what happened to pacific-basin emerging markets after the 1997 financial crisis?

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This article was downloaded by: [University of Kent] On: 18 November 2014, At: 17:50 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Applied Financial Economics Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rafe20 What happened to pacific-basin emerging markets after the 1997 financial crisis? Joo Ha Nam a , Ky-hyang Yuhn b & Sang Bong Kim c a Department of Economics , Sogang University , Seoul, Korea b Department of Economics , Florida Atlantic University , Boca Raton, FL 33431 c Department of Economics , Texas A&M University, College Station , TX 77843 Published online: 21 Apr 2008. To cite this article: Joo Ha Nam , Ky-hyang Yuhn & Sang Bong Kim (2008) What happened to pacific-basin emerging markets after the 1997 financial crisis?, Applied Financial Economics, 18:8, 639-658, DOI: 10.1080/09603100701222275 To link to this article: http://dx.doi.org/10.1080/09603100701222275 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

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Page 1: What happened to pacific-basin emerging markets after the 1997 financial crisis?

This article was downloaded by: [University of Kent]On: 18 November 2014, At: 17:50Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Applied Financial EconomicsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/rafe20

What happened to pacific-basin emerging marketsafter the 1997 financial crisis?Joo Ha Nam a , Ky-hyang Yuhn b & Sang Bong Kim ca Department of Economics , Sogang University , Seoul, Koreab Department of Economics , Florida Atlantic University , Boca Raton, FL 33431c Department of Economics , Texas A & M University, College Station , TX 77843Published online: 21 Apr 2008.

To cite this article: Joo Ha Nam , Ky-hyang Yuhn & Sang Bong Kim (2008) What happened to pacific-basin emerging marketsafter the 1997 financial crisis?, Applied Financial Economics, 18:8, 639-658, DOI: 10.1080/09603100701222275

To link to this article: http://dx.doi.org/10.1080/09603100701222275

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Page 2: What happened to pacific-basin emerging markets after the 1997 financial crisis?

Applied Financial Economics, 2008, 18, 639–658

What happened to pacific-basin

emerging markets after the 1997

financial crisis?

Joo Ha Nama, Ky-Hyang Yuhnb,* and Sang Bong Kimc

aDepartment of Economics, Sogang University, Seoul, KoreabDepartment ofEconomics,FloridaAtlanticUniversity,BocaRaton,FL33431cDepartment of Economics, Texas A &MUniversity, College Station,

TX 77843

The stock prices of Asian emerging markets have been at tandem with

sharp moves of the US market since the 1997 financial crisis. This study

investigates how the 1997 crisis has changed Asian emerging markets by

focusing on price and volatility spillovers from the US market to five

Pacific-Basin emerging markets, Hong Kong, Singapore, South Korea,

Malaysia, and Taiwan. We have used daily stock prices from 3, January

1995 to 24, April 2001 and compared the spillover effects between the

prior- and post-crisis periods employing an EGARCH model. The

influence of US innovations on stock prices in the region increased after

the 1997 financial crisis (only with the exception of the Malaysian market),

but the influence of US shocks on market volatility decreased substantially

after the crisis (only with the exception of the Korean market). South

Korea and Malaysia pursued different approaches to coping with the

financial crisis, and their different programs led to opposite shifts in price

and volatility spillovers after the crisis.

I. Introduction

The Asian financial crisis that occurred in the late

1990s has affected Asian financial markets in a

significant way. In particular, Asian emerging

markets including the Korean and Malaysian

markets underwent seismic changes in the wake of

the financial turmoil. Many regulations and

restrictions on trading activities in these markets

have been eased or eliminated since the financial

crisis, and Asian emerging markets have become

much more globalized and liberalized. Foreign

portfolio investment in these markets has grown at

a galloping pace as these markets have

been increasingly integrated into the world financial

market.1 Asian emerging markets have emerged

*Corresponding author. E-mail: [email protected] The most dramatic developments occurred in the Korean market. Since the Korean government began to open its stockmarket to foreign investors in 1992, it raised the limit of foreigners’ investment in a stock traded on the Korean StockExchange six times to 26% of shares of the stock until the 1997 financial crisis hit the Korean economy, but an individual’sacquisition of a Korean stock was limited to 7%. As the financial crisis was looming ahead, the limit of foreigners’ investmentwas raised to 50% on 11, December 1997, then to 55% on 30, December 1997 and finally the restrictions on foreigninvestment in Korean stocks were completely eliminated on 25, May 1998. The market value of Korean stocks held by foreigninvestors was 10 692.2 billion Won at the end of 1998, but the holdings of Korean stocks by foreign investors increased to95 115.4 billion Won as of 16, April 2004, recording a 789.6% increase during this period. The market value of stocks of the 10largest business groups held by foreign investors accounts for 50.3% of the total market value of Korean stocks traded on theKorea Stock Exchange and the KOSDAQ.

Applied Financial Economics ISSN 0960–3107 print/ISSN 1466–4305 online � 2008 Taylor & Francis 639http://www.tandf.co.uk/journalsDOI: 10.1080/09603100701222275

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Page 3: What happened to pacific-basin emerging markets after the 1997 financial crisis?

as an important segment of the world financial

system.The growing integration of Asian emerging mar-

kets raises several fundamental questions. Does the

globalization of financial markets precipitate thetransmission of information from advanced markets

into the emerging markets? If an emerging market

becomes more globalized and liberalized, then

information on stock prices produced in a leading

market such as the US market will be more rapidly

disseminated into the emerging market, thus prompt-ing price spillovers. In fact, since the 1997 financial

crisis changed the financial landscape in Asia, the

influence of advanced stock markets on the Asian

emerging markets has gained steadily. Notably, the

stock prices of the emerging markets have tended tosynchronize with the sharp moves of US stock prices.

What is of particular interest from both practical

and theoretical perspectives is whether such co-

movements in stock prices amplify the volatility of

emerging markets. The co-movements of stock prices

across markets may change stock prices above orbelow the levels dictated by market fundamentals,

potentially creating market volatility. However, if

investors become more informed as a result of

globalization of markets, the increased interdepen-

dence and linkage of financial markets could reduce

the transmission of volatility from one market toanother. Thus, one interesting hypothesis to be tested

concerns whether or not an increased integration of

financial markets leads to a reduction in market

volatility in the emerging markets.The main purpose of this study is to investigate

how five Pacific-Basin emerging markets (Hong

Kong, Singapore, South Korea, Malaysia and

Taiwan) have been affected by the 1997 financial

crisis. We are particularly interested in whether the

influences of shocks originated in the US markets on

prices and market volatility in the five Pacific-Basinmarkets increased or decreased after the 1997 crisis.2

It is well documented that the volatility of the Pacific-

Basin markets was historically high. Interestingly

enough, the volatility of these markets have been

much dampened since 2000 when the aftermath of the

financial crisis has been substantially subdued. ‘Forthe first time in nearly two years, European markets

have surpassed most emerging countries in terms of

trading volatility, measured by the percentage gains

or drops from one day to the next’ (The Wall Street

Journal, 3, October 2002). This study aims to explore

whether much of the slowdown in Asian markets’

volatility after the financial crisis in 1997 is a

fundamental shift or a temporary fad.These emerging markets present some contrasting

features. First, Hong Kong and Singapore had the

least regulated financial systems, whereas South

Korea (hereafter referred to as Korea) and

Malaysia enforced strict controls on capital transac-

tions until the 1997 financial crisis. Taiwan was in

between in terms of market regulation. Second,Korea and Malaysia severely suffered from spec-

ulative attacks on their currencies that plunged the

countries into a full-scale financial crisis in 1997, but

Hong Kong, Singapore and Taiwan were less affected

by the financial crash. Korea and Malaysia pursuedcompletely different and opposite approaches to

coping with the financial crisis, but both countries

have successfully overcome the crisis.3

Third, South Korea and Malaysia changed their

exchange rate regime in the opposite direction in the

midst of the financial crisis. South Korea shiftedgears toward a floating exchange rate system from

a market average exchange rate system in December,

1997, whereas Malaysia changed its exchange rate

regime from a floating exchange system to a fixed

exchange rate system in March, 1998. Hong Kong

and Taiwan also stand at opposites. Hong Kong haspegged its currency to the US dollar since October,

1983, whereas Taiwan has maintained its flexible

exchange rate system since April, 1989. Singapore has

operated under a multiple-currency basket system

since 1981.4 Finally, Hong Kong, Singapore andMalaysia are geopolitically more proximate than

the other two countries.There has been a spate of studies on the interaction

and interdependence of stock prices and volatility

among advanced markets. There are also a number of

studies that examine price (or return) and volatilityspillovers from advanced markets to emerging

markets. For example, see Ng (2000), In et al.

(2001) Edwards and Susmel (2001), Darrat and

Zhong (2002) and Worthington and Higgs (2004).

However, there is little literature that investigates

what happened to Asian emerging markets after the1997 financial crisis. This study offers some new

evidence on how the Pacific-Basin emerging markets

responded to shocks produced in the United States

after the crisis. We compare spillover effects between

2An anonymous referee has noted that during the financial crisis period, the US market was also subjected to above-averagevolatility. We have tested for price and volatility spillovers from the five emerging markets to the US market in Section V.3Malaysia instituted capital controls in 1998 to restrict outflows of capital in the aftermath of the financial crisis.4 Exchange rates seem to have played some role in the transmission of shocks from the United States, but this issue is not thefocus of this study.

640 J. H. Nam et al.

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the prior- and post-crisis periods using daily datafrom 3, January 1995 to 24, April 2001. To this end,we utilize an EGARCH model which is known to besuitable for modeling the asymmetric transmission ofvolatility.

The evidence offered in this article indicates thatthe 1997 financial crisis has had profound impacts onthe emerging stock markets. First, the Hong Kongand Singapore markets showed strikingly similarpatterns in price and volatility spillovers. The pricespillover effect increased marginally, but the volatilityspillover effect weakened considerably after the crisisin these markets. In view of the fact that these twomarkets are the most liberalized emerging markets inthe Pacific-Basin region, it is not surprising to findstrong evidence for the transmission of prices andvolatility from the US market to these markets inboth the prior- and post-crisis periods. TheMalaysian market deviates slightly from this trend.Price and volatility spillovers from the US market tothe Malaysian market were evident in all samples,but, unlike the Hong Kong and Singapore markets,both price and volatility spillovers slowed down afterthe crisis.

The Taiwanese market showed no shifts in priceand volatility spillovers between the prior- and post-crisis periods. A price spillover effect, thoughmoderate, was observed in the Taiwanese marketboth before and after the crisis, but the volatilityspillover did not occur at all both before and after thecrisis. As far as market volatility is concerned, theTaiwanese market was immune from US shocksthroughout the entire sample period.

The most dramatic shifts between the prior- andpost-crisis periods took place in Korea. The Koreanmarket was completely insulated from US shocksbefore the crisis, but it became most vulnerable to USshocks after the crisis. There was no price spillovereffect before the crisis, but the price spillover effectappeared most strongly in Korea after the crisis.There was no volatility spillover effect before thecrisis, but the volatility spillover effect showed upmost strongly in Korea after the crisis.

These different paths and trends in price andvolatility spillovers among the emerging marketsappear to be due to different institutional andindustrial factors and different phases of marketliberalization. In particular, it is interesting to observethat the Korean market and the Malaysian marketare on the opposite ends of the spectrum in price andvolatility spillovers from the US market. Price andvolatility spillovers from the US market werestrengthened in the Korean market after the crisis,but weakened in the Malaysian market after thecrisis. On the other hand, the differing results between

Korea and Taiwan confirm the Feenstra et al (2003)conjecture that the response of markets to large‘competitive shocks’ such as the 1997 financial crisiscan be different depending on different types ofindustry structure. They suggest that temporaryshocks are not expected to have permanent effectson financial markets in Taiwan where business firmsare weakly integrated, but the effects of such shockson markets will be much more severe in Korea wherebusiness firms are strongly vertically integrated.

The article is organized as follows: In Section II webriefly review the literature on price and volatilityspillovers. Section III discusses the methodologyemployed in this study. Section IV describes thedata used in this study and analyses basic character-istics of the data. Section V presents empirical resultsand their implications. Concluding remarks areprovided in Section VI.

II. A Brief Review of the Literature

Earlier studies focused on the interdependence ofstock prices among advanced markets. Jaffe andWesterfield (1985) studied spillover effects among theUS, U.K, Australian, Canadian and Japanese mar-kets using daily closing prices and found interdepen-dence among these markets. Eun and Shim (1989)investigated price spillovers in nine advanced marketsusing a VAR model and found that innovations in theUS market were rapidly transmitted to other markets,whereas no single foreign market significantlyexplained US market movements. Barclay et al.(1990) found positive correlations between theNew York and Tokyo markets using daily stockprices. King and Wadhwani (1990) provided someevidence in support of the ‘contagion effect’ in theNew York, London and Tokyo markets, showingthat negative shocks in one market were immediatelytransmitted to other markets. Koch and Koch (1991)analysed the lead-lag relationship among eight stockmarkets. Their study revealed that there was atendency for the regional interdependence of stockmarkets to increase and that the spillover effect of theNew York market on the Tokyo market waspronounced.

More recent studies concentrate on the interna-tional interactions of stock returns and volatility interms of the first and second moments of returnsutilizing recent advances in time series analysis suchas GARCH-type models. For example, Hamao et al.(1990), using a single-variable GARCH-M model,have examined price spillovers (interdependences ofthe first moments) and volatility spillovers

Pacific-basin emerging markets after the 1997 financial crisis 641

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Page 5: What happened to pacific-basin emerging markets after the 1997 financial crisis?

(interdependences of the second moments) among theNew York, London and Tokyo markets. Their studyhas confirmed the presence of a spillover effect fromthe New York and London markets to the Tokyomarket, but found no spillover effect from the Tokyomarket to either the New York or London market.Subsequent studies such as Ng et al. (1991),Theodossiou and Lee (1993), Engle and Susmel(1994) have also presented evidence for spillovereffects mainly in advanced markets.

However, most of previous studies failed toincorporate asymmetry in price and volatility spil-lovers. Nelson (1991) has developed an EGARCHmodel to study the asymmetrical effects of shocks onstock return volatility in the US market. He hasdiscovered that in the US market negative shocks hadlarger impacts on volatility than positive shocks.Koutmos and Booth (1995), noticing that a market’svolatility responds asymmetrically to its own pastshocks, have shown that negative shocks originatedin one market exert greater spillover effects on othermarkets than do positive shocks. More specifically,they have used a multivariate EGARCH model toanalyse spillovers of daily stock prices and volatilityamong the New York, Tokyo and London marketsand confirmed an asymmetrical spillover effect fromthe New York market to the Tokyo and Londonmarkets, from the Tokyo market to the London andNew York markets and from the London market tothe New York and Tokyo markets. Thus, they haveconcluded that price and volatility spillovers aregenerally reciprocal in the sense that two marketsinfluence each other.

Several studies have examined price and volatilityspillovers among emerging markets. Ng (2000) hasstudied the magnitude and changing nature ofvolatility spillovers from the United States andJapan to six Pacific-Basin countries (Hong Kong,South Korea, Malaysia, Singapore, Taiwan andThailand) using weekly equity indexes denominatedin US dollars and found evidence for the impact ofthe world factors and significant spillovers from theregion to many of the Pacific-Basin markets. In, Kimet al. (2001) have examined volatility spilloversamong three emerging markets, South Korea, HongKong and Thailand during the 1997 to 1998 periodwhen the Asian financial crisis spread uncontrollably.They have used a multivariable VAR-EGARCHmodel and observed that there were reciprocalspillovers of volatility between the Hong Kong andKorean markets and one-directional spillovers fromthe Korean market to the Thai market.

Edwards and Susmel (2001) have analysed thebehaviour of volatility in Argentina, Brazil, Chile,Hong Kong and Mexico using weekly equity indexes

denominated in US dollars from the last week ofAugust 1989 to the third week of October 1999. Theyhave found strong evidence of volatility co-move-ments across countries, especially among theMercosur countries. Darrat and Zhong (2002) haveinvestigated whether the US or Japanese market (orboth) is the main driving force behind major move-ments in 11 Asia-pacific emerging markets usingweekly data from November 1987 to May 1999. Theyhave confirmed a robust cointegrating relationlinking each of the emerging markets with the twomatured markets of the United States and Japan.Worthington and Higgs (2004) have examined thetransmission of stock returns and volatility amongAsian markets: three developed markets (HongKong, Japan and Singapore) and six emergingmarkets (Indonesia, South Korea, Malaysia, thePhilippines, Taiwan and Thailand) using daily datafrom 15, January 1988 to 6, October 2000 and foundthat all Asian equity markets are highly integrated.They have further discovered that mean spilloversfrom the developed to the emerging markets are nothomogeneous across the emerging markets.

Although, there is a proliferation of the literatureon the transmission of prices (or returns) andvolatility among countries, few studies have investi-gated what happened to Pacific-Basin emergingmarkets during and after the 1997 Asian financialcrisis. This study aims to compare price and volatilityspillovers from the US market to the Pacific-Basinemerging markets between the prior- and post-crisisperiods. This study is also concerned with thecomparison of asymmetric spillovers between goodand bad news from the US market to the Pacific-Basin markets.

III. Methodology

The transmission of information from one market toanother market can be explored in two differentways. One can look at the price spillover effect or thevolatility spillover effect. The fact that informationon US stock prices is transmitted to stock prices inother markets implies that information on US stockprices can be helpful in predicting stock pricemovements in other markets. On the other hand, anincrease in volatility indicates excessive responses ofstock prices to new information. The spillover ofvolatility from the US market to an emerging marketimplies that excessive responses of stock prices in theemerging market are linked to excessive responsesof US stock prices.

642 J. H. Nam et al.

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It is frequently observed in asset markets that

periods of large volatility are followed by periods of

low volatility and vice versa (volatility clustering).

The ARCH-type model recognizes the presence of

successive periods of volatility and stability. Engle

et al. (1990) ascribe volatility clustering to two

factors. The first explanation for volatility clustering

is that information itself comes in a cluster. In such a

case, even if market participants react to market

conditions rationally and stock prices reflect all the

available information, successive inflows of

information result in a volatility clustering. Another

explanation for volatility clustering is provided by

non-synchronous trading among market participants

who possess different volumes of information. Even if

the same information is disseminated in the market,

volatility associated with the information persists

because market participants may have different

perceptions toward the information and behave

differently.Spillover effects can be asymmetrical. Suppose that

the stock prices of country A increase by �% when

the US stock prices increase by �%.When we observe

that the stock prices of country A fall by more than

�% when the US stock prices fall by �%, we have an

asymmetric price spillover effect. Such an asymmetric

spillover effect can also occur in the transmission of

volatility. For example, if negative shocks generated

in the US market have greater impacts on the

volatility of market A than do positive shocks, an

asymmetric volatility spillover exists.This study adopts a two-variable EGARCH model

to investigate asymmetric price and volatility spil-

lovers. We first define Rit as

Rit ¼ 100 lnPt

Pt�1

� �ð1Þ

where Pt� 1 is the closing price on the previous

trading day and Pt is the closing price on the current

trading day. Thus, Rit represents the daily close-to-

close return in market i at time t. Let �it be the

average rate of return in the market and �2it be the

variance of Rit at time t, conditional on market

information available at t�1 (It� 1). Then, "it is a

shock or innovation which is given by the difference

between Rit and �it.

"it ¼ Rit � �it ð2Þ

We can standardize the innovation as

zit ¼"it�it

ð3Þ

A two-variable EGARCH model can be repre-sented by the following set of equations:5

Rit ¼ �i, 0 þ��i, j"j, t�1 þ "i, t for i, j ¼ 1, 2 ð4Þ

�2it ¼ exp �i, 0 þ��i, j fjðzj, t�1Þ þ �i ln �2

i, t�1

� �h ifor i, j ¼ 1, 2 ð5Þ

fjðzj, t�1Þ ¼ ðjzj, t�1j � Eðjzj, t�1jÞ þ �jzj, t�1Þ

for j ¼ 1, 2 ð6Þ

Equation 4 expresses the daily return in market i as avector moving average. That is, the conditional meanof the rate of return in market i is expressed as afunction of its past innovations as well as the pastinnovations of other markets. Thus, coefficients �i, jfor i 6¼ j measure the magnitude of a price (or return)spillover across markets.

Equation 5 represents the conditional-varianceequation. The effect of a shock in market j (say, theUS market) on the volatility of market i (say, anemerging market) is determined by the coefficient offj, �ij. Note that fj consists of jzj,tj �E(jzj,tj) and �j zj,t.The term jzj,tj �E(jzj,tj) which is given by thedeviation of standardized errors in market j(in absolute value) from their mean measures thesize effect of a volatility spillover and the term �j zj,tmeasures the sign effect of a volatility spillover.Asymmetry in the spillover effect is present if �j isnegative and statistically significant (assuming thata negative shock exerts a larger impact on volatilitythan a positive shock). If �j is negative and zj isnegative, then the positive value of �j zj reinforces thesize effect. However, if �j is negative and zj is positive,then the negative value of �j zj offsets partially thesize effect.

The asymmetric spillover effect of a shock inmarket j on the volatility of market i (expressedin logs) is measured by

@ ln �2i, t

@zj, t

!¼ �ij

@fj@zj, t

� �ð7Þ

It follows from Equation 6 that

@fj@zj, t

¼ 1þ �j for zj40 ð8aÞ

and

@fj;t@zj, t

¼ �1þ �j for zj50 ð8bÞ

Thus, the asymmetric effect of a positive shock onmarket i ’s volatility (ln�2it) is given by �ij (j1+ �jj)

5 The EGARCH specification employed in this study follows that of Koutmos and Booth (1995).

Pacific-basin emerging markets after the 1997 financial crisis 643

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Page 7: What happened to pacific-basin emerging markets after the 1997 financial crisis?

and the asymmetric effect of a negative shock is given

by �ij (j�1+ �jj). The importance of the asymmetric

effect of a negative shock relative to a positive shock

or leverage effect is then given by

j�1þ �jj

j1þ �jj

We can consider three possibilities:

(1) If �j¼ 0, a negative shock has the same effect

on volatility as a positive shock.(2) If �j50, a negative shock has a larger effect on

volatility than a positive shock.(3) If �j>0, a positive shock has a greater effect

on volatility than a negative shock.

Finally, � in Equation 5 measures the persistence of

volatility. If the conditional variance depends on the

previous conditional variance, then a GARCH effect

is confirmed. If � is less than one, the unconditional

variance is finite; if � is equal to one, the uncondi-

tional variance does not exist and the conditional

variance follows I(1).Researchers are typically concerned with a

situation in which price and volatility spillovers

occur from the US market to emerging markets,

ruling out the possibility of the reverse direction.

Although such an assumption may be plausible in

light of the fact that the size of individual emerging

markets is small relative to that of the US market,

that assumption is not necessarily warranted.

However, since the spillovers that run from the

US market to emerging markets is a predominant

pattern, the main focus of this study is on the

following form of spillovers. (We report the results

on price and volatility spillovers from the five

emerging markets to the US market, particularly

during the crisis period in Section V.)

R1t ¼ �1, 0 þ �1, 1"1, t�1 þ "1, t ð9Þ

R2t ¼ �2, 0 þ �2, 1"1, t�1 þ �2, 2"2, t�1 þ "2, t ð10Þ

where 1 represents the US market and 2 an emerging

market. By the same token, Equation 5 can be

rewritten as

�21t ¼ exp �1, 0 þ �1, 1f1ðz1, t�1Þ þ �i ln �2

1, t�1

� �h ið11Þ

�22t¼ exp

h�2,0þ�2,1f1ðz1,t�1Þþ�2,2f2ðz2,t�1Þ

þ�i ln �22,t�1

� �i ð12Þ

Equation 6 is also reformulated as

f1ðz1, t�1Þ ¼ ðjz1, t�1j � Eðjz1, t�1jÞ þ �1z1, t�1Þ ð13Þ

f2ðz2, t�1Þ ¼ ðjz2, t�1j � Eðjz2, t�1jÞ þ �2z2, t�1Þ ð14Þ

We estimate the EGARCH model in two steps. In

the first stage, we estimate Equation 9 (US return

equation) and obtain OLS residuals for the US

market. We then estimate Equation 11 (US condi-

tional-variance equation) after we calculate standar-

dized errors and substitute Equation 13 into

Equation 11. In the second stage, we estimate

Equation 10 (return equation for emerging

market i) and calculate standardized errors for

emerging market i. We then estimate Equation 12

(conditional-variance equation for emerging market

i) using standardized errors of the US and emerging

market i after we substitute Equation 13 and 14 into

Equation 12. We have computed the values of

�j (j¼ 1, 2) using GAUSS and standardized errors

using the Delta Method.6

6We have used E-VIEWS and GAUSS to estimate the set of equations and the Delta Method to estimate the SEs of �j(j¼ 1, 2). Letting �¼ f(�) and �^¼ f(�^), then cov(�^)¼F(�^)cov(�^)F(�^)’ where

�^ ¼a1, 1a1, 1�1

� �

and

covð�̂Þ ¼var a1, 1

� �cov a1, 1, a1, 1�1

� �cov a1, 1, a1, 1�1

� �var a1, 1�1

� �� �

since

f �^ð Þ ¼a1, 1

a1, 1�1� �

=a1, 1

� �

and

F �^ð Þ ¼1 0

� a1, 1�1� �

=a21, 1 1=a1, 1

� �

Similarly, we have calculated the SEs of a2,1 using the var-cov matrix of F(�^).

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IV. Analysis of Data

The trading time in the United States is from9:30 a.m. to 4:00 p.m. This trading time correspondsto the time from 11:30 p.m. to 6:00 a.m. (during theday light saving time period) in Korea and Taiwan.Thus, at the time when the Korean and Taiwanesestock markets open at 9:00 a.m., information onchanges in the prices of stocks traded in the USmarket on the previous trading day is available totraders in the Korean and Taiwanese markets and thearrival of new information can exert some effects onthe behaviour of traders in the Korean andTaiwanese stock markets. The Hong Kong marketopens one hour later than the Korean and Taiwanesemarkets. The Malaysian market opens half an hourbefore the Hong Kong market and the Singaporemarket opens one hour later than the Hong Kongmarket. Thus, information produced in theNew York market becomes a part of the informationset of traders in the emerging markets.

This study uses daily closing prices to calculate thedaily returns of the US market and the five emergingmarkets. The data we have used include the DowJones Industrial Average Index (US.), the Hang SengIndex (Hong Kong), the Stock Exchange ofSingapore All Share Index (SESASI, Singapore), theKorea Composite Stock Price Index (KOSPI, Korea),the Kuala Lumpur Stock Exchange Composite Index(KLSECI, Malaysia) and the Taiwan Stock ExchangeWeighted Price Index (TSEWPI, Taiwan). Theseprice indexes have been collected from http://www.datastream.com.

We have broken down the sample into three sub-samples: (1) sample 1: 3, January 1995–30,September 1997; (2) sample 2: 1, October 1997–30, September 1998 and (3) sample 3: 1, October1998–24, April 2001. The first sample periodroughly corresponds to the period prior to theAsian financial crisis. The second sample periodfalls under the height of the financial crisis. Thethird sample period deals with a period duringwhich some financial reforms were under way afterthe financial turmoil.

Table 1 presents some basic statistics for thevariables used in this study. In order to testwhether the returns in each market are normallydistributed, we have conducted the Jarque–Beratest.7 The null hypothesis that the returns arenormally distributed is rejected for all markets. Thisfinding is broadly consistent with most of previousstudies that have tested for the distribution of stockreturns.

We have also tested whether the return series ineach market are white noise using the Ljung–Box test. To this end, we have investigated theautocorrelation of Rit and the square of Rit for 8,16 and 24 lags, respectively. The Ljung–Box Q-statistic follows a chi-square distributionunder the null hypothesis that the series exhibitwhite noise processes.8 Tables 2 and 3 report theLjung–Box Q statistics for each market. Noautocorrelation is present in the Dow Jones returns,but the return series in all the emerging markets areserially correlated regardless of the length of lags.We also reject the null hypothesis for the squaredreturns in all markets including the US market.9

These test results suggest that once volatility getslarger, such large volatility persists for a certainperiod of time.

Figure 1 shows dynamic movements in daily stockreturns in each market.10 As evidenced by thediagram, the daily stock returns of the East Asianmarkets showed wide fluctuations during and afterthe financial crisis. The pattern of successive periodsof large volatility followed by successive periods oflow volatility is pronounced. Thus, the GARCHappears to be suitable for modelling such volatilityclustering.

V. Empirical Results

In order to examine price and volatility spilloversfrom the US market to the five emerging markets,we have estimated five sets of the EGARCH model:(1) Dow Jones–Hang Seng Index (Hong Kong),

7 The Jarque–Bera statistic is given by JB¼ (n� k) [S2/6+ (K� 3)2/24], where n indicates the number of observations, k thenumber of parameters estimated, K the kurtosis of the distribution, and S the skewness of the distribution. The Jarque–Berastatistic follows a chi-square distribution with 2 degrees of freedom under the null hypothesis that the variable is normallydistributed.8 The Ljung–Box Q-statistic is given by QLB¼ n (n+2)��2k/(n� j), where �k is autocorrelation between �t and �t� k.The Ljung–Box Q-statistic is distributed as 2 with n degrees of freedom under the null hypothesis that �1¼ �2¼ � � � ¼ �k¼ 0.9 The squared return series can be viewed as a proxy for the variance of the series.10 The origin on the X-axis starts with 3, January 1995. The 500th observation corresponds to 3, December 1996, the 1000thobservation to 3, November 1998, and the 1500th observation to 3, October 2000.

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Table 1. Basic statistics for stock returns

Dow Jones Hong Kong KOSPI Malaysia Singapore Taiwan

Mean 0.060935 0.029332 �0.037341 �0.032196 �0.005765 �0.014629Median 0.047363 0.000000 0.000000 �0.006025 0.000000 0.000000SD 1.054781 1.919333 2.318577 2.042872 1.545474 1.660211Skewness �0.476997 0.186225 0.063259 0.582646 0.484729 �0.099989Kurtosis 7.473534 12.76423 5.784139 33.20498 13.42410 5.769531Jarque–Bera

(Probability)1434.943(0.000000)

6548.261(0.000000)

532.7166(0.000000)

62664.48(0.000000)

7516.849(0.000000)

528.79178(0.000000)

Sample size 1646 1646 1646 1646 1646 1646

Fig. 1. Movements in daily returns

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(2) DOW Jones–SESASI (Singapore), (3) DowJones–KOSPI (Korea), (4) Dow Jones–KLSECI(Malaysia) and (5) Dow Jones–TSEWPI (Taiwan).For each set of the EGARCH model, we haveestimated 14 coefficients.

GARCH Effects

The persistence of volatility (GARCH effect) in eachmarket is measured by � i, and the estimated values of� i for each market are as follows: (The valuesof �i can be found in Tables 4–8).

The GARCH effect is confirmed in most markets,only except for the Korean and Singapore markets

during the sample period 2 (period of the financialcrisis). Thus, there was a tendency for volatility to

persist in the emerging markets as well as in the USmarket, which renders support to the GARCHspecification. Second, the magnitude of the GARCH

effect is more or less of the same order in mostmarkets, ranging from 0.738 (Taiwan in sample 1) to

1.004 (Taiwan in sample 2), ignoring 0.170 inMalaysiain sample 2 which is not significant at the conventional

level of significance.

Asymmetric price and volatility spillovers

The coefficients which pertain to price and volatility

spillovers are as follows:

(1) �2,1 measures the effect of past innovations in

the US market on the price of an emerging

market at t.(2) �2,1 determines the overall volatility effect

from the US market to an emerging market

at t. It includes the size effect and the

asymmetric effect of US innovations on

the volatility of an emerging market. In this

section, we evaluate the overall volatility

spillover effect (�ij) without separating

the asymmetric effect of a positive

(�ij j1+ �jj) or negative shock (�ijj�1+ �jj).(We will address the leverage effect in Sections

III and IV.)

As a whole, the Hong Kong and Singapore

markets exhibited similar patterns in price and

volatility spillovers; the Korean and Malaysian

markets moved in the opposite direction in both

Table 2. Ljung–Box statistics for returns

Dow Jones Hong Kong KOSPI Malaysia Singapore Taiwan

Q(8) 12.799 (0.119) 28.067 (0.000) 28.071 (0.000) 46.585 (0.000) 47.134 (0.000) 29.019 (0.000)Q(16) 32.044 (0.010) 36.283 (0.003) 37.203 (0.002) 64.995 (0.000) 76.744 (0.000) 46.152 (0.000)Q(24) 38.516 (0.031) 42.297 (0.012) 45.823 (0.005) 75.885 (0.000) 85.109 (0.000) 67.707 (0.000)

Table 3. Ljung–Box statistics for the squares of returns

Dow Jones Hong Kong KOSPI Malaysia Singapore Taiwan

Q(8) 184.07 (0.000) 546.96 (0.000) 321.27 (0.000) 720.17 (0.000) 196.61 (0.000) 175.49 (0.000)Q(16) 247.03 (0.000) 590.38 (0.000) 518.37 (0.000) 775.29 (0.000) 347.30 (0.000) 266.65 (0.000)Q(24) 303.03 (0.000) 607.22 (0.000) 689.68 (0.000) 785.98 (0.000) 412.01 (0.000) 378.25 (0.000)

US: 0.974* (sample 1); 0.906* (sample 2); 0.965* (sample 3)Hong Kong: 0.960* (sample 1); 0.979* (sample 2); 0.956* (sample 3)Korea: 0.929* (sample 1); �0.440 (sample 2); 0.840* (sample 3)Singapore: 0.959* (sample 1); 0.489 (sample 2); 0.932* (sample 3)Taiwn: 0.738* (sample 1); 1.004* (sample 2); 0.941 (sample 3)Malaysia: 0.986* (sample 1); 0.170*** (sample 2); 0.932* (sample 3)

* indicates significance at the 1% level, ** significance at the 5% level and *** significance atthe 10% level.

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Table

4.Spillovers

form

theDowJones

totheHongKongmarket

Period1

Period2

Period3

Coefficients

z-statistics

Coefficients

z-statistics

Coefficients

z-statistics

�1,0

0.086548

3.073868

(0.0021)*

�0.035206

�0.588266

(0.5564)

0.011319

0.243679

(0.8075)

�1,1

0.067315

1.628687

(0.1034)

0.046409

6.59235

(0.5097)

0.050082

1.268649

(0.2046)

�1,0

�0.096103

�3.621758

(0.0003)*

�0.019836

�0.285474

(0.7753)

�0.047912

�1.990913

(0.0465)**

�1,1

0.108922

3.459312

(0.0005)*

0.062060

0.787044

(0.4313)

0.074115

2.360998

(0.0182)**

�1

0.974104

137.1002

(0.0000)*

0.905550

39.76530

(0.0000)*

0.965123

80.71877

(0.0000)*

� 1�0.595949

�2.49519**

�5.303223

�0.74557

�1.433087

�2.11813**

�2,0

0.086076

2.337290

(0.0194)**

�0.524232

�3.653122

(0.0003)*

0.046111

0.705549

(0.4805)

�2,1

0.611408

12.95063

(0.0000)*

0.549650

4.561296

(0.0000)*

0.666019

14.68543

(0.0000)*

�2,2

0.030585

0.750631

(0.4529)

0.046136

0.703314

(0.4819)

0.026032

0.679069

(0.4971)

�2,0

�0.116213

�4.648566

(0.000)*

0.000839

0.016091

(0.9872)

0.049443

1.970530

(0.0488)**

�2,1

0.097696

5.122591

(0.0000)*

0.016889

3.459464

(0.0005)*

0.023761

2.481240

(0.0131)**

�2,2

�0.158999

4.907841

(0.0000)*

0.068960

1.453022

(0.1462)

�0.011037

�0.423956

(0.6716)

�2

0.960284

87.50616

(0.0000)*

0.979178

100.7546

(0.0000)*

0.956218

63.69098

(0.0000)*

� 2�0.349782

�1.62191

�2.456424

�1.46612

�0.217903

�0.16117

Diagnosticsonstandardized

residuals

Dow

Jones

HongKong

Dow

Jones

HongKong

Dow

Jones

HongKong

Mean

0.011041

1.87001

0.017055

0.048679

0.010567

0.001222

SD

1.004009

1.305720

1.009316

0.989933

1.000430

0.994389

Skew

ness

�0.415682

8.982218

�0.477560

0.069968

�0.190155

�0.055787

Kurtosis

4.288916

139.9960

3.718919

4.649182

3.805566

3.526148

LB(12)

12.173

(0.351)

13.630

(0.254)

8.8027

(0.640)

16.041

(0.141)

9.8372

(0.545)

6.1863

(0.861)

LB2(12)

11.848

(0.375)

0.1497

(1.000)

7.9668

(0.716)

12.071

(0.358)

12.168

(0.351)

5.7559

(0.818)

Notes:Thenumbersin

parentheses

representp-values.

Thesignificance

of� j:*indicatessignificance

atthe1%

level,**significance

atthe5%

level.

***significance

atthe10%

level.

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Table

5.Spillovers

from

theDowJones

totheSingapore

market

Period1

Period2

Period3

Coefficients

z-statistics

Coefficients

z-statistics

Coefficients

z-statistics

�1,0

0.086548

3.073868

(0.0021)*

�0.035206

�0.588266

(0.5564)

0.011319

0.243679

(0.8075)

�1,1

0.067315

1.628687

(0.1034)

0.046409

6.59235

(0.5097)

0.050082

1.268649

(0.2046)

�1,0

�0.096103

�3.621758

(0.0003)*

�0.019836

�0.285474

(0.7753)

�0.047912

�1.990913

(0.0465)**

�1,1

0.108922

3.459312

(0.0005)*

0.062060

0.787044

(0.4313)

0.074115

2.360998

(0.0182)**

�1

0.974104

137.1002

(0.0000)*

0.905550

39.76530

(0.0000)*

0.965123

80.71877

(0.0000)*

� 1�0.595949

�2.49519**

�5.303223

�0.74557

�1.433087

�2.11813**

�2,0

0.001058

0.029632

(0.9764)

�0.062176

�0.325826

(0.7446)

0.082370

1.284502

(0.1990)

�2,1

0.330497

8.109450

(0.0000)*

0.558797

4.054434

(0.0001)*

0.450713

9.833429

(0.0000)*

�2,2

0.190875

5.095551

(0.0000)*

0.153266

2.362272

(0.0182)**

0.083161

2.045372

(0.0408)**

�2,0

�0.087862

�3.488786

(0.0005)*

0.544083

4.889840

(0.0000)*

�0.085266

�3.576595

(0.0003)*

�2,1

0.079740

3.774823

(0.0002)*

0.040016

1.791347

(0.0732)***

0.020798

1.956535

(0.0504)***

�2,2

0.98892

3.311910

(0.0009)*

0.319347

2.501030

(0.0124)**

0.173936

4.061359

(0.0000)*

�2

0.959149

63.99523

(0.0000)*

0.489378

4.795264

(0.1505)

0.932646

33.04485

(0.0000)*

� 2�0.135613

�0.30256

�0.394540

�1.91764***

�0.050628

�0.39752

Diagnosticsonstandardized

residuals

Dow

Jones

Singapore

Dow

Jones

Singapore

Dow

Jones

Singapore

Mean

0.011041

0.177160

0.017055

�0.080496

0.010567

�0.012764

SD

1.004009

1.121634

1.009316

1.004549

1.000430

1.000454

Skew

ness

�0.415682

5.386172

�0.477560

2.500997

�0.190155

0.099160

Kurtosis

4.288916

44.78669

3.718919

21.62859

3.805566

4.082188

LB(12)

12.173

(0.351)

22.367

(0.022)

8.8027

(0.640)

8.1568

(0.699)

9.8372

(0.545)

10.132

(0.519)

LB2(12)

11.848

(0.375)

1.5167

(1.000)

7.9668

(0.716)

4.7711

(0.942)

12.168

(0.351)

15.186

(0.174)

Notes:Thenumbersin

parentheses

representp-values.

Thesignificance

of� j:*indicatessignificance

atthe1%

level,**significance

atthe5%

level,

***significance

atthe10%

level.

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Table

6.Spillovers

from

theDowJones

totheKoreanmarket

Period1

Period2

Period3

Coefficients

z-statistics

Coefficients

z-statistics

Coefficients

z-statistics

�1,0

0.086548

3.073868

(0.0021)*

�0.035206

�0.588266

(0.5564)

0.011319

0.243679

(0.8075)

�1,1

0.067315

1.628687

(0.1034)

0.046409

0.659235

(0.5097)

0.050082

1.268649

(0.2046)

�1,0

�0.096103

�3.621758

(0.0003)*

�0.019836

�0.285474

(0.7753)

�0.047912

�1.990913

(0.0465)**

�1,1

0.108922

3.459312

(0.0005)*

0.062060

0.787044

(0.4313)

0.074115

2.360998

(0.0182)**

�1

0.974104

137.1002

(0.0000)*

0.905550

39.76530

(0.0000)*

0.965123

80.71877

(0.0000)*

� 1�0.595949

�2.49519**

�5.303223

�0.74557

�1.433087

�2.11813**

�2,0

�0.068254

�1.325546

(0.1580)

�3.03386

�1.260269

(0.2075)

0.072768

0.687059

(0.4920)

�2,1

0.070259

1.177434

(0.2390)

0.573186

3.185783

(0.0014)*

0.671079

7.820526

(0.0000)*

�2,2

0.141084

3.511372

(0.0004)*

0.096152

1.558422

(0.1191)

0.061035

1.437068

(0.1507)

�2,0

�0.074458

�3.038440

(0.0024)*

3.607515

2.695470

(0.0070)*

0.216709

1.762406

(0.0780)***

�2,1

0.013907

0.549990

(0.5822)

�0.005413

�0.356681

(0.7213)

0.054422

2.913042

(0.0036)*

�2,2

0.126615

3.506077

(0.0005)*

�0.148491

�1.215266

(0.2243)

0.097348

1.900301

(0.0574)***

�2

0.928756

37.21082

(0.0000)*

�0.439620

�0.785542

(0.4321)

0.839597

11.09373

(0.0000)*

� 2�0.660325

�3.17215**

�0.055411

�0.11408

�0.081943

�0.29491

Diagnosticsonstandardized

residuals

Dow

Jones

Korea

Dow

Jones

Korea

Dow

Jones

Korea

Mean

0.011041

�0.001977

0.017055

0.000312

0.010567

�0.004727

SD

1.004009

0.998900

1.009316

1.002896

1.000430

1.001577

Skew

ness

�0.415682

-0.034441

�0.477560

0.231580

�0.190155

�0.040344

Kurtosis

4.288916

3.670566

3.718919

3.946973

3.805566

3.560481

LB(12)

12.173

(0.351)

12.199

(0.349)

8.8027

(0.640)

12.777

(0.308)

9.8372

(0.545)

5.8857

(0.881)

LB2(12)

11.848

(0.375)

10.555

(0.481)

7.96658

(0.716)

21.103

(0.032)

12.168

(0.351)

17.816

(0.105)

Notes:Thenumbersin

parentheses

representp-values.

Thesignificance

of� j:*indicatessignificance

atthe1%

level,**significance

atthe5%

level,

***significance

atthe10%

level.

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Table

7.Spillovers

from

theDowJones

totheMalaysianmarket

Period1

Period2

Period3

Coefficients

z-statistics

Coefficients

z-statistics

Coefficients

z-statistics

�1,0

0.086548

3.073868

(0.0021)*

�0.035206

�0.588266

(0.5564)

0.011319

0.243679

(0.8075)

�1,1

0.067315

1.628687

(0.1034)

0.046409

0.659235

(0.5097)

0.050082

1.268649

(0.2046)

�1,0

�0.096103

�3.621758

(0.0003)*

�0.019836

�0.285474

(0.7753)

�0.047912

�1.990913

(0.0465)**

�1,1

0.108922

3.459312

(0.0005)*

0.062060

0.787044

(0.4313)

0.074115

2.360998

(0.0182)**

�1

0.974104

137.1002

(0.0000)*

0.905550

39.76530

(0.0000)*

0.965123

80.71877

(0.0000)*

� 1�0.595949

�2.49519**

�5.303223

�0.74557

�1.433087

�2.11813**

�2,0

�0.015959

�0.409032

(0.6825)

�0.497510

�3.010674

(0.0026)*

0.014014

0.231189

(0.8172)

�2,1

0.257677

5.272365

(0.0000)*

0.969906

10.15068

(0.0000)*

0.233182

5.792718

(0.0000)*

�2,2

0.119809

3.221963

(0.0013)*

�0.117616

�1.975544

(0.0482)**

0.159681

4.099448

(0.0000)*

�2,0

�0.064610

�3.811172

(0.0001)*

1.209674

�8.521047

(0.0000)*

�0.103778

�4.893542

(0.0000)*

�2,1

0.037722

2.606012

(0.0092)*

�0.063532

�8.521047

(0.0000)*

0.021793

2.099472

(0.0358)**

�2,2

0.090422

4.157613

(0.0000)*

0.822283

6.284086

(0.0000)*

0.205459

5.529926

(0.0000)*

�2

0.986084

295.4194

(0.0000)*

0.170452

1.744003

(0.0812)***

0.931639

45.06447

(0.0000)*

� 2�0.737243

�3.81954**

0.3241305

2.229161**

�0.233463

�2.24858**

Diagnosticsonstandardized

residuals

Dow

Jones

Malaysia

Dow

Jones

Malaysia

Dow

Jones

Malaysia

Mean

0.011041

�0.006189

0.017055

0.364385

0.010567

0.013073

SD

1.004009

0.983236

1.009316

1.224138

1.000430

1.001164

Skew

ness

�0.415682

0.024901

�0.477560

2.674199

�0.190155

0.127152

Kurtosis

4.288916

5.891242

3.718919

13.13744

3.805566

4.777941

LB(12)

12.173

(0.351)

14.323

(0.215)

8.8027

(0.640)

6.1594

(0.860)

9.8372

(0.545)

16.804

(0.114)

LB2(12)

11.848

(0.375)

11.709

(0.386)

7.9668

(0.716)

15.187

(0.174)

12.168

(0.351)

8.1842

(0.697)

Notes:Thenumbersin

parentheses

representp-values.

Thesignificance

of� j:*indicatessignificance

atthe1%

level,**significance

atthe5%

level,

***significance

atthe10%

level.

Pacific-basin emerging markets after the 1997 financial crisis 651

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Table

8.Spillovers

from

theDowJones

totheTaiwanesemarket

Period1

Period2

Period3

Coefficients

z-statistics

Coefficients

z-statistics

Coefficients

statistics

�1,0

0.086548

3.073868

(0.0021)*

�0.035206

�0.588266

(0.5564)

0.011319

0.243679

(0.8075)

�1,1

0.067315

1.628687

(0.1034)

0.046409

6.59235

(0.5097)

0.050082

1.268649

(0.2046)

�1,0

�0.096103

�3.621758

(0.0003)*

�0.019836

�0.285474

(0.7753)

�0.047912

�1.990913

(0.0465)**

�1,1

0.108922

3.459312

(0.0005)*

0.062060

0.787044

(0.4313)

0.074115

2.360998

(0.0182)**

�1

0.974104

137.1002

(0.0000)*

0.905550

39.76530

(0.0000)*

0.965123

80.71877

(0.0000)*

� 1�0.595949

�2.49519**

�5.303223

�0.74557

�1.433087

�2.11813**

�2,0

�0.061589

1.185424

(0.2358)

�0.111064

-�1.128546

(0.2591)

�0.001596

�0.022566

(0.9820)

�2,1

0.128030

1.996125

(0.0459)**

0.321291

4.751784

(0.0000)*

0.282520

5.088653

(0.0000)*

�2,2

�0.004090

�0.089495

(0.9278)

0.082955

1.405370

(0.1599)

0.034677

0.893184

(0.3718)

�2,0

�0.027327

�0.938312

(0.3481)

0.023578

1.874197

(0.0609)***

�0.004502

�0.178930

(0.8580)

�2,1

�0.002631

�0.068630

(0.9453)

�0.003285

�0.572868

(0.5667)

�0.003030

�0.189059

(0.8500)

�2,2

0.232033

4.221268

(0.0000)*

�0.047987

�7.875853

(0.0000)*

0.091149

3.790502

(0.0002)*

�2

0.738363

10.59213

(0.0000)*

1.004466

189.8882

(0.0000)*

0.940939

11.09373

(0.0000)*

� 2�0.607031

�4.6677**

3.040844

0.233271

�1.40959

�2.05275**

Diagnosticsonstandardized

residuals

Dow

Jones

Taiwan

Dow

Jones

Taiwan

Dow

Jones

Taiwan

Mean

0.011041

�0.028852

0.017055

0.170979

0.010567

�0.196742

SD

1.004009

1.000115

1.009316

1.166011

1.000430

0.990034

Skew

ness

�0.415682

�0.354908

�0.477560

4.076019

�0.190155

3.358597

Kurtosis

4.288916

6.129250

3.718919

24.68851

3.805566

21.01841

LB(12)

12.173

(0.351)

13.752

(0.247)

8.8027

(0.640)

25.196

(0.009)

9.8372

(0.545)

37.916

(0.000)

LB2(12)

11.848

(0.375)

5.8893

(0.881)

7.9668

(0.716)

10.983

(0.0530)

12.168

(0.351)

4.8492

(0.938)

Notes:Thenumbersin

parentheses

representp-values.

Thesignificance

of� j:*indicatessignificance

atthe1%

level,**significance

atthe5%

level,

***significance

atthe10%

level.

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price and volatility spillovers; and the Taiwanesemarkets revealed several distinctive features. First, inthe Hong Kong and Singapore markets, the pricespillover effect gained strength after the crisis, but thevolatility spillover effect became much weaker afterthe crisis. Second, in the Malaysian market, bothprice and volatility spillovers dwindled after the crisis.In contrast, the Korean market is the only marketwhere both price and volatility spillovers increaseddramatically after the crisis. Third, the Taiwanesemarket had a strong price spillover effect for allsample periods, but no volatility spillover effect inall samples.

These differing features seem to be attributed to thedistinctive industry structures. Before the 1997financial crisis, Korea had stringent capital controlsin place, designed to discourage the acquisition ofKorean assets by foreign residents. Most of theserestrictions were removed after the crisis. In theTaiwanese economy, small- and medium-sized firmshave been a driving force behind the rapid growth ofthe economy and the market volatility of these firmsare believed to be relatively less affected by externalshocks.

In this regard, Feenstra et al. (2003) havepresented an interesting proposition. They havenoted that both Korea and Taiwan have verticallyand horizontally-integrated industry groups, but theform of industry structure in the two countries isquite different, which could lead to differentresponses to financial shocks. According toFeenstra et.al., Korea has some of the largest andmost vertically-integrated industry groups (V-groups), whereas industry groups in Taiwan aremuch smaller and concentrated in upstream sectors(U-groups). The responses of industry groups tolarge external shocks such as the 1997 financialcrisis can be different between V-groups and U-groups. They suggest that the equilibria of U-groups (Taiwan) are stable, so that a temporaryshock will not have permanent effects on markets.However, the equilibria of V-groups (Korea) areunstable, so that the effects of a competitive shockwill be much more severe. Our test results givesome empirical content to their proposition con-cerning which business structure will experiencefinancial difficulty in the presence of large shocks.

Price and volatility spillovers from the US market to

the Hong Kong market. The null hypotheses that�2,1¼ 0 and �2,1¼ 0 are both rejected at the 1% levelof significance for all samples, indicating that strongprice and volatility spillovers from the US market arepresent in the Hong Kong market. The magnitudeof the price spillover effect remained quite stable

between the prior- and post-crisis periods (�2,1: 0.611before crisis! 0.667 after crisis). However, themagnitude of the volatility spillover effect (�2,1)declined significantly from 0.098 (before crisis)to 0.024 (after crisis).

Price and volatility spillovers from the US market to

the Singapore market. The effects of US shocks onthe prices and volatility of the Singapore market werehighly significant in all samples. Price and volatilityspillovers in the Singapore market parallel those ofthe Hong Kong market: The price spillover effectbecame marginally stronger after the crisis (�2,1: 0.330before crisis! 0.450 after crisis), but the volatilityspillover effect was substantially reduced after thecrisis (�2,1: 0.080 before crisis! 0.021 after crisis).

Price and volatility spillovers from the US market to

the Korean market. We have obtained somewhatdifferent results for the Korean market from those ofthe Hong Kong and Singapore markets. For sample 1(period prior to the crisis), we are not able to rejectthe null hypotheses that �2,1¼ 0 and �2,1¼ 0. Thus,no evidence of price and volatility spillovers from theDow Jones to the KOSPI is found. In fact, theKorean market is the only market among the fiveemerging markets where no price spillover effect fromthe US market was present before the financial crisis.During the financial crisis (sample 2), the spillovereffects from the US market to the Korean market aremixed: We have found a positive spillover effect forstock prices, but no evidence is found for volatilityspillovers. After the financial crisis (sample 3), thetransmission of US shocks to the prices and volatilityof the Korean market picked up most strongly in theregion (�2,1: 0.070 before crisis! 0.671 after crisis;�2,1: 0.014 before crisis! 0.054 after crisis).

Price and volatility spillovers from the US market to

the Malaysian market. The pattern of price andvolatility spillovers from the US market to theMalaysian market is in sharp contrast with that ofthe Korean market. There were strong spillovereffects of US shocks on both prices and volatilityfor all sample periods. The price spillover effectdecreased after the financial crisis (�2,1: 0.258 beforecrisis! 0.233 after crisis) and the volatility spillovereffect also declined significantly after the financialcrisis (�2,1: 0.038 before crisis! 0.022 after crisis).

Price and volatility spillovers from the US market to

the Taiwanese market. The Taiwanese market dis-tinguishes itself from the other markets in the patternof price and volatility spillovers. First, there wasa strong positive price spillover effect for all sample

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periods at the conventional level of significance. Thep-values of the coefficients are less than 0.05 in allsamples. However, as far as the volatility spillover isconcerned, we have a different picture. None of thecoefficients is significant in all samples at anyreasonable level of significance, indicating that theTaiwanese market was insulated from the volatility ofthe US market throughout the entire sample period.

Price and volatility spillovers from the emerging

markets to the US market. Figure 1 reveals thatthe US market also seemed to be subjected to above-average volatility during the 1997 Asian financialcrisis. It is possible to conjecture that the transmissionof prices and volatility from the emerging markets tothe US market occurred during the crisis period.Interestingly enough, we have found that there wasonly a significant price spillover from Hong Kongand Singapore to the United States during the crisisperiod. There was no price spillover from the rest ofthe emerging markets to the US market and there wasno volatility spillover from any of the emergingmarkets to the US market. (The numbers inparentheses are p-values.)

Thus, we can argue that the US market was

affected to some extent by the Asian financial shockvia the Hong Kong and Singapore markets during thecrisis period.

Own asymmetric volatility and the leverage effect

In this section we discuss how shocks occurredin an emerging market affect its own volatility.An own asymmetric volatility effect is measured by�i,i and �i

@ ln �2it

@zit¼ �it

@fi@zit

� �¼ �iiðj1þ �ijÞ for a positive shock

¼ �iiðj � 1þ �ijÞ for a negative shock

(1) �1,1 measures the effect of past innovationsoriginated in the US market on the volatility ofthe US market at t and �2,2 measures the effect

of past innovations originated in an emergingmarket on its own volatility at t.

(2) �1 determines the own asymmetric effect of ashock on market volatility in the US marketand �2 determines the own asymmetric effect ofa shock on market volatility in an emergingmarket.

As we discussed, if �i50, a negative shock has agreater effect on market volatility than a positive

shock. If �i>0, a positive shock has a larger effect

on market volatility than a negative shock. Thus, if

�1,1 is significant and �1 is significant and negative,

asymmetry in volatility exists in the US market.

Similarly, if �2,2 is significant and �2 is significant

and negative, asymmetry in volatility is present in

an emerging market under consideration. The �icoefficient is negative in all markets with only two

exceptions that occurred during the period of the

financial crisis: Malaysia’s �i is 0.3241 which is

significant at the 5% level and Taiwan’s �i is 3.0408

which is insignificant at any reasonable level of

significance. Thus, the dominant evidence shows

that bad news in each market could have a greater

impact on its own market volatility than good

news. Also, there is one episode in which �i is

significant and smaller than �1 (�i5�1): The

estimate of �i in the Taiwanese market was

�1.410 during the sample period 3. In all other

markets and samples, �i lies between �1 and zero

(�15�i50).We are particularly concerned with the leverage

effect which is given by |�1+ �i|/j1+ �ij. It

measures how large the effect of a negative shock

on volatility is relative to the effect of a positive

shock. For example, if the size of the leverage effect

is 2, then the effect of a negative shock on

volatility is twice as large as the effect of a positive

shock on volatility. The estimated leverage effect

for each market is presented in Table 9.The own leverage effect in each market tends to

have tapered off substantially after the Asian

financial crisis only except for the Taiwanese

market: Hong Kong: 2.076 (before the

crisis)! 1.557 (after the crisis); Korea:

4.888! 1.178; Malaysia: 6.612! 1.609; Singapore:

1.314! 1.107; Taiwan: 4.090! 5.882. However, a

significant own leverage effect where both �2,2 and

�2 are significant at the 5% level was present in the

Korean, Malaysian and Taiwanese markets before

the crisis, but after the crisis, a significant leverage

effect showed up only in the Malaysian and

Taiwanese markets.

Hong Kong!US: �i,j : 0.057* (0.000); �i,j :�0.0078 (0.546)Singapore!US: �i,j : 0.043* (0.004); �i,j : 0.0120 (0.433)Korea!US: �i,j :� 0.0106 (0.382); �i,j : � 0.0089 (0.379)Malaysia!US: �i,j : 0.014 (0.333); �i,j : 0.0211 (0.097)Taiwan!US: �i,j : 0.017 (0.125); �i,j : �0.0147 (0.289)

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Asymmetric volatility spillovers from the US marketto the emerging markets: cross leverage effect

Finally, we have figured out the asymmetric

spillover effect of a shock originated in the US

market on the conditional volatility of each

emerging market based on our empirical results.

The coefficients which pertain to such asymmetric

volatility spillovers are �2,1 and �1. Following

Equation 7, we can calculate the magnitude of the

spillover effect of good news (1% market advances)

and bad news (1% market declines) from the US

market on each market’s volatility as follows:

Positive shock

@ ln �22t

@z1t¼ �2ðj1þ �1jÞ ð15Þ

Negative shock

@ ln �22t

@z1t¼ �ðj � 1þ �1jÞ ð16Þ

First, we note that the spillover effect of a

negative shock (market declines) from the

US market outweighed the spillover effect of a

positive shock (market advances). This asymmetric

spillover effect appears strongly in all markets

both before and after the financial crisis. For

example, the spillover effect of a +1% and �1%

innovation in the US market (after the crisis) is as

follows:Second, the asymmetric spillover effect declined

considerably in the Hong Kong, Malaysian and

Singapore markets after the crisis, while the asym-

metric spillover effect increased significantly in the

Korean market after the crisis. For example, a �1%

shock in the US market increased the conditional

volatility of the Korean market from 0.022% to

0.1324%, while a �1% shock in the US market

decreased the conditional volatility of the Hong Kong

market from 0.1559 to 0.0578%. These findings

indicate that the Korean market became most

vulnerable to shocks generated in the US marketafter the financial crisis (Tables 10 and 11).

It is also worthwhile noting that when theasymmetric effect of a shock originated in anemerging market on its own volatility (own leverageeffect) is strong, then the asymmetric spillover effectof a US shock on an emerging market’s volatility(cross leverage effect) disappears and vice versa. (Thisphenomenon does not occur only in the Malaysianmarket.) For example, before the crisis, a significantown leverage effect was present in the Korean,Malaysian and Taiwanese markets, whereas a sig-nificant cross leverage effect was present in the HongKong, Malaysian and Singapore markets. After thecrisis, a significant own leverage effect was presentonly in the Malaysian and Taiwanese markets,whereas a significant cross leverage effect was presentin the Hong Kong, Korean, Malaysian andSingapore markets. Thus, when the effect of adomestic shock on market volatility dominates,the effect of a foreign shock (US shock) on marketvolatility diminishes. Conversely, when the effect ofa foreign shock (US shock) gains ground, then theeffect of a domestic shock on market volatility losesstrength.

Contagion effects

Several studies have investigated the contagion effectof the financial crisis that originated in Thailand inJuly, 1997, mainly focusing on the transmission of the

Table 9. Own leverage effect

Before crisis (Period 1) Crisis (Period 2) After crisis (Period 3)

Dow Jones 3.949875* �1.46477 5.14004*Hong Kong 2.075892** �2.37323 1.557229KOSPI 4.887974* 1.117322 1.178514Malaysia 6.6116* 0.510425* 1.609136*Singapore 1.313777** 2.303271** 1.106655**Taiwan 4.090006* 0.505054** 5.88211*

Notes: Both �jj and �j are significant at the 5% level; **: Only �jj is significant at the 5% level*.

Positive shock Negative shock

Hong Kong 0.0103% 0.0578%Korea 0.02365% 0.1324%Malaysia 0.0094% 0.0530%Singapore 0.0090% 0.0506%Taiwan Insignificant Insignificant

Pacific-basin emerging markets after the 1997 financial crisis 655

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financial crisis to foreign exchange markets. We canconsider two types of the contagion effect. The firsttype is the fundamental-based contagion effect that isassociated with economic interdependence amongcountries. This type of the contagion effect is quitecommon, because countries are increasingly inter-dependent through trade, common creditors andsimilar macroeconomic trends. The second type ofthe contagion effect is the pure contagion effect thatarises from a panic, herd behaviour or self-fulfillingexpectations.

Most of the studies on the contagion effect of the1997 financial crisis have tested for the fundamental-based contagion effect and confirmed the contagioneffect of the crisis. For example, Glick and Rose(1998) and Kaminsky and Reinhart (1998) showedthat trade linkages between Thailand and othercountries and the role of common creditors werethe important determinants of the transmission of thefinancial crisis to other foreign exchange markets.Kaminsky and Schmukler (1999) and Baig andGoldfajn (1999) found that during the crisis period,the rates of return on stocks in East Asian countrieswere highly sensitive to news that originated in thecrisis-inflicted countries.

Unlike the existing studies that relate the rate ofchange of stock prices to variables that are thought tobe the sources of the contagion, we directly addressthe contagion effect of the 1997 financial crisis onstock prices in our EGARCH framework by inves-tigating whether prices and volatility were trans-mitted from Thailand to the five emerging marketsduring the crisis period and whether prices andvolatility were transmitted to one another amongthe five emerging markets. Our test results show thatthere was evidence for the transmission of eitherprices or volatility or both from Thailand to theemerging markets during the crisis period only exceptfor the Korean market. We have also found strongevidence for the transmission of either prices orvolatility or both among the five emerging marketsduring the crisis period, but evidence for such

contagion effects before the crisis and after thecrisis is much weaker. Our finding is roughly inconformity with the previous studies concerning thecontagion effect. Our result further suggests that thereturn of the Hong Kong and Singapore markets to arelatively ‘normal’ level after the crisis (in terms of

reduced volatility) might be the result of the dyingoff of the contagion.11

VI. A Summary and Concluding Remarks

The main purpose of this study has been toinvestigate price and volatility spillovers from the

US market to five Pacific-Basin emerging marketsbefore and after the 1997 Asian financial crisis: HongKong, Singapore, South Korea, Malaysia andTaiwan. The existing literature on the effects of the1997 financial crisis on emerging markets is few andfar between, despite the fact that the importance and

influence of these markets in the world financialmarket have continued to increase.

The Hong Kong and Singapore markets are oftencategorized as ‘developed markets.’ As expected,there is strong evidence for the transmission ofstock prices and volatility from the US market tothese markets both before and after the crisis. The

Korean and Malaysian markets that were hit mosthard by the financial turmoil underwent majorchanges in the behaviour of price and volatilityspillovers from the US market. Interestingly enough,these two markets experienced opposite shifts in priceand volatility spillovers after the financial crisis,

although Malaysia’s experience was not as dramaticas that of Korea. The Taiwanese market, on the otherhand, showed no major shifts in the path of price andvolatility spillovers from the US market between theprior- and post-crisis periods. These differing patternsamong the Pacific-Basin countries appear to be

attributed to differences in institutional and industrial

Table 10. Effect of +1% innovations in Dow Jones on the volatility of each market (in %)

Before crisis (Period 1) Crisis (Period 2) After crisis (Period 3)

Hong Kong 0.0395* 0.0727** 0.0103*KOSPI 0.0056 0.2333 0.0236*Malaysia 0.0152* 0.2734** 0.0094*Singapore 0.0322* 0.1722 0.0090*Taiwan 0.0011 0.0141 0.0013

Notes: * The coefficients of both volatility spillovers (�2,1) and asymmetry (�1) are significant at the 5% level; **: Only thecoefficient of volatility spillovers (�2,1) is significant at the 5% level.

11 Empirical results are not reported here, but available from the authors upon request.

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factors and differences in the phase of market

liberalization in these countries.Hong Kong is a metropolitan country which has

played the role of the financial hub in Asia. It is well

known that the Hong Kong market is one of the least

regulated markets in Asia. Not surprisingly, innova-

tions originated in the United States had significant

effects on the prices and volatility of the Hong Kong

market for all sample periods. The price spillover

effect became marginally stronger, but the volatility

spillover effect weakened after the financial crisis.

The Singapore market has shared a similar trend with

the Hong Kong market: The price spillover effect

picked up a little bit after the crisis, but the volatility

dependence of the Singapore market on the US

market became less pronounced after the crisis.The Korean market was the only market whose

prices as well as volatility were immune from shocks

produced in the US market before the 1997 financial

crisis. However, price and volatility spillover effects

showed up most strongly in the Korean market after

the crisis. This finding supports the Feenstra et al.

(2003) proposition that the effect of an external shock

will be much more severe in V-groups such as South

Korea. Several factors might coalesce for such shifts.

First, the Korean market was most closed in the

region before the crisis, but the Korean government

took a series of drastic actions to remove many

restrictions on capital transactions in the wake of the

financial crisis. The consequence of such actions was

massive inflows of foreign funds into the Korean

market. Currently foreigners’ portfolio investment

accounts for more than 50% of the market value of

stocks (10 largest business groups) traded in

the Korean market. In addition to the financial

factors, the real sector of the Korean economy is

heavily dependent on the United States with the

United States being the largest trading partner of

Korea.The Taiwanese market also distinguished itself

from the other markets as far as the transmission of

shocks originated in the United States is concerned.

In the Taiwanese market, the price spillover effect

was evident in all samples, but the volatility spillover

effect was not present in all samples. This result is

also consistent with Feenstra, Huang and Hamilton’s

conjecture that an external shock will not have strong

effects on markets for U-groups such as Taiwan. One

possible explanation for the absence of the volatility

spillover effect during the entire sample period is that

the Taiwanese economy has operated under the pivot

of small- and medium-sized firms and that the

interdependence of the Taiwanese economy with

the US economy is not that strong.The Korean and Malaysian experiences deserve

a special attention. These two countries took different

paths toward dealing with the crisis and both

countries have been assessed to be successful in

coping with the crisis. Korea adopted a market-

oriented strategy recommended by the IMF, elim-

inating many restrictions on market activities such as

controls on the foreign acquisition of domestic

stocks. On the other hand, Malaysia went its own

way, refusing the IMF prescriptions for curing the

financial malaise. These different programs have led

to different patterns in price and volatility spillovers

from the US market: The price spillover effect picked

up significantly in the Korean market while the price

spillover effect decreased in the Malaysian market

after the crisis and the volatility spillover effect

increased dramatically in the Korean market while

the volatility spillover effect decreased substantially

in the Malaysian market after the crisis.To sum up, new information on stock prices

originated in the US market was more rapidly

transmitted to the Pacific-Basin emerging markets

(with the exception of the Malaysian market) after

the crisis, but the transmission of volatility from the

US market to the emerging markets was considerably

reduced (with the exception of the Korean market)

after the crisis. Asymmetry in the spillover effect of

US shocks on market volatility was pronounced in all

markets only except for the Taiwanese market after

the financial crisis.

Table 11. Effect of �1% US innovations on the volatility of each market (in %)

Before crisis (Period 1) Crisis (Period 2) After crisis (Period 3)

Hong Kong 0.1559* 0.1065** 0.0578*KOSPI 0.0222 0.0341 0.1324*Malaysia 0.0602* 0.4005** 0.0530*Singapore 0.1273* 0.2522 0.0506*Taiwan 0.0042 0.0207 0.0074

Notes: *: The coefficients of both volatility spillovers (�2,1) and asymmetry (�1) are significantat the 5% level; **: Only thecoefficient of volatility spillovers (�2,1) is significant at the 5% level.

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Acknowledgements

We thank Soo-yon Lee and Kyong Ha Lee for theirexcellent research assistance. We are grateful toseminar participants at the Korea EconomicResearch Institute (KERI), Sogang University andFlorida Atlantic University for their constructivecomments and suggestions. All remaining errors areour own. The authors are also grateful to ananonymous referee for valuable comments whichimproved this article substantially.

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