Financial Market Contagion in the Asian Crisis1
Author: Mr. Taimur Baig and Mr. Ilan Goldfajn

Contributor Notes

Author’s E-Mail Address, baig@uiuc.edu, igoldfajn@imf.org
Publication Date:
01 Nov 1998
eISBN:
9781451857283
Language:
English
Keywords:
WP; e.g Thailand-Philippines; Malaysia-Philippines case; Malaysia-Thailand equity; Korea regression; stock index; Lr test; reaction in Indonesia
Download PDF (2.5 MB)

This paper tests for evidence of contagion between the financial markets of Thailand, Malaysia, Indonesia, Korea, and the Philippines. Cross-country correlations among currencies and sovereign spreads are found to increase significantly during the crisis period, whereas the equity market correlations offer mixed evidence. A set of dummy variables using daily news is constructed to capture the impact of own-country and cross-border news on the markets. After controlling for own-country news and other fundamentals, the paper shows evidence of cross-border contagion in the currency and equity markets.

Abstract

This paper tests for evidence of contagion between the financial markets of Thailand, Malaysia, Indonesia, Korea, and the Philippines. Cross-country correlations among currencies and sovereign spreads are found to increase significantly during the crisis period, whereas the equity market correlations offer mixed evidence. A set of dummy variables using daily news is constructed to capture the impact of own-country and cross-border news on the markets. After controlling for own-country news and other fundamentals, the paper shows evidence of cross-border contagion in the currency and equity markets.

I. Introduction

Following the collapse of the Thai baht’s peg on July 2, 1997, the financial markets of East and South-East Asia — in particular Thailand, Malaysia, Indonesia, the Philippines, and Korea— headed in a similar, downward direction during late 1997 and early 1998. The regional markets faced increasing pressure in the aftermath of the devaluation of the baht, and this pressure was reflected in the subsequent unraveling of the managed currencies in Malaysia and Indonesia. As the crises became full-blown, intense foreign exchange and stock market turmoil spread in the entire region, culminating in the collapse of the Korean won. News of economic and political distress, particularly bank and corporate fragility, became commonplace in the affected countries, and it appeared as though anything that brought one market down put additional pressure on the other markets as well.

What was the driving force behind this transmission of shocks from one country to the other? Was it fundamentals driven, or was it a case of irrational, herd mentality displayed by panic-stricken investors? Could the reaction of the markets simply be explained away by their historically close relationships? Finally, did some countries play a larger role in terms of cross- border impact than others? These questions provide the motivation behind this paper. We carry out three sets of analysis to tackle these issues. First, we use correlations and VARs to see the extent of co-movement in the markets during the crises. Second, we test if the correlations in these markets increased significantly during the crises. Finally, we estimate the impact of own country and cross-border news on selected financial markets of the region.

We use three and a half years’ of daily data (1995-1998) from the five selected countries for our empirical analysis. We first study the correlation between the countries of their respective foreign exchange, equity, interest rate, and sovereign debt markets, examining which markets seemed more affected and postulating why this was the case. We apply a Vector Auto Regression (VAR) methodology to estimate the impulse responses to shocks in each of the currency and stock markets. This allows us to see if there was indeed significant transmission of pressure in the respective markets, as well as how persistent those shocks were.

Then, we test if the correlations in the various markets increase significantly during the crisis period in comparison to historical, “tranquil” period levels. If there is no significant increase in the correlation, then it is likely that the pressure felt by the markets is more due to some common cause or spillover effects. The policy implication would be to focus on the source of the shock and try to tackle that first. On the other hand, if the increase in correlation is significantly and substantially higher than the historical correlations, then there is reason to suspect that sentiments have shifted. Under such circumstances, there is an avenue for measures to calm the markets.

Finally, we distinguish between the impact of fundamentals and possible herd behavior on stock markets and exchange rates. But our use of high frequency daily data limits our capacity to obtain many representations of fundamentals. We remedy this by creating a set of dummy variables to take into account the significant, market moving news for the respective countries.

These dummies serve a dual purpose: they are proxies of own-country fundamentals, as well as serving as a source of contagion for other countries. We estimate the impact of these dummies, as well as other selected fundamentals, on the financial markets through country-by-country regressions. We further analyze the residuals of these regressions to see the extent of cross-border correlation after controlling for fundamentals.

The rest of the paper is organized as follows. Section II addresses some conceptual issues, such as distinguishing between various concepts of contagion, as well as the arguments involving procedures to test for significant increases in correlation between two time periods. Section III examines the correlation among the exchange rate, equity, and interest rate markets, and presents the results of the VAR analysis. Section IV tests for increased correlation during the crisis period as compared to the tranquil period. Section V introduces the news dummy variables, and analyzes the results of the regressions with the dummies and other fundamentals. Section VI contains some concluding remarks. The data and methodology description, and news chronology are provided in the appendix.

II. Conceptual Discussion of Contagion

The fact that the financial crises in the Asian countries occurred almost at the same time has led to the widespread use of terms like the Asian “Flu,” with the implication that this is a case of contagion, where one country’s ill fate transmits to other, vulnerable countries. Use of such terminology, however, tends to obscure several pertinent issues involving simultaneous occurrence of financial crises. The term contagion itself is too broad, as there are several distinct forms of shock that can transfer across borders, each with very different policy implications.2 Masson (1997) highlights the various concepts of contagion. The simultaneous movement of markets could be explained by common external factors (e.g. a rise in U.S. interest rates or the devaluation of the yen), trade linkage or third market competition related spillovers, or market sentiments. While any of these factors could lead to what is perceived as contagious financial crises, it is crucial to identify which one of them is actually driving the market mayhem. One also needs to take into account if the presence of high degree of correlations is sufficient proof of contagion. If markets are historically cross-correlated, then a sharp change in one market will have an expected change in given magnitude in the other markets. If there is no appreciable increase in correlations during the crisis period, then the markets are simply reacting to each other, dictated by their traditional relationship. The scenario is quite different if the correlations change substantially subsequent to the onset of the crises, in which case one can indeed make the case for contagion. In this section, we analyze the relevance of these various concepts in the context of the Asian crises.

External or “monsoonal” effects, like the rise in German interest rates in 1992 (in the context of the ERM crises) or the U.S. interest rate hike in 1994 (for the Tequila episode), have been widely held to be triggers of contagious currency crises among countries that are commonly affected.3 It has been argued that the sustained depreciation of the Japanese yen vis-à- vis the U.S. dollar, beginning in the summer of 1995, was a significant external factor contributing to the pressure faced by Asian markets. This argument is highlighted by the fact that the five most affected countries —Thailand, Malaysia, Indonesia, South Korea, and the Philippines— had substantial trade linkages with Japan and the U.S. (see Table 1). The yen’s depreciation led to real appreciation of the currencies that were predominantly pegged to the U.S. dollar, thus hurting the export sectors of these countries. The declining exports in turn put pressure on the currencies ahead of the 1997 crises. There are, however, several problems with this argument. As argued in Chinn (1997) and Baig (1998), notwithstanding the depreciation of the yen, the real exchange rates of the affected economies (with the exception of Thailand) did not show any clear case of over-valuation relative to their historical movements. Furthermore, there was a substantial time lag between the yen’s depreciation and the onset of the crises in Asia.

Table 1.

Exports Share of the Asia 5 in 1997

(As a percentage of total exports)

article image
Source: Direction of Trade Statistics Quarterly: International Monetary Fund (June, 1998).

While trade linkages between countries with geographic proximity can have an impact in explaining spillover effects (see Eichengreen et al (1996), Glick and Rose (1998)), they are not adequate to account for what happened in East Asia. The trade linkages among the five countries in discussion are not very striking (see Table 1). Consider the fact that the financial markets in the region came under severe pressure after the collapse of the Thai baht on July 2, 1997. It is difficult to reconcile the trade linkage argument with the transmission of exchange rate pressure from Thailand to other countries of the Asia 5. The export share to Thailand constituted less than 4 percent of total exports for each of the four countries in discussion, making intra-country trade an unlikely source of pressure on financial markets.

Since the Asia 5 countries exported a large portion of their goods to US and Japan, it is tempting to believe that some indirect trade linkage due to third market competition was instrumental in repeated rounds of competitive devaluation. We don’t find much evidence in support of this argument either. The Asia 5 countries do not share very similar third-country export profiles that would amount to severe competitiveness pressures. Going back to the Thai case, even after taking into account bilateral trade and competition in third country, the importance of Thailand is rather small for the countries concerned.

In addition to external shocks and spillovers, there exists a strand of explanation that looks at the markets from the point of view of global investors. Calvo (1996) argues that emerging markets are susceptible to herd mentality by investors. Since it is too costly for investors to address the state of each economy, it is optimal for them to pull out of a group of related markets simultaneously when they spot signs of nervousness in just one of them. Masson (1997) argues somewhat along the same lines in his explanation of investor psychology in the context of a multiple equilibria framework. He also argues that small triggers can be precipitating factors for investors, leading to across the board loss of confidence and a higher perceived risk of holding investment in a set of countries. As investors follow each other and pull their money out, the herd behavior pushes these countries to the bad equilibrium of financial distress.

The following sections empirically test for the existence of possible investor herd behavior in Asia. We use two types of tests. The first test verifies if there is a significant increase in correlations between the pre-crisis and the crisis period. We use a two sample or heteroscedastic t-test for this purpose. If the correlations have increased significantly, then there are grounds for believing that the markets have moved away from the relationships dictated by traditional movements of fundamentals. On the other hand, if the correlations are not significantly different, then markets are simply reacting to shocks that are common-cause or spillover generated. The hypothesis behind this test is that the correlation between the fundamentals has not increased substantially after the crisis and, therefore, we can assign the increase in co-movements to shifts in market sentiments affecting the entire region. We also apply a log-likelihood ratio test for the significance of groupwise correlations. In the second test, we check whether after controlling for own country news and other fundamentals, there is still an impact of cross border news on the markets. The assumption is that own country news and the selected variables capture the essential movements in fundamentals, and the other country dummy coefficients capture contagion effects.

In the recent literature on the Asian crises, an alternative interpretation for the contagion was advanced, stating that the spread of the crisis to several Asian countries was the consequence of a “wake-up call” effect. Accordingly, after the collapse of the Thai baht, investors started perceiving other countries differently, interpreting the same fundamentals to be a sign of weaker economies. Since the observed fundamentals have not changed, the paper will not be able to distinguish between the herd behavior and the wake- up call effect. Therefore, one could interpret the results regarding herd behavior in the rest of the paper as possible evidence in favor of the wake-up call effect.

III. Financial Market Correlations (Evidence and Tests)

A. Currency Market Correlations

We begin our analysis by estimating correlation coefficients of the daily change in nominal exchange rates. The sample period begins from the day of the baht devaluation, July 2, 1997, and extends up to May 18, 1998. After calculating the overall correlation in the sample period, we extend our analysis by repeating the exercise for sub-samples consisting of three month windows, and rolling them till the end date. This allows us to take a deeper look at the dynamics of cross-border correlations.

The full sample (see Table 2) shows positive coefficients for all pairs, with seven out of ten pairs with correlations of .25 or higher. Indonesia’s cross-correlations with the other countries stands out, with correlation coefficients of its daily change in exchange rates with Korea, Malaysia, Philippines, and Thailand being .25, .36, .26, and .28, respectively. The other cases of sizable correlations are Malaysia’s with the Philippines and Thailand, .28 and .35 respectively, and Thailand-Philippines (.31). It is interesting to note that despite the mayhem associated with the Korean won’s downward plunge in the between October 1997 and January 1998, the full sample correlation matrix shows barely any influence of the won on regional currencies.

Table 2.

Exchange Rate Correlation (Full sample and rolling panel with three month window)

article image

The problem with using the full sample is that it smooths out a lot of shorter duration interactions between the markets. For instance, events in Korea and Indonesia had substantial impacts on the markets for periods of three to four months during certain phases of the crises, but those movements are diminished by the use of the full sample. The rolling correlations alleviate this problem to some extent (see Table 2). It is instructive to note that the correlation between Indonesia and Korea is barely different from zero in the first three months of the crisis. Subsequently, the correlation increases substantially from November onwards, as both countries came under severe exchange rate pressure. Korea’s correlation with Thailand nearly doubles from late 1997, and similar increases are seen vis-à-vis Malaysia during the first three months of 1998. The rolling correlations also reveal very high volatility in the region. The correlation coefficient between Thailand and the Philippines go from -.22 to .75 from July-September 1997 to December-February 1998.

The results also reveal that the Indonesian and Malaysian currencies were the most consistently and highly correlated through the sample. Except for isolated sample windows and with the exception of Indonesia, the Korean currency seems to be the roughly uncorrected with the rest of the currencies. Finally, despite being the primary source of the shock that triggered the Asian crises, the Thai baht shows no sign of appreciable correlation with other currencies, with the exception of Malaysia, until the October-December window. The correlations become noticeably large in the last month of 1997 and the first three months of 1998.

B. Stock Market Correlations

The full sample panel with cross-border correlations for changes in stock indices reveal fairly high level of co-movement in the region’s equity markets (see Table 3). As with the currency correlations, the Malaysian and the Indonesian markets have the highest degree of correlation. This is perhaps surprising given the fact that the countries do not export more than 1.5 percent of their total exports to each-other (see Table 1), and there are significant structural and political differences between the two countries, as well as differing levels of financial sector development. The two countries also display sizable correlations with the rest of the countries under consideration.4

Table 3.

Stock Index Correlation (Full sample and rolling panel with three month window)

article image

In the rolling correlations, from August 1997 onwards, the Malaysian and Thai stock markets demonstrate strikingly high degrees of correlation, up to .70 in the December-February window. This mimics their close relationship in the foreign exchange market during the same period. Similarly high degrees of correlations are seen in the Malaysia-Philippines case. Overall, the stock market correlations (both full sample and rolling panel) are larger when compared to the respective correlations in the currency markets. For instance, the Malaysia-Thailand equity returns correlations in various windows are greater by .1 to .2 than the currency market correlations counterparts.

C. Interest Rate Correlations

We look at the cross-border correlations of interest rates with some reservations. The overnight call rates used in this exercise may not be comparable given the variation in the way they are set across countries. Besides, interest rates were widely used as tools of monetary policy in all the countries in discussion; thus the rates reflected the policy stance rather than market determined levels. During specific periods of severe market mayhem, interest rates were raised to very high levels for a short period to tackle speculative attacks in Indonesia, Malaysia and the Philippines, resulting in extreme outliers in the data.5 As they were used as monetary policy instruments, the interest rates are not necessarily reflective of market forces. As illustrated in Table 7, the interest rate correlations vary widely from pair to pair, with 5 of the correlations negative and the other 5 positive. The Indonesia-Korea, Indonesia-Thailand and Thailand-Malaysia interest rate correlations appear to be consistent with their currency and stock market relationships. Other than these, it is hard to discern much from the results.6

D. Sovereign Spread Correlations

A superior alternative to domestic interest rates in investigating the market assessment of country risk is the interest rates on foreign currency denominated debt that is traded in off-shore markets. We obtain such rates on selected dollar denominated debt for the five countries, and then calculate the spread by subtracting the U.S. treasury bill yield with corresponding maturity (see Appendix 1 for details). The resulting spreads are ideal proxies for pure default risk for the respective countries.

The cross-correlation matrix of the sovereign spreads presents striking results (see Table 4). The cross country correlations are extremely high, ranging from .51 (Malaysia-Thailand) to as much as .91 (Indonesia-Malaysia). Previously observed high correlations between Indonesia-Malaysia continue to demonstrate similar results. Even pairs that show relatively small degree of correlation in the currency and the stock markets, e.g Thailand-Philippines, are marked by remarkably high coefficients (.90 in this case). This extremely high degree of correlation between the spreads indicates that the global investors treated these five countries’ financial fragility with a broad stroke by demanding high risk premiums for all of them during the crisis. The probability of private debt default was perceived to have increased dramatically in all of these countries, and nervousness about one market transmitted to other markets readily.

Table 4.

Sovereign Spreads Correlation (Full sample and rolling panel with three month window)

article image

The rolling correlations reveal salient aspects of the market dynamics. Beginning with the Thai crisis, the cross-border correlations among Korea, Indonesia, and Thailand go up substantially, and remain uniformly strong until early 1998.7 The most glaring illustration of this is the September-November window, when the cross-correlations between Korea-Indonesia, Thailand-Indonesia, and Korea-Thailand are .92, .95, and .97, respectively. While the Philippines’ stock and currency markets do not show very high degree of correlation with these three countries, its risk premium appears to be markedly tied to the fortune of them. From July to December 1997, the Philippines spreads are strongly correlated with these three countries.

Following the correlations of Malaysia with the rest of the pack reveals that until early 1998, they were relatively less correlated with the markets in Thailand and the Philippines, while remaining fairly well correlated with Indonesia and Korea. However, as the spreads for the other countries came down and showed some stability, Malaysia’s spreads kept rising and persisted at very high levels (see Figure 9). In the January-March window, Malaysia’s spreads were negatively correlated with all the countries in the sample, ranging from -.09 with Indonesia to -.63 with the Philippines. The correlations recover somewhat in April. It must be noted here though that the negative correlations do not necessarily reflect a comparatively worse financial state in Malaysia. During the last few months of the sample when it appears as though Thailand was recovering while Malaysia remained stuck in financial distress, the latter has consistently commanded relatively lower spreads.

E. VAR Analysis

The Asian crises were marked by periods of market mayhem when currencies and stock markets in the region tumbled in waves, with declining markets pushing each-other in a circular and mutually reinforcing manner. It is very difficult to isolate the magnitude of shocks that transmitted from one market to the other. In order to discern the patterns of currency and stock market pressure, we take advantage of the Vector Auto-regression (VAR) methodology. The methodology is useful in this context as it recognizes the endogeneity of all the variables in the system. It also moves away from our earlier focus on contemporaneous correlations, and allows for the impact of lagged values of the variables. To keep the analysis simple, we do not estimate VARs that include overlapping markets (i.e. incorporating both exchange rates and stock market returns on the right hand side), but rather look at the interactions between the five countries one market at a time. For a given country, the sample starts from the day that country’s currency peg unraveled8 and ends on May 18, 1998.9 We then run a five variable VAR for the exchange rates, obtain the estimated impulse response function for the shocks originating from the given country, and then do the same for the stock market data. We choose a lag length of one day, and do not find improvement in our model by including more lags. This exercise was repeated for all five countries, giving us a total of 10 impulse response graphs. By virtue of this, we make use of the data that spans a country’s financial turmoil phase, and follow the impact of one standard deviation innovation in its currency and stock market on the rest of the markets under study. The issue of ordering the variables for generating the impulse response functions turned out to be inconsequential, as changing the ordering did not have any significant impact in the results.

Figures 1 through 5 present the impulse response graphs. Shocks originating from Thailand’s currency market (see Figure 1) have a significant impact on the markets of Malaysia, Indonesia and the Philippines, i.e. a depreciation of the baht led to an immediate depreciation of the currencies in these countries. The impact from the shocks tends to disappear after about 4 days. In the stock market, Thailand’s movements had a significant and corresponding reaction from all of the countries in discussion.

FIGURE 6.
FIGURE 6.

NOMINAL EXCHANGE RATE (vis-avis US$)

Tranquil vs Crisis

Citation: IMF Working Papers 1998, 155; 10.5089/9781451857283.001.A001

Source: Bloomberg, IMF staff estimates
FIGURE 7.
FIGURE 7.

STOCK MARKET INDICES

Tranquil vs Crisis

Citation: IMF Working Papers 1998, 155; 10.5089/9781451857283.001.A001

Source: Bloomberg, IMF staff estimates
FIGURE 8.
FIGURE 8.

OVERNIGHT CALL RATES (in percent per annum)

Tranquil vs Crisis

Citation: IMF Working Papers 1998, 155; 10.5089/9781451857283.001.A001

Source: Bloomberg, IMF staff estimates
FIGURE 9.
FIGURE 9.

SOVEREIGN SPREADS (in basis points)

Citation: IMF Working Papers 1998, 155; 10.5089/9781451857283.001.A001

Source: Bloomberg, IMF staff estimates

Of the remaining impulse response functions, Malaysia (see Figures 2) demonstrates similar results. All the four countries responded to shocks in its currency and stock markets with the right sign and significance. Indonesia (see Figures 3) had the most impact on the markets of Thailand and Malaysia, whereas the evidence of its impact on the Philippines and Korea is weaker. Korea (Figures 4) stands out in this exercise as the country that did not react to or impact significantly upon the rest of the countries. The Philippines (Figure 5) had only a modest impact on Malaysia and Thailand.

The common element in the impulse response functions is the relatively stronger reaction by the equity markets to shocks in a given country, when compared with corresponding results in the currency markets. This is consistent with our earlier results. However, it must be noted that evidence of strong interactions between markets is not sufficient evidence of contagion. As seen in the next section, despite the higher correlations, stock market dynamics changed relatively less than the currency markets’ during the Asian crises.

The following contains a summary of the correlations and impulse response results.

CORRELATIONS SUMMARY

article image

IMPULSE RESPONSE SUMMARY

article image

IV. Testing for Significant Increase in Correlations

While the full sample and rolling correlations help us identify the pattern of contagion, they don’t tell us whether these correlations are significantly different from market behavior in tranquil times. To address this issue, we apply the two-sample or Heteroscedastic t-test described in the appendix. For the currency, equity price index, and interest rates, we define the crisis period as the one analyzed above, which is July 2, 1997 to May 18, 1998. For the tranquil phase, we obtain the corresponding data from January 1, 1995 to December 31, 1996.10 We run the same cross-correlations, and then test for a significant increase in correlations during the crisis period. The results are presented in Tables 5-8. The crisis period correlations that are greater than the corresponding tranquil period correlation within a 1 percent level of significance are highlighted. Due to data limitations, we restrict the crisis sample for sovereign spreads from April 11, 1997 to June 30, 1997. While this is a considerably shorter period than the other cases, we believe that it nevertheless captures the market dynamics prior to the crisis.

Table 5.

Exchange Rates

article image
LR test attempts to reject the null that all pairwise correlations are zero. Each pairwise correlation is tested for the null that the correlation is zero.*, ** and *** denote rejection at a 10%, 5%, and 1% levels respectively.Source: Bloomberg and IMF Staff Estimate
Table 6.

Stock Market Returns

article image
LR test attempts to reject the null that all pairwise correlations are zero. Each pairwise correlation is tested for the null that the correlation is zero.*, ** and *** denote rejection at a 10%, 5%, and 1% levels respectively.Source: Bloomberg and IMF Staff Estimate
Table 7.

Interest Rates

article image
LR test attempts to reject the null that all pairwise correlations are zero. Each pairwise correlation is tested for the null that the correlation is zero.*, ** and *** denote rejection at a 10%, 5%, and 1% levels respectively.Source: Bloomberg and IMF Staff Estimate
Table 8.

Sovereign Spreads

article image
LR test attempts to reject the null that all pairwise correlations are zero. Each pairwise correlation is tested for the null that the correlation is zero.*, ** and *** denote rejection at a 10%, 5%, and 1% levels respectively.Source: Bloomberg and IMF Staff Estimate

The tranquil period correlations for the exchange rates in every single pair are barely different from zero. This observation must however be seen in the context of the practice of managed exchange rates prior to the crises in all the countries in discussion. In light of the fact that most of the currencies moved very little during the tranquil period, it is hardly surprising that the correlations in the crisis period are significantly greater than every single pairwise correlation in the tranquil period (see Table 5).

The stock market tests, however, paint a very different picture. In six out of the ten pairs, the stock market correlations are positive and large. Among the striking correlations, Indonesia-Malaysia, Indonesia-Thailand, and Malaysia-Thailand are notable, with coefficients of .37, .32, and .41 respectively. Despite historically high levels of correlation, we find evidence that in the cases of Indonesia-Malaysia and Indonesia-Thailand, the correlations were significantly higher in the crisis period (see Table 6). The Philippines showed large correlations with all the countries (except Korea) during both the tranquil and crisis period, and none of these results increase significantly in the latter period. Overall, the evidence for contagion in the stock markets is mixed at best, as the analysis of the tranquil period demonstrated that there was substantial historical co-movement in many of the markets.

In the case of the interest rates, with the exception of Korea-Thailand (.37), there are no cases of noticeable correlations in the tranquil phase (see Table 7). In six out often cases, cross-border correlations are significantly greater in the crisis period.

The tranquil period correlation matrix for the sovereign spreads, despite being limited by the sample size, is instructive (see Table 8). While Indonesia-Malaysia (.47) and Thailand-Malaysia (.48) are the only two countries with large correlations in the tranquil phase, all of the pairwise correlations increased significantly and substantially in the crisis phase. Thus the choice of dividing the samples from the day of the baht devaluation is deemed sensible, as it captures the breaking point in market behavior in all the different variables studied in this section.

In sum, the analysis demonstrates that there was a clear case of increased correlations in the currency markets. This result comes with the caveat that the currencies’ movements were minimal prior to the crises due to the existence of pegs. The evidence is not very clear in the case of the equity markets and the domestic call rates. The spreads on dollar denominated debt, representing pure default risk, display the most striking degree of correlations and evidence of contagion.

V. Incorporating Dummy Variables

A. Dummy Variables

Following Ganapolsky and Schmukler (1998) and Kaminsky and Schmukler (1998), we use dummy variables to quantify the impact of policy announcements and other news on the respective markets. A set of ten dummy variables, representing good news and bad news in each country, is used to estimate the impact of news. For every country, we assume that its own news is a proxy for changes in fundamentals, whereas changes in the fortune of another country is a potential source of contagion. Our objective is twofold: first, we regress the variables under consideration on own country dummies and other selected fundamentals. We then take the residuals of these regressions and analyze their cross-correlations. Second, we repeat the exercise with all ten dummies on the right hand side to evaluate the impact of cross-border news.

In order to create the dummy variables, we scrutinized the daily reports from Reuters, Bloomberg, Financial Times, CNN-fn, and IMF departmental news archives, and took into account significant country-specific news events. We did not simply seek out the news behind every occasion when the markets moved significantly. Rather than relying on market commentaries that invariably contain some explanation for a given day’s market performance, we concentrated on news purely based on the criteria of whether the news event represented changes in the fundamentals of an economy. We broke down the news in two broad categories of good and bad news. In order to filter out the noise associated with daily news events from content that represents fundamentals of a given country, we used the following specific criteria:

Good News:

  1. Successful formation of bailout arrangements;

  2. Announcement of rescue package by international organizations;

  3. Better-than-expected economic news;

  4. Specific measures to stabilize the markets.

Bad news:

  1. Collapse of the currency regime or of long-standing financial arrangements;

  2. Breakdown in negotiation with multilateral agencies;

  3. Large scale bankruptcy or firm closure;

  4. Credit rating downgrade;

  5. Worse than expected announcements about debt exposure, inflation, or growth prospects, confusing policy moves;

  6. Threats or announcement of capital controls imposition;

  7. Resignation or firing of high profile officials;

  8. Civil unrest.

News that came out at the end of a business day was dated the following day. The news were checked across more than one source to verify date and content. The information was then used to create five good news and five bad news dummies for the respective countries. A chronology of the news events used to construct the dummy variables is provided in Appendix 2.

B. Impact of Own News and Other Fundamentals

In this section, we present the results of the impact of own country news and other fundamentals on the financial markets. In addition to the own country dummies, we add two more variables on the right hand side: the daily stock market return in the U.S. (S&P 500) and the yen-dollar exchange rate. These two variables are included as additional proxies of fundamentals. The yen-dollar rate also accounts for the monsoonal effect.

Table 9-A presents the results of the exchange rate regressions. The results are strong across the board. Bad news from own country had strong downward impact on the exchange rates in all the countries in discussion. Perhaps more interestingly, with the exception of Korea and the Philippines, the other three countries’ exchange rates reacted favorably and significantly to good news events. The exchange rate reaction to negative news was 1.7 percent and 2 percent for Malaysia and Thailand respectively. Indonesia’s exchange rate, marked by extraordinary volatility even by the standards of the regional mayhem, reacted with greater magnitude in both directions. The bad news dummy coefficient is 0.044, while the good news coefficient estimate is -0.059. The U.S. stock market impacted favorably on the currencies of Thailand and Malaysia. The estimates of the impact of the yen-dollar exchange rate are quite strong. Except for Indonesia, each of the four other countries’ exchange rates faced pressure whenever the yen depreciated. This is hardly surprising, given their large trade shares with Japan. A one percentage point depreciation of the yen brought a 0.35 to 0.82 percent depreciation of the currencies.

Table 9-A.

Regression Results with own country Dummy and other Fundamentals

Dependent Variable: Change in Nominal Exchange Rate

article image
Absolute values of t-statistics in parenthesis; * denotes 10% significance level, ** denotes 5% significance level Except for the dummies, all variables are in log first differences

The residuals from these regressions, having controlled for fundamentals, represent another measure of contagion. In Table 9-B we present the residual correlations. The results appear to have diminished somewhat from the original correlations observed in Table 2. However, LR test reveals statistically significant groupwise correlation of the residuals. Thus, despite controlling for fundamentals, the correlation between the currencies remain substantial and significant. Contagion effects persist well above and beyond the identified fundamentals. The evidence also proves that the financial markets correlations are not principally driven by some big news events.

Table 9-B.

Residuals Correlation

article image
LR test attempts to reject the null that all pairwise correlations are zero. * and** imply rejection at a 10% and 5% level, respectively.

The regression results with the stock prices are also strong (see Table 10-A). Except for the Phillippines, all the other stock markets react significantly, with the right sign to bad and good news events. In Thailand, Korea, and Indonesia, reactions to bad news were of a greater magnitude than to good news. All five stock markets in the sample were strongly correlated the U.S. stock market. The yen-dollar rate, on the other hand, was significant only for Korea. The negative coefficient associated with the yen implies that a percentage depreciation of the Japanese currency led to a 0.6 percent decline in the Korean stock market. This result is reinforced by the yen’s persistent decline during the entirety of the Asian crises, which put inordinate pressure on the Korean exports industry. After controlling for these variables, the residual correlations (see Table 10-B) remain relatively high and statistically significant. Once again, own country news and selected fundamentals do not account adequately for the correlations observed among the regional stock markets.

Table 10-A:

Regression Results with own country Dummy and other Fundamentals

Dependent Variable: Change in Stock Market Index

article image
Absolute values of t-statistics in parenthesis; * denotes 10% significance level, ** denotes 5% significance level Except for the dummies, all variables are in log first differences
Table 10-B.

Residuals Correlation

article image
LR test attempts to reject the null that all pairwise correlations are zero. * and** imply rejection at a 10% and 5% level, respectively.

We extend our analysis to domestic interest rates and sovereign spreads (see Tables 11 and 12). In these regressions, the news dummies do not reveal any consistent patterns. Very few of the regressors are significant, and often have counter-intuitive signs. The regressions fail to explain much of the movements in interest rates or spreads. The residual correlations are virtually identical to the raw correlations. The results for the interest rates can be reconciled with our earlier argument that the interest rates used in this exercise are not reflective of market forces. Therefore, we don’t expect them to react like other market variables, such as the exchange rate or stock market index. The lack of a consistent result on the sovereign spreads, on the other hand, seems to indicate that the debt market is not driven by fundamentals. This argument is supported by the fact that the raw correlations between the spreads, observed in the previous sections, were very high. This high degree of correlation indicates that the sovereign debt market is more prone to be driven by contagion factors along the lines of Masson (1997). The co-movements of the spreads are well above anything that can be accounted through changing fundamentals, and are possibly due to investor herd behavior.

Table 11-A.

Regression Results with own country Dummy and other Fundamentals

Dependent Variable: Interest Rates

article image
Absolute values of t-statistics in parenthesis; * denotes 10% significance level, ** denotes 5% significance level Except for the dummies and the dependent variable, all variables are in log first differences
Table 11-B.

Residuals Correlation

article image
LR test attempts to reject the null that all pairwise correlations are zero. * and ** imply rejection at a 10% and 5% level, respectively.
Table 12-A.

Regression Results with own country Dummy and other Fundamentals

Dependent Variable: Sovereign Spreads, in basis points

article image
Absolute values oft-statistics in parenthesis; * denotes 10% significance level, ** denotes 5% significance level Except for the dummies and the dependent variable, all variables are in log first differences
Table 12-B:

Residuals Correlation

article image
LR test attempts to reject the null that all pairwise correlations are zero, * and ** imply rejection at a 10% and 5% level, respectively.

C. Impact of Cross-border News

Tables 13 through 16 present the results of regressions with the complete set of news dummies on the right hand side. These regressions were done to quantify the impact of cross-border news on the markets. In addition to the news dummies of the Asia 5, we also include good news and bad news dummies of Japan. This inclusion is interesting given the strong trade linkage and financial ties Japan has with the countries in discussion. Additionally, during the sample period, a large number of news events took place in Japan that had far reaching implication for the regional markets. The U.S. stock market makes up the last independent variable in this exercise.

Table 13.

Regression results using across the board Dummy Variables

article image
Absolute values of t-statistics in parenthesis; * denotes 10% significance level, ** denotes 5% significance level
Table 14.

Regression results using across the board Dummy Variables

article image
Absolute values of t-statistics in parenthesis; * denotes 10% significance level, ** denotes 5% significance level
Table 15.

Regression results using across the board Dummy Variables

article image
Absolute values of t-statistics in parenthesis; * denotes 10% significance level, ** denotes 5% significance level
Table 16.

Regression results using across the board Dummy Variables

article image
Absolute values oft-statistics in parenthesis; * denotes 10% significance level, * * denotes 5% significance level