volatiliry spill over and contagion asian crises

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Volatility Spillovers and Contagion during the Asian Crisis: Evidence from Six Southeast

Asian Stock MarketsAuthor(s): Kanokwan Chancharoenchai and Sel DiboogluReviewed work(s):Source: Emerging Markets Finance & Trade, Vol. 42, No. 2 (Mar. - Apr., 2006), pp. 4-17Published by: M.E. Sharpe, Inc.Stable URL: http://www.jstor.org/stable/27750488 .

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Emerging Markets Finance and Trade, vol. 42, no. 2,March-April 2006, pp. 4-17.? 2006 M.E. Sharpe, Inc.All rightsreserved.ISSN 1540^96X/2006 $9.50 + 0.00.

Kanokwan Chancharoenchai andSel DiboogluVolatility Spillovers and ContagionDuring theAsian CrisisEvidence from ix SoutheastAsianStockMarketsAbstract: Using a multivariate generalized autoregressive conditional heteroskedasticity(GARCH-M) model, we investigate volatility spillovers in six Southeast Asian stock markets around the time of the 1997Asian crisis. We focus on interactions with theU.S. marketas a world financial market, and with the Japanese market as a regional financial market.We also use bivariate GARCH-M models to examine the behavior of individual markets andtheir interactions with other markets in the region. All models lend support to the idea ofthe uAsian contagion" which started in Thailand and rapidly spread to other markets.

Key words: Asian financial crisis, contagion, stock markets, time series models.

The Southeast Asian economies were theenvy ofmany countries before thefinancial and currencycrisis of July 1997,which began inThailand and spread rapidlytoMalaysia, thePhilippines, Indonesia, Korea, Taiwan, andHong Kong. The crisishad a surprisingand dramatic effecton both thefinancial and real sectors of theafflicted countries. At its core were the large-scale foreign capital inflows intoSoutheast Asian financial systems,which became vulnerable topanic and suddenreversals of market confidence (Charumilind et al. 2006; Jeon and Seo 2003;

Kanokwan Chancharoenchai is a lecturer in the Faculty of Economics atKasetsart University, Bangkok, Thailand. Sei Dibooglu ([email protected]) is an associate professor at theUniversity of Missouri-St. Louis. Sei Dibooglu acknowledges research supportfrom theCenter for International Studies at theUniversity ofMissouri-St. Louis. The opinions expressed herein are solely those of the authors.4

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MARCH-APRIL 2006 5

Nagayasu 2001). Most of the economic activity that thecapital inflows supportedin the affected countrieswas highly productive, so the loss of economic activityfrom the sudden inflow reversalwas enormous. This forced the economies intosharpdownturns. The crisis prompted the largestfinancial bailouts inhistory,anditwas themost severe financial crisis tohit thedeveloping world since the 1982debt crisis. Indonesian gross domestic product (GDP) contractedbymore than 15percent in 1998, and theKorean and Thai economies contractedby approximately7 and 10percent, respectively.The crisis also threatened thegrowthof otheremergingand transition conomies, and as such, it is importanttounderstand thepatternof volatility spillovers, theextent towhich such spilloversmight have influencedotherwise sound economies, and how these effects could bemitigated.1This paper explores thecontagion effects of theAsian crisis onAsian regionalstockmarkets, including Japan, and global markets, as proxied by theU.S. stockmarket. To capture the interactionsamong the largermarkets and emergingmarkets,we use amultivariategeneralized autoregressive conditional heteroskedasticity(GARCH-M) framework.As the crisis startedfromThailand and rapidly spread tootherneighboring countries,we examine thedynamics of contagion from theThaistockmarket tofiveAsian emergingmarkets using a bivariate GARCH-M model.We use data from six emergingAsian stockmarkets: Thailand (TH), thePhilippines (PH), Indonesia (IN),Malaysia (MA), Korea (KO), and Taiwan (TW). As apreliminary step,we explore thedynamic interactionsof each of theAsian marketswith twomajor stockmarkets, those of Japan (JP) and the nited States (US).We thenexamine thecontagion and spillover effects of theAsian crisis betweeneach possible pair of countries in thesample: Thailand, thePhilippines, Indonesia,Malaysia, Korea, and Taiwan. We consider two samples: data prior to theAsiancrisis (January3, 1994, toDecember 31, 1996), and an extended sample (January3, 1994, toDecember 31, 1999).Data and MethodologyTo investigate thebehavior of excess returnvolatility and volatility spillovers,weconsider the daily closing price of six emergingAsian markets. All indices aredenominated in local currencyand expressed indaily percentages. The daily stockprice indices are all drawn fromDatastream. To proxy the risk-freeratesof return,we use the three-month -bill rate for thePhilippines, the six-monthmiddle deposit rate for Indonesia, the interbankovernight rate forThailand, the interbanktwo-monthoffered rateforMalaysia, thenegotiable certificateof deposit (NCD)ninety-one-day yield forKorea, themoney market 180-daymiddle rate for Taiwan, the three-monthmiddle rateT-bill rate forJapan, and the three-month -billsecondmarketmiddle rate for theUnited States. The portfolioweights reflect therelative size of themarkets and are calculated frommonthlymarket capitalizationdata inU.S. dollars. Daily data forboth the interestrate andmarket capitalizationare also from Datastream.



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6 EMERGINGMARKETS FINANCEANDTRADEThe excess returns, rt, are the stock returns, Rs t,net of the risk less rate, Rf t,

rt=Rsj-Rf,n (1)where Rs t s thefirstdifference of stockprices in logarithms, \nSPIt InSPI^, andall data used comprise daily closing values of stock market indices.Let the conditional variance of theexcess returnsand covariance between thedomestic market portfolio and foreignmarket portfolio be ht and cov(rd,rfit),espectively. he relationbetween theexpected excess return nd bothht nd cov(rd,rft)can be estimated by regressingboth theexcess returnsrt n thepredictable component of stock market volatility, and the covariance, cov(rd,rfit).With daily data from differentgeographical regions, tradinghours generallyoverlap in a calendar day. The Asian markets open before theU.S. market does.Therefore, theU.S. market returnsmay predict emergingmarket and Japanese

market returns. Because the Asian markets close before the U.S. market does?more specifically, on theprevious calendar day?the Asian market returnsdo nothelp toexplain previous-day U.S. returns.As inChan et al. (1992), to account forthe lack of synchronization in tradinghours, one lagged disturbance of theU.S.returns is incorporated in theAsian returns. In addition, returnsfor each countrydepend on one lagged disturbance to capture theeffects of infrequent tradingonthedynamics of index returns.2 or our three-dimensionalmodel, with a majorregionalmarket (Japan), a global market (theUnited States), and each of the sixPacific Asia emerging stock markets (/?),the typical conditional mean equationcan be expressed as

rpj = +aprPJ-l +?Pl?PJ-l +dp2?usJ-l+^pla)pJhPJ+ ?USl ?W {rP,t 'rUSJ + ?py Vjpj C0V {rpj >rjP,t )+ ?p,t

rus,t=

ûs0 +ausrus,t-\ +dUs[?us,t-\ + ÛSÛSlKlSJ+ ?PlMpj C0V(rPj>rus.t+ ?jPl jpj cov(rw,rjN + eusKJP.r= X.iPo +(X.ipriP.t-\ +^Pl?jpa-l+djP2?us^+^jp^jpahJPa+ ?P2a)pj(^t^jpj^?usûsj c?v(rusnrjp) eJpJ

[et]~N(0,[Ht]),where [Ht] is the variance-covariance matrix, and [et] is thevector of error termsfrom estimating r ,rus, and rjp.Formulated this way, themonthly excess returns rp ton a typicalAsian emergingmarket portfoliomay be influencedby nondomesticfactors.



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To implement themodel empirically, it is important o specify thedynamics ofconditional variance and covariance.Extending the standard (univariate)GARCH-Mmodel, Bollerslev et al. (1988) propose amultivariate GARCH-M specificationthat llows the covariance terms to influence the domestic returnprocess. For thethree-dimensional case (trivariateGARCH-M process), the conditional variancecovariance specification can be expressed as

Vec [//Jh

c?v(rP,t>rus,t)c?v(rp,t>ruP,t)

(


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8 EMERGINGMARKETS FINANCEANDTRADEtional variances or prediction errors,following theempirical findings ofFrench etal. (1987), who show thatusing aGARCH(2,1) tomodel conditional variance ofexcess returns/zrdoesnot appear todiffersignificantlyfromusing aGARCH(1,1).The trivariate conditional variance-covariance specification of Equation (3)allows the conditional variances to depend only on past squared residuals, andcovariances to depend on past products of error terms. The important cross-marketeffects,highlighted byHamao et al. (1990) for the national stockmarkets of theUnited States, United Kingdom, and Japan, are not sufficientlyincluded in theBollerslev et al. (1988) trivariate ARCH-M model. Even thougha more generalprocess could be specified tocapture cross-market spillover effects,positive semidefiniteness of the conditional covariance matrix in thatprocess isnot assured. Toreflectmore general dynamics inmodel (3), the [a] and [y]matrices would have toinclude nonzero, off-diagonal elements. The conditional variance-covariance specification is, therefore, especified intoEquation (4), as originally proposed byBabaet al. (1989), denoted as BEKK below:

[Ht] [Pl[[P] [Fl [Ht_x][F][G]' >_,][et_{]'G], (4)where [Ht]denotes the 3x3 variance-covariance matrix conditional on informationat time t, nd denotes thevector of disturbances fromEquation (2). Theterm [P] is an upper triangularmatrix of threecoefficients,whereas [F] and [G]are free (square) matrices of coefficientswith nine parameters for each. Unlikefullparameterization, thisapproach economizes thenumber ofparameters inEquation (3) (twenty-four, ncluding the interceptparameter for the trivariatesystemused here), and guarantees thatthe covariance matrices are positive definite.

Consequently, themodel, with the threeequations above, the conditionalmean(2), and theconditional variance (3) and (4), allow for considerable dynamics intherisk-premiumrelation between themarket portfolio of own assets and the covariance of returnswith othermarkets. This covariance is a weighted average ofthe variance of themarket portfolio of domestic assets and the covariance of thereturnson themarket portfolio of domestic assets with themarket portfolio offoreign assets (foreign influences),where theweights are theproportions of domestic and other stocks in theworld market portfolio.Empirical ResultsIdeally,ourmodel should estimate an eight-variablemultivariate GARCH-M modelof the full set of stock excess returns,to account forcontagion or spillover effectsamong theeightmarkets. Unfortunately, thiswould require estimating 80 parameters inthefirstmoment and 162parameters in the secondmoment, which is impossiblewith prevailing computing technologyand numerical methods. Therefore, thisstudyfocuses on twosubsystems:three-variablend two-variableGARCH-M modelson thedaily stockexcess returns. he three-variable (trivariate ARCH-M) model



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includes one of the six emerging stockmarkets inAsia and twodeveloped markets,Japan (using theTokyo Stock Exchange as a regionalmarket) and the nited States(using theNew York Stock Exchange as theglobal market), to capture volatilitytransmissionfromregional and world markets to thesixAsian emerging stockmarkets.We use the two-variable (bivariate GARCH-M) model to explore volatilityspilloversbetween anygiven pair of theemergingmarkets. ThemultivariateGARCHIV!approach shows therelationbetween the variance of therespectivemarket andthe variance of othermarkets and describes theeffect thatthecovariance betweenthemarkets has on theexcess returns n thatmarket as a volatility spillover effect.The portfolio weights coare based on daily capitalization data inU.S. dollars, relativeto thevalue of thesumof therelevantmarkets, and reflect therelative size of themarkets investigated.Even thoughwe alluded to excess returnshaving amultivariate ^-distribution,Chan et al. (1992) showed that restoring normality in the sample by removingoutliers did not significantly change the results. Given their results and convergence problems, we use themultivariate normal distribution in theoptimizationroutine.

Evidence from Trivariate GARCH-M ModelsTable 1presents estimation results fortheThailand-Japan-United States trivariateGARCH-M model using theBEKK parameterization,3 relatingeach stockmarketof the sixAsian emergingmarket indices to theregional (Japan) and global (UnitedStates) stock markets. In themean equations ineach estimatedmodel, the intercept parameters are mostly negative. These large negative-intercept terms are notsurprising,because reduced capital gains taxes on long-term ssets provide incentives tohold thoseassets, despite otherwise unfavorable ratesof returns(Bollerslevet al. 1988). They also reflect thatequity holders did consistentlyworse over thesample period. Furthermore, the time-series analysis indicates thatmost countrystock index returnsexhibit first-orderserial correlation,which can be explainedby institutionalfactors, such as bid-ask spreads and nonsynchronous trading inindividual stocks.4The significant coefficients of one lagged disturbance for theemergingmarket (d{) and theUnited States (d2) also show that theasynchronismin trading times is successfully captured in themodel, as Chan et al. (1992) suggest. This finding thus stronglysupports the effect of different alendar days andcannot be ignored in the analysis. In addition, themean equations show that theeffects of conditional variance in each individualmarket?the values of theXxparameter?are, with fewexceptions, all positive, but haveweak explanatorypowerfor market excess returns in almost all cases, according to asymptotic ^-statistics.This lack of significance of the coefficienton thevariance is somewhat surprisingand implies that time variation in theconditional variance of the entiremarket isnot an important source of variation in excess returns. However, this weak marketpremium effectdoes not necessarily indicate the absence of a premium associated



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Table

TrivariateARCH-Mstimatesromailyxcesseturnsfhailand,apan,ndhenitedtates

Estimatesfoefficientsfonditionalxcesseturnsrp,t XP0+prpX-\?Pi?p,t-1 ?p2Fus,t-Jl xp^>p,thp,t+?us^>us,t cov(rp,fw) ?jp^jpj ?v(rP,t>rjp,t)?p,t

rus,t= XusQ ausrus,t-: ?us/us,t^ xus^us,thus,t +?p^}p,tco^rp,t'rusf+ ?jp2a,jp,tcow(rus,t>rjp,t)i:us,trjptjp0jprjp,t-ljpAEjp,t^jp2cus,t^jp^wjp,thip,tp2a>p,tov(rP,f>rP.t)us2?>us,tov(w-0p.r)jp,t

Beforehesianrisis(January,994,oecember1,996)

Thailand

Unitedtates

Japan

ExtendedampleJanuary1,994,oecember1,999)

Thailand

Unitedtates

Japan

-0.134-0.56).409(3.73)**

-0.312(-2.55)**

0.509(5.99)**12.1811.19) -4.491

(-1.02)1.105(1.37)

-0.243(-1.16).2140.66)

-0.119-0.36) 1.3621-32)11.636

(0.21) 0.039(0.02)

-0.083-0.76)0.174(1.26)

-0.146-1.01)0.320

(6.32)**0.253

(0.82)5.4850.35).4190.34)

-0.008-0.06).208(2.24)*-0.085

(-0.87).421(8.21)**

-0.398-0.07)-0.145

(-0.74)0.078

(0.14)

0.019(0.56)

0.071(0.30).024(0.10) 0.096

(1.80)*10.079(-0.63)

1.1342.27)**

0.112(1.56).136(1.58)0.114-1.29).35112.31)**.008

(0.04)-60.087-3.29)**.3101.44)



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Estimatesfoefficientsfariance-covarianceatrix[Ht=P]P]F]HM][F]G]>M'[G]

1.136 (2.24)**0.039-0.01)0.086(0.33)0.007 (0.02e~1)13 0.162 (0.68)-0.073-0.01)22.5763.15)**.0610.16)P230.045 (-0.20)-0.150-0.06)

33.002e-10.02e-5).0020.01~3).457(12.21)**0.216 (15.99)**G12.0040.19).0324.55)**.051

(2.17)*0.053 (5.48)**

G21.084 (0.86)0.093-2.57)**

G220.019 (-0.47)0.24214.71)**23 0.070

(1.25)0.053 (2.80)**31 0.258

(2.67)**0.218 (8.09)**G32 0.066

(1.32)0.002 (0.14)

G33.3447.95)**0.096-7.77)**Fn .132 (0.92)-0.073-1.50)120.047 (-0.53)-0.167-5.90)**130.241 (-3.62)**0.491-21.34)**F21 0.426 (-0.20)0.3802.578)**F22 0.220

(0.19)0.903 (36.89)**23 0.130 (-0.12)-0.332-4.75)**F310.240 (-1.54)1.78821.56)**32 0.006 (-0.08)-0.098-1.88)*

33 0.871(16.65)**0.242 (4.73)**

Notes: Portfolio weights reflect relative sizes of the three markets.Numbersinarentheses are ^-statisti

** Statistically significant at the 1 percentlevel.*Statistically sig



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12 EMERGINGMARKETSFINANCEAND TRADEwith nondiversifiable risk in themovements of the totalmarket. Itmay be a signofmulticollinearity, orweak evidence of time variation inmarket risk.While themarket's variance (X{)has a positive effect ingeneral, the coefficientson thecovariance termshave negative and positive signs. Specifically, in thecomplete sample, Indonesian expected excess returnsdepend negatively on the covariance of Indonesian excess returnswith Japan,or cov(r/n ,rjp), nd positively on thecovariance with U.S. excess returns, or cov(rjnT,rust). Japanese excess returns receive volatility fromMalaysia and transmitvolatility toKorea in theprecrisis period.Similarly,U.S. volatility transmits o the aiwanese market inthesame period.When theentire sample isconsidered,Malaysia transmitsvolatilitytoJapan,whichtransmits tto theUnited States. Both Japanand theUnited States transmit olatility to Indonesia. Japan receives a considerable volatility effect fromMalaysiathrough ov(rmat,rjp) nboth sample periods. This finding generally contrastswiththe literature, nwhich developed markets have amajor effecton emergingmarkets,but not theotherway around. These volatility transmissionpaths are summarized inFigure 1.

Despite the literature, urmodel demonstrates thattherewas some interdependence involatility between emergingmarkets and developed markets before andafter the sian crisis.The covariance termsimplya Shockwave that traveledthroughthe differentfinancial markets.Evidence fromBivariate GARCH-M ModelsThis section uses the bivariate GARCH-in-mean model to examine volatilityspillover effectsfromoneAsian emergingmarket toanother:Thailand, thePhilippines, Indonesia, Malaysia, Korea, and Taiwan. There are fifteen separate regressions,with eight parameters in the conditional mean equation and eleven in theconditional variance-covariance equation for each regression. Because the sixemergingmarkets are approximately in the same geographical region,we are notconcernedwith asynchronous trading nd do not include lagged disturbance terms.Results for thePhilippines-Thailand model are given5 inTable 2.The mean equations show that the interceptparameters overall are negative insignbut statistically insignificantfor all markets. The significance of the coefficients suggests that ll markets, except Taiwan, exhibit first-order erial correlation.Perhaps most interesting, ime-varying ariance in therespective stockmarketis not theonly importantsource of variation in the stock excess returns. verall,the value of theA1?the conditional variance coefficient, tends tohave theexpectedpositive sign. For thecovariance, theThai excess returnsdo not depend on anyotherAsian emerging stockmarkets' behavior, thoughat the same time, theThaimarket does apparently cause positive volatility throughcov(rthnrpU) nthePhilippinemarket during thepost-financial crisis period inAsia at the 10percent level.This is strongevidence for the belief of rapid spread of the fallout fromThailandtoneighboring countries during theAsian crisis.



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Figure 1.The Pathways ofVolatilityTransmissions intheTrivariate Model(a)Before the sian crisis (January , 1994, toDecember 31, 1996)

Malaysia Japan w+ KoreaUnited States Taiwan

(b)Extended sample (January , 1994, toDecember 31, 1999)Indonesia

Malaysia * Japan m+ United States

In addition, over thepre-financial crisis period inAsia, thePhilippine and Taiwanese markets receive volatility fromeach other throughcov(rph ,rm.). he Philippinemarket transmitsvolatility to theKorean market throughco\(rpllt,rko). heKorean market also receives volatility fromthe aiwanese market, and transmits tback in turn,throughcov(rw,rm.,),but the influence appears tobe weak. Finally,theMalaysian market receives volatility through cov(rjnPrmat) and cov(r/mu,rm.,).These results are evidence of interdependence among the six emergingmarkets inAsia, even before thecrisis.For the conditionalmean equations over theextended sample period, the statistics show a number of interestingresults regarding thecovariance terms.The expected excess returns on theMalaysian and Taiwanese market are stronglyinfluenced by theMalaysian excess returns through cov(rlna t,rko )and cov(rm, nr,a t),respectively.The Taiwanese market transmitsvolatility through ov(r/>; rM )to theIndonesian market. TheMalaysian market transmitsvolatility through ov(rjn t,rfm)to the Indonesian market; at thesame time, itsexpected excess returnsdepend onIndonesian volatility through cov(rin t,rmflJ).he pathways of volatility transmission are given inFigure 2.We also estimate bivariate models fordaily stockexcess returnsamong the sixemergingmarkets fromJanuary 1, 1997, toDecember 31, 1999. The results aregiven inPanel C ofFigure 2. There is strongevidence of contagion after the crisis.The Thai stockmarket's volatility spillover suggests that thas played an increasingly importantrole in theAsian stock markets after itsmid-1997 fallout. Theemergingmarkets also seem tobe highly connected to regional capital markets.Our results indicate that thereturn omovements among East Asian stockmarketswere strongprior to the sian crisis and continued unabated after the sian crisis.These results are broadly in linewith those ofYang and Lim (2004). Moreover,



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14 EMERGINGMARKETSFINANCEAND TRADE

Table 2Estimates from the Bivariate GARCH-M Model with the Philippines andThailand

Estimates of thecoefficients f the conditionalexcess returnsrd,t *d0 +adrd,t^ +^-(?f,t)hd,t +Xd2Uf,t c?v(rd,t>rf,t)+ ?d,trf,t= \ + afrf,t-\+f,thf,t +V1-(0f,t)Q)djcov(rd,t>rf,t) + ef,t

Before theAsian crisis Extended sample(January , 1994, to (January , 1994, toDecember 31, 1996) December 31, 1999)Philippines Thailand Philippines Thailand

a

k9

0.013(0.16)0.168(5.12)**

-0.027(-0.10)0.042(0.32)

-0.141(-1.34)

0.071(2.07)*0.055(0.57)0.407(1.19)

-0.020(-0.47)

0.170(7.86)**

-0.031(2.12)*0.131(1.70)***

-0.105(-1.19)

0.098(4.81)**0.054(1.04)-0.008

(-0.09)Estimates of the coefficients of the variance-covariance matrix

[Ht]H [P] [F] Ht_,][F][G]'eMJ e?][G]with [Hf] = "f,t J{rd,t>rf,t))[v{rd,t>rf,t) ht,t

P11^12P'22?nG12G21G22F?Fl2

0.0670.6650.704

-0.096-0.1480.3080.441

-0.6920.2450.227

-0.787

(0.34)(11.15)*(8.62)*(-1.84)*(-2.97)*(8.24)*(8.69)*(-9.94)*(3.31)*(4.98)*

;-15.05)*

0.006-0.230-0.1340.2300.3630.152

-0.0030.2801.1430.550

-0.346

(0.02e-2)(-0.22)(-0.16)(14.94)**(14.41)**(15.12)**(-0.18)(5.11)**

(26.32)**(17.28)**(-6.32)**

Notes: Daily Thai and Philippine excess returns are calculated in local currency:/=Thailand, d = Philippines. Portfolio weights reflect relative size of the two markets.Numbers in parentheses are r-statistics. *** Statistically significant at the 10 percentlevel. ** Statistically significant at the 1 percent level. * Statistically significant at the 5percent level.



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Figure2. The Pathways ofVolatilityTransmissions in theBivariate Model(a) Before the sian crisis (January , 1994, toDecember 31, 1996)

Philippines Korea

TaiwanV

Indonesia Malaysia

(b)Extended sample (January , 1994, toDecember 31, 1999)

Thailand wm PhilippinesIndonesia Malaysia

t tTaiwan Korea

(c)Asian crisisperiod (January , 1997, toDecember 31, 1999)

Thailand ^ Philippines Taiwan4 * I

Malaysia ^ Korea Indonesia



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16 EMERGINGMARKETSFINANCEAND TRADEthere re contagion effects in theregion,despite capital controls imposed by somecountries, such as Malaysia.

Finally, the significance of thediagonal elements of the [F] and [G]matricesindicates thatGARCH effects are prevalent and strong.On the other hand, theassumption of cross-market effectscan be confirmedby thehigh degree of significance of theoff-diagonal elements. This high degree of significance is consistentwith the results ofHamao et al. (1990), who show that the cross-market effectcannot be neglected. This empirical finding confirms the estimation results fromthe conditional mean equations.ConclusionsWe examine volatility spillovers inSoutheast Asian emerging stockmarkets in thecontextof themid-1997 financial crisis, using amultivariate GARCH process withBEKK parameterization tomodel thevolatility of excess returns.This procedurereflects thewell-known autoregressive behavior involatility series, and accountsfor spillover effects between various equitymarkets. The significantdegree ofthese effectscan reveal the relative size and openness of a particularmarket.The excess returns xhibit first-order erial correlation,which can be explainedby institutional factors.The effect of nonsynchronous tradinghours is found tohave a significanteffect in explaining index returns. In addition, results indicatesignificantforeign influences on the time-varyingriskpremiums in all specificationsand models. Similarly, thebivariateGARCH-M model provides strong evidence of reactionsamong thesixneighboringmarkets inSoutheastAsia. The suddenfallout inThailand seems tohave played an importantrole in the variation in excess returns nother SoutheastAsian markets. This supports the idea of the"Asiancontagion," suggesting that the crisis started inThailand and spread tootherfinancial markets.

Notes1.We use "contagion" in the broad sense, including cross-country transmission ofshocks or cross-country spillover effects. In our model, spillovers are indicated by significant cross-country covariance terms. For alternative definitions and a brief survey, see Yangand Lim (2004).2. For details on asynchronous trading, see Chan et al. (1992), Wei et al. (1995), andKim etal. (2000).3. Results for the United States and Japan together with each of the following Southeast Asian countries are detailed in tables 3-7 in an appendix, available from the authors

upon request: the Philippines, Indonesia, Taiwan, Malaysia, and Korea.4. An extensive discussion can be found in Scholes and Williams (1977) and inCohenet al. (1986).5. Results for all other possible pairwise combinations of countries are detailed intables 8-21 of the appendix, available from the authors upon request.



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