Directory UMM :Data Elmu:jurnal:I:International Review of Economics And Finance:Vol9.Issue4.2000:

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International Review of Economics and Finance 9 (2000) 299–322

The relationship between developed equity markets

and the Pacific Basin’s emerging equity markets

Baekin Cha

a,

_{*, Sekyung Oh}

b

a_{Korea Institute of Finance, 7th Fl., KFB Bldg., 4-1, 1-Ga, Myong-Dong, Chung-Gu,}

Seoul, 100-021, Korea

b_{Kunkook University, 93-1, Mojin-Dong, Kwangjin-Gu, Seoul, 143-701, Korea}

Received 8 October 1998; accepted 4 October 1999; revised 14 June 1999

Abstract

Using a trivariate vector autoregression (VAR) model with a proper control for hetero-scedasticity, this paper investigates the relationships between the two largest equity markets in the world—the U.S. and Japan—and the four Asian emerging equity markets: Hong Kong, Korea, Singapore, and Taiwan. Evidence indicates that the links between the developed markets and the Asian emerging markets (AEMs) began to increase after the stock market crash in October 1987, and have significantly intensified since the outbreak of the Asian

JEL classification:G15

Keywords:Asian emerging markets; Asian financial crisis; Vector autoregression

1. Introduction

The predominant feature of the stock market crash of October 1987 was its global scale. Equity markets around the world reacted to the collapse of the Dow Jones Industrial Average of the New York Stock Exchange with their own versions of a crash. Based on this phenomenon, scholars and market participants have developed an increasing interest in examining the relationships among national equity markets. Various methods have been used in this study. For example, Eun and Shim (1989) use vector autoregression to study the interdependence among world equity markets and find evidence of the U.S. market leading worldwide trends. Arshanapalli and Doukas (1993), using a cointegration analysis, also report an increasing degree of

* Corresponding author. Tel.:1822-3705-6313; fax:1822-3705-6309.

E-mail address: [email protected] (B. Cha)

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interdependence among world capital markets since the Crash. While many other researchers report similar results [see, for example, Brady (1988); Hamao, Masulis, & Ng (1991); and Cheung & Ng (1992)], other researchers, such as Malliaris and Urrutia (1992) argue that there are no lead-lag relationships for the major market indices either before or after the Crash.

Recently, the speed of integration among world capital markets seems to have accelerated, due to the gradual lifting of restrictions on capital flows and the relaxation of exchange controls in many countries. Major progress in computer technology and telecommunications has also expedited the international flow of information and lowered transaction costs. Recent trends of economic unification and regionalization have created various economic blocs that have also contributed to the integration of world capital markets.

Many researchers suggest that the substantially increased use and integration of international stock markets has also enhanced the efficiency of global financial mar-kets. For example, Bailey (1990) examines the effect of U.S. monetary shocks on the Pacific-rim stock markets and shows that the stock indices of countries with relatively few barriers to investment flows exhibit stronger reactions than those with strict capital flow controls. Kohers and Kohers (1995) find that 11 European stock markets are linked with both each other and the rest of the world, and that the presence of these distinct systematic relationships has increased overall market efficiency so that abnormal returns are less common in these markets today.

The studies mentioned above have mainly focused on the interdependence among stock markets of the developed countries such as the U.S., Japan, and Western Europe. Although the Asian stock markets are currently suffering from the recent financial crisis, the importance of stock markets in the AEMs has still grown tremendously in recent years.

More recent studies have investigated stock market correlations between developed countries and AEMs. Cheung and Mak (1992) find that the U.S. market is a global factor, affecting both developed and emerging markets. Liu and Pan (1997) investigate the mean and volatility spillover effects from the U.S. and Japan on the four AEMs (Hong Kong, Singapore, Taiwan, and Thailand). Using data from 1984 to 1991, they find that the spillover effects increased substantially after the Crash of October 1987, and that the U.S. market is more influential than the Japanese market in transmitting returns and volatilities to the four AEMs. Wu and Su (1998) report that returns in large markets lead to returns in small markets and that the Japanese market has a fairly strong influence on other markets in cases where the U.S. impact is isolated. While it is a common belief that the U.S. stock market is the single most influential market in the world, in a study of Asian markets, examining the impact of the Japanese stock market is also important.

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markets, it would be interesting to analyze how the crisis altered the relations between markets of developed countries and AEMs.

This paper empirically investigates several aspects of the influence of the U.S./ Japanese markets on Asian markets. The paper seeks to determine the size of the impact, the speed of transmission, and which market, the American or Japanese, has the greater influence. Presumably, the influence from the U.S. and Japan would be different depending on whether an AEM had been a victim of the financial crisis or not. It is likely that the stock market of a crisis country might have become more sensitive to unexpected movements of the U.S. and the Japanese stock markets than that of a country which had escaped crisis. To test this hypothesis, we compare the case of Korea, which was hit hard by the financial crisis, with Hong Kong, Singapore, and Taiwan, which successfully averted a crisis.

As mentioned above, the greater a country’s restrictions on international capital flows, the lesser the degree of integration between that country’s stock market and the world stock markets. A number of Asian countries have put strict restrictions on foreign ownership, which has the effect of blocking foreign direct investment and other foreign exchange transactions. While Singapore and Hong Kong opened up their stock markets early and have guaranteed almost free capital flows without any restrictions,1_{Korea and Taiwan have historically imposed many restrictions on foreign}

ownerships.2 _{Since the financial crisis began, Korea has lifted most restrictions on}

foreign direct investment and foreign ownership, and it will be very interesting to examine whether such deregulation has caused any change in the channel through which the Korean stock market is influenced by the American and Japanese stock markets.3

In investigating the issues mentioned above, this paper uses the vector autoregres-sion (VAR) method with a proper control for heteroscedasticity. The VAR model is particularly well suited for our purpose since it avoids the problems inherent in the single-equation method yet still yields useful econometric evidence with which to examine the relative importance of the two major markets on the AEMs.

The rest of the paper is organized as follows: Sections 2 and 3 describe the data and the methodology employed, respectively. Section 4 presents the empirical results, and Section 5 concludes the paper.

2. Data

2.1. Data

The stock markets analyzed in this paper are those of the four AEMs: the Stock Exchange of Hong Kong (SEHK), the Korean Stock Exchange (KSE), the Stock Exchange of Singapore (SES) and the Taiwanese Stock Exchange (TSE). In terms of market capitalization expressed in U.S. dollars, these are the four largest Asian markets after the Japanese and Australian markets.

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Fig.

1.

Stock

price

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the SES, the Weighted Index for the TSE, the S&P 500 Index for the U.S., and the Nikkei 225 Index for Japan. Weekly stock returns are computed as the percentage log difference of the closing prices on Fridays. Weekly stock returns are used as opposed to daily ones, to avoid the problem of nonsynchronous trading in some thinly traded stocks.

The study covers the period from January 4, 1980, to September 18, 1998. To examine the stability of the results, the whole sample period is divided into three subperiods: January 4, 1980–September 25, 1987 (Period I), November 6, 1987–June 27, 1997 (Period II), and July 4, 1997–September 18, 1998 (Period III). The Crash of October 1987 separates Periods I and II. The period of October 1987 is excluded from the sample. Periods II and III are separated by the start of the Thai collapse on July 2, 1997, the date the central bank of Thailand changed the Thai exchange rate system from the multicurrency basket system to the managed floating system. In retrospect, it is clear that this is the event that triggered the devaluations in other Asian countries and eventually caused the Asian foreign exchange and financial crises. Figs. 1 and 2 show the level of stock price indices and return rates over all three periods.

To test for the stationarity of stock returns, the Dickey-Fuller test (DF) and the augmented Dickey-Fuller test (ADF) are used. In these tests, the null hypothesis that the national stock indices have a unit root is tested against the alternative hypothesis that they do not. The results confirm the presence of a unit root in the levels of all stock price indices, but there is no evidence of a unit root in their first differences in any of the subperiods or in the whole sample period.4

2.2. Diagnostic statistics

Table 1 shows the mean and the standard deviation of the rate of return for each market during the sample period. For the whole sample period as well as for each subperiod, all four AEMs have higher levels of risk than either the Japanese or the U.S. markets. Only Taiwan, however, shows a higher return for the whole sample period; the rates of return in the other three AEMs are all lower than the U.S. market’s rate of return. In other words, Hong Kong, Singapore, and Korea have the unfavorable combination of higher risks and lower returns than the U.S. market. The reason for this is that the rates of return of the AEMs began deteriorating after the October Crash of 1987, and have declined particularly sharply since the outbreak of the Asian financial crises. During Period I, the AEMs performed quite well against the developed markets: Hong Kong, Korea, and Taiwan recorded higher rates of return than either the U.S. or Japanese markets, while the rates of return in the Singapore market were higher than the U.S. but lower than Japan. The rates of return in the AEMs started deteriorating only in Period II, when only the Hong Kong market managed to maintain a higher rate of return than the U.S.. The Japanese market also tumbled during Period II and exhibited a negative rate of return, reflecting the decade-long economic stagnation during this period.

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Fig.

2.

Stock

return

rates

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Table 1

Mean and standard deviation of weekly return rates (unit: %) Whole Sample Period: 80/1/4,98/9/18

US JAPAN HONG KONG KOREA SINGAPORE TAIWAN

Mean 0.231 0.078 0.222 0.113 0.077 0.258

Standard Deviation 2.013 2.474 4.070 3.343 3.148 4.508

Period I: 80/1/4,87/9/25

Mean 0.273 0.333 0.375 0.391 0.288 0.508

Period II: 87/11/6,97/6/27

Mean 0.250 20.021 0.380 0.075 0.173 0.237

Period III: 97/7/4,98/9/18

Mean 0.169 20.566 21.093 21.508 21.234 20.441

The Korean market in particular recorded the lowest return rate and the highest level of risk among the four AEMs. It is interesting that Taiwan, which has relatively more restrictions on international capital movements than other countries, both successfully escaped a crisis and also exhibited the lowest level of risk and the smallest fall in rates of return.

2.3. Contemporaneous correlations

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Table 2

Contemporaneous correlation coefficients

Whole Sample Period: 80/1/4,98/9/18

US 1 0.330 0.243 0.106 0.333 0.112

JAPAN 1 0.218 0.198 0.313 0.210

HONG KONG 1 0.122 0.487 0.150

KOREA 1 0.128 0.049

SINGAPORE 1 0.219

TAIWAN 1

Period I: 80/1/4,87/9/25

US 1 0.378 0.213 0.041 0.235 0.072

JAPAN 1 0.231 0.138 0.230 0.098

HONG KONG 1 0.013 0.319 0.095

KOREA 1 0.082 0.025

SINGAPORE 1 0.083

TAIWAN 1

Period II: 87/11/6,97/6/27

US 1 0.302 0.238 0.121 0.291 0.036

JAPAN 1 0.193 0.187 0.326 0.218

HONG KONG 1 0.156 0.533 0.111

KOREA 1 0.165 0.039

SINGAPORE 1 0.224

TAIWAN 1

Period III: 97/7/4,98/9/18

US 1 0.361 0.450 0.274 0.472 0.473

JAPAN 1 0.273 0.310 0.254 0.297

HONG KONG 1 0.236 0.675 0.399

KOREA 1 0.151 0.472

SINGAPORE 1 0.116

TAIWAN 1

The correlation coefficients between the AEMs and Japan also increased over the sample period. With the exception of Hong Kong in Period II and Singapore in Period III, all other correlation coefficients between the AEMs’ return rates and Japanese return rates consistently increased over the three periods. In Period III, the coefficients were 0.273 in Hong Kong, 0.310 in Korea, 0.254 in Singapore, and 0.297 in Taiwan.

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and Taiwan, which have had relatively more restrictions on international capital flows than other AEMs, the return rates exhibited only weak correlation with those in the U.S. and Japan before the Asian financial crisis. Since the crisis, however, the correla-tion has become much greater.

3. Method

3.1. Vector autoregression (VAR)

In general, a k-th order vector autoregression (VAR) model for an n31 vector Y is written as shown in Eq. (1),

Yt5 Dt1

o

k

s51

BsYt2s1et, t51, . . .,T (1)

whereDtis an n31 deterministic vector, andetis an n31 serially uncorrelated residual

vector with E[et]50 and E[etet9] 5 S ,∞.5Here, the residual vector e is said to be

the innovation (shock) inYbecause it is the component inYthat cannot be predicted from past values of variables in the system.6_{Then, either by a polynomial lag division}

or by a successive substitution, the corresponding moving average representation (MAR) is derived from Eq. (2),

Yt5 Ft1

o

∞

s50

As et2s, t51, . . ., T (2)

whereFtis the corresponding deterministic part and A05In.

In this paper, for each of the four AEMs, Yt is defined as a 331 vector such as

given in Eq. (3),

Yt5 [YU,t,YJ,t, YA,t]9 (3)

where YU,t, YJ,t, YA,t are the stock return series for the U.S. market, the Japanese

market, and the AEM in concern, respectively. Thus, the VAR system [Eq. (1)] for an AEM is written as given in Eq. (4),

While the estimated coefficients bm

n,s in the VAR system [Eq. (4)] provide little

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but in a form which is relatively easy to understand. In this paper, the MAR was used in two ways. First, it was used to compute the proportion of the forecasting error variance of the AEM’s return rate that can be attributed to shocks in the U.S. and Japan, and the AEM’s own domestic shocks, in a process known as variance decomposi-tion. This variance decomposition method provides useful econometric evidence for explaining the relative importance of the U.S. and Japanese markets to the AEMs. Second, it was used to compute the dynamic responses of the AEM’s return rate to random shocks in the U.S. and Japanese markets. The dynamic impulse responses investigate how unexpected changes in U.S. and Japanese return rates change the return rates of the Asian emerging markets over time.

3.2. VAR with autoregressive conditional heteroscedasticity (ARCH)

For Hong Kong, Korea, Singapore, and Taiwan, and for each subsample period, Eq. (1) was initially estimated with four lags and a constant term for the deterministic part.7

An important assumption underlying the usual unrestricted VAR is that the residu-als are not serially correlated. The trends of the stock return rates in Fig. 2, however, clearly question the presence of autoregressive conditional heteroscedasticity (ARCH). In this context, an attempt was made to test the ARCH effect by modeling the residual series as ARCH processes such as shown in Eq. (5):

Var(eU,t)5 aU,01 aU,1e2U,t211 aU,2e2U,t221. . .1 aU,pe2U,t2p

Var(eJ,t) 5 aJ,01 aJ,1e2J,t211 aJ,2e2J,t221 . . .1 aJ,qe2J,t2q

Var(eA,t)5 aA,01 aA,1e2A,t211 aA,2e2A,t221. . .1 aA,re2A,t2r (5)

Eq. (5) indicates that different orders of lags were allowed for residual series within each VAR system. Applying the test proposed by Engle (1982) with one to 12 lags for each residual series has confirmed that the ARCH effect is indeed present.8

In order to generate the correct residuals that will be used in variance decomposition and impulse response functions in the presence of the ARCH effect, the optimal lag length of each ARCH process in Eq. (5) should be determined first. We applied the Schwarz (1978) information criterion (SIC) to determine the optimal lags; that is, p5 pˆ, q5 qˆ,r 5rˆ in Eq. (5).9_{Table 3 presents the results of the optimal lags and}

the chi-square statistics for each equation in the four VAR systems. It is shown in Table 3 that the numbers of optimal lags in the ARCH process for the residual series are one and five for the U.S. and the Japanese return rates, respectively, in all four VAR systems. But, those for the AEMs’ return rates were different across the VAR systems; that is, one for the Hong Kong and the Singaporean return rates, eight for the Korean return rate, and three for the Taiwanese return rate.

Finally, with the optimal lags in Table 3, Eqs. (4) and (5) were jointly estimated by the pseudo-maximum likelihood method using the BHHH algorithm and the residu-als were saved.10 _{In the next section, these residuals were used to form the MAR,}

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Table 3

Optimal lag length for the autoregressive conditional heteroscedasticity (ARCH) process for residual series

VAR Country Lags x2_Statistics

HONG KONG US 1 81.40*

Notes:(1) (*) denotes statistical significance of the ARCH effect at the 5% level.

(2) The lag length was determined by the Schwarz Information Criterion (SIC).

4. Empirical results

4.1. Variance decomposition

With the estimated residuals and the MAR [Eq. (2)], we decomposed the forecasting error variance of the AEM’s return rate in order to compute the relative contribution of shocks in the U.S., Japan, and the AEM itself. First, the innovations were orthogo-nalized in order to “isolate” a shock in each variable. This was done by expressing the second term of the MAR [Eq. (2)] as given by Eq. (6),

whereCs5AsGandGG9is a factorization of the covariance matrix ofe.In computing

the orthogonalized innovationsu5G21_e_{, the Choleski factorization was used where}

the G matrix was chosen to be lower triangular. Then, with weekly return rates, the k-week ahead forecast error of Y at time t becomes as shown by Eq. (7):

Ck21ut111 . . .1C0ut1k 5

o

k21

s50

Csut1k2s. (7)

LetCij

s be the (i, j) element of Cs. Then, the variance of this k-week ahead forecast

error is

_o

s)2. Therefore, the component of the error variance in the k-week

ahead forecast of Yi_{, which is accounted for by innovations in} _Yj _becomes

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Table 4 presents the results for variance decomposition, where up to 24-week forecast error variances of the four AEMs’ return rates are decomposed. The explana-tory power of each country is measured as a percentage, so that the horizontal sum of each row is 100.

While the forecast error variance itself does not depend on the factorization of the covariance matrix ofe, the decomposition of this forecast error variance does. With the Choleski factorization, there is a different factorization for each ordering of the variables. The results in Table 4 are based on the ordering of YU,t, YJ,t, YA,t in the

orthogonalizing process. In practice, orderings are determined based on a “semi-structural” interpretation of the model: If movements in A precede movements in B within a single period, A precedes B in the ordering [see Doan (1995)]. Therefore, the chosen ordering ofYU,t,YJ,t, YA,tfeatures the recursive causal ordering suggested

by theory: the U.S. first, then the Japanese market, then the emerging market.11

Table 4 shows a dramatic increase in the importance of American and Japanese shocks in explaining unexpected movements in stock returns in all four AEMs. This change came only after the outbreak of the Asian financial crisis. Before the crisis broke out, most of the forecast error variances of the AEMs’ return rates could be explained by domestic factors. Since the outbreak of the crisis, however, U.S. and Japanese influences explain a significant portion of the forecast error variance of the AEMs’ return rates. For example, U.S. shocks explained just 6.67% of Hong Kong’s variance in Period I and 7.11% in Period II. In Period III, however, U.S. shocks accounted for more than 28% of variance. In Singapore, where the U.S. impact was historically largest among the four AEMs (10.78% in Period I and 13.29% in Period II), U.S. shocks have become even more influential with the explanatory power of 28.07% in Period III.

The same trend of increasing influence also appears in Korea and Taiwan, whose stock markets were historically thought by observers to have moved somewhat inde-pendently of the developed stock markets. Table 4 shows that before the sudden outbreak of the Asian financial crisis, the U.S. impact was negligible in Korea and Taiwan. In Korea, the U.S. market had an explanatory power of only 1.36% and 1.98% in Periods I and II, respectively, while in Taiwan, the explanatory power was 1.61% and 1.89% in Periods I and II, respectively. Since the Asian financial crisis, the U.S. explanatory power has jumped to more than 10% and 29% in Korea and Taiwan, respectively.

The Japanese impact has also increased since the crisis in all AEMs but Taiwan. In Periods I and II, the explanatory power of Japanese fluctuations was only 3.11% and 1.67% in Hong Kong, 1.87% and 4.12% in Korea, and 2.73% and 6.76% in Singapore, respectively. In Period III, the Japanese influence had increased to 4.20% in Hong Kong, 9.08% in Korea, and 8.34% in Singapore. Interestingly, the Asian financial crisis seems not to have increased the Japanese influence on the Taiwanese stock market.

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Table 4

Percentage of k-week ahead forecast error variance of each Asian market’s return accounted for by the US, Japanese, and domestic innovations

HONG KONG Period I: 80/1/4,87/9/25

k Standard Error US JAPAN HONG KONG

1 4.03813 4.92 2.05 93.02

4 4.12045 6.68 2.45 90.87

8 4.14111 6.67 3.10 90.23

12 4.14115 6.67 3.11 90.23

16 4.14115 6.67 3.11 90.23

20 4.14115 6.67 3.11 90.23

24 4.14115 6.67 3.11 90.23

Period II: 87/11/6,97/6/27

1 3.07704 5.64 1.48 92.88

4 3.12638 7.04 1.58 91.38

8 3.13582 7.11 1.67 91.22

12 3.13590 7.11 1.67 91.22

16 3.13590 7.11 1.67 91.22

20 3.13590 7.11 1.67 91.22

24 3.13590 7.11 1.67 91.22

Period III: 97/7/4,98/9/18

1 5.39934 23.72 1.43 74.85

4 5.68375 28.09 2.97 68.94

8 5.76791 28.62 4.17 67.21

12 5.77079 28.64 4.20 67.16

16 5.77086 28.64 4.20 67.16

20 5.77087 28.64 4.20 67.16

24 5.77087 28.64 4.20 67.16

KOREA

Period I: 80/1/4,87/9/25

k Standard Error US JAPAN KOREA

1 2.70818 0.43 1.34 98.23

4 2.73397 1.34 1.71 96.96

8 2.73799 1.35 1.87 96.77

12 2.73802 1.36 1.87 96.77

16 2.73802 1.36 1.87 96.77

20 2.73802 1.36 1.87 96.77

24 2.73802 1.36 1.87 96.77

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Table 4 (continued)

Period II: 87/11/6,97/6/27

1 2.91338 1.56 3.31 95.13

4 2.93535 1.86 3.84 94.30

8 2.94337 1.98 4.12 93.90

12 2.94340 1.98 4.12 93.90

16 2.94340 1.98 4.12 93.90

20 2.94340 1.98 4.12 93.90

24 2.94340 1.98 4.12 93.90

Period III: 97/7/4,98/9/18

1 5.59997 1.37 6.32 92.31

4 5.99069 9.99 9.03 80.98

8 6.08760 10.72 9.12 80.16

12 6.10733 10.89 9.09 80.03

16 6.11202 10.92 9.08 80.00

20 6.11330 10.93 9.08 80.00

24 6.11364 10.93 9.08 80.00

SINGAPORE

Period I: 80/1/4,87/9/25

k Standard Error US JAPAN SINGAPORE

1 2.68836 6.37 1.79 91.83

4 2.78761 10.84 2.22 86.95

8 2.80737 10.78 2.73 86.49

12 2.80764 10.78 2.73 86.48

16 2.80765 10.78 2.73 86.48

20 2.80765 10.78 2.73 86.48

24 2.80765 10.78 2.73 86.48

Period II: 87/11/6,97/6/27

k Standard Error US JAPAN SINGAPORE

1 2.18749 9.16 5.69 85.14

4 2.26603 13.25 6.59 80.16

8 2.26925 13.29 6.76 79.95

12 2.26926 13.29 6.76 79.95

16 2.26926 13.29 6.76 79.95

20 2.26926 13.29 6.76 79.95

24 2.26926 13.29 6.76 79.95

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Table 4 (continued)

Period III: 97/7/4,98/9/18

Standard Error US JAPAN SINGAPORE

1 5.01981 27.16 0.04 72.80

4 5.47872 27.41 7.37 65.22

8 5.61658 27.95 8.22 63.84

12 5.62759 28.06 8.32 63.61

16 5.62881 28.07 8.34 63.59

20 5.62893 28.07 8.34 63.59

24 5.62895 28.07 8.34 63.58

TAIWAN

Period I: 80/1/4,87/9/25

k Standard Error US JAPAN TAIWAN

1 2.55266 0.95 0.33 98.72

4 2.65281 1.73 0.41 97.86

8 2.75171 1.62 1.16 97.22

12 2.76080 1.61 1.22 97.17

16 2.76183 1.61 1.22 97.17

20 2.76196 1.61 1.22 97.17

24 2.76198 1.61 1.22 97.17

Period II: 87/11/6,97/6/27

1 5.25462 0.03 4.56 95.41

4 5.36258 1.73 4.89 93.38

8 5.38120 1.89 5.12 92.99

12 5.38129 1.89 5.12 92.99

16 5.38129 1.89 5.12 92.99

20 5.38129 1.89 5.12 92.99

24 5.38129 1.89 5.12 92.99

Period III: 97/7/4,98/9/18

1 3.14478 28.41 0.02 71.58

4 3.30596 31.75 2.59 65.66

8 3.51622 29.96 2.40 67.63

12 3.54105 29.75 2.45 67.80

16 3.54589 29.70 2.45 67.85

20 3.54683 29.69 2.45 67.86

24 3.54701 29.69 2.45 67.86

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Singa-porean stock returns. Another interesting observation is that in Hong Kong and Singapore, the U.S. impact dominates the Japanese impact in all three periods. On the other hand, the Japanese impact exceeds the U.S. impact in Period III in Korea and in Periods I and III in Taiwan.

These results suggest that an emerging market’s sensitivity to shocks from developed markets is related to its degree of openness. As is well known, there are no restrictions on equity investment for either foreigners or domestic residents in Hong Kong and Singapore. These two AEMs also permit foreign currency to be either imported or exported. Through these venues, shocks from the U.S. can be more directly transmitted to these two Asian financial centers. The higher level of restrictions and regulations on international capital flows in Korea and Taiwan appear to have made their stock markets less sensitive to foreign shocks.

Given their initial insulation from foreign shocks, one would expect the opening of markets after the crisis to have a greater impact on Korea and Taiwan than on Hong Kong and Singapore, which were already quite open before the crisis. This prediction is borne out by the data in Table 4. While U.S. influence after the crisis increased by 4.0 and 2.1 times in Hong Kong and Singapore, respectively, its influence in Korea and Taiwan increased by 5.5 and 15.7 times, respectively. It is particularly interesting to note that the U.S. impact has increased more in Taiwan than in Korea, even though Taiwan was able to ward off the crisis and Korea wasn’t. Such a result appears to be related to the fact that the Japanese impact increased by more than five times in Korea after the crisis, while it has been reduced in Taiwan. Since it was Japanese banks’ refusal to roll over the Korean short-term liabilities that triggered the Korean financial crisis, the Japanese influence would naturally be much bigger in Korea than in Taiwan. Conversely, the relative influence of the U.S. market should be smaller in Korea than in Taiwan.

4.2. Impulse response functions

Next, we compute the dynamic impulse responses of the AEMs’ return rates to random shocks in the U.S. and Japanese return rates. In Eq. (6), computing the dynamic responses is equivalent to tracing out its coefficients Cs. Specifically, the

response ofY at t 5k to an initial shock of size l in theu vector isClk. Similarly,

the response ofYat t5k to a shock of size one touj_{is the j-th column of}_C

k. Thus,

Cij

k is the dynamic impulse response of Yi in k quarters to a positive shock of one

standard deviation in Yj_{, which is regarded as a measure of dynamic causality from}

Yj_to_Yi_.

Fig. 3 presents impulse responses for the AEMs’ return rates to a 1% positive shock in the U.S., Japanese, and domestic return rates. The responses are computed for up to 24 weeks and drawn using the same scale. Figures on the vertical axis are in percentage terms. Table 5 also presents the minimum and the maximum values of these responses.

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Fig.

3.

Responses

of

stock

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Fig.

3.

(continued

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Fig.

3.

(continued

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Fig.

3.

(continued

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Table 5

Minimum and maximum values of impulse responses Responses to a 1% US Shock

Period I Period II Period III

MIN MAX MIN MAX MIN MAX

HONG KONG 20.156 0.241 20.013 0.244 20.524 0.274

KOREA 20.072 0.077 20.110 0.100 20.698 0.234

SINGAPORE 20.067 0.323 20.023 0.306 20.381 0.382

TAIWAN 20.002 0.080 20.022 0.347 20.232 0.316

Responses to a 1% Japanese Shock

HONG KONG 20.173 0.247 20.046 0.021 20.185 0.249

KOREA 20.005 0.108 20.054 0.063 20.183 0.539

SINGAPORE 20.147 0.115 20.036 0.068 20.216 0.672

TAIWAN 20.183 0.038 20.078 0.078 20.041 0.185

Responses to a 1% Domestic Shock

HONG KONG 20.053 1.020 20.065 1.015 20.073 1.158

KOREA 20.071 1.020 20.035 0.055 20.110 1.183

SINGAPORE 0.000 1.021 20.055 1.016 20.174 1.171

TAIWAN 0.001 1.021 20.052 1.016 20.093 1.171

Periods I and II, the responses are small and tend to start to dampen after two weeks, before dying out in six or seven weeks. In Period III, however, the responses of the AEMs’ return rates to the U.S. and the Japanese shocks are larger and last much longer. The responses to the U.S. shocks persist longer than those to the Japanese shocks during Period III.

The reactions to shocks in the developed countries’ stock markets did not always have predictable effects on the AEMs. For example, the 1% unexpected increase in U.S. return rates caused Korean rates to oscillate from10.7% to21.27% in Period III. The Korean market also shows a bigger response to Japanese shocks than the Taiwanese market in Period III, which is consistent with the results of the variance decomposition analysis.

5. Conclusion

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two largest equity markets in the world: the U.S. and Japan. Evidence indicates that the degree of association between the developed markets and the Asian emerging markets (AEMs) began to change after the October Crash of 1987, and that the relationships have been significantly intensified since the outbreak of the Asian finan-cial crisis in July 1997.

Following the October Crash of 1987, the U.S. equity market began to have a more significant impact on the Hong Kong and Singaporean markets, though U.S. influence on the Korean and the Taiwanese markets was unchanged. Relatively, the Japanese equity market did not have much impact on the four AEMs until the Asian financial crisis. The impact of both developed markets on the AEMs, however, has dramatically increased since the outbreak of the crisis. Also, in all four AEMs, the responses of return rates to U.S. and the Japanese shocks have become much larger and more persistent since the outbreak of the Asian financial crisis. On the one hand, the results are consistent with existing literature that concludes that the U.S. market is a global factor. On the other hand, the results suggest that an emerging market’s sensitivity to shocks from the developed markets is related to its degree of openness. While Singapore and Hong Kong opened up their stock markets early and have guaranteed almost free capital flows in and out, Korea and Taiwan have historically put many restrictions on international capital flows and foreign ownership. Therefore, the U.S. influence could be more directly transmitted to Hong Kong and Singapore before the crisis, and U.S. influence on Korea and Taiwan has increased sharply since the crisis.

Acknowledgments

We wish to thank anonymous referees for insightful comments on earlier draft(s) of this paper. Responsibility for any remaining errors rests with us.

Notes

1. While Singapore and Hong Kong place no restrictions on equity investment for either foreigners or domestic residents, some limited restrictions on foreign ownership still exist for companies that are considered strategically important to national interests.

2. The Taiwanese stock market was opened to foreigners on January 1, 1991, although foreign investors still face restrictions such as a limit on total cash inflows and a 10% limit on aggregate foreign ownership. The Korean stock market was opened on January 1, 1992, but foreigners’ ownership on listed companies on the Korean Stock Exchange was limited to 10% of the total outstanding shares (8% for public corporations) of a company and to 3% per foreigner (1% for public corporations). The limit was gradually increased to 26% per company and 7% per foreigner in November 1997.

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in December 1997. In January 1998, according to the agreement with the Interna-tional Monetary Fund, authorities permitted up to 55% foreign ownership (25% for public corporations). The limit for private corporations was completely abolished in May 1998, though a limit of 30% per stock and 3% per person was retained for public corporations.

4. The results are not reported here but are available from the authors upon request.

5. For details, see Doan (1995).

6. For a stochastic process {Yt},etis said to be the innovation inYtif and only if

et5Yt2E(Yt|Yt2i, i>1), where E(Yt|Yt2i, i>1) is defined to be the limit

Yˆtof linear combinations of {Yt2i, i>1}, which minimizes the variance of (Yt2

Yˆt). That is,et is a one-step-ahead forecast error ofYtas of time t 21. Since

et21is again a limit of linear combinations of {Yt2i, i>2},etis serially uncorrelated.

7. Considering the cross-equation nature of the model, we tested alternative lags (one, two, and three lags) as restrictions on the four lags that we used. The likelihood ratio test was applied, whose statistics are asymptotically distributed as chi-square with degrees of freedom equal to the number of restrictions. The null hypotheses of the alternative lags were all rejected. The results are not reported in the paper.

8. The ARCH test proposed by Engle (1982) is based on the Lagrange multiplier principle. After estimating each equation in Eq. (4) by ordinary least squares (OLS) for observations t5 2m11,2m12, . . ., T, the squares of the estimated residuals is regressed on a constant and m of its own lagged values. The product of the sample size T and the uncentered R2_{converges in distribution to a}

chi-square variable with m degrees of freedom under the null hypothesis that the residual term is i.i.d. normally distributed with mean 0 and variance s2_{. The}

results are not reported in the paper, but are available from the authors. 9. See Schwarz (1978).

10. See Berndt et al. (1974) and Engle (1982).

11. We also checked whether or not the results in Table 4 favored a different ordering in the orthogonalization, and found that they are not sensitive to the alternate orderings of the variables.

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