(0.004)

(0.007)

(0.002)

(0.005)

(0.007)

(0.002)

(0.004)

(0.007)

(0.002)

(0.005)

(0.009)

(0.002)

(0.005)

(0.008)

(0.002)

(0.005)

(0.008)

(0.002)

(0.004)

(0.007)

(0.002)

(0.005)

(0.008)

(0.002)

(0.000)

(0.006)

(0.002)

(0.005)

(0.007)

(0.002)

(0.004)

(0.008)

(0.002)

(0.004)

(0.008)

(0.002)

(0.004)

(0.006)

0.220

0.264

0.170

0.249

0.282

0.253

(0.000)

(0.005)

(0.008)

(0.002)

(0.005)

(0.008)

(0.002)

(0.004)

(0.006)

(0.002)

(0.004)

(0.008)

0.187 0.240 0.230 0.257 0.202 0.213 0.179 0.240

0.182

0.228 0.300 0.280 0.224 0.358 0.237 0.259 0.196

0.184

0.279 0.182 0.213 0.244 0.278 0.271 0.185 0.271

0.147

0.215 0.276 0.238 0.254 0.299 0.224 0.208 0.271

0.148

0.241 0.169 0.177 0.274 0.159 0.190 0.178 0.225

0.286

0.226 0.220 0.189 0.251 0.222 0.121 0.272 0.121

0.331

0.153

0.053

0.000

0.169

0.110

0.133

(0.002)

(0.000)

(0.003)

(0.003)

(0.004)

(0.007)

(0.002)

(0.000)

(0.003)

(0.003)

(0.004)

(0.007)

(0.002)

(0.004)

(0.005)

(0.002)

(0.004)

(0.007)

(0.002)

(0.004)

(0.006)

(0.002)

(0.003)

(0.006)

(0.002)

(0.004)

(0.006)

(0.002)

(0.004)

(0.008)

(0.003)

(0.006)

(0.007)

(0.004)

(0.006)

(0.012)

(0.003)

(0.004)

(0.008)

(0.003)

(0.008)

(0.014)

0.161 0.205 0.076 0.253 0.181 0.171

0.036 0.135 0.051 0.089 0.093 0.035

0.010 0.101 0.040 0.054 0.044 0.076

0.114 0.179 0.075 0.217 0.108 0.066

0.140 0.194 0.056 0.261 0.174 0.238

0.177 0.182 0.062 0.295 0.251 0.244

0.061

0.001

0.000

(0.004)

(0.008)

(0.128)

0.089

--0.076

--0.259

--0.273

(0.002)

(0.004)

(0.005)

(0.002)

(0.005)

(0.009)

(0.002)

(0.007)

(0.009)

(0.002)

(0.006)

(0.011)

(0.002)

(0.005)

(0.008)

(0.002)

(0.005)

(0.010)

(0.002)

(0.005)

(0.011)

(0.002)

(0.004)

(0.008)

(0.002)

(0.005)

(0.003)

(0.002)

(0.005)

(0.009)

(0.002)

(0.005)

(0.008)

(0.002)

(0.005)

(0.008)

(0.004)

(0.005)

(0.007)

(0.000)

(0.007)

(0.009)

(0.004)

(0.007)

(0.014)

(0.004)

(0.009)

(0.017)

0.204 0.417 0.340 0.478 0.169 0.449 0.131 0.296

0.115 0.497 0.400 0.316 0.097 0.316 0.060 0.189

0.611 0.424 0.408 0.027 0.351 0.050 0.254

0.450 0.348 0.412 0.076 0.403 0.085 0.330

0.423 0.394 0.542 0.191 0.430 0.113 0.283

0.418 0.456 0.548 0.205 0.514 0.070 0.369

0.163

0.116

0.076

0.154

0.199

0.184

(0.004)

(0.006)

(0.010)

(0.004)

(0.007)

(0.014)

(0.002)

(0.004)

(0.006)

(0.002)

(0.004)

(0.009)

(0.003)

(0.007)

(0.012)

(0.003)

(0.007)

(0.012)

(0.003)

(0.007)

(0.008)

(0.004)

(0.006)

(0.009)

(0.000)

(0.006)

(0.010)

(0.000)

(0.005)

(0.011)

(0.000)

(0.010)

(0.017)

(0.004)

(0.010)

(0.019)

(0.012)

(0.005)

(0.008)

(0.010)

0.149 0.205 0.270 0.203 0.335 0.115

0.120 0.198 0.176 0.334 0.337

---

0.129 0.183 0.020 0.303 0.278 0.009

0.096 0.184 0.291 0.244 0.326 0.021

0.204 0.271 0.166 0.175 0.283 0.096

0.289 0.323 0.098 0.290 0.259 0.070

0.214

0.268

0.185

0.301

0.143

0.125

(0.006)

(0.012)

(0.023)

(0.006)

(0.009)

(0.013)

(0.002)

(0.001)

(0.004)

(0.002)

(0.003)

(0.006)

(0.007)

(0.009)

(0.005)

0.387

--0.400

(0.004)

(0.003)

(0.007)

(0.011)

(0.003)

(0.007)

(0.013)

(0.002)

(0.004)

(0.006)

(0.002)

(0.004)

(0.008)

(0.004)

--

--

(0.003)

(0.007)

(0.011)

0.035 0.050 0.378 0.115 0.006

Average Total number of countries, with estimates: Number of countries with positive difference: Sign Test (p-value )

0.468

0.139

(0.002)

(0.005)

Israel

0.222

0.022 0.010 0.398 0.132

--

0.050 0.016 0.344 0.141 0.001

0.061 0.038

0.105 0.096

0.041

0.146 0.096

0.038 0.001

(0.000)

0.385 0.216 0.143

-0.069

0.005

0.138

-0.022

0.093

0.140

0.059

-0.095

-0.074

0.028

0.013

-0.053

0.036

-0.131

0.038 -0.024

0.008

-0.103

-0.003

0.050

0.007

0.097

-0.199

-0.056

0.011

-0.047

-0.150

0.029

-0.081

0.087

0.031

0.029

-0.150

0.029

0.018

0.083

-0.034

0.102

0.184

0.016

0.057

0.133

-0.047

0.104

0.167

-0.026

0.059

0.081

-0.001

0.005

0.022

-0.036

0.172

0.241

-0.073

0.081

0.207

-0.105

0.203

0.168

--

--

--

-0.128

0.144

0.184

0.033

-0.074

-0.193

0.008

-0.006

0.032

-0.066

0.226

0.140

-0.093

0.095

0.178

-0.046

0.114

0.163

-0.046

0.053

0.021

0.034

0.094

0.115

-0.009

0.083

0.108

-0.053

0.084

0.160

-0.021

0.073

0.140

0.021

-0.010

0.078

0.041

-0.159

-0.013

-0.009

-0.054

-0.019

-0.094

--

0.062

0.087

-0.125

-0.060

0.026

0.019

-0.012

-0.011

--

-0.015

0.009

0.002

0.041

-0.010

0.014

0.075

0.090

--

0.142

-0.008 42 23 0.220

0.025 39 27 0.005

0.061 42 30 0.001

Table 12B Exceedance Correlations: Difference between Negative Tail and Postive Tail This table reports the difference between negative tail and positive tail exceedance correlation estimates for investable and non-investable for each threshold parameter. Positive difference, denoting larger correlation for negative tail, is depicted in bold typeface. The last row provides p-value for the sign test of equal probability of higher and lower correlations against the alternative of higher correlation in negative tail. The crisis country is Thailand.

Std Dev.

Investable EMDB Data 0 1 1.5

Non-Investable EMDB Data 0 1 1.5

Emerging Markets:

Argentina

0.019

0.061

0.132

0.022

--

0.010

Brazil

-0.049

0.114

0.179

-0.040

0.094

0.109

Chile

-0.028

0.051

0.093

-0.036

0.064

0.182

0.031

-0.032

-0.030

--

--

0.000

Mexico

-0.035

0.176

0.274

-0.044

0.051

0.093

Peru

-0.066

0.039

0.203

--

--

0.088

Venezuela

-0.105

0.170

0.140

-0.114

0.215

0.148

China

-0.077

0.366

0.203

--

-0.001

0.000

India

-0.133

0.190

0.198

-0.136

0.198

0.200

Indonesia

0.043

-0.072

-0.192

-0.008

-0.102

-0.161

Korea

0.011

-0.038

-0.047

-0.034

0.061

0.069

Malaysia

-0.064

0.182

0.223

-0.071

0.164

0.053

Pakistan

-0.075

0.104

0.189

-0.107

--

0.157

Philippines

-0.016

0.133

0.116

-0.079

0.122

0.144

Sri Lanka

-0.021

0.003

0.016

-0.055

0.022

0.088

0.041

0.044

0.094

0.027

0.086

0.166

Czech Republic

-0.018

0.089

0.030

0.011

-0.058

-0.097

Greece

-0.054

0.104

0.144

-0.045

0.111

0.086

Hungary

-0.017

0.040

0.132

0.012

0.010

--

Poland

0.019

0.004

0.076

0.104

-0.145

--

Portugal

0.052

-0.161

-0.039

-0.015

-0.023

0.087

Russia

0.008

-0.067

-0.305

0.003

0.044

-0.186

-0.051

--

0.094

-0.031

--

--

Israel

0.085

-0.124

-0.116

-0.047

0.048

0.062

Jordan

0.013

0.031

0.007

0.016

0.026

-0.033

--

0.000

--

--

--

0.001

-0.025

--

-0.019

0.137

-0.095

--

South Africa

0.009

0.000

0.037

-0.023

0.044

-0.043

Turkey

0.011

-0.019

0.004

--

--

--

Zimbabwe

0.112

--

0.118

--

0.144

0.124

Average

-0.013

0.051

0.067

-0.023

0.047

0.054

29 13 0.644

27 18 0.026

29 22 0.001

24 8 0.924

23 17 0.005

25 18 0.007

Colombia

Taiwan

Slovakia

Morocco Egypt

Total number of countries with estimates: Number of countries with positive difference:

Sign Test (p-value )

Table 12C Investable Stock Index Return Exceedance Correlation vs. Non-Investable Stock Index Return Exceedance Correlation This table reports the correlation estimates and its standard errors, for each threshold parameter. The crisis country is Thailand. The standard errors are in parentheses. Positive differences, denoting higher correlations with investable, are

Std Dev. Argentina Brazil Chile Colombia Mexico Peru Venezuela China India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan Czech Republic Greece Hungary Poland Portugal Russia Slovakia Israel Jordan Morocco Egypt South Africa Turkey Zimbabwe

Investable EMDB Data Positive Tail Negative Tail 0 1 1.5 0 1

1.5

0.158

0.043

0.000

0.177

0.104

0.132

0.053

0.020

0.000

0.074

--

0.010

(0.002)

(0.005)

(0.003)

(0.003)

(0.004)

(0.007)

(0.002)

(0.003)

(0.003)

(0.003)

(0.000)

(0.003)

(0.002)

(0.004)

(0.003)

(0.003)

(0.004)

(0.007)

(0.002)

(0.003)

(0.000)

(0.003)

(0.004)

(0.005)

(0.002)

(0.004)

(0.004)

(0.002)

(0.004)

(0.007)

(0.002)

(0.003)

(0.003)

(0.002)

(0.004)

(0.008)

(0.003)

(0.006)

(0.008)

(0.003)

(0.004)

(0.005)

0.291

0.172

0.071

--

(0.076)

(0.004)

(0.000)

(0.000)

(0.002)

(0.000)

(0.004)

(0.003)

(0.004)

(0.008)

(0.002)

(0.003)

(0.003)

(0.002)

(0.004)

(0.007)

(0.003)

(0.006)

(0.007)

(0.004)

(0.005)

(0.012)

(0.003)

(0.000)

(0.008)

(0.000)

(0.005)

(0.009)

(0.003)

(0.003)

(0.008)

(0.003)

(0.007)

(0.011)

(0.003)

(0.003)

(0.009)

(0.003)

(0.009)

(0.015)

0.158

0.201 0.074 0.256

0.186 0.161

0.038 0.135

0.081 0.064 0.106 0.022

0.002 0.089 0.049 0.017

0.044 0.077

0.109 0.173 0.105

0.221 0.120 0.056

0.152

0.186 0.049 0.240 0.145

0.192

0.181 0.182 0.019

0.247

0.216

0.132 0.141

---

0.058 . 0.172

0.027

0.017

0.054

0.092

0.015

--

0.105

0.000

0.017

0.043

0.128

0.000

0.028

--

0.074

0.058

0.121 0.117

0.001 0.122 0.078

0.242

0.126 0.197

0.000 0.136 0.089 0.222

0.445

0.077

0.089

0.368

0.443

0.292

--

0.001

0.000

0.000

0.001

0.000

(0.002)

(0.004)

(0.006)

(0.000)

(0.006)

(0.013)

(0.000)

(0.014)

(0.000)

(0.006)

(0.000)

(0.000)

(0.003)

(0.006)

(0.008)

(0.003)

(0.007)

(0.012)

(0.003)

(0.006)

(0.009)

(0.003)

(0.000)

(0.012)

(0.002)

(0.007)

(0.009)

(0.002)

(0.006)

(0.012)

(0.002)

(0.007)

(0.011)

(0.002)

(0.007)

(0.011)

(0.002)

(0.006)

(0.011)

(0.002)

(0.007)

(0.012)

(0.002)

(0.006)

(0.012)

(0.003)

(0.007)

(0.013)

(0.001)

(0.006)

(0.011)

(0.002)

(0.004)

(0.008)

(0.002)

(0.000)

(0.011)

(0.002)

(0.005)

(0.009)

(0.002)

(0.005)

(0.003)

(0.003)

(0.005)

(0.009)

(0.002)

(0.000)

(0.004)

(0.003)

(0.005)

(0.008)

(0.002)

(0.005)

(0.009)

(0.002)

(0.005)

(0.009)

(0.002)

(0.005)

(0.009)

(0.002)

(0.005)

(0.008)

(0.003)

(0.006)

(0.008)

(0.003)

(0.006)

(0.009)

(0.004)

(0.005)

(0.000)

(0.004)

(0.006)

(0.011)

(0.004)

(0.008)

(0.014)

(0.004)

(0.008)

(0.016)

(0.004)

(0.007)

(0.013)

(0.004)

(0.008)

(0.017)

0.226 0.426 0.366 0.487 0.156

0.431 0.108 0.290

0.101 0.497

0.443 0.349 0.081 0.294 0.088 0.235

0.082 0.587

0.483 0.325

0.022 0.318 0.062 0.262

0.093 0.469 0.377

0.423 0.081 0.415 0.087

0.331

0.291 0.425 0.405

0.531 0.185 0.427

0.091 0.279

0.280 0.395

0.436 0.548

0.211 0.434 0.078 0.356

0.215

0.339 0.391 0.419

0.134 . 0.418 0.141 0.303

0.100

0.089

0.379

0.079

0.518

0.375

0.331

0.408

0.309

0.357

0.408

0.348

0.000

0.312

0.027

0.355

0.056

0.339

0.037

0.179

0.086

0.200

0.330

0.298 0.277

0.436 0.473 0.137

0.434 0.079 0.265

0.289 0.357 0.477

0.461 0.157

0.499 0.125

0.366

0.211

0.170

0.065

0.193

0.259

0.095

0.065

0.130

0.107

0.077

0.072

0.010

(0.004)

(0.007)

(0.009)

(0.004)

(0.000)

(0.010)

(0.004)

(0.007)

(0.000)

(0.003)

(0.007)

(0.010)

(0.002)

(0.004)

(0.006)

(0.002)

(0.004)

(0.009)

(0.002)

(0.007)

(0.098)

(0.003)

(0.000)

(0.000)

(0.003)

(0.007)

(0.011)

(0.003)

(0.006)

(0.012)

(0.003)

(0.009)

(0.000)

(0.004)

(0.006)

(0.017)

(0.003)

(0.007)

(0.008)

(0.003)

(0.006)

(0.009)

(0.003)

(0.008)

(0.000)

(0.004)

(0.004)

(0.007)

(0.003)

(0.006)

(0.010)

(0.003)

(0.005)

(0.010)

(0.003)

(0.007)

(0.010)

(0.003)

(0.005)

(0.011)

(0.006)

(0.013)

(0.022)

(0.005)

(0.013)

(0.010)

(0.006)

(0.013)

(0.020)

(0.006)

(0.013)

(0.020)

(0.006)

(0.000)

(0.017)

(0.000)

(0.009)

(0.017)

(0.009)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

0.209

0.268

0.241

0.294

0.144

0.125

0.111

0.035

0.000

0.064

0.084

0.062

(0.006)

(0.012)

(0.000)

(0.006)

(0.009)

(0.013)

(0.006)

(0.013)

(0.000)

(0.008)

(0.009)

(0.011)

(0.002)

(0.000)

(0.002)

(0.002)

(0.003)

(0.004)

(0.002)

(0.002)

(0.004)

(0.002)

(0.003)

(0.003)

(0.013)

--

--

--

--

--

(0.013)

0.000

--

0.095

--

--

--

--

0.146 0.188 0.270

0.209 0.330 0.092

0.038 0.000

0.110 0.188 0.161 0.325

0.343

--

0.021 0.000

0.121 0.163 0.021 0.305 0.305

0.002

0.030

--

0.092 0.171

0.289 0.261 0.338 0.042

0.052

--

0.214 0.228 0.165

0.164 0.276 0.087

0.051

0.000

0.295

0.098 0.266 0.000 0.096

0.037

0.001

0.108 0.059

0.208 0.337 0.047

0.035

0.000

0.021

0.000

0.097 . 0.173

0.238 0.300

0.022

--

0.383

0.399

0.381

--

(0.000)

(0.013)

(0.003)

(0.007)

(0.011)

(0.003)

(0.007)

(0.013)

(0.006)

(0.016)

(0.002)

(0.005)

(0.006)

(0.002)

(0.004)

(0.007)

(0.002)

--

(0.000)

-0.012

(0.000)

(0.004)

--

--

(0.003)

(0.008)

(0.011)

--

--

0.008

--

0.081 0.001

0.095

0.120

0.102 0.145

0.085

0.119

0.053 .

0.016

0.051

0.000

(0.006)

0.121

0.340

0.058

(0.013)

0.084

0.193

--

(0.010)

0.289

0.163

0.272

--

(0.008)

--

0.120

0.227

0.032

0.344

0.059

--

0.019

0.399

0.001

0.104

0.005

0.374

--

0.265

0.057

Average Total number of countries with estimates: Number of countries with positive difference (or difference in difference): Sign Test (p-value )

1.5

Non-Investable EMDB Data Positive Tail Negative Tail 0 1 1.5 0 1

0.255

--

0.107

0.028 0.215

0.344 0.001

0.048 0.001

0.086 0.186 0.049 0.314 0.086 0.001

0.025

0.001

--

0.137

0.000 .

--

(0.008)

(0.008)

(0.000)

(0.006)

(0.016)

(0.022)

0.003

(0.000)

-0.012

(0.004)

(0.008)

(0.007)

--

(0.006)

(0.009)

0.236 .

0.132

(0.025)

0.266

.

0.299 0.098 0.132

0.193 0.145 0.127

Difference: Inv-- Non-Inv Positive Tail Negative Tail 0 1 1.5 0 1

Diff in Diff 1.5

0

0.105

0.023

0.000

0.103

--

0.122

0.003

0.026

0.012

-0.015

0.017

0.031

0.055

0.009

0.060

0.082

0.074

0.068

0.069

-0.015

-0.008

--

--

0.048

0.088

0.048

0.018

0.084

-0.007

-0.026

0.093

0.118

0.155

0.128

--

0.043

--

0.067

0.158

-0.011

-0.006

0.002

-0.002

-0.050

-0.006

--0.009 --0.009 --

1 --

1.5 -0.122

-0.019

-0.070

0.013

0.089

--0.125 --

0.030 -0.181 -0.115

0.044

0.008

--

0.076

0.089

0.368

0.442

0.292

-0.366

-0.203

0.011

0.001

-0.007

0.014

-0.007

-0.009

-0.003

0.008

0.002 0.031

0.087

0.118

0.069

0.138

0.148

0.038

-0.051

-0.030

-0.025

0.068

0.075

0.020

-0.031

-0.041

-0.045

0.099

0.116

0.068

0.040

-0.083

0.075

0.058

0.087

-0.007

-0.018

-0.170

0.022

--

0.022

0.054

0.048

0.054

-0.032

--

-0.032

0.013

-0.018

-0.037

0.076

-0.007

-0.065

-0.063

-0.011

0.028

-0.033

0.032

0.025

0.001

0.012

-0.047

-0.034

0.020

0.072

-0.013

0.056

0.062

0.001

0.014

-0.010

-0.014

0.042

0.072

0.146

0.040

--

0.116

0.187

0.085

0.029

-0.147

-0.127

0.042

0.089

0.121

0.033

0.082

0.179

0.009

0.007

-0.058

0.080

0.091

--

0.051

0.121

0.109

0.029

-0.030

--

0.211

--

--

0.126

0.137

0.049

0.085

-0.149

--

0.001

0.087

0.078

0.068

-0.051

-0.048

-0.067

0.138

-0.007

0.043

0.033

-0.002

-0.068

-0.086

-0.005

0.111

0.119

0.045

--

--

0.026

0.086

0.095

0.019

--

--

0.126

0.098

0.233

0.241

0.230

0.061

0.063

-0.132

0.172

0.178

0.004

-0.002

-0.028

0.001

0.003

0.012

0.003

-0.005

-0.040

0.000

--

--

--

-0.001

0.000

--

--

0.057

-0.090

--

--

--

--

0.162

--

0.085

0.144

0.108

0.117

0.100

0.188

-0.032

0.044

---0.080

0.032

--

--

--

0.004

-0.060

--

--

--

--

--

-0.002

0.132

0.013

-0.008

--

--

0.006

0.047 27 21 0.001

0.092 22 17 0.002

0.100 23 16 0.017

0.075 26 24 0.000

0.091 28 21 0.002

0.095 29 18 0.068

0.036 21 11 0.332

0.047 24 13 0.271

-0.007 24 9 0.846

Table 13 Regime-switching Model (Thailand) The columns to the left report the difference in volatility and correlation across the two states of a Markov regime-switching model while the columns to the right report estimated t-statistics. In the table, "C" refers to crisis source country, "W" refers to world index, and "T" refers to target country. "Diff in Diff" referrs to the difference in the difference across investable and noninvestable indexes and states. "Mean LL" is the mean likleihood function while "N" is the number of observations used in the estimation. The difference in volatility has been annualized by mulitplying the estimated difference using weekly data by the square-root of 52. Statistically siginificant t-statistic values are denoted in bold typeface. Panel A: Emerging Market Countries Difference In Estimated Moments Across States Volatility Correlation Investable Noninvestable

Region

Difference in Correlation High Vol St

Volatility

C W (11a) (12a)

C W I N (1b) (2b) (3b) (4b)

C W (5b) (6b)

C (7b)

W (8b)

C W C W (9b) (10b) (11b) (12b)

t-stats Correlation Investable Noninvestable

Diff in Diff

Diff in Corr High Vol St

Mean LL LL

N

(13)

(14)

C W I N (1a) (2a) (3a) (4a)

C W (5a) (6a)

C (7a)

W (8a)

C W (9a) (10a)

0.11 0.34 0.29 0.28 0.36 0.35 0.35

-0.03 0.11 0.07 0.07 0.10 0.08 0.09

-0.63 0.45 0.00 0.28 0.22 0.16 0.43

-0.98 0.31 0.06 0.30 0.33 0.24 0.49

0.26 0.14 0.20 0.05 0.17 0.10 0.17

0.45 0.12 0.34 0.07 0.26 0.06 -0.17

0.09 0.14 -0.06 -0.04 0.01 0.03 0.09

0.21 0.13 0.00 0.10 0.04 0.07 -0.15

0.16 0.00 0.26 0.08 0.15 0.07 0.08

0.23 0.00 0.34 -0.03 0.22 -0.01 -0.01

0.15 0.01 0.26 0.11 0.15 0.17 0.12

0.21 0.04 0.35 0.04 0.35 0.19 0.12

2.67 6.99 7.46 4.53 6.77 3.06 6.22

-1.83 6.63 4.63 4.01 5.38 1.95 4.16

-6.11 3.42 0.11 7.44 4.65 3.06 3.35

-7.76 4.17 0.79 7.15 3.92 4.34 3.63

3.19 1.77 2.42 0.48 1.99 0.80 2.05

4.96 1.63 3.75 0.83 2.54 0.42 -1.93

0.94 1.79 -0.43 -0.51 0.17 0.21 1.07

2.33 1.63 0.02 1.11 0.50 0.57 -1.93

1.84 0.04 2.03 0.90 2.32 0.61 0.87

2.44 -0.10 3.57 -0.32 2.64 -0.05 -0.16

3.14 0.39 2.01 2.17 2.78 2.21 2.10

4.34 1.35 3.50 0.75 5.38 2.09 2.25

6.96 7.71 9.22 8.23 8.22 8.05 7.17

665 665 665 556 665 456 612

Asia-Pacific China South Asia India Indonesia Korea Malaysia Pakistan Philippines Sri Lanka Taiwan, China Czech Rep. Hungary Poland Portugal Russia Slovakia

0.37 0.30 0.26 0.23 0.40 0.32 0.43 0.36 0.27 0.37 0.41 0.23 0.39 0.43 0.31

0.12 0.08 0.08 0.08 0.09 0.07 0.10 0.10 0.09 0.10 0.12 0.09 0.08 0.03 -0.04

0.28 0.11 0.59 0.39 0.36 0.20 0.23 0.22 0.14 0.22 0.35 0.41 0.15 0.59 -0.10

0.54 0.10 0.74 0.38 0.37 0.19 0.31 0.12 0.14 0.27 0.48 0.53 0.19 0.77 -0.12

-0.13 0.12 0.26 0.43 0.17 0.04 0.19 0.15 0.37 0.13 0.17 0.12 0.25 0.06 -0.07

-0.12 0.19 0.14 0.20 0.04 0.00 0.25 0.07 0.25 0.33 0.34 -0.01 0.22 0.35 0.13

0.00 0.12 0.08 0.42 0.24 0.08 0.35 0.15 0.35 0.11 -0.10 0.08 0.23 0.04 -0.07

-0.08 0.18 0.03 0.18 0.08 0.07 0.27 0.20 0.24 0.09 -0.05 0.12 0.17 0.23 0.10

-0.13 0.00 0.18 0.01 -0.07 -0.05 -0.16 -0.01 0.03 0.03 0.28 0.05 0.02 0.02 0.00

-0.04 0.00 0.11 0.01 -0.04 -0.07 -0.02 -0.14 0.01 0.24 0.38 -0.13 0.05 0.12 0.03

0.30 0.01 0.18 0.02 0.02 0.03 -0.04 -0.03 0.02 0.18 0.30 0.08 0.05 0.09 0.07

0.33 0.01 0.11 0.01 0.00 0.02 -0.01 -0.08 0.02 0.35 0.53 -0.01 0.10 0.32 0.07

4.21 8.90 7.82 5.79 6.89 8.89 7.19 2.31 4.20 3.35 5.69 3.49 4.80 5.33 4.27

2.32 6.24 6.69 6.55 4.72 4.84 4.99 2.15 4.67 1.38 4.54 3.22 3.22 1.14 -1.75

3.25 2.82 6.04 6.18 4.64 5.13 2.86 1.24 3.97 2.15 4.92 4.70 4.74 3.59 -3.11

1.40 2.59 6.84 5.89 5.06 7.89 4.32 0.45 3.99 1.13 4.00 6.49 5.54 3.52 -2.57

-0.91 1.06 1.85 4.26 2.19 0.34 2.40 0.65 3.21 1.27 1.60 1.09 1.15 0.52 -0.46

-0.83 1.90 1.50 2.51 0.51 0.00 3.34 0.56 2.58 1.74 4.20 -0.06 1.52 3.44 0.91

0.01 1.06 0.52 4.13 3.07 0.84 4.24 0.89 2.94 0.39 -0.89 0.66 1.06 0.32 -0.49

-0.69 1.86 0.29 2.29 0.98 0.68 3.10 1.41 2.43 0.81 -0.33 1.34 0.93 2.06 0.65

-0.72 0.08 2.49 0.54 -1.63 -0.67 -3.69 -0.10 2.00 0.11 2.42 0.60 0.36 0.17 0.02

-0.20 0.41 2.16 0.56 -1.26 -1.02 -0.40 -0.71 0.91 1.19 2.97 -1.67 0.77 0.97 0.22

2.06 0.62 2.57 0.75 1.11 0.88 -1.42 -0.82 1.73 1.38 2.91 1.75 1.21 1.07 0.56

2.41 1.06 2.17 0.57 0.05 0.42 -0.56 -2.03 1.40 1.68 4.42 -0.24 2.18 2.86 0.59

7.36 10.13 8.49 9.21 8.87 8.38 8.45 8.60 10.04 8.11 7.97 7.47 9.36 6.14 7.31

455 464 574 508 665 548 665 456 333 404 455 401 534 242 242

Others

0.30 0.45 0.27 0.34 0.35 0.45 0.09 0.15

-0.06 0.10 -0.02 0.08 -0.03 0.13 0.06 0.03

-0.24 0.20 0.13 0.16 -0.10 0.18 -0.30 0.50

-0.04 3.60 0.50 0.14 -0.14 0.51 -7.72 3.94

-0.45 0.38 -0.29 0.10 0.22 0.47 0.28 -0.02

-0.29 0.36 -0.06 0.16 -0.09 0.34 -0.04 0.20

-0.31 0.07 -0.18 0.10 0.03 0.08 0.18 -0.29

-0.17 -0.11 -0.09 0.15 0.07 0.24 0.29 -0.10

-0.14 0.31 -0.11 0.00 0.19 0.39 0.11 0.27

-0.12 0.46 0.03 0.01 -0.15 0.10 -0.34 0.30

-0.05 0.33 0.07 0.00 0.06 0.35 0.09 0.24

-0.08 0.47 0.14 0.01 -0.15 0.26 0.09 0.26

5.11 7.80 2.35 7.04 5.92 4.92 2.89 1.56

-2.31 4.90 -0.95 4.37 -1.23 4.75 5.06 1.08

-8.41 4.17 2.66 5.97 -3.24 3.05 -1.39 3.48

-2.20 2.85 4.40 5.17 -4.41 6.09 -7.12 2.98

-3.51 3.38 -1.37 1.21 1.42 4.44 2.41 -0.06

-2.00 4.15 -0.38 2.20 -0.45 3.42 -0.26 0.62

-2.30 0.87 -1.27 1.24 0.20 0.65 1.33 -1.56

-1.30 -1.40 -0.57 1.96 0.42 1.78 2.10 -0.25

-1.10 3.15 -0.78 -0.10 1.01 3.59 0.67 0.64

-1.04 4.60 0.23 0.43 -0.62 0.96 -1.81 0.62

-0.47 3.87 0.81 -0.05 0.56 3.85 1.87 0.56

-0.84 5.06 1.77 0.32 -0.75 3.63 2.04 0.55

8.38 7.62 7.31 10.20 8.87 8.32 5.84 7.94

242 584 247 665 242 229 408 430

29 0

21 1

23 4

21 5

11 1

11 1

4 1

6 0

7 1

6 0

11 0

13 1

Latin America

Country

Diff in Diff

Argentina Brazil Chile Columbia Mexico Peru Venezuela

Egypt Greece Israel Jordon Morocco South Africa Turkey Zimbabwe

Average 0.13 0.14 (0.07) (0.08) Number of Positive and Significant Values: Number of Negative and Significant Values: (Total Number of Values =30) Number of Positive differences: Sign Test of equal probability of higher/lower correlations:

0.07 0.05

0.11 0.14

30 26 25 25 26 23 23 23 22 18 26 25 0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.001 0.003 0.100 0.000 0.000

Table 13. Continued

Panel B: Developed Countries

Region

Country Australia Belgium Canada France Germany Hong Kong Italy Japan Netherlands Spain Sweden Switzerland UK US

C 0.34 0.33 0.39 0.34 0.34 0.40 0.34 0.32 0.36 0.38 0.36 0.34 0.34 0.38

Difference In Estimated Moments Across States Volatility Correlation Investable W I C W 0.08 0.04 0.30 0.20 0.09 0.08 0.15 0.08 0.10 0.13 0.16 0.16 0.08 0.08 0.30 0.25 0.08 0.10 0.24 0.26 0.09 0.20 0.16 0.15 0.08 0.05 0.22 0.41 0.08 0.09 0.34 -0.29 0.09 0.11 0.22 0.09 0.09 0.10 0.24 0.26 0.08 0.15 0.19 0.29 0.08 0.07 0.18 0.10 0.08 0.05 0.26 0.15 0.10 0.11 0.25 0.19

Average 0.23 0.17 Number of Positive and Significant Values: Number of Negative and Significant Values: (Total Number of Values =14) Number of Positive differences: Sign Test of equal probability of higher/lower correlations:

t-stats for Difference in Moments Across States Volatility Correlation Investable C W I C W 9.29 6.44 2.66 4.25 2.95 9.59 6.13 5.29 2.08 1.18 8.53 5.20 5.79 1.85 2.11 7.74 5.68 3.79 3.97 5.07 8.97 6.40 4.77 3.19 5.46 10.19 4.61 7.44 1.70 2.05 8.93 5.91 2.05 2.57 6.58 10.06 6.85 5.59 4.16 -6.07 3.25 2.53 2.21 2.35 1.93 5.88 3.91 2.41 1.19 3.88 7.36 3.95 4.57 2.03 4.63 8.99 4.95 3.61 2.02 1.66 9.36 6.06 3.22 3.36 3.07 7.03 4.57 4.34 3.27 3.61

14 0

14 0

14 0

14 14 14 0.000 0.000 0.000

0 0

11 0

10 1

14 13 0.000 0.000

Mean LL

N

6.76 6.88 6.99 6.82 6.71 6.37 6.47 6.75 7.10 6.74 6.53 6.89 6.97 7.10

665 665 665 665 665 665 665 665 665 665 665 665 665 665