Are the East Asian markets integrated? Evidence from the ICAPM · 2019. 5. 11. · Journal of...

Journal of Economics and Business 55 (2003) 585–607

Are the East Asian markets integrated?Evidence from the ICAPM

Bruno Gérarda,∗, Kessara Thanyalakparkb,1, Jonathan A. Battenc,2

a Department of Financial Economics, Norwegian School of Management,BI, Elias Smiths vei 15, P.O. Box 580, N-1302 Sandvika, Norway

b Department of Banking and Finance, Faculty of Commerce and Accountancy,Chulalongkorn University, Bangkok, Thailand

c College of Business Administration, Seoul National University,151-742 San 56-1, Sillim-Dong, Kwanak-Wu, Seoul, South Korea

Abstract

We test a conditional international asset pricing model with both world market and domestic riskincluded as independent pricing factors for five East Asian markets, the US and World markets. Wemodel second moments and risk exposures using a bi-diagonal multivariate GARCH(1,1) process. Wedocument that this novel GARCH specification provides a significantly better fit of the return pro-cess than a standard diagonal specification. Although exposure to world market risk carries a signif-icant premium across all markets, we find little support for the hypothesis that exposure to residualcountry risk is rewarded. However, residual country returns are significantly related to exchange ratechanges. Hence, we find surprisingly little evidence of market segmentation in East Asia over the period1985–1998.© 2003 Elsevier Inc. All rights reserved.

JEL classification: C32; F30; G12

Keywords: International capital market integration; South East Asia; GARCH

∗ Corresponding author. Tel.:+47-67-55-71-05; fax:+47-67-55-76-75.E-mail addresses: [email protected] (B. Gerard), [email protected] (K. Thanyalakpark),

[email protected] (J.A. Batten).1 Tel.: +66-2-218-5744; fax:+66-2-218-5913.2 Tel.: +82-2-880-8530; fax:+82-2-882-0547.

0148-6195/$ – see front matter © 2003 Elsevier Inc. All rights reserved.doi:10.1016/S0148-6195(03)00055-9

586 B. Gerard et al. / Journal of Economics and Business 55 (2003) 585–607

1. Introduction

A persistent issue in the field of international finance is the extent to which internationalfinancial markets are integrated. In fully integrated capital markets, the same asset pricingrelationships apply in all countries, and firms should use similar decision rules and evaluationcriteria regardless of their geographical location (Brealey, Cooper, & Kaplanis, 1999). Whenmarkets are segmented on the other hand, the risk return relationship varies across countries anda project which might be considered to provide an attractive return in one country, could proveto be unsatisfactory in another. Financial market segmentation could arise, for example, frommarket imperfections, differences in taxes or other restrictions on the ownership of securities(e.g.,Eun, 1985; Eun & Janakiramanan, 1986). Research over the last two decades suggests thatdifferent national markets exhibit different level of integration to international financial marketsand that the degree of integration vary over time (e.g.,Bekaert & Harvey, 1995; Carrieri, Errunza,& Hogan, 2002; Hodrick, 1981; Stulz & Wasserfallen, 1995). These results have two importantimplications for firms and investors: First, the cost of capital can be substantially differentamong mildly segmented capital markets. Second, if national stock markets are segmented,then international portfolios should provide superior risk adjusted performance since some ofthe domestic systematic risk can be diversified away by investing internationally without payinga price in terms of lower returns.

Empirical studies investigating financial integration have tended to focus on developed mar-kets (e.g.,Bekaert & Harvey, 1995; Campbell & Hamao, 1992; Carrieri, Errunza, & Sarkissian,2002; Jorion & Schwartz, 1986; Korajczyk & Viallet, 1989). Recently, more papers have focusedon emerging markets, and several studies have documented the high returns and low correla-tions of these markets with the rest of the world, suggesting significant benefits from addingemerging markets to global portfolios (e.g.,Bekaert, 1999; Bekaert, Erb, Harvey, & Viskanta,1998; De Santis & Imrohoroglu, 1997). However, the potential benefits of diversifying intoemerging markets may be jeopardised by the direct and indirect forms of investment barriersapplied to foreign investors (Bekaert & Harvey, 2000). Such restrictions on capital flows arewidely believed to make emerging markets at least mildly segmented (Bekaert & Urias, 1996;Errunza & Losq, 1985, 1989). Documenting the existence of barriers to investments however,is insufficient by itself to prove segmentation as either these barriers may not be binding orinvestors may find innovative ways to circumvent legal restrictions (Bonser-Neal, Brauer, Neal,& Wheatley, 1990; Glassman & Riddick, 1996). Determining the extent to which a nationalequity market is segmented from international financial markets is thus an empirical questionof great interest to both investors and researchers.

The aim of this paper is to investigate whether key markets in the East Asian region are fullyintegrated into or partially segmented from the world financial markets. Our study is conductedwithin the framework of the partially segmented international asset pricing model ofErrunzaand Losq (1985, 1989)which takes into account the fact that some markets may not be fully inte-grated in world markets. If capital markets are fully integrated, the expected return of a countryportfolio should solely be determined by the country’s exposure to world covariance risk. In con-trast, segmentation implies that the risk-return relation in each national market is determined pri-marily by domestic factors. Thus, when capital markets are partially segmented, expected returnswould be determined by the country’s exposure to both world and country specific risk factors.

B. Gerard et al. / Journal of Economics and Business 55 (2003) 585–607 587

A growing body of literature documents the time-varying nature of expected returns andrisk exposures both in a purely domestic setting (e.g.,Bollerslev, Engle, & Wooldridge, 1988;Ferson, 1994) and in international markets (e.g.,Bekaert & Harvey, 1995; De Santis & Gerard,1997, 1998; Dumas & Solnik, 1995; Ferson & Harvey, 1993). To accommodate this featureof the data, we estimate a conditional version of the asset pricing model, in which both theprices of risk and the risk exposures change over time. We use global information variables tocondition the price of world risk and local variables to condition the price of domestic risk.Since some of the local variables are correlated with the degree of development and openness ofthe local equity market, this specification implicitly allows the degree of integration to changeover time. We use the parsimonious stationary diagonal generalized autoregressive conditionalheteroskedasticity (GARCH)-in-mean approach developed byDe Santis and Gerard (1997)toaccommodate time variations of the returns covariance process and hence of the risk exposures.

Our study contains several contributions. First we present a simultaneous analysis of someof the largest emerging and developed markets in East Asia as well as the world and US marketsin which both the conditional measures of risk and their prices are time-varying. Second, weimplement a novel specification of the diagonal GARCH process (that we call bi-diagonalGARCH) which while remaining very parsimonious allows for a differential impact of localreturn surprises on the covariance between two emerging markets and on the covariance betweenemerging and developed markets. Finally, since our method is fully parametric we can studythe dynamics and relative magnitudes of global and local risk premiums.

We investigate whether, over the period January 1985 to December 1998, five key markets inthe East Asian region are fully integrated into or partially segmented from the world financialmarkets. The five markets include the three emerging markets of Korea, Malaysia and Thailandand the two developed markets of Hong Kong and Japan. These five markets are among thelargest equity markets in the region in either nominal terms, or relative to gross domesticproduct (GDP), and have been subject to significant investment flows.1 We study whether thesefive Asian markets are fully integrated or partially segmented relative to a world portfolio ofdeveloped equity markets as well as relative to the US equity market.

We find surprisingly little evidence of either partial or total market segmentation for the fiveAsian markets in our study. Although the premium for world market risk is significant for allassets, the prices and associated premiums for domestic risks are not significant. However, wefind that residual returns are significantly related to exchange rate variables. This suggests thatalthough domestic returns volatility is not priced, exposure to currency risk may underlie thecross-country differences in expected returns. We also find that the bi-diagonal specificationof the GARCH process fits the data significantly better than the simple diagonal GARCHspecification. This suggests that, not surprisingly, local return shocks in emerging markets havedifferential impact on the return covariance between emerging markets than on their covariancewith developed markets. Hence, while these countries financial markets may well be integrated,they display a low lever of interrelatedness with developed markets.

Our study is most closely related to the work ofCarrieri, Errunza, and Hogan (2002). Carrieriet al. document that, over the period from 1976 to 2000, the degree of financial integrationof eight emerging markets with world markets increase significantly, and that this increasedintegration coincide with the different market liberalization. This is consistent with our findingwhich pertains to the second half of their sample. Although Carrieri et al. use an asset pricing


framework similar to ours, they perform univariate tests, while we conduct our investigation ina multivariate setting which presumably yields more power. However, our approach is unableyet to deal with time-varying integration as they do.

The paper is organized as follows.Section 2describes the conditional version of the Interna-tional Capital Asset Pricing Model (ICAPM) where both world market risk and domestic riskare priced.Section 3presents the empirical methodology. The data is described inSection 4.The results are reported inSection 5. Section 6concludes.

2. The conditional version of the ICAPM

Consider first a fully integrated international financial market in which purchasing powerparity holds. Under these assumptions, several authors (Adler & Dumas, 1983; Solnik, 1977;Stulz, 1981; Wheatley, 1988, 1989among others) have extended the domestic Capital AssetPricing Model ofSharpe (1964)andLintner (1965)to an international setting. Formally, aconditional version of the model can be written as2

E(Rit|It−1)− Rft = δm,t−1 cov(Rit, Rmt|It−1), ∀i (1)

whereRit is the return on asseti, Rft is the risk free rate andRm,t−1 is the return on worldmarket portfolio from timet − 1 andt, It−1 is the information set available at timet − 1 andδm,t−1 is the price of world market risk. One can view the price of market risk as the expectedcompensation that an investor would receive for taking on a unit of world covariance risk.Under the usual assumption of investor risk aversion,δm,t−1 is equal to the world aggregate riskaversion coefficient, and thus has to be positive. Along the lines suggested byMerton (1973)andBekaert and Harvey (1995), we will use this fact to impose a non-negativity constraint onthe price of market risk during estimation. All returns are expressed in a common currency,which is this study we choose to be the US dollar. Since PPP is assumed to hold, investors bearno currency risk and the risk return relationship is unaffected by the choice of the referencecurrency (Sercu, 1980). In this model of fully integrated markets, only world covariance riskis priced in international equity markets and expected returns are not affected by domesticfactors.

However, the existence of explicit restrictions to capital flows in emerging markets, andthe empirical record (e.g.,Bekaert & Harvey, 1995, 1997) suggests that international capitalmarkets, especially emerging markets, may not be fully integrated.Errunza and Losq (1985,1989)extend the international CAPM to account for mild segmentation between markets: asubset of the assets is available to all investors, while ownership of the remaining assets isrestricted to a subset of the investors. Under these assumptions, expected returns are a functionof two risk factors: exposure to global market risk and exposure to nondiversifiable local risk.The second risk factor is the component of an asset idiosyncratic volatility which cannot bediversified away because of market segmentation. The conditional version of this model can bewritten as follows:

E(Rit|It−1)− Rft = δm,t−1 cov(Rit, Rmt|It−1)+ δdi,t−1 var(Resit|It−1), ∀i (2)


whereRit are the returns on the local market portfolio from any mildly segmented country.3

The second factor (var(Resit)) captures the local market nondiversifiable risk uncorrelated toglobal risk. Hence,

var(Resit) = var(Rit)− cov(Rit, Rmt)2

var(Rmt).

δdi,t is the price of domestic risk, that is, the additional reward investors require for taking onone unit of country specific nondiversifiable risk. When a national market is fully integrated inworld markets, idiosyncratic domestic risk is fully diversifiable and its associated price wouldbe zero.

3. Empirical methods

Equation (2)seems to be the natural relation to use in empirical tests of market integration, asit takes into account investors use of new information to make investment decisions. Intuitivelyinvestors will use all the information at their disposal including country specific and globalinformation variables. However, for the sake of parsimony, we assume that global informationvariables are used to condition the price of market risk (δm) and local information variablesare used to estimate the price of domestic risk (δdi). The asset pricing model requires thatthe risk return relationship in (2) hold for all assets including the world market portfolio. Ifthe world economy encompassesL countries,L + 1 pricing restrictions have to hold in eachperiod:

E(R1t|It−1)− Rft = δm,t−1 cov(R1t, Rmt|It−1)+ δd1,t−1 vart−1(Res1t|It−1)

......

...

E(RLt|It−1)− Rft = δm,t−1 cov(RLt, Rmt|It−1)+ δdL,t−1 vart−1(ResLt|It−1)

E(Rmt|It−1)− Rft = δm,t−1 var(Rmt|It−1)

(3)

In empirical work, although ideally all assets should be included, any subset ofN − 1 assetsplus the market portfolio can be used. The trade-off is between manageability of the estimationand loss of information in the cross-correlations and a reduction in the power of the test of theasset pricing restrictions.

DenoteRt the (N × 1) return vector of (N − 1) country portfolios and the world marketportfolio. Then the following system of equations can be used to estimate and test the conditionalversion of the partially segmented international CAPM:

Rt − Rfti = δm,t−1hNt + δd,t−1 ∗ qt + εt, εt|It−1 ∼ N(0, Ht) (4)

where

qt = D(Ht)− hNt ∗ hNt

hNNt

and asterisk (∗) denotes the Hadamard (element by element) matrix product,i is anN-dimen-sional vector of ones,Ht is the (N × N) conditional covariance matrix of asset returns.hNt is


theNth column theHt and contains the (N × 1) vector of the conditional covariances of eachasset with the world market portfolio.δd,t−1 is the (N × 1) vector of the prices of domesticrisk, qt is the (N × 1) vector of the residuals country volatility,D(Ht) the diagonal componentsin Ht, andhNNt the (N,N) element of the covariance matrix which contains the variance of theworld portfolio.

Equation (4)follows directly from the asset pricing relation in (2). However, the model doesnot specify the dynamics of the conditional second moments. To complete the parameterizationof the model, we use the parsimonious GARCH-in-mean specification developed byDe Santisand Gerard (1997). This specification has two main features. First, the conditional secondmoments are assumed to follow a diagonal GARCH(1,1) process. Second the system is assumedto be covariance stationary. Hence the process for theHt matrix can be written as

Ht = H0 ∗ (ii′ − aa′ − bb′)+ aa′ ∗ εt−1ε′t−1 + bb′ ∗Ht−1 (5)

whereH0 is the unconditional covariance matrix of residuals anda andb are (N × 1) vectorsof unknown parameters.

This specification has two advantages. First, it requires estimation of only 2N parameters andcan thus be implemented for (relatively) large cross-sections of assets. Second it constraints theestimated time-varying covariance to average to the sample unconditional covariance matrix.One crucial drawback of the specification is that it may be too restrictive, as the same parametersdrive both variance and covariance processes. To address this concern while retaining parsimony,we use the following specification of the GARCH(1,1), which we call the bi-diagonal GARCH:

Ht =C′C + [aa′ ∗ I + (a0a0′) ∗ (1 − I)] ∗ εt−1ε

′t−1

+ [bb′ ∗ I + (b0b0′) ∗ (1 − I)] ∗Ht−1 (6)

whereI is the identity matrix,1 is an× n matrix of ones and

CC′ = H0 ∗ (ii′ − [aa′ ∗ I + (a0a0′) ∗ (1 − I)]′ − [bb′ ∗ I + (b0b0′

) ∗ (1 − I)])

The typical elements of the covariance processes are computed as follows:

hiit = cii + a2i ε

2it−1 + b2

i hit−1, ∀ihijt = cij + a0

i a0jεit−1εjt−1 + b0

i b0jhijt−1, ∀i,j

wherea0 andb0 are (N × 1) vectors of unknown parameters.When we estimate the model, we will set all the elementsa0 andb0 equal to the elementsa

andb, except for the coefficients that correspond to the three emerging markets. This featureis built on a sensible intuition, which suggests that the conditional covariance process of anemerging market with another may differ from its covariance process with a developed country.

We useEqs. (3) and (6)as our benchmark model. We estimate the model and conduct all ourtests using the quasi-maximum likelihood approach of Bollerslev and Wooldridge (1992). Sta-tistical inference is carried out by computing robust Wald statistics. Optimization is performedin gauss using the BHHH algorithm.


4. Data

Our dataset includes two distinct groups of data: the returns series on which the asset pricingmodel is estimated and tested, and the global and local information variables used to conditionthe estimation. We describe them separately.

4.1. Country returns

We use monthly dollar denominated returns on stock indices for three developed markets,the US, Japan and Hong Kong, for three emerging market, Thailand, Malaysia and Korea, as forthe world portfolio. The developed market returns as well as the world portfolio returns seriesare from Morgan Stanley Capital International (MSCI), and the emerging market returns data isfrom the International Financial Corporation (IFC). Our sample covers the period from January1985 to December 1998. AsHarvey and Zhou (1993)point out, there are some differencesin the construction of these indices. For example, MSCI select firms based on liquidity floatand cross-ownership, while IFC select firms based on size. Despite these differences, the indexreturns are highly correlated. Over the period when they are both available, the MSCI and IFCindices for the emerging countries have a correlation greater than 0.95. Returns are measured inexcess of the return on one month euro dollar deposits. The one month euro dollar deposit rateis from the BIS and Datastream. To convert local currency returns into US dollar returns, we usethe end of month exchange rates used by MSCI and the IFC for their respective indices. Note thatour proxy for world equity market portfolio is the MSCI world index, which is a value-weightedportfolio of 20 developed markets, and hence does not include the three emerging markets inour sample.

Table 1reports summary statistics for the US dollar returns on the six countries and theworld portfolio. Panel A in the table contains means, standard deviations, skewness, kurtosis,Bera–Jacque (B–J in the Table) statistics for normality, and the sample unconditional correla-tions. Kurtosis indexes show that the unconditional distribution of excess returns has heaviertails than the normal distribution in most countries, except Japan. The resulting non-normalitycondition is also found in the Bera–Jacque statistics which uniformly reject the normality ofthe excess returns. Panel B reports the returns correlations. Panels C and D report autocorrela-tions for the returns and returns squared were also calculated. No significant autocorrelation isdetected in the return series, while squared returns exhibit significant autocorrelations at lag 1.Cross-correlations of squared returns, at all 6 leads and 6 lags, between the world and the othercountries were also calculated and are reported in Panel D ofTable 1. Only contemporaneouscross-correlations between each country and the world were significant. This suggests that inour sample, an AR correction in the mean equation is not necessary, while a GARCH modelfor the second moment process may be appropriate.

4.2. Information variables

To reflect the information available to investors and condition our estimation we need toselect both global and local information variables. These variables should, according toHarvey(1991), approximate the information that investors use to set prices and should also have some


Table 1Summary statistics of US dollar returns

US Japan Korea Thailand Malaysia Hong Kong World

Panel A: Summary statisticsMean 0.93 0.18 0.13 0.09 −0.35 0.99 0.75Standard deviation 4.40 7.35 11.15 11.75 10.31 9.18 4.26Skewness −1.53 0.04 0.29 −0.66 −0.31 −1.72 −0.98Kurtosis 9.60 3.31 7.16 5.54 6.52 13.14 6.18B–J 370.69 0.73 123.63 57.60 89.53 802.78 97.71Q 10.97 13.49 11.97 39.96 29.26 26.17 13.77

Panel B: Unconditional correlation ofritUS 1Japan 0.25 1Korea 0.21 0.38 1Thailand 0.37 0.25 0.38 1Malaysia 0.46 0.22 0.25 0.67 1Hong Kong 0.53 0.22 0.18 0.60 0.62 1World 0.77 0.76 0.32 0.40 0.45 0.53 1

Panel C: Autocorrelations ofritJapan and Korea

Lag 1 0.00 0.08 0.07 0.16 0.13 0.00 0.03Lag 2 −0.06 −0.04 0.09 0.13 0.21 −0.03 −0.06Lag 3 −0.07 0.04 −0.04 −0.07 −0.09 −0.03 −0.06Lag 4 −0.16 0.03 0.01 −0.14 −0.06 −0.13 −0.09Lag 5 0.06 0.06 0.03 −0.08 −0.04 −0.13 0.07Lag 6 −0.06 −0.04 0.13 −0.03 −0.19 −0.04 −0.10

Panel D: Autocorrelations and cross-correlations ofr2itAutocorrelations ofr2it

Lag 1 0.09 0.14 0.50 0.21 0.14 −0.02 0.02Lag 2 0.02 0.10 0.34 0.35 0.19 0.00 0.05Lag 3 0.01 −0.02 0.22 0.19 0.37 0.00 −0.04Lag 4 0.02 −0.03 0.11 0.33 0.22 0.00 0.01Lag 5 −0.02 0.05 0.09 0.20 0.05 0.02 0.03Lag 6 −0.01 0.08 0.12 0.13 0.41 −0.03 0.04

Cross-correlations ofr2it: world and assetjLag−6 −0.01 0.19 −0.03 0.05 0.23 −0.02Lag−5 −0.03 0.08 0.00 −0.03 −0.03 −0.02Lag−4 0.04 0.01 0.00 −0.01 0.01 0.02Lag−3 0.00 −0.01 0.08 0.06 0.03 −0.02Lag−2 0.02 0.08 −0.01 0.09 0.08 −0.04Lag−1 0.00 −0.03 0.05 0.09 0.02 0.02Lag 0 0.87 0.40 0.03 0.49 0.48 0.67Lag 1 0.05 0.13 −0.03 0.02 0.13 −0.01Lag 2 0.04 0.15 0.12 0.13 −0.02 0.12Lag 3 −0.02 −0.01 0.12 0.01 0.04 −0.07Lag 4 −0.04 −0.01 0.10 0.01 −0.02 −0.05Lag 5 0.00 0.08 0.07 −0.04 −0.05 −0.03Lag 6 0.05 −0.04 −0.04 0.01 0.04 0.00

Monthly US dollar returns on the equity indices of six countries and the value-weighted world index are fromMSCI and IFC. Excess returns are obtained by subtracting the Eurodollar one-month rate. All returns are in percentper month. The sample covers the period January 1985 to December 1998 (168 observations). B–J denote theBera–Jacque statistic for normality andQ is the Ljung–Box test statistic of order 12.


ability to predict returns. While it may be common to use financial market based informationvariables, such as dividend yield or bond yield,Dumas (1994)suggests the use of economicvariables, which are “external” to the financial market, and reflect general economic conditionsaffecting a country or the world. Since expected returns are influenced by expected real activity(e.g.,Vassalou, 2000), variables that forecast expected real activity should also forecast equityreturns. This suggestion has intuitive appeal. For example, the aggregate degree of risk aversionand hence the price of market risk, may be higher during a recession. Therefore, it would seemsnatural that variables that are directly linked to the performance of the real economy wouldhave predictive power for stock returns.

We distinguish two groups of information variables: global and local information variables.The model we estimate decompose an asset expected risk premium in two components, thepremium for exposure to world market risk and the premium for exposure to the nondiversifiablelocal risk. We will use global variables to condition the price of market risk, while we will uselocal variables to condition the local premium.

4.2.1. World information variablesThe world information variables are a set of variables common to all investors and pertaining

to all securities. We use the global information variables to condition the price of market risk. Asdiscussed earlier, the price of market risk is the reward that that investors receive for taking on aunit of covariance risk. It also measures the aggregate degree of risk aversion. The selection ofthe common information variables was drawn from previous studies in the international financeliterature (see, among others,Bekaert & Harvey, 1995; Bekaert & Hodrick, 1992; Ferson &Harvey, 1993). Even though most of our variables are related to the US,Harvey (1991)findsthat US market variables are at least as good predictors of worldwide rates of returns as countryspecific information variables.Harvey (1991)further shows that measures of the US termstructure have 87% correlation with GDP weighted measures of the world term structure. Inparticular our global instruments include the following:

X∆PRW is the lagged US dollar denominated MSCI World dividend price ratio in excess ofthe return on one month euro dollar deposits.

∆USTP is the month-to-month change in the US term premium, where the term premium iscomputed as the yield difference between the ten-year T-note and the three-month Treasurybill.

∆Euro$ is the month-to-month change in the one-month Euro$ deposit rate. Given the highproportion of US market capitalization in the world index, the change in the US interestrate may be important in predicting change in returns world wide.

USDP is the US default spread, measured as the difference in the yield to maturity on Moody’sBaa and Aaa rated bonds. This variable tracks changes in default risk as well as changesin investors risk aversion.

4.2.2. Country specific information variablesTo the extent that markets are segmented, local economic conditions will affect local asset

returns beyond the impact of global factors. In particular we use local variables to conditionthe price of domestic risk. By nature, the set of local information variables we collect may notrepresent all country specific information available to investors, as we are restricted to the subset


of variables available at monthly frequency for all countries over the whole sample period. Ofcourse, some of these variables will be correlated with the world information variables. Forexample, local economic growth may dependent on whether the rest of the world economiesare in an expansion or a recession. However, the degree of correlation is usually small. Forexample,Ferson and Harvey (1993)find less than 40% average correlation among dividendyields in the MSCI countries. We select variables that reflect macroeconomic data, financialdata, scaled prices, and exchange rate related data. All definitions are given below.

Two variables are chosen to reflect local macroeconomic conditions. The first, denoted SIis the month-to-month change in the local risk free short-term interest rate. It primarily re-flects changes in local inflation expectations. The second is the difference between the US realshort-term interest rate and the local real short-term interest rate. This variable is denoted LCFRI.The real short term rate is the difference between the beginning of the month risk free interestrate and the previous month realized inflation rate. LCFRI reflects difference in time preferencesacross countries, and hence should be informative about the domestic price of risk. All interestrates and inflation variables are taken from the International Financial Statistics (IFS).

Scaled prices variables reflect local stock market conditions. AsFerson and Harvey (1993)suggest, if expected returns differ across countries with investability, one might expect differ-ences in valuation ratios to be related to differences in expected returns. Further scaled pricevariables have been shown to have good predictive power for future returns in all countries (see,for example,Fama & French, 1989). For each countries we compute the local currency dividendprice ratio in excess of the local short-term interest rate (X�PRL). The dividend calculated asthe difference between the change in the local currency total return index and the change in thelocal currency price index over a given month multiplied by the level of the price index. Thedata come from MSCI and IFC.

If one assumes that purchasing power parity (PPP) does not hold, then in addition to global andlocal risk, expected returns depend on deviations from PPP (Adler & Dumas, 1983). Recentevidence shows this is likely the case (De Santis & Gerard, 1998; Dumas & Solnik, 1995;Kollmann, 1995). In this case investors do not perceive domestic and foreign assets as perfectsubstitutes and will demand a currency risk premium to compensate for accepting risk exposure.To control for this possibility we include in the local information set the change in the local priceof the US$ (XRATE). Note also that in this case uncovered interest rate parity (UIP) may nothold, and that the exchange risk premium may be related to deviation from UIP. Therefore, thevariable LCFRI defined earlier as the difference between local and US real interest rates mayalso be related to a possible currency risk premium. For the US market we use the differencebetween the real rate on the one-month Euro$ deposit and on the one-month T-Bill. Correlationsof the global and local information variables, not reported here for the sake of brevity, are low,which suggests that they reflect distinct elements of the investors information set.

5. Results

We conduct our empirical investigation in three steps. First, we investigate the dynamics ofthe conditional second moments of returns and test whether the diagonal or the bi-diagonalmultivariate GARCH model provides a better description of the data. We then proceed to our


estimation of the international CAPM with partial segmentation and discuss the results of ourtests of financial integration. Finally we conduct robustness tests and investigate whether otherfactors may explain our results.

5.1. Diagonal versus bi-diagonal GARCH(1,1) model

We estimate the partially segmented International CAPM first with a diagonal and thenwith a bi-diagonal stationary multivariate GARCH(1,1) specification for the dynamics of thecovariance process. As our earlier discussion indicates, the diagonal specification constraintsthe coefficients of the covariance processes to be identical to the coefficients of the varianceprocess, while the bi-diagonal specification relaxes this restriction. Panel A ofTable 2shows theresults of a likelihood ratio test of the diagonal versus bi-diagonal specification. The test rejectsthe diagonal in favor of the bi-diagonal specification at any conventional level of significanceand indicates that the latter fits the data significantly better. Although not reported here, similarinferences were conveyed by the Akaike and Schwarz criterions. Panel B ofTable 2displaysthe parameter estimates of the bi-diagonal stationary multivariate GARCH(1,1). The GARCHprocess parameters ofai and bi obtained from all assets are significant and estimates ofbicoefficients are larger thanai as is typical in most studies that use GARCH models and displayhigh persistence. All estimates satisfy the stationary conditionsaiaj+bibj < 1 for all i andj. Thebi-diagonal parametersa0

i andb0i are significant for Thailand and Malaysia, but not for Korea.

Panel C reports joint test of the significance of the diagonal and bi-diagonal coefficients of theAR and MA terms of the covariance process. The tests show that the bi-diagonal parametersa0 andb0 are jointly significantly different from zero. Furthermore for Korea, Thailand andMalaysia, the test rejects the equality of the coefficients of the variance processes and thecoefficient of the covariance processes.

Diagnostic statistics are presented in Panel D ofTable 2. Whether one uses the diagonalor bi-diagonal specification, the residual statistics are for the most part unchanged. The majordifference is that average mean residual is much closer to zero using the bi-diagonal rather thanthe diagonal specification, confirming the superior fit of the former. Pseudo-R2, not reportedin the table, also improve substantially when using the bi-diagonal specification. For mostcountries, skewness measures of the first moment and second moment were negative, implyingthat the distribution has a long left tail. The Ljung–Box statistic was also computed to test thenull hypothesis of zero autocorrelation, for a maximum of 12 lags, in both the standardizedresiduals (Q(z)) and the standardized residuals squared (Q(z2)). The results imply that, in themean returns, autocorrelation is left unexplained in Thailand and Hong Kong whereas, in thereturns volatility process autocorrelation is left unexplained for Japan.

Overall these results imply that using a conditional covariance process that accommodatesdifferences between the variance and covariance process yields a superior fit to the time-varyingsecond moments of the returns in our sample.

5.2. Segmented conditional international CAPM and mean returns

This section reports and discuss the results of the estimation and tests a conditional versionof the segmented international CAPM. Previous evidence suggests that the price of market risk


Table 2Test of the covariance process specification diagonal versus bi-diagonal GARCH(1,1)

χ2 df p value

Panel A: Likelihood ratio testBi-diagonal versus diagonal GARCH(1,1) 28.894 6 0.0001


Panel B: Covariance process coefficient estimates, bi-diagonal specificationa 0.091 0.299 0.453 0.308 0.424 0.294 0.252SE 0.055 0.051 0.096 0.051 0.028 0.076 0.035a 0 0.168 0.200 0.216SE 0.154 0.061 0.050b 0.961 0.735 0.690 0.882 0.818 0.812 0.729SE 0.019 0.278 0.186 0.030 0.030 0.033 0.247b 0 0.165 0.976 0.973SE 0.443 0.038 0.028

χ2 df p value

Panel C: Specification testsAre thea coefficients jointly equal to zero?

H0: a = 0 426.40 7 0.0000Are thea 0 coefficients jointly equal to zero?

H0: a0= 0 20.25 3 0.0002Are theb coefficients jointly equal to zero?

H0: b = 0 5422.10 7 0.0000Are theb 0 coefficients jointly equal to zero?

H0: b0 = 0 1347.96 3 0.0000Are a andb jointly equal toa0 andb0?

H0: a = a0, b = b0 24.74 6 0.0004


Panel D: Residual summary statisticsDiagonal GARCH(1,1)

Average 1.01 1.02 0.86 0.92 0.96 1.01 1.00Skewness −1.49∗ −0.05 −0.01 −0.44∗ −0.99∗ −1.68∗ −0.97∗Kurtosis 6.19∗ 0.56 1.15∗ 1.93∗ 4.31∗ 10.40∗ 3.16∗B–J 128.48∗ 41.60∗ 23.79∗ 13.28 38.40∗ 433.70∗ 26.00∗Q(z) 8.56 10.26 7.56 23.18 17.32 26.05 15.07Q(z2) 2.94 33.71 16.37 12.76 9.62 0.82 3.14ENLM 2.50 6.62 9.57 4.21 5.80 1.22 2.48

Bi-diagonal GARCH(1,1)Average −0.01 −0.09 −0.02 −0.78 −0.01 −0.00 −0.02Skewness −1.46∗ −0.06 0.02 −0.47∗ −1.01∗ −1.75∗ −1.00∗Kurtosis 5.95∗ 0.17 1.15∗ 1.86∗ 4.38∗ 10.68∗ 3.39∗B–J 116.30∗ 56.10∗ 23.80∗ 15.11∗ 40.80∗ 466.29∗ 28.47∗Q(z) 6.72 10.87 8.08 23.71 16.70 25.37 14.37Q(z2) 3.23 32.73 19.32 12.22 10.24 1.22 3.34ENLM 4.55 2.30 12.83 3.64 5.98 1.25 2.64

We estimate the conditional International CAPM with time-varying risk (Eq. (4)) using monthly dollar-denominated returns from January 1985to December 1998. Data for country equity indices and the world portfolio are from MSCI. The model relates the asset excess returnrit to its worldcovariance risk covt−1(rit ,rmt ) = hiNt and its country-specific risk vart−1(Resit ) = qit . The prices of risk are functions of a number of instruments,Zt−1, included in the investor’s information set. The world instruments (Zmt−1) include a constant, the world index dividend yield in excess of theone-month Eurodollar rate (XDPRW), the change in the US term premium (�USTP), the change in the one-month Eurodollar rate (�Euro$), and

the US default premium (USDP). The country specific instruments (Zdit−1) includes the month-to-month change in the short-term interest rate (SI)

and the local dividend price ratio in excess of short-term interest rate (XDPRL):rit = δm,t−1hiNt + δdi,t−1 ∗ qit + εit, εt |It−1 ∼ N(0, Ht)whereδm,t−1 = exp(K′

mZmt−1), δdi,t−1 = exp(K′

diZdit−1), andqt = D(Ht)− (hNt ∗ hNt)/hNNt.

The model is estimated with two alternative specifications of the covariance process.Diagonal GARCH(1,1):Ht = H0 ∗ (ii′ − aa′ − bb′)+ aa′ ∗ εt−1ε

′t−1 + bb′ ∗Ht−1

Bi-diagonal GARCH(1,1)Ht = C′C + [aa′ ∗ I + (a0a0′) ∗ (1 − I)] ∗ εt−1ε

′t−1 + [bb′ ∗ I + (b0b0′) ∗ (1 − I)] ∗Ht−1

whereCC′ = H0 ∗ (ii′ − [aa′ ∗ I + (a0a0′) ∗ (1 − I)]′ − [bb′ ∗ I + (b0b0′) ∗ (1 − I)]), H0 is the unconditional covariance matrix of residuals,i isa (N × 1) unit vector,I is the identity matrix,1 is a matrix of ones anda, a0, b andb0 are (N × 1) vectors of unknown parameters. Robust standarderrors are reported in italics. The specification tests are performed as robust Wald on the estimated coefficients. B–J is the Bera–Jacque test statisticfor normality, whileQ(z) andQ(z2) are the Ljung–Box test statistic of order 12 for the standardized residuals and standardized residuals squared,respectively. ENLM is the Engle–Ng test of predictability of the conditional second moments using the instruments.

∗ Statistical significance at the 5% level.


may be time-varying (see, for example,Bekaert & Harvey, 1995). Adler and Dumas (1983)show the price of world market risk to be a weighted average of the coefficients of risk aversionof all national investors. Since evidence suggests that most investors are risk averse the priceof market risk must be positive. Therefore, we model the dynamics of the price of market risk(δm) as a positive function of common information variable,δm,t−1 = exp(K′

mZmt−1), where

Zmt−1 is a set of global information variables observed at the end of timet − 1. The instrumentsinclude a constant, lagged excess world dividend price ratio (X�PRW), the change in the USterm premium (�USTP), change in one-month Euro US$ deposit rate (�Euro$), and the USdefault premium: Baa–Aaa (USDP). Since instrument set includes a constant, a test of whetherthe price of risk is constant can be performed by testing the hypothesis whether the coefficientsof all the time-varying variables in the price of risk are jointly equal to zero.

To estimate the price of domestic risk in a time-varying fashion it is necessary to allowδdi,t−1 = exp(K′

diZdit−1), whereZdit−1 is a set of local information variables for countryi observed

at the end of timet − 1. In the model ofErrunza and Losq (1985), the price of domestic riskmust be positive. For parsimony, we include a constant, the excess local dividend price ratio(X�PRL) and the changes in the level of short-term interest rate (SI) in each country localinformation set. Clearly these local information variables will not fully reflect local economicconditions; hence tests based on their ability to predict the price of domestic risk may be biased.

Table 3, Panel A reports the mean equation parameter estimates while Panel B reports theresults of specification tests. Turn first to the price of market risk. The parameter estimates arereported in the last column of Panel A. The point estimates of the constant and the coefficientsof XDPRW,�USTP and USDP are statistically significant while the coefficient of�Euro$is not. Moreover the robust Wald tests reported in Panel B indicate that all parameters of theprice of market risk are jointly different from zero. This suggests that the price of market riskis different from zero. Second the tests show that the coefficients of the time-varying variablesconditioning the price of market risk are also jointly different from zero. This indicates that theprice of market risk vary significantly over time.

Turning next to the price of local residual risk, we find that none of the coefficient pointestimates are significant. The joint tests reported in Panel B confirm these results. First thehypothesis that the estimated coefficients of the price of local risk are equal to zero jointlyfor all markets cannot be rejected any level of significance. This suggest that domestic riskis not a priced factor. This results is confirmed by the single country tests: for none of thecountries included in the sample was the local risk priced, whether we considered developed oremerging markets. In brief, no evidence of segmentation is detected by our formal statistical testsof the partially segmented international CAPM. This suggests that over the sample period theEast-Asian markets considered were fully integrated components of the world financial markets.

Pseudo-R2 for the estimated model are reported at the bottom of Panel A. They are com-puted as the ratio of the estimated model sum of squares to the total sum of squares, andare used to measure the success of the model in fitting returns in sample.R2

M measures theexplanatory power of the estimated market risk factor only, whileR2

M+C measures the ex-planatory power of both global and local factors jointly. Including the domestic risk factorworsens the model’s fit for four out of six equity markets. Only for Korea and Thailand doesthe inclusion of the domestic risk factor improves the model fit, even though, based on theWald, local risk is not statistically for these two countries. For Korea, the pseudo-R2 more


Table 3Quasi-maximum likelihood estimates of the segmented conditional International CAPM with time-varying pricesof risk

Kdi Km


Panel A: Mean equation parameters estimatesConstant 0.003 0.008 0.000 0.012 0.008 −0.005 −5.66SE 0.045 0.030 0.010 0.010 0.010 0.014 1.43SI 0.007 0.009 0.020 −0.013 0.020 −0.009SE 0.211 0.220 0.030 0.024 0.060 0.040XDPRL(W) −0.018 −0.002 −0.000 −0.001 0.001 0.015 3.66SE 0.010 0.004 0.001 0.002 0.002 0.030 1.89�USTP −2.08SE 0.78�Euro$ 1.31SE 1.98USDP 3.44SE 1.27

R2M −1.37 7.84 2.41 0.49 −2.56 −1.98 3.53R2M+D −1.85 3.74 4.91 3.46 −4.17 −2.23

χ2 df p value

Panel B: Specification tests(a) Price of world market risk

Is the price of market risk equal to zero?H0: Km,k = 0, ∀ k 60.638 5 0.000

Is the price of market risk constant?H0: Km,k = 0, ∀ k > 1 13.449 4 0.000

(b) Price of local market residual risk—joint testAre all the coefficients of country specific risk jointly equal to zero?

H0: Kdi,l = 0, ∀ i,l 0.070 18 1.000

(c) Price of local market residual risk—single country testsIs the price of US domestic risk equal to zero?

H0: KUS,l = 0, ∀ l 0.000 3 1.000Is the price of Japan domestic risk equal to zero?

H0: Kjapan,l = 0, ∀ l 0.000 3 1.000Is the price of Korea domestic risk equal to zero?

H0: Kkorea,l = 0, ∀ l 0.029 3 0.998Is the price of Thai domestic risk equal to zero?

H0: Kthai,l = 0, ∀ l 0.031 3 0.998Is the price of Malaysia domestic risk equal to zero?

H0: Kmalay,l = 0, ∀ l 0.001 3 0.998Is the price of Hong Kong domestic risk equal to zero?

H0: KH.K.,l = 0, ∀ l 0.000 3 1.000

We estimate the segmented conditional International CAPM with time-varying risk using monthly dollar-denominated returns from January 1985to December 1998. Data for country equity indices and the world portfolio are from MSCI. The model relates the asset excess returnrit to its worldcovariance risk covt−1(rit ,rmt ) = hiNt and its country-specific risk vart−1(Resit ) = qit . The prices of risk are functions of a number of instruments,Zt−1, included in the investor’s information set. The world instruments (Zmt−1) include a constant, the world index dividend yield in excess of theone-month Eurodollar rate (XDPRW), the change in the US term premium (�USTP), the change in the one-month Eurodollar rate (�Euro$), andthe US default premium (USDP). The country specific instruments (Z

dit−1) includes the month-to-month change in the short-term interest rate (SI)

and the local dividend price ratio in excess of short-term interest rate (XDPRL):rit = δm,t−1hiNt + δdi,t−1 ∗ qit + εit, εt |It−1 ∼ N(0, Ht)whereδm,t−1 = exp(K′

mZmt−1), δdi,t−1 = exp(K′

diZdit−1) and qt = D(Ht)− (hNt ∗ hNt)/hNNt.

The conditional covariance process is parameterised as bi-diagonal GARCH(1,1):Ht = C′C + [aa′ ∗ I + (a0a0′) ∗ (1 − I)] ∗ εt−1ε

′t−1 + [bb′ ∗ I + (b0b0′) ∗ (1 − I)] ∗Ht−1

whereCC′ = H0 ∗ (ii′ − [aa′ ∗ I + (a0a0′) ∗ (1 − I)]′ − [bb′ ∗ I + (b0b0′) ∗ (1 − I)]), H0 is the unconditional covariance matrix of residuals,i isa (N × 1) unit vector,I is the identity matrix,1 is a matrix of ones anda, a0, b andb0 are (N × 1) vectors of unknown parameters. Robust standarderrors are reported in italics.R2

M is the pseudo-R2 when world market risk is the only priced factor, andR2M+D is the pseudo-R2 when both world

and local market risk are priced factors. The specification tests are performed as robust Wald on the estimated coefficients.


than doubles, from 2.41% to 4.91% when the local factor is included. For Thailand the in-crease is even more substantial, from 0.49% for the world market factor alone to 3.46% forthe world plus the local factor. These results suggests that local factors may be important inexplaining some emerging markets returns, even tough our statistical tests may lack power todetect it.

To further investigate the ability of the model to explain country specific risk, we performa robust generalized least square regression (GLS) of the model’s estimated residuals on thelocal information variables. The estimated GARCH covariance matrix is used as the weightingmatrix for each time period. The results of these regressions, not reported for the sake of brevity,suggest that local information variables have no power in explaining the model residual returnsfor developed markets. However, we find that local variables have some power, albeit limitedin explaining emerging market residual returns. This finding, coupled with the earlier findingof the importance of local factor in terms of pseudo-R2, suggests that our model may lack theflexibility to fully detect the importance of local risk, possibly due to the time-varying natureof the degree of financial integration.

5.3. Robustness checks: exchange risk and local factors

Recent evidence (De Santis & Gerard, 1998; Dumas & Solnik, 1995) suggests that in additionto world market risk, currency risk is priced in international equity returns. To investigatewhether this is the case in our sample, we expand our pricingEq. (3)to include, in addition tomarket and domestic risk, a set of exchange rate related information variables:

E(Rit|It−1)−Rft = φdiZXit−1+δm,t−1 cov(Rit, Rmt|It−1)+δdi,t−1 var(Resit|It−1), ∀i (7)

whereZXit−1 is countryi exchange rate fluctuation information variables.Panel A ofTable 4reports the estimates of the parameters of the mean equations. The

results for the price of market risk are similar to those obtained inTable 3, when exchange ratevariables were not included. The coefficients on XDPRW,�USTP and USDP are significantat the 5% level while the coefficient on�Euro$ is not. In contrast to the previous table, forthe price of domestic risk, the coefficient of XDPRL is significant in four out of six countriesat the 5% level. Turning to exchange rate risk related variables, the coefficient estimates ofXRATE and LCFRI are statistically significant at any standard level for every country,4 exceptfor the coefficient of the real interest rate differential in Japan. These results must be takenwith caution however, for Thailand and Hong Kong as their exchange rates were pegged tothe dollar prior to the crisis. However, the evidence suggests that, for these two countries, atleast after the inception of the crisis, equity returns were significantly related to exchange ratechanges.

Panel B ofTable 4report specification tests forEq. (7). As in Table 3, the tests indicatethat even when exchange risk is explicitly included, the coefficients of the price of world mar-ket risk are jointly highly significantly different from zero and that the price of market risk istime-varying. In this specification as well, we cannot reject the hypothesis that domestic resid-ual risk is not priced. Although the evidence suggests that the equity markets in our sampleare fully integrated, it is important to point out that our results maybe partially biased becausecurrency risk is not included as a pricing factor in the estimated model, even though exchange


Table 4Currency risk and the segmented conditional International CAPM


Panel A: Mean equation parameters estimatesCoefficients of the price of local residual risk,Kdi

Constant 0.009 0.007 0.000 0.014 0.010 0.003SE 0.006 0.030 0.010 0.010 0.010 0.011SI 0.007 0.110 0.022 −0.010 0.012 −0.013SE 0.330 0.210 0.060 0.020 0.059 0.040XDPRL −0.022 0.003 0.007 −0.003 0.001 0.020SE 0.010 0.004 0.001 0.001 0.002 0.003

Currency risk factor proxy variables coefficients,φdiXRATE – 1.71 15.87 −9.76 53.43 381.90SE – 0.33 0.00 0.01 0.06 0.01LCFRI 0.10 0.22 0.13 0.53 0.55 0.11SE 0.01 0.03 0.21 0.00 0.01 0.03

Const XDPRW �USTP �Euro$ USDP

Coefficients of the price of world market risk,KmKm −5.78 3.83 −2.14 −1.01 3.51SE 1.87 2.04 0.71 2.22 1.56


R2M+X 0.46 7.85 0.85 −0.05 1.05 −1.63 3.88R2M+D+X 0.36 3.83 8.74 3.28 0.04 −1.97

χ2 df p value

Panel B: Specification tests(a) Price of world market risk

Is the price of market risk equal to zero?H0: Km,k = 0, ∀ k 50.430 5 0.000

Is the price of market risk constant?H0: Km,k = 0, ∀ k > 1 12.802 4 0.012

(b) Price of local market residual risk—joint testAre all the coefficients of country specific risk jointly equal to zero?

H0: Kdi,l = 0, ∀ i,l 0.204 18 1.000

(c) Local currency risk factorAre all the coefficients of the local currency risk proxy variables jointly equal to zero?

H0: φdi,q = 0, ∀ i,q 43.001 11 0.000Are the coefficients of the local exchange rate change jointly equal to zero?

H0: φdi,XRATE = 0, ∀ I 13.251 5 0.025Are the coefficients of the local real risk free differential jointly equal to zero?

H0: φdi,LCFRI = 0, ∀ i 34.155 6 0.000

We estimate an augmented version of the segmented conditional International CAPM which relates the asset excess returnrit to its world covariancerisk covt−1(rit ,rmt ) = hiNt , its country-specific risk vart−1(Resit ) = qit as well as variables proxying for a currency risk factor. The price of globalrisk is a functions of global instruments (Zmt−1) which include a constant, the world index dividend yield in excess of the one-month Eurodollar rate(XDPRW), the change in the US term premium (�USTP), the change in the one-month Eurodollar rate (�Euro$), and the US default premium(USDP). The price of country risk is conditioned on local instruments (Z

dit−1), the month-to-month change in the short-term interest rate (SI) and the

local dividend price ratio in excess of short-term interest rate (XDPRL). The currency risk variables are the change in the local exchange rate to theUS$ (XRATE) and the difference between local and US real risk free interest rates (LCFRI):rit = φ′

diZXit−1 + δm,t−1hiNt + δdi,t−1 ∗ qit + εit, εt |It−1 ∼ N(0, Ht)

whereδm,t−1 = exp(K′mZ

mt−1), δdi,t−1 = exp(K′

diZdit−1), andqt = D(Ht)− (hNt ∗ hNt)/hNNt.

The conditional covariance process is parameterized as bi-diagonal GARCH(1,1):Ht = C′C + [aa′ ∗ I + (a0a0′) ∗ (1 − I)] ∗ εt−1ε

′t−1 + [bb′ ∗ I + (b0b0′) ∗ (1 − I)] ∗Ht−1

whereCC′ = H0 ∗ (ii′ − [aa′ ∗ I + (a0a0′) ∗ (1 − I)]′ − [bb′ ∗ I + (b0b0′) ∗ (1 − I)]), H0 is the unconditional covariance matrix of residuals,i isa (N × 1) unit vector,I is the identity matrix,1 is a matrix of ones anda, a0, b andb0 are (N × 1) vectors of unknown parameters. Robust standarderrors are reported in italics.R2

M+X is the pseudo-R2 when world market currency risk are the priced factor, andR2M+D+X is the pseudo-R2 when both

world and local market risk and currency risk are priced factors. The specification tests are performed as robust Wald on the estimated coefficients.


rate changes are included as explanatory variable. AsDe Santis and Gerard (1998), amongothers, have found, currency risk premiums are significantly different from zero and, therefore,models of international asset pricing that only include market risk are misspecified. Althoughwe found it impossible with current technology to estimate a model similar toDe Santis andGerard (1998)in which exposure to currency risk is explicitly included as a time-varyingpricing factor, including exchange rate variables as conditioning variables in the mean equa-tions provide evidence of the importance of currency risk for equity returns. The Wald testsshow that the two exchange rate related conditioning variables are both jointly and individ-ually highly significant across markets. Furthermore, including these variables improves the

Table 5Test of intercepts in the conditional International CAPM


Panel A: Mean equation parameters estimateCountry specific interceptsαi 0.499 0.330 0.240 1.264 0.561 1.130 0.441SE 0.462 0.689 0.732 1.101 0.831 0.950 0.450

Const XDPRW �USTP �Euro$ USDP

Coefficients of the price of world market risk,KmKmi −7.95 3.92 −2.35 −2.03 4.93SE 2.81 2.45 1.12 2.54 1.69


R2M −0.085 7.41 2.11 −0.08 −2.38 −1.77 3.89

χ2 df p value

Panel B: Hypothesis testsAre the intercepts jointly equal to zero?

H0: αi = 0, ∀ i 2.757 7 0.907Is the price of market risk equal to zero?

H0: Km,k = 0, ∀ k 32.612 5 0.000Is the price of market risk constant?

H0: Km,k = 0, ∀ k > 1 12.141 4 0.016

We estimate a conditional version of the traditional International CAPM which relates the asset excess returnritto its world covariance risk covt−1(rit ,rmt) = hiNt , only. We include an intercept in the mean equation to proxy foromitted local variables. The price of global risk is a functions of global instruments (Zmt−1) which include a constant,the world index yield dividend yield in excess of the one-month Eurodollar rate (XDPRW), the change in the US termpremium (�USTP), the change in the one-month Eurodollar rate (�Euro$), and the US default premium (USDP):rit = αi + δm,t−1hiNt + εit, εt |It−1 ∼ N(0, Ht)whereδm,t−1 = exp(K′

mZmt−1).

The conditional covariance process is parametrized as bi-diagonal GARCH(1,1):Ht = C′C + [aa′ ∗ I + (a0a0′) ∗ (1 − I)] ∗ εt−1ε

′t−1 + [bb′ ∗ I + (b0b0′) ∗ (1 − I)] ∗Ht−1

whereCC′ = H0 ∗ (ii′ − [aa′ ∗ I + (a0a0′) ∗ (1 − I)] ′ − [bb′ ∗ I + (b0b0′) ∗ (1 − I)]), H0 is the unconditionalcovariance matrix of residuals,i is a (N×1) unit vector,I is the identity matrix,1 is a matrix of ones anda, a0, b andb0 are (N × 1) vectors of unknown parameters. Robust standard errors are reported in italics.R2

M is the pseudo-R2

when the world market is the only priced factor.


model’s pseudo-R2 for all markets except Thailand. The results for Thailand are not surpris-ing as the bath was pegged to the dollar for most of the sample period. Overall these re-sults underscore the significant impact of exchange rate risk on international equity returnsand the importance of explicitly including currency risk in test of international asset pricingmodels.

As a further robustness test, we estimate a simple conditional version of the internationalCAPM in which world market risk exposure is the single common pricing factor of equity re-turns but which includes as well a country specific constant. Formally, the model can be writtenas follows:

E(Rit|It−1)− Rft = αi + δm,t−1 cov(Rit, Rmt|It−1), ∀i (8)

whereαi is a set of country specific constants.Including country specific constants can be interpreted as a measure of mild segmentation or

as an average measure of other factors that cannot be captured by our model, like differences intax treatment, information or transaction costs across countries. A finding of significant countryspecific constants can be interpreted as a rejection of the international CAPM and/or of thehypothesis of market integration.Table 5contains the estimation results for this version of themodel. Panel A reports parameters estimates for the mean equation. Panel B reports the results ofjoint hypothesis tests. As far as the price of market risk is concerned, both the coefficients pointestimates and the results joint tests are similar to those reported inTables 3 and 4where residualcountry risk was also considered. The evidence confirms the importance of world market riskin pricing the equity markets included in our sample. Turning to the market specific constants,the Table shows that none of the estimated intercepts are individually significantly differentfrom zero. Moreover, the Wald test indicates that the intercepts are not jointly significant.Overall our results suggest that our sample of East Asian markets are integrated in worldequity markets and that the world covariance risk is a significant pricing factor across all thesemarkets.

6. Conclusion

This study tests a conditional version of the international CAPM where the world market anddomestic risk are explicitly parameterized as independent pricing factors. We model conditionalsecond moments using a novel bi-diagonal multivariate GARCH(1,1) process. We documentthat this novel GARCH specification provides a significantly better fit of the covariance processof emerging market returns than a standard diagonal specification. Our methodology is fullyparametric enabling us to use the model estimates to investigate the relative magnitude and thedynamics of both the domestic and world risk premiums. Surprisingly, little or no evidence ofmarket segmentation in South East Asia is uncovered over the period from 1985 to 1998, al-though the last 18 months of our sample cover the Asian crisis that, according to most observers,started in July 97 with the floating of the Thai baht.

Conceptually, the integration process should affect a number of financial variables. When acapital market becomes more integrated, the firm has more opportunities to attract investmentfunds and inefficient operations should be abandoned. This likely decreases the cost of capital


and risk in the firm. In the context of an international CAPM this will be experienced asa decline in price of domestic risk and asset volatility. However, when a market is openedto international investors, it may become more sensitive to world events, which drives thecovariance of asset’s return and the world portfolio. For these reasons, changes in correlationsbetween emerging markets and the world market may shed some light on the issues of capitalmarket liberalization and integration. Fortunately, our model provides estimates of the timeseries cross-border correlation.Fig. 1contains plots of the estimated correlations between theworld market and the six equity markets included in the study. To filter out some of the highfrequency estimation error in the point estimates of correlation, we also plot the H–P filteredcorrelations (Hodrick & Prescott, 1997).

Despite the previous finding of market integration in emerging market returns, the esti-mated correlations suggests a low level of relatedness of these markets with the rest of thedeveloped markets. Estimated correlations between developed countries and the world marketoften exceed 0.7 while for most emerging markets, these correlations are on average lowerthan 0.5. In our study, by far the highest estimated correlation is observed for the US (0.9on average), as would be expected given the high portion of US capitalization in the worldindex. Japan also exhibits correlations over 0.7 in most periods. Hong Kong and Malaysiahave an average estimated correlation of about 0.5, while for Korea and Thailand the aver-ages are 0.3 and 0.4, respectively. Despite the fact that several financial liberalization programswere implemented in South East Asia emerging markets during our sample period, there isno evidence of increased relatedness with developed countries in the period prior to the on-set of the Asian crisis. In fact the filtered series in the plot reveals that the correlations arerelatively unchanged in most of emerging market countries. This evidence suggests that al-though our asset pricing tests do not detect segmentation, East Asian emerging markets maynot be fully integrated. The evidence of some segmentation in these markets is consistentwith the investment environment. For example, despite its recent liberalization, the Koreanmarket is still not fully accessible to foreign investors. Foreign ownership is limited to only10% in so-called unlimited industries and 8% in limited industries (which includes commu-nications and defense). Recently, the 10% ceiling was raised to 25% for 45 firms that had hitthe 10% cap. In the case of Thailand, most Thai stocks still have foreign ownership lim-its (seeBailey & Jagtiani, 1994for details on foreign investment restrictions in Thailandprior to 1993). Among emerging markets in the sample, Malaysia appears to have enjoyedthe most liberalized investment climate over most of the sample period. Although foreigninvestment was limited by the Foreign Investment Committee to 30% of equity, it appearsthat foreigners still played a large role in the Malaysian market. By the end of 1992, for-eign participation in Malaysian equities was 27%. However, this liberalization process hascome to an abrupt halt in September 1998, when severe restrictions to foreign investmentwere imposed. All this suggests that East Asian emerging markets may not yet be fully inte-grated.

The scope of our conclusions may be limited for several reasons. First currency-risk is notspecifically included as a pricing factor in the model, although we provide some evidence of theimportance of exchange risk in explaining the cross-section of expect equity returns. Second,the selected local information variables may not capture adequately expectations about localeconomic conditions in each emerging market. Hence, special care must be taken in interpreting


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Fig. 1. Estimated correlations with the world portfolio, raw and HP filtered.

our findings. Our implementation has also ignored dynamic interactions between changes inbarriers to investment and market returns, or time variation in the degree of market segmen-tation.Carrieri, Errunza, and Sarkissian (2002)provide evidence that this may of importance.Furthermore, the MSCI world market index we use may not be a good representative of the


World market portfolio in relation to the set of assets investigated. AsHarvey (1991)states,the MSCI world market portfolio is really an industrial world market portfolio. Lastly, the IFCindices reflect the local prices of (investable) equities included in the index.Bailey, Chung andKang (1999)provide evidence that emerging market stocks that are traded internationally maycommand a vastly different price when traded among foreign investors than on their domesticmarkets. This suggests that returns we use in our tests may not reflect all the pricing effects ofbarriers to investments. Further research is needed to address these issues.

Notes

1. The stock market capitalization as a percent of GDP was 244.8% for Hong Kong, 30.7%for Korea, 26.3% for Thailand and 134.4% for Malaysia in 1999. These four countriesalso accounted for US $38.3 billion of new equity issues in the period from 1996 to 2000compared with US $31.4 billion in Japan during the same period.

2. Although the model was initially developed in a single period framework,Merton (1973)shows that it can be extended to an intertemporal setting by assuming for example thatinvestors have log utility.

3. Equation (2), although correct for country index portfolios, is not strictly valid for in-dividual risky securities. However, it can be generalized for any asset by substituting tothe variance of the nondiversifiable local market risk, the covariance between the assetreturns and the component of its local market portfolio returns orthogonal to the worldmarket returns.

4. This result is unchanged with the substitution of SI to RI.

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