The Effects of the Asian Crisis on Global Equity Markets

Financial The Review

EFA Eastern Finance

Association The Financial Review 36 (2001) 125-142

The Effects of the Asian Crisis on Global Equity Markets

Sorin A. Tuluca" Burton Zwick

Fairleigh Dickinson University

Abstract

We investigate the comovement of daily returns from 13 Asian and non-Asian markets before and after the advent of the Asian crisis in July 1997. For individual pairs of markets, our analysis shows a seven-fold increase in feedback relations. For the markets as a group, we find a reduction in the number of common factors that generate returns. Since the post- crisis period included the collapse of the Russian market and attack on the Brazilian real, we also analyze six three-month subperiods surrounding the crisis. We find that the perceived increase in comovement during the post-crisis interval was the result of subperiod transitory shocks.

Keywords: international equity markets, Asian crisis, Granger-causality, factor analysis

JEL Classifications: G 1 1/G 15

1. Introduction The Asian crisis began with the collapse of the Thai baht in July 1997 and the

further erosion in Hong Kong and other Asian markets in October of that year. When the crisis began, the volatility of daily returns in global equity markets increased considerably. In analyzing the cause of large daily price changes, Kaminsky

*Corresponding aurhor: Fairleigh Dickinson University, Samuel J. Silberman College of Business, 285 Madison Ave. M104A, Madison, New Jersey, 07940; Phone: (973) 443-8995, E-mail: [email protected] du.edu We thank an anonymous referee, the editor, and Jacob Dreyer for helpful comments on earlier drafts. All remaining errors are solely the authors' responsibility. Sorin A. Tuluca acknowledges partial funding of this research by the Silberman College of Business competitive Summer Research Grant Program.

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126 S.A. Tuluca and B. Zwick/The Financial Review 36 (2001) 125-142

and Schmukler (1999) note the role of local and neighboring-country news, particularly news about agreements with international organizations. However, they argue that the largest daily changes seem to be driven in part by herding, or an “overreaction” to bad news.

Ghosh, Saidi, and Johnson (1999) analyze several months immediately after the crisis began, and they note not only increased volatility but increased comovement of equity markets. Several emerging Asian equity markets move with the Japanese market, and others Asian markets move more with the U.S. market. In their recent study of the crisis, using closed-end country funds, Pan, Chan, and Wright (forthcoming) note different share price and net asset value responses. They interpret these as different responses by domestic and global investors to the Asian financial crisis.

In the case of an earlier shock, the stock market crash of 1987, Le (1991), Malliaris and Urmtia (1992), Arshanapelli and Doukas (1993), Lau and McInish (1993), Chan, Gup, and Pan (1997), and Meric and Meric (1997) used simple correlation, Granger-causality and principal component analyses to analyze the effect of the shock on the comovement of global equity returns. For this paper, we use daily returns, observed at 15-month intervals surrounding July 1997, from 13 Asian and non-Asian equity markets to examine the effect of the Asian crisis on the comovement of global equity returns. We use three forms of statistical analysis- Granger-causality, factor analysis, and Box-M tests-to compare the ‘ ‘pre-crisis” and “post-crisis” intervals. We apply Granger-causality analysis to 156 pairs or combinations of equity markets from the group of 13 countries from Asia, North America, South America, and Europe. For the post-crisis period, we find a seven- fold increase in bidirectional causality (feedback). This dramatic increase in the bilateral comovement of returns suggests an increase in the general comovement of global equity returns following the advent of the Asian crisis.

To more directly analyze the general comovement of global equity markets, we use factor analysis rather than principal components analysis. Although both techniques are designed to express the larger group of original variables in terms of a smaller group, they differ fundamentally from each other.’ For this study of the Asian crisis, we find fewer common factors generating market returns in the post-crisis interval.

Previous analyses of the 1987 crash and the Asian crisis have treated the post- crash or post-crisis periods as a single homogeneous interval. Certainly for the Asian crisis, actual events suggest there may be a problem with this approach. The Asian crisis was a series of events. Following an interval of intense crisis from July 1997- October 1997, financial markets stabilized somewhat early in 1998 only to be

’ Lee and Kim (1993) and Meric and Meric(1997) are recent studies that use principal components to analyze the comovement of equity markets. These analyses follow Philippatos, Christofi, and Christofi (1983). For a detailed discussion of the differences between factor analysis and principal components analysis, see Dunteman (1989).

S. A. Tuluca and B. Zwick/The Financial Review 36 (2001) 125-142 127

buffeted once again by the collapse of the Russian market in July-August 1998 and the attack of the Brazilian real shortly thereafter.

To analyze the pattern of comovement after the Asian crisis began, we divide the post-crisis interval into five subperiods. We use a sixth subperiod to cover the interval just before the crisis. Using both factor analysis and the Box-M test of correlation matrix equality, we analyze not only the longer-term intervals of 15 months used in the Granger and factor analyses, but also the six subperiods of three months from before and after the onset of the crisis. The study suggests that the apparent persistency of the increase in stock market comovement after the beginning of the Asian crisis is actually the result of a series of periodic increases in comovement after short-lived shocks, rather then a long-term post-crisis effect.

Section 2 describes the data and the three methodologies used in the analysis. In Sections 3 and 4, we use equal pre- and post-crisis intervals of 15 months to test for the effects of the Asian crisis on the equity returns of the 13 markets. The pre- crisis interval extends from April 1996-June 1997. The post-crisis interval runs from November 1997-January 1999. Section 3 presents the results of Granger- causality analysis, and Section 4 gives the results of factor analysis. Section 5 analyzes the subperiods using factor and Box-M analysis. Section 6 summarizes and concludes.

2. Data and methodology

We use daily equity returns from 13 markets to analyze stock market returns. We obtain daily returns in local currency from the following daily price data: the United States (the S&P500), Canada (Toronto 300 Composite), Mexico (IPC), Brazil (Bovespa), the United Kingdom (FTlOO), Japan (Nikkei 300), Hong Kong (Hang Seng Index), Singapore (Singapore Straits Times Index), Taiwan (Weighted Com- posite), Korea (Korea Composite EX), Malaysia (Kuala Lumpur SE Stock Index), Indonesia (Jakarta Composite), and Thailand (Bangkok SET). Haver Analytics, New York, NY, provided the data. Their original sources were The Wall Street Journal and the Financial Times of London. For observations that were missing because of market closings, we use closing prices from the previous day.

Local currency results show the transmission of shocks between markets, and may be more appropriate for an investor with a global or multicurrency frame of reference. For any investors who are trading with U.S. currency, U.S. dollar returns are more useful for comparing investment activities and opportunities. We use both local currency and U.S. dollars returns in this analysis of the Asian crisis.2 Since

* The exchange rate data to convert local currency returns into dollar returns was obtained from J.P. Morgan.


the results using local currency and U.S. dollar returns are similar, we show only the local returns here. The dollar returns are noted when appr~priate.~

We use three different methodologies in the analysis. To test the bilateral comovements of the selected markets, we use the Granger (1969) causality methodology. A time series X causes another time series Y in the Granger sense even if, with information about past values of Y, we can better predict Y using past values of X. If the reverse is true, then Y causes X. If both relationships are true, there is a feedback effect between X and Y. For this study, we let X,Y be any of the possible pairs of country stock prices indices and let AXi, AYj, (i # j ) be the corresponding pair of returns computed as the log of the price relative.

We conduct Augmented Dickey- Fuller and Phillips-Peron tests for each period and for the entire sample. The unit root hypothesis could not be rejected at acceptable levels of significance for any of the time series. Since equity indexes are nonstationar- ity, we conduct our analysis in terms of returns.

We use the following script): [z:] =

vector autoregressive (VAR) model (t is the time sub-

Since the structure the system described in Equation (1) is generally unknown, an important concern is the selection of the lag length k. Following Enders (1995), we use the Sims likelihood ratio test to determine the maximum lag length.4 The number of lags included varies from four to 12, depending on the pair of variables under consideration.

We base the test for Granger-causality between AY and AX (with AY being predicted) on an F-statistic appropriately adjusted for degrees of freedom. F is the ratio of the residual sum of squares using past values of AY alone and the residual sum of squares using past values of both AX and AY. The formal hypotheses are:

(2) 1 2 3 k

1 2 3 k

Hex: p12+12=@12=.... .+ 12=0

HOY:P21=P21=P21= .....=P21= 0

where the Hox tests the null hypothesis that all AY lagged coefficients are jointly zero, and the HOY tests the same for the AX coefficients.

This is consistent with a comprehensive study of lead-lag relationships of daily returns of stock markets around the world in which Copeland and Copeland (1998) use both local currency and dollar returns and find little difference between the two sets of results.

Other methods (multivariate generalizations of the Akaike Information or Schwarz Information Criteria could he used to determine the number of lags in the system. The selection of the lag structure depends on the method used. However, Geweke, Meese, and Dent (1979) demonstrate that Granger-causality results are not sensitive to the selection of lags.


In the following analysis, a Granger-causality relation is accepted if the F- statistic allows the null hypothesis of no Granger-causality to be rejected at a 0.05 level of significance.

The study of the bilateral comovements cannot capture changes in the general comovement of the 13 Asian and non-Asian markets. Therefore, we use factor analysis to capture these changes. The method expresses each variable AXi as a linear combination of k common factors, 4, and a unique factor score, q. If pij are the elements of matrix B then AXi is given by:

k

In the extraction of common factors, our current analysis follows that of Kaiser (1958), dropping those factors corresponding to the latent roots that are less than one. The assumptions5 required by the factor analysis, and are consistent with the Arbitrage Pricing Theory (APT) of Ross (1979). The technique we use in this study is similar to the APT empirical work of Roll and Ross (1980). Therefore, when there is a change in the number of factors generating returns in the pre- and post- crises intervals, a change occurs not only in the comovement of stock markets, but also in the structure of an asset pricing model of the APT type.

Several methods can be used to extract common factors. This study shows the results from the image method of extraction, because this method separates the common part and the unique part of a variable by computing the common part as a linear combination of all the other variables in the set.6 To enhance the interpretabil- ity of results and obtain an orthogonal structure for the factors, we apply the varimax method of rotation. There may be cases in which the same correlation structure would give a varying number of common factors (Kim and Muller, 1978). To ensure that the pre- and post-crises correlation structure has changed, we can test directly for the equality of the correlation matrices using the Box-M test. The formal hypothesis in the Box-M test is: Ho: Rl=..Rk where Ri are p-dimensional correlation matrices. If R is the pooled estimate of the common correlation matrix and Ni the number of observations, the test statistic is:

k

M =I: (Ni - 1)lnlR 1 - x ( N i - l)lnlRi I (4)

With a scale factor, Box-M is approximately distributed as a x2 variate. However, this distribution is good if k and/or p are less than five. In our analysis of the Asian

i i= l

The assumptions are: EV)=E(E)=E(ef’)=O, ECfS)=I, E(EE’)=E, where E is the mathematical expectation operator.

We used other methods (maximum likelihood, least square, alpha factoring) to test for the robustness of the results. Although the number of factors was almost identical across methods, the loadings were slightly different.


Table 1

Market hours This table list the trading hours for the 13 equity markets from Asia, Europe, North America, and South America from earliest to latest. All hours are U.S. Eastern Standard Time.

Opening Closing

1:00 a.m. Japan 7:OO p.m. Korea 7:30 p.m. 1:00 a.m. Taiwan 8:OO p.m. 11:OO p.m. Hong Kong 8:OO p.m. 3:OO p.m. Singapore 8:30 p.m. 3:OO a.m. Indonesia 8:30 p.m. 400 a.m. Malaysia 8:30 pm. 4:OO a.m. Thailand 9:OO p.m. 430 a.m. United Kingdom 3:30 a.m. 11:30 a.m. Brazil 9:OO a.m. 4:OO p.m. Canada 9:30 a.m. 4:OO p.m. Mexico 9:30 a.m. 4:OO p.m. U.S. 9:30 a.m. 4:OO p.m.

crisis, k is two but p is 13. Under the circumstances the x2 is not appropriate, so we use a variant of the test proposed by Box that follows an F distribution’ and is available in the multiple discriminant analysis of the SPSS package. Box-M test will determine if the change in the factor structure is due factor extraction not producing unique solutions, or to the fact that the correlation structures are indeed different.

3. G ra nger-ca usa I i ty resu I ts

In a Granger-causality analysis that uses daily stock market returns, we must account for differences in the hours of market operation across the 13 countries used in the analysis. Table 1 lists the markets hours (Eastern Standard Time) for the various exchanges.

For markets with similar closing hours, such as the four markets of the Americas, there is no need to take time differences into account. The data from both markets can be set at time t. In the case of markets with different trading hours, an impulse or shock in the market that closes earlier is transmitted to the later market at time t, but an impulse in the market that closes later cannot be transmitted to the earlier market until time t+l. It is necessary, in the Granger-causality test that the earlier market causes the later market, to set the earlier closing market at t+l and the later closing market at t.

For a Box-M, F-test discussion that includes comments on the power of such tests, see Momson (1990). Box-M methodology following a x2 distribution was previously applied to the 1987 crash by several researchers including Lau and McInish (1993) and Meric and Meric (1997).


For example, events in time t+l in an earlier closing market, such as Japan, are essentially lagged relative to events in time t+l in the U.S. market. Unless the U.S. is set at t and Japan remains at t+l, the series of lagged values for Japan in the Granger-causality test that Japan causes the U.S. will exclude the most recent, and presumably most important, lagged value. To adjust for the time difference problem, in all Granger-causality tests that any of the eight Asian markets cause the United Kingdom or any of the four markets of the Americas, we set the United Kingdom and the Americas at t and the Asian markets at t+ 1. In tests that the United Kingdom causes the four markets of the Americas, we set the Americas at t and the United Kingdom at t+l. In tests that Taiwan causes the other seven Asian markets, we set the seven Asian markets at t and Taiwan at t+l. In tests that Korea and Japan cause Hong Kong, Singapore, Malaysia, Indonesia, and Thailand, we set the five affected countries at t and Japan and Korea at t+1.*

Table 2 presents the results of the Granger-causality tests. The right-hand side of the table shows the results for the post-crisis interval. The first column lists cases of feedback or bidirectional causality. The second column shows cases of one-way causality. The results for the pre-crisis interval are shown in the left-hand side of the table.

Following the advent of the Asian crisis, the number of relations showing Granger-causality (0.05 level of statistical significance) rose to 82 in the post-crisis interval (out of 156 possible relations among 13 countries) from 38 in the pre-crisis interval. Even more striking is the increase in cases of feedback to 26 from only four in the pre-crisis interval. The 26 bidirectional relations in the post-crisis interval include all four feedback relations from the pre-crisis interval, which demonstrates the consistency of the results.

Of the three major markets, the United Kingdom and Japan experienced dramatically increased feedback during the post-crisis interval. Bidirectional relations for the United Kingdom increased to ten in the post-crisis period from only two before the crisis. For Japan, bidirectional relations rose to three in the post-crisis interval from zero during the pre-crisis interval.

The U.S. experience was quite different. During the post-crisis interval, the U.S. experienced bidirectional causality only with the United Kingdom, a relationship that also existed in the pre-crisis interval. On the other hand, the U.S. in Granger- causality tests caused six markets during the pre-crisis interval and seven in the post-crisis interval.

The preponderance of Granger-causality from the U.S. to other countries, mainly in the Pacific region, throughout the pre- and post-crisis intervals supports the Liu and Pan (1997) study. Using data from before and after the 1987 crash, they report that the U.S. market was more influential than Japan in transmitting

* The use of realignment was suggested by the referee. Our method of realignment, which adjusts for non-overlapping trading hours, is similar to Liu and Pan (1997).


Table 2

Granger causality results This table lists cases of feedback (bilateral Granger causality) and one-way Granger causality for the pre-crisis interval (April 1996-June 1997) and the post-crisis interval (November 1997-January 1999). That X Granger causes Y is accepted in this analysis if the null hypothesis that X does not Granger cause Y can be rejected at the 0.05 level of significance. For the cases of one-way causality, the symbol > means causes. For example, X>Y means that X Granger causes Y.

Re-Crisis Post-Crisis

Cases with Feedback One-way Causality Cases with Feedback One-way Causality

Hong Kong-Mexico JapamIndonesia Hong Kong-UK JapamMalaysia Singapore-Canada Japan>Hong Kong US.-UK Japan>UK

KoreDUK TaiwanzHong Kong IndonesiaTaiwan Malay sia>Korea MalaysiDHong Kong ThailanbTaiwan Thailand>Indonesia Thailand>Mexico U.S.>Japan U.S.>Indonesia U.S.>Malaysia U.S.>Hong Kong U.S.>Singapore U.S.>Brazil CanadaAndonesia CanadDMalaysia CanadDHong Kong Mexico>Taiwan Mexico>Indonesia Mexico>Singapore BrazibMalaysia BrazibHong Kong BrazibSingapore UbTaiwan UbCanada UbBraz i l

Japan-Canada Japan-Brazil Japan-UK Korea-Thailand Korea-Brazil Korea-UK Taiwan-Thailand Taiwan-Malaysia Taiwan-Hong Kong Indonesia-Canada Malaysia-Canada Malaysia-Mexico Malaysia-UK Thailand-Canada Thailand-UK Hong Kong-Canada Hong Kong-Mexico Hong Kong-Brazil Hong Kong-UK Singapore-Canada Singapore-Mexico Singapore-UK

Canada-UK Mexico-UK Brazil-UK

U.S.-UK

JapanSndonesia JapwHong Kong Japan>Singapore Korea >Indonesia KoreaBMalay sia KoreDHong Kong K0reaN.S. TaiwamIndonesia Taiwan>Singapore Taiwan>Brazil Thailand>Malaysia ThailanbHong Kong Thailand>Mexico Singapore>Korea U.S.>Japan U.S.>Taiwan U.S.>Indonesia U.S.>Malaysia U.S.>Thailand U.S.>Hong Kong U.S.>Singapore CanadaTaiwan Mexico>Japan Mexico>Korea Mexico>Taiwan Mexico>Indonesia Brazil >Indonesia BrazibSingapore UGTaiwan UK>Indonesia

returns and volatilities to four Asian markets (Hong Kong, Singapore, Taiwan, and Thailand).

After the July 1997 crisis onset, the hardest hit markets the ASEAN-4 of Thailand, Malaysia, Singapore, and Indonesia show strong feedback relationships with other Asian markets and with markets outside the region. Before the crisis, there was no feedback between ASEAN-4 markets and the Americas, and very little


causality running from Asia to the Americas. In the post-crisis period, six new feedback relationships emerge between the ASEAN-4 and the Americas. The other affected Asian markets, Taiwan, Korea, and Hong Kong, show similar patterns. Hong Kong’s three feedback relationships in the post-crisis interval make it the most influential Asian market in terms of Asian-Americas feedback.

Results with U.S. dollar returns are similar to results using local currency returns. The number of relations that show Granger-causality rises to 83 in the post- crisis interval from 36 in the pre-crisis interval. The number of feedback relations rises to 29 in the post-crisis period from five in the pre-crisis period. The regional effects are also almost unchanged.

We note that, although several studies use cointegration analysis in their Granger-causality inferences (for example, Malliaris and Urmtia, 1992), there are several reasons why we do not use cointegration. The analysis of daily returns is short-term, covering an interval of less than three years. Moreover, the crisis induces a structural break that further weakens the power of the cointegration results for this analysis. Despite the limitation mentioned above, we augment the Granger analysis with cointegration. The incorporation of the error-correcting term does not reverse the strong evidence of increased post-crisis feedback or one-way Granger- causality. This is consistent with Pan, Chan, and Wright (forthcoming). In their Asian crisis study of closed-end funds, they report that the inclusion of an error- correction term does not affect Granger-causality results.

The increase in Granger-causality and the even more dramatic increase in feedback during the post-crisis interval suggest that the Asian crisis triggered a dramatic increase in the comovement of equity returns. The increase in comovement was clearly global in nature, since pairings of Asian and non-Asian markets account for many of the feedback relations. The Granger-causality analysis of bilateral comovements cannot capture changes in the general comovement of the 13 Asian and non-Asian markets. To capture such changes this study turns to the multivariate method of factor analysis.

4. Factor analysis results

Panel A, Table 3, presents the results of factor analysis for the pre-crisis interval of 15 months. They reveal five factors with a cumulative explained variance of 60.4%. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy of 0.61 is borderline acceptable, falling somewhat below the Kaiser-Meyer-Olkin level of 0.7 that generally assures the usefulness of factor analysis for a given sample. We use varimax rotation to facilitate the interpretation of the common factors. This procedure ensures that factors are orthogonal and that, therefore, the correlation between any pair of factors is zero. Factor analysis aids the interpretation by standardizing all the variables to have unit variance. Thus, the reported factor loadings represent both the sensitivity and the correlation of each variable with the respective factor. To


obtain the correlation between any two variables, we sum the products of their respective sensitivities with each factor.

In Table 3, the relevant factor loadings, those along the diagonal, appear in bold. These bolded factor loadings show that factor 1, which explains 19.6% of the variance, is predominantly linked to five of the 13 markets-the four markets of the Americas and the United Kingdom. Factors 2, 3, 4, and 5 are mostly linked to the eight Asian markets. Therefore, the correlation structure of the Americas and the United Kingdom can be recovered mostly from the first factor, but the correlation structure of the Asian markets requires four factors to be recovered.

For a group of stock market returns, the possible linkage of common factors to economic variables enables factor analysis results to approximate an economic model of expected returns (see Chen, Roll, and Ross, 1986). If the five factors in our analysis can be linked to economic variables, then the regional arrangement of the factors suggests that these economic variables are segmented geographically. The Americas and United Kingdom region are aligned to one economic variable or common factor (factor 1). The Pacific region is segmented into several economic variables or common factors: factor 2 for the well developed equity markets of Japan and Hong Kong, factor 4 for the Korea and Taiwan, and factors 3 and 5 for the ASEAN-4 markets.

Panel B, Table 3, reports the results for the post-crisis interval. In the post- crisis interval, the number of factors is reduced to three. One for the four markets of the Americas plus the United Kingdom explains 32.6% of total variance and two others are linked to the eight Asian markets. Despite the reduction of factors from five to three, the cumulative explained variance of 58.3% is only slightly lower than the 60.4% explained for the pre-crisis interval. Moreover, the Kaiser-Meyer- Olkin measure of sampling adequacy rises to the very acceptable level of 0.8 (compared to the level of 0.61 in the pre-crisis interval). The fact that the correlation structure of the Asian stock markets can be mostly recovered from two rather than four factors suggests that the Asian markets had stronger comovements in the post- crisis period.

The reduction in factors might occur because the same correlation matrix generates alternative factor structures in both the pre- and post-crisis intervals. Box- M test of correlation matrix equality allows us to test this hypothesis and shows a statistically significant difference between the correlation matrices of the pre- and post-crisis intervals. The approximate F-statistic of 14.49 (inferred from Box-M statistic appropriately adjusted for degrees of freedom) rejects at virtually a zero level of significance the hypothesis that the periods are similar. This result confirms that the number of common factors changed because of different correlation structures in the pre- and post-crisis periods, not because an unchanged correlation matrix generated alternative factor structures.’

Since factor analysis cannot test any formal null hypothesis, the results of this technique should always be regarded as tentative.


Table 3

Factor analysis of global equity returns This table shows the factors and factor loadings of the system of 13 equity markets from Asia, Europe, North America, and South America for the pre-crisis interval (April 1996-June 1997) and the post-crisis interval (November 1997-January 1999). Keyser-Meyer-Olkin is the measure of sampling adequacy. 0.60-0.70 is the lower range of acceptability for this measure; 0.70 or higher is desirable. Bolded loadings represent the highest loadings for that factor.

Panel A: Pre-Crisis

Factor 5 Factor 3 Factor 4 Factor 1 Factor 2

Canada 0.81 0.16 0.01 0.11 0.22 u s . 0.74 -0.05 0.00 0.05 0.11 United Kingdom 0.47 0.38 -0.03 -0.08 -0.16 Brazil 0.42 0.06 0.05 -0.02 -0.28 Mexico 0.38 -0.14 0.22 0.16 -0.06 Japan 0.00 0.60 -0.02 0.03 -0.06 Hong Kong 0.03 0.46 0.20 0.35 0.08

Thailand -0.10 -0.08 0.51 0.00 -0.13 Singapore 0.17 0.07 0.44 0.14 0.19 Indonesia 0.16 0.26 0.41 -0.11 0.12

Korea Taiwan

0.17 -0.04 -0.13 0.48 0.00 -0.04 0.09 0.1 1 0.41 -0.08

Malaysia 0.01 -0.03 0.03 -0.07 0.53 Kaiser-Meyer-Olkin = 0.61 Cumulative variance explained = 0.604 Variance explained by Factor 1 = 0.196

Panel B: Post-Crisis

Factor 1 Factor 2 Factor 3

U.S. 0.79 0.15 0.01 Brazil 0.72 0.09 -0.21 Canada 0.71 0.26 0.09 Mexico 0.69 0.11 -0.15 United Kingdom 0.46 0.36 -0.08 Hong Kong 0.17 0.73 0.03 Singapore 0.17 0.67 0.05 Thailand 0.08 0.62 -0.02 Indonesia 0.07 0.52 0.04 Taiwan 0.05 0.37 0.07 Japan 0.14 0.33 0.11 Korea 0.13 0.31 0.01

Malaysia -0.09 0.07 0.34

Kaiser-Meyer-Olkin = 0.80 Cumulative variance explained = 0.583 Variance explained by Factor 1 = 0.326


Results using U.S. dollar returns are similar to those using local currency returns. The number of factors drops from five in the pre-crisis interval to three in the post-crisis interval. The corresponding statistics on Kaiser-Meyer-Olkin and explained variance are also similar to those using local currency returns. The Box- M statistic using U.S. dollar returns rejects at a zero level of significance the hypothesis that the pre- and post-crisis intervals are similar.

This harmonization in the Asian stock markets might be explained by the “herding” effect noted by Kaminsky and Schmukler (1999). In their research, Kaminsky and Schumkler remark on the role of local and neighboring-country news, particularly news about agreements with international organizations. However, they argue that the largest daily changes seem to be driven in part by herding, or an “overreaction” to bad news. In this case, the economic variables, which are mim- icked in normal periods by the common factors, would have been overshadowed by herding. The lower number of common factors that drive the returns might be caused by changes in investors’ sentiment, rather than changes in fundamentals. However, the difficulty in distinguishing real economic effects from overreaction suggests that this explanation should be viewed as tentative.

5. An analysis of subperiods

So far, analyses of the 1987 crash and the Asian crisis confirm the effect of financial shocks on the volatility and comovement of global equity markets. As mentioned above, the Asian crisis from July 1997 through early 1999 was not a single event, but a series of events. The crisis began with the collapse of the Thai baht in July 1997 and the attack on other Asian currencies in October of that year. The markets then stabilized somewhat in early 1998 only to be buffeted once again by the beginning of the Russian crisis in July 1998, the collapse of the Russian ruble in August and the follow-up attack on the Brazilian real in the fall of 1998. In both 1997 and 1998, the month of July exhibited extraordinary market volatility.

Other analysts tend to view the Asian crisis and the aftermath of the earlier 1987 crash as a single event. We believe that the Asian crisis provides an opportunity to analyze shorter-term effects of shocks by looking at subperiods within the longer interval of crisis. To further analyze the effects of the Asian crisis on global equity markets, we extract six subperiods (three months in length) from the longer interval, beginning in April 1997 and running through January 1999. The six subperiods are April-June 1997, August-October 1997, November 1997-January 1998, April-June 1998, August-October 1998, and November 1998-January 1999. The first period occurred just before the Asian crisis began, and the others occur after. We omit July of 1997 and 1998 from the subperiods because of their extreme volatility.’o

‘“We experimented with subperiod lengths of three, four, and five months. The four- and five-month subperiod results did not show any commonality among subperiods, presumably because four- and five- month intervals did not correspond to the length of subperiod shocks and their after-effects.


Table 4

Factor analysis of the subperiod equity returns This paper indicates the number of commons factors, the variance explained by these factors, and the KMO (Kaiser-Meyer-Olkin) measure of sampling adequacy for six intervals of three months between April 1997-January 1999. The first interval precedes the start of the Asian crisis in July 1997; the others begin in August 1997.

Interval

~

Number of Variance Factors Explained (%)

KMO Sampling Adequacy

April 1997-June 1997 5 August 1997-0ctober 1997 3 November 1997-January 1998 3 April 1998-June 1998 3 August 19984ctober 1998 3 November 1998-January 1999 3

68.3 68.8 67.8 65.5 65.6 60.8

0.43 0.69 0.72 0.73 0.73 0.70

Table 4 presents the results of factor analysis. As with the results for the longer post- and pre-crisis intervals, the single pre-crisis subperiod is distinctly different from all of the post-crisis subperiods. For the pre-crisis subperiod, the number of factors is five. For all of the subperiods during or after the crisis began, the number of factors drops to three. Despite the reduction in factors, the percentage of explained variance for the crisis and post-crisis subperiods is close to that for the pre-crisis subperiod. The Kaiser-Meyer-Olkin measure of sample adequacy of 0.69 or higher for all five crisis subperiods is dramatically higher than the Kaiser-Meyer-Olkin measure of sample adequacy of only 0.43 for the pre-crisis subperiod.

The results from factor analysis might indicate that the correlation matrices are similar in the whole post-crisis period. However, as before, the same factor structure can be the result of different correlation matrices. Again, we use Box-M test to verify the hypothesis that the subperiod correlation matrices are equal for all post-crisis periods.

Table 5 gives the results of Box-M tests. The approximate F-statistics (as implied by Box-M statistic appropriately adjusted for degrees of freedom) for the 15 pair-wise combinations is presented on the left-hand side of each cell. The level of statistical significance appears to the right. The Box-M subperiod results support the results of the factor analysis, showing a major difference between the pre-crisis subperiod (Column 1) and all of the others. Almost all of the largest F-statistics appear in Column 1 and result from statistically significant differences between the single pre-crisis subperiod and each of the crisis subperiods (Columns 2,3,4, and 5). The F-statistics in Column 1 range from 2.11 to 3.56, rejecting at a virtually zero level of significance the hypothesis of no difference between the pre-crisis subperiod and all of the other five subperiods.

The second subperiod (August 1997-October 1997) includes the after-shock of the collapse of the Thai baht and the October attack on several other Asian


currencies, including the Hong Kong dollar. Column 2 shows that the F-statistics of 1.40 to 1.81 are smaller than those in Column 1, but still allow rejection of the null hypothesis of no difference from the four subsequent subperiods at a virtually zero level of significance in two cases, a 0.002 level of significance in the third, and a 0.008 in the fourth.

The subsequent subperiods (Columns 3, 4, and 5) are quite different from the Columns 1 and 2. The November 1997-January 1998 subperiod (Column 3) is much less significant and different from the three subsequent subperiods. In one case, the comparison with the November 1998-January 1999 subperiod, the hypothesis of no difference cannot be rejected except at the 0.1436 level. The November 1997- January 1998 and November 1998-January 1999 periods are similar, following immediately after the two major shocks within the longer post-crisis interval. The November 1997-January 1998 subperiod follows the major shocks from July- October of 1997. The November 1998-January 1999 subperiod follows the second round of shocks, including the collapse of the Russian market and the wholesale attack on the Brazilian real. The similarity (or lack of statistically significant difference) of these subperiods, especially comparing to the pre- and post-crisis differences in Column 1, suggests a pattern in the response of equity markets to global financial shocks.

At first glance, the results using factor analysis for subperiods appear almost identical to the earlier results that use 15-month intervals. All four of the post-crisis subperiods are similar to each other and differ from the single pre-crisis subperiod. The longer period analysis shows the same statistically significant difference between the pre- and post-crisis interval. Despite this similarity between the subperiod and longer period results, the subperiod results using Box-M analysis suggest that the results using the 15-month intervals need to be reinterpreted. The subperiod evidence suggests that what seems to be a sustained effect on markets of one major shock is, in fact, the effect of markets that realign themselves only during much shorter intervals following a series of shocks. The statistically insignificant difference (F- statistic of 1.16 andp-value of 0.1436) between the two subperiods that immediately follow the two most turbulent episodes within the longer post-crisis interval show that the comovement of world equity markets increased in a similar way following the two shocks.

Shocks such as the 1987 crash or the Asian crisis induce greater comovement among markets. They represent a risk to investors because they undermine the effectiveness of global diversification strategies. The subperiod results on the Asian crisis suggest that the crisis-induced increase in the comovement of returns results mostly from subperiod effects, rather than effects for the longer period. Since these subperiod effects are relatively short-term, the results suggest that global shocks represent less of a threat to long-term global investment strategies than previously believed.

The subperiod results may also reconcile the findings of Meric and Meric (1997) with those of Chan, Gup, and Pan (1997) regarding the 1987 stock market

140 S.A. Tuluca and B. Zwick/The FiPtancial Review 36 (2001) 125-142

crash. Using principal components analysis, Meric and Meric (1997) (and other researchers) find that, following the crash, there is a long-term, persistent rise in the comovement of equity markets. Using co-integration analysis, Chan, Gup, and Pan (1997) find no increase in the cointegration of equity returns following the 1987 crash.

In both principal components analysis and factor analysis, the input for analysis is the correlation matrix of returns. Correlation measures linear association and does not take into account the time order of returns. Therefore, even short periods of intense comovements of returns might affect the correlation if it is computed over a longer period of time. The result might give the impression of long-term persistency arising from a temporary increase in correlation due to a single shock. As emphasized in the theory of cointegration, short-term deviations from equilibrium exist but are corrected in subsequent periods. In cointegration analysis, where the analysis of long-term relations does include time ordering, we cannot infer any persistent change from analyses based on correlation if the changes in comovements are transitory in nature. Clearly, factor analysis uncovers short-term changes in comovements, and cointegration uncovers changes in the long-term equilibrium relationships. If crises have only short-term effects on the increase of stock market comovements, as both our study and that of Chan, Gup, and Pan (1997) suggest, these effects will not be revealed in cointegration analysis. Therefore, the importance of such changes for long-term international diversification is less than previously believed.

6. Summary and conclusion

Volatility and comovement of returns among global markets increased following the 1987 stock market crash. In our analysis of the daily equity market returns from 13 Asian and non-Asian markets, we show a similar increase in comovement after the Asian crisis began in July 1997.

For individual pairs of markets, Granger-causality analysis shows a seven-fold increase in bidirectional causality. The uncertainty surrounding the crisis dramatically increased the transmission of disturbances from one market to another, and this transition was clearly global. Links between Asian and non-Asian markets accounted for much of the feedback.

In examining the comovement for the 13 markets as a group, our factor analysis showed a 40% reduction (from five to three) in the number of separate factors that generated returns. The non-Asian countries were grouped in one factor, but the Asian markets are now characterized by two, rather than four, additional factors.

When we used short-term intervals, the results of Box-M tests show a statistically significant difference between the pre-crisis subperiod and all crisis subperiods. These results also show lack of any difference between the pair of subperiods that immediately follow two major shocks within the longer post-crisis interval. This particular juxtaposition of significant and insignificant differences reinforces the argument that the Asian crisis affected the global equity markets, but that the effect


was the result of a series of shorter subperiod effects rather than a single long one. If the increased comovement following shocks is primarily a series of short-term effects rather than longer ones, the effects may be less important for longer-term investment strategies than previously believed.

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The Effects of the Asian Crisis on Global Equity Markets

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