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: 17:50Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK
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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
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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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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
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(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.
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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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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.
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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.
Pacific-basin emerging markets after the 1997 financial crisis 657
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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.
References
Baig, T. and Goldfajn, I. (1999) Financial marketcontagion in the Asian crisis, IMF Staff Papers, 46,167–95.
Barclay, M. J., Litzenberger, R. H. and Warner, J. B.(1990) Private information, trading volume, and stock-return variances, The Review of Financial Studies, 31,233–53.
Darrat, A. F. and Zhong, M. (2002) Permanent andtransitory driving forces in the Asian-pacific stockmarkets, The Financial Review, 37, 35–52.
Edwards, S. and Susmel, R. (2001) Volatility dependenceand contagion in emerging equity markets, Journal ofDevelopment Economics, 66, 505–32.
Engle, R. F., Ito, T. and Lin,W.L. (1990)Meteor showers orheat waves? Heteroskedastic intra-daily volatility in theforeign exchange market, Econometrica, 58, 525–42.
Engle, R. F. and Susmel, R. (1994) Hourly volatilityspillovers between international equity markets,Journal of International Money and Finance, 13, 3–25.
Eun, C. and Shim, S. (1989) International transmission ofstock market movements, Journal of Finance andQuantitative Analysis, 24, 241–56.
Feenstra, R. C., Huang, D. -S. and Hamilton, G. G. (2003)A market-power based model of business groups,Journal of Economic Behavior and Organization, 51,459–85.
Glick, R. and Rose, A. (1998) Contagion and Trade NBERWorking Paper, No. 6806.
Hamao, Y. R., Masulis, R. W. and Ng, V. (1990)Correlations in price changes and volatility acrossinternational stock markets, The Review of FinancialStudies, 3, 281–307.
In, F., Kim, S., Yoon, J. and Viney, C. (2001) Dynamicinterdependence and volatility transmission of Asianstock markets: Evidence from the Asian crisis,International Review of Financial Analysis, 10, 87–96.
Jaffe, J. and Westerfield, R. (1985) The week-end effect incommon stock returns: The international evidence,Journal of Finance, 40, 433–54.
Kaminsky, G. and Reinhart, C. (1998) On crises, contagionand confusion, Mimeo, George WashingtonUniversity and the University of Maryland.
Kaminsky, G. and Schmukler, S. (1999) What triggersmarket jitters? A Chronicle of the Asian crisis, Mimeo,World Bank.
King, M. A. and Wadhwani, S. (1990) Transmission ofvolatility between stock markets, The Review ofFinancial Studies, 3, 5–33.
Koch, P. D. and Koch, T. W. (1991) Evolution in dynamiclinkages across daily national stock indexes, Journal ofInternational Money and Finance, 10, 231–51.
Koutmos, G. and Booth, G. G. (1995) Asymmetricvolatility transmission in international stock markets,Journal of International Money and Finance, 14,747–62.
Nelson, D. B. (1991) Conditional heteroskedasticity in assetreturns: A new approach, Econometrica, 59, 347–70.
Ng, A. (2000) Volatility spillover effects from Japan and theUS to the Pacific-Basin, Journal of InternationalMoney and Finance, 19, 207–33.
Theodossiou, P. and Lee, U. (1993) Mean and volatilityspillovers across major national stock markets: furtherempirical evidence, Journal of Financial Research, 16,337–50.
Worthington, V. and Higgs, H. (2004) Transmission ofequity returns and volatility in Asian developedand emerging markets: A multivariate GARCHanalysis, International Journal of Finance andEconomics, 9, 71–80.
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