Are the East Asian markets integrated? Evidence from the ICAPM

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

Are the East Asian markets integrated? Evidence from the ICAPM Bruno Gérard a,∗ , Kessara Thanyalakpark b,1 , Jonathan A. Batten c,2 a

b

Department of Financial Economics, Norwegian School of Management, BI, Elias Smiths vei 15, P.O. Box 580, N-1302 Sandvika, Norway 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 risk included as independent pricing factors for five East Asian markets, the US and World markets. We model second moments and risk exposures using a bi-diagonal multivariate GARCH(1,1) process. We document that this novel GARCH specification provides a significantly better fit of the return process than a standard diagonal specification. Although exposure to world market risk carries a significant premium across all markets, we find little support for the hypothesis that exposure to residual country risk is rewarded. However, residual country returns are significantly related to exchange rate changes. Hence, we find surprisingly little evidence of market segmentation in East Asia over the period 1985–1998. © 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. G´erard), [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

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1. Introduction A persistent issue in the field of international finance is the extent to which international financial markets are integrated. In fully integrated capital markets, the same asset pricing relationships apply in all countries, and firms should use similar decision rules and evaluation criteria regardless of their geographical location (Brealey, Cooper, & Kaplanis, 1999). When markets are segmented on the other hand, the risk return relationship varies across countries and a project which might be considered to provide an attractive return in one country, could prove to be unsatisfactory in another. Financial market segmentation could arise, for example, from market 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 that different national markets exhibit different level of integration to international financial markets and 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 important implications for firms and investors: First, the cost of capital can be substantially different among mildly segmented capital markets. Second, if national stock markets are segmented, then international portfolios should provide superior risk adjusted performance since some of the domestic systematic risk can be diversified away by investing internationally without paying a price in terms of lower returns. Empirical studies investigating financial integration have tended to focus on developed markets (e.g., Bekaert & Harvey, 1995; Campbell & Hamao, 1992; Carrieri, Errunza, & Sarkissian, 2002; Jorion & Schwartz, 1986; Korajczyk & Viallet, 1989). Recently, more papers have focused on emerging markets, and several studies have documented the high returns and low correlations of these markets with the rest of the world, suggesting significant benefits from adding emerging markets to global portfolios (e.g., Bekaert, 1999; Bekaert, Erb, Harvey, & Viskanta, 1998; De Santis & Imrohoroglu, 1997). However, the potential benefits of diversifying into emerging markets may be jeopardised by the direct and indirect forms of investment barriers applied to foreign investors (Bekaert & Harvey, 2000). Such restrictions on capital flows are widely 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 or investors may find innovative ways to circumvent legal restrictions (Bonser-Neal, Brauer, Neal, & Wheatley, 1990; Glassman & Riddick, 1996). Determining the extent to which a national equity market is segmented from international financial markets is thus an empirical question of great interest to both investors and researchers. The aim of this paper is to investigate whether key markets in the East Asian region are fully integrated into or partially segmented from the world financial markets. Our study is conducted within the framework of the partially segmented international asset pricing model of Errunza and Losq (1985, 1989) which takes into account the fact that some markets may not be fully integrated in world markets. If capital markets are fully integrated, the expected return of a country portfolio should solely be determined by the country’s exposure to world covariance risk. In contrast, segmentation implies that the risk-return relation in each national market is determined primarily by domestic factors. Thus, when capital markets are partially segmented, expected returns would be determined by the country’s exposure to both world and country specific risk factors.

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A growing body of literature documents the time-varying nature of expected returns and risk 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 feature of the data, we estimate a conditional version of the asset pricing model, in which both the prices of risk and the risk exposures change over time. We use global information variables to condition 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 of the local equity market, this specification implicitly allows the degree of integration to change over time. We use the parsimonious stationary diagonal generalized autoregressive conditional heteroskedasticity (GARCH)-in-mean approach developed by De Santis and Gerard (1997) to accommodate 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 some of the largest emerging and developed markets in East Asia as well as the world and US markets in which both the conditional measures of risk and their prices are time-varying. Second, we implement a novel specification of the diagonal GARCH process (that we call bi-diagonal GARCH) which while remaining very parsimonious allows for a differential impact of local return surprises on the covariance between two emerging markets and on the covariance between emerging and developed markets. Finally, since our method is fully parametric we can study the dynamics and relative magnitudes of global and local risk premiums. We investigate whether, over the period January 1985 to December 1998, five key markets in the East Asian region are fully integrated into or partially segmented from the world financial markets. The five markets include the three emerging markets of Korea, Malaysia and Thailand and the two developed markets of Hong Kong and Japan. These five markets are among the largest equity markets in the region in either nominal terms, or relative to gross domestic product (GDP), and have been subject to significant investment flows.1 We study whether these five Asian markets are fully integrated or partially segmented relative to a world portfolio of developed 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 five Asian markets in our study. Although the premium for world market risk is significant for all assets, the prices and associated premiums for domestic risks are not significant. However, we find that residual returns are significantly related to exchange rate variables. This suggests that although domestic returns volatility is not priced, exposure to currency risk may underlie the cross-country differences in expected returns. We also find that the bi-diagonal specification of the GARCH process fits the data significantly better than the simple diagonal GARCH specification. This suggests that, not surprisingly, local return shocks in emerging markets have differential impact on the return covariance between emerging markets than on their covariance with 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 of Carrieri, Errunza, and Hogan (2002). Carrieri et al. document that, over the period from 1976 to 2000, the degree of financial integration of eight emerging markets with world markets increase significantly, and that this increased integration coincide with the different market liberalization. This is consistent with our finding which pertains to the second half of their sample. Although Carrieri et al. use an asset pricing

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framework similar to ours, they perform univariate tests, while we conduct our investigation in a multivariate setting which presumably yields more power. However, our approach is unable yet to deal with time-varying integration as they do. The paper is organized as follows. Section 2 describes the conditional version of the International Capital Asset Pricing Model (ICAPM) where both world market risk and domestic risk are priced. Section 3 presents the empirical methodology. The data is described in Section 4. The results are reported in Section 5. Section 6 concludes.

2. The conditional version of the ICAPM Consider first a fully integrated international financial market in which purchasing power parity holds. Under these assumptions, several authors (Adler & Dumas, 1983; Solnik, 1977; Stulz, 1981; Wheatley, 1988, 1989 among others) have extended the domestic Capital Asset Pricing Model of Sharpe (1964) and Lintner (1965) to an international setting. Formally, a conditional version of the model can be written as2 E(Rit |It−1 ) − Rft = δm,t−1 cov(Rit , Rmt |It−1 ),

∀i

(1)

where Rit is the return on asset i, Rft is the risk free rate and Rm,t−1 is the return on world market portfolio from time t − 1 and t, It−1 is the information set available at time t − 1 and δm,t−1 is the price of world market risk. One can view the price of market risk as the expected compensation 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 risk aversion coefficient, and thus has to be positive. Along the lines suggested by Merton (1973) and Bekaert and Harvey (1995), we will use this fact to impose a non-negativity constraint on the 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 bear no currency risk and the risk return relationship is unaffected by the choice of the reference currency (Sercu, 1980). In this model of fully integrated markets, only world covariance risk is priced in international equity markets and expected returns are not affected by domestic factors. However, the existence of explicit restrictions to capital flows in emerging markets, and the empirical record (e.g., Bekaert & Harvey, 1995, 1997) suggests that international capital markets, especially emerging markets, may not be fully integrated. Errunza and Losq (1985, 1989) extend the international CAPM to account for mild segmentation between markets: a subset of the assets is available to all investors, while ownership of the remaining assets is restricted to a subset of the investors. Under these assumptions, expected returns are a function of 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 be diversified away because of market segmentation. The conditional version of this model can be written as follows: E(Rit |It−1 ) − Rft = δm,t−1 cov(Rit , Rmt |It−1 ) + δdi ,t−1 var(Resit |It−1 ),

∀i

(2)

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where Rit 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 to global 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 on one unit of country specific nondiversifiable risk. When a national market is fully integrated in world markets, idiosyncratic domestic risk is fully diversifiable and its associated price would be zero. 3. Empirical methods Equation (2) seems to be the natural relation to use in empirical tests of market integration, as it takes into account investors use of new information to make investment decisions. Intuitively investors will use all the information at their disposal including country specific and global information variables. However, for the sake of parsimony, we assume that global information variables are used to condition the price of market risk (δm ) and local information variables are used to estimate the price of domestic risk (δdi ). The asset pricing model requires that the risk return relationship in (2) hold for all assets including the world market portfolio. If the world economy encompasses L countries, L + 1 pricing restrictions have to hold in each period: 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 var t−1 (ResLt |It−1 )

(3)

E(Rmt |It−1 ) − Rft = δm,t−1 var(Rmt |It−1 ) In empirical work, although ideally all assets should be included, any subset of N − 1 assets plus the market portfolio can be used. The trade-off is between manageability of the estimation and loss of information in the cross-correlations and a reduction in the power of the test of the asset pricing restrictions. Denote Rt the (N × 1) return vector of (N − 1) country portfolios and the world market portfolio. Then the following system of equations can be used to estimate and test the conditional version of the partially segmented international CAPM: Rt − Rft i = δm,t−1 hNt + 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 an N-dimensional vector of ones, Ht is the (N × N) conditional covariance matrix of asset returns. hNt is

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the Nth column the Ht and contains the (N × 1) vector of the conditional covariances of each asset with the world market portfolio. δd ,t−1 is the (N × 1) vector of the prices of domestic risk, qt is the (N × 1) vector of the residuals country volatility, D(Ht ) the diagonal components in Ht , and hNNt the (N,N) element of the covariance matrix which contains the variance of the world portfolio. Equation (4) follows directly from the asset pricing relation in (2). However, the model does not specify the dynamics of the conditional second moments. To complete the parameterization of the model, we use the parsimonious GARCH-in-mean specification developed by De Santis and Gerard (1997). This specification has two main features. First, the conditional second moments are assumed to follow a diagonal GARCH(1,1) process. Second the system is assumed to be covariance stationary. Hence the process for the Ht matrix can be written as Ht = H0 ∗ (ii − aa − bb ) + aa ∗ εt−1 ε t−1 + bb ∗ Ht−1

(5)

where H0 is the unconditional covariance matrix of residuals and a and b are (N × 1) vectors of unknown parameters. This specification has two advantages. First, it requires estimation of only 2N parameters and can thus be implemented for (relatively) large cross-sections of assets. Second it constraints the estimated 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 parameters drive 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 + (a0 a0 ) ∗ (1 − I)] ∗ εt−1 ε t−1

+ [bb ∗ I + (b0 b0 ) ∗ (1 − I)] ∗ Ht−1

(6)

where I is the identity matrix, 1 is a n × n matrix of ones and



CC = H0 ∗ (ii − [aa ∗ I + (a0 a0 ) ∗ (1 − I)] − [bb ∗ I + (b0 b0 ) ∗ (1 − I)]) The typical elements of the covariance processes are computed as follows: hiit = cii + ai2 ε2it−1 + bi2 hit−1 ,

∀i

hijt = cij + ai0 aj0 εit−1 εjt−1 + bi0 bj0 hijt−1 , ∀i,j where a0 and b0 are (N × 1) vectors of unknown parameters. When we estimate the model, we will set all the elements a0 and b0 equal to the elements a and b, except for the coefficients that correspond to the three emerging markets. This feature is built on a sensible intuition, which suggests that the conditional covariance process of an emerging market with another may differ from its covariance process with a developed country. We use Eqs. (3) and (6) as our benchmark model. We estimate the model and conduct all our tests using the quasi-maximum likelihood approach of Bollerslev and Wooldridge (1992). Statistical inference is carried out by computing robust Wald statistics. Optimization is performed in gauss using the BHHH algorithm.

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4. Data Our dataset includes two distinct groups of data: the returns series on which the asset pricing model is estimated and tested, and the global and local information variables used to condition the 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 for the world portfolio. The developed market returns as well as the world portfolio returns series are from Morgan Stanley Capital International (MSCI), and the emerging market returns data is from the International Financial Corporation (IFC). Our sample covers the period from January 1985 to December 1998. As Harvey and Zhou (1993) point out, there are some differences in the construction of these indices. For example, MSCI select firms based on liquidity float and cross-ownership, while IFC select firms based on size. Despite these differences, the index returns are highly correlated. Over the period when they are both available, the MSCI and IFC indices for the emerging countries have a correlation greater than 0.95. Returns are measured in excess of the return on one month euro dollar deposits. The one month euro dollar deposit rate is from the BIS and Datastream. To convert local currency returns into US dollar returns, we use the end of month exchange rates used by MSCI and the IFC for their respective indices. Note that our proxy for world equity market portfolio is the MSCI world index, which is a value-weighted portfolio of 20 developed markets, and hence does not include the three emerging markets in our sample. Table 1 reports summary statistics for the US dollar returns on the six countries and the world 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 correlations. Kurtosis indexes show that the unconditional distribution of excess returns has heavier tails than the normal distribution in most countries, except Japan. The resulting non-normality condition is also found in the Bera–Jacque statistics which uniformly reject the normality of the excess returns. Panel B reports the returns correlations. Panels C and D report autocorrelations for the returns and returns squared were also calculated. No significant autocorrelation is detected 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 other countries were also calculated and are reported in Panel D of Table 1. Only contemporaneous cross-correlations between each country and the world were significant. This suggests that in our sample, an AR correction in the mean equation is not necessary, while a GARCH model for the second moment process may be appropriate. 4.2. Information variables To reflect the information available to investors and condition our estimation we need to select both global and local information variables. These variables should, according to Harvey (1991), approximate the information that investors use to set prices and should also have some

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Table 1 Summary statistics of US dollar returns US

Japan

Korea

Thailand

Malaysia

Hong Kong

World

0.18 7.35 0.04 3.31 0.73 13.49

0.13 11.15 0.29 7.16 123.63 11.97

0.09 11.75 −0.66 5.54 57.60 39.96

−0.35 10.31 −0.31 6.52 89.53 29.26

0.99 9.18 −1.72 13.14 802.78 26.17

0.75 4.26 −0.98 6.18 97.71 13.77

Panel B: Unconditional correlation of rit US 1 Japan 0.25 1 Korea 0.21 0.38 Thailand 0.37 0.25 Malaysia 0.46 0.22 Hong Kong 0.53 0.22 World 0.77 0.76

1 0.38 0.25 0.18 0.32

1 0.67 0.60 0.40

1 0.62 0.45

1 0.53

0.07 0.09 −0.04 0.01 0.03 0.13

0.16 0.13 −0.07 −0.14 −0.08 −0.03

0.13 0.21 −0.09 −0.06 −0.04 −0.19

0.00 −0.03 −0.03 −0.13 −0.13 −0.04

0.03 −0.06 −0.06 −0.09 0.07 −0.10

Panel D: Autocorrelations and cross-correlations of rit2 Autocorrelations of rit2 Lag 1 0.09 0.14 0.50 Lag 2 0.02 0.10 0.34 Lag 3 0.01 −0.02 0.22 Lag 4 0.02 −0.03 0.11 Lag 5 −0.02 0.05 0.09 Lag 6 −0.01 0.08 0.12

0.21 0.35 0.19 0.33 0.20 0.13

0.14 0.19 0.37 0.22 0.05 0.41

−0.02 0.00 0.00 0.00 0.02 −0.03

0.02 0.05 −0.04 0.01 0.03 0.04

0.05 −0.03 −0.01 0.06 0.09 0.09 0.49 0.02 0.13 0.01 0.01 −0.04 0.01

0.23 −0.03 0.01 0.03 0.08 0.02 0.48 0.13 −0.02 0.04 −0.02 −0.05 0.04

−0.02 −0.02 0.02 −0.02 −0.04 0.02 0.67 −0.01 0.12 −0.07 −0.05 −0.03 0.00

Panel A: Summary statistics Mean 0.93 Standard deviation 4.40 Skewness −1.53 Kurtosis 9.60 B–J 370.69 Q 10.97

Panel C: Autocorrelations of rit Japan and Korea Lag 1 0.00 Lag 2 −0.06 Lag 3 −0.07 Lag 4 −0.16 Lag 5 0.06 Lag 6 −0.06

0.08 −0.04 0.04 0.03 0.06 −0.04

Cross-correlations of rit2 : world and asset j Lag −6 −0.01 0.19 Lag −5 −0.03 0.08 Lag −4 0.04 0.01 Lag −3 0.00 −0.01 Lag −2 0.02 0.08 Lag −1 0.00 −0.03 Lag 0 0.87 0.40 Lag 1 0.05 0.13 Lag 2 0.04 0.15 Lag 3 −0.02 −0.01 Lag 4 −0.04 −0.01 Lag 5 0.00 0.08 Lag 6 0.05 −0.04

−0.03 0.00 0.00 0.08 −0.01 0.05 0.03 −0.03 0.12 0.12 0.10 0.07 −0.04

1

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

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ability to predict returns. While it may be common to use financial market based information variables, such as dividend yield or bond yield, Dumas (1994) suggests the use of economic variables, which are “external” to the financial market, and reflect general economic conditions affecting 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 equity returns. This suggestion has intuitive appeal. For example, the aggregate degree of risk aversion and hence the price of market risk, may be higher during a recession. Therefore, it would seems natural that variables that are directly linked to the performance of the real economy would have 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, the premium for exposure to world market risk and the premium for exposure to the nondiversifiable local risk. We will use global variables to condition the price of market risk, while we will use local variables to condition the local premium. 4.2.1. World information variables The 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. As discussed earlier, the price of market risk is the reward that that investors receive for taking on a unit of covariance risk. It also measures the aggregate degree of risk aversion. The selection of the common information variables was drawn from previous studies in the international finance literature (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) finds that US market variables are at least as good predictors of worldwide rates of returns as country specific information variables. Harvey (1991) further shows that measures of the US term structure have 87% correlation with GDP weighted measures of the world term structure. In particular our global instruments include the following: X∆PRW is the lagged US dollar denominated MSCI World dividend price ratio in excess of the return on one month euro dollar deposits. ∆USTP is the month-to-month change in the US term premium, where the term premium is computed as the yield difference between the ten-year T-note and the three-month Treasury bill. ∆Euro$ is the month-to-month change in the one-month Euro$ deposit rate. Given the high proportion of US market capitalization in the world index, the change in the US interest rate 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’s Baa and Aaa rated bonds. This variable tracks changes in default risk as well as changes in investors risk aversion. 4.2.2. Country specific information variables To 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 condition the price of domestic risk. By nature, the set of local information variables we collect may not represent all country specific information available to investors, as we are restricted to the subset

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of variables available at monthly frequency for all countries over the whole sample period. Of course, some of these variables will be correlated with the world information variables. For example, local economic growth may dependent on whether the rest of the world economies are in an expansion or a recession. However, the degree of correlation is usually small. For example, Ferson and Harvey (1993) find less than 40% average correlation among dividend yields in the MSCI countries. We select variables that reflect macroeconomic data, financial data, scaled prices, and exchange rate related data. All definitions are given below. Two variables are chosen to reflect local macroeconomic conditions. The first, denoted SI is the month-to-month change in the local risk free short-term interest rate. It primarily reflects changes in local inflation expectations. The second is the difference between the US real short-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 interest rate and the previous month realized inflation rate. LCFRI reflects difference in time preferences across countries, and hence should be informative about the domestic price of risk. All interest rates and inflation variables are taken from the International Financial Statistics (IFS). Scaled prices variables reflect local stock market conditions. As Ferson and Harvey (1993) suggest, if expected returns differ across countries with investability, one might expect differences in valuation ratios to be related to differences in expected returns. Further scaled price variables 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 dividend price ratio in excess of the local short-term interest rate (XPRL). The dividend calculated as the difference between the change in the local currency total return index and the change in the local currency price index over a given month multiplied by the level of the price index. The data come from MSCI and IFC. If one assumes that purchasing power parity (PPP) does not hold, then in addition to global and local risk, expected returns depend on deviations from PPP (Adler & Dumas, 1983). Recent evidence 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 perfect substitutes 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 price of the US$ (XRATE). Note also that in this case uncovered interest rate parity (UIP) may not hold, and that the exchange risk premium may be related to deviation from UIP. Therefore, the variable LCFRI defined earlier as the difference between local and US real interest rates may also be related to a possible currency risk premium. For the US market we use the difference between the real rate on the one-month Euro$ deposit and on the one-month T-Bill. Correlations of 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 of the conditional second moments of returns and test whether the diagonal or the bi-diagonal multivariate GARCH model provides a better description of the data. We then proceed to our

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estimation of the international CAPM with partial segmentation and discuss the results of our tests of financial integration. Finally we conduct robustness tests and investigate whether other factors 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 then with a bi-diagonal stationary multivariate GARCH(1,1) specification for the dynamics of the covariance process. As our earlier discussion indicates, the diagonal specification constraints the coefficients of the covariance processes to be identical to the coefficients of the variance process, while the bi-diagonal specification relaxes this restriction. Panel A of Table 2 shows the results of a likelihood ratio test of the diagonal versus bi-diagonal specification. The test rejects the diagonal in favor of the bi-diagonal specification at any conventional level of significance and indicates that the latter fits the data significantly better. Although not reported here, similar inferences were conveyed by the Akaike and Schwarz criterions. Panel B of Table 2 displays the parameter estimates of the bi-diagonal stationary multivariate GARCH(1,1). The GARCH process parameters of ai and bi obtained from all assets are significant and estimates of bi coefficients are larger than ai as is typical in most studies that use GARCH models and display high persistence. All estimates satisfy the stationary conditions ai aj +bi bj < 1 for all i and j. The bi-diagonal parameters ai0 and bi0 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 the AR and MA terms of the covariance process. The tests show that the bi-diagonal parameters a0 and b0 are jointly significantly different from zero. Furthermore for Korea, Thailand and Malaysia, the test rejects the equality of the coefficients of the variance processes and the coefficient of the covariance processes. Diagnostic statistics are presented in Panel D of Table 2. Whether one uses the diagonal or bi-diagonal specification, the residual statistics are for the most part unchanged. The major difference is that average mean residual is much closer to zero using the bi-diagonal rather than the diagonal specification, confirming the superior fit of the former. Pseudo-R2 , not reported in the table, also improve substantially when using the bi-diagonal specification. For most countries, skewness measures of the first moment and second moment were negative, implying that the distribution has a long left tail. The Ljung–Box statistic was also computed to test the null hypothesis of zero autocorrelation, for a maximum of 12 lags, in both the standardized residuals (Q(z)) and the standardized residuals squared (Q(z2 )). The results imply that, in the mean returns, autocorrelation is left unexplained in Thailand and Hong Kong whereas, in the returns volatility process autocorrelation is left unexplained for Japan. Overall these results imply that using a conditional covariance process that accommodates differences between the variance and covariance process yields a superior fit to the time-varying second 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 version of the segmented international CAPM. Previous evidence suggests that the price of market risk

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Table 2 Test of the covariance process specification diagonal versus bi-diagonal GARCH(1,1) χ2 Panel A: Likelihood ratio test Bi-diagonal versus diagonal GARCH(1,1)

28.894 US

Japan

Panel B: Covariance process coefficient estimates, bi-diagonal specification a 0.091 0.299 SE 0.055 0.051 0 a SE b 0.961 0.735 SE 0.019 0.278 0 b SE

Bi-diagonal GARCH(1,1) Average Skewness Kurtosis B–J Q(z) Q(z2 ) ENLM

p value

6

0.0001

Korea

Thailand

Malaysia

Hong Kong

World

0.453 0.096 0.168 0.154 0.690 0.186 0.165 0.443

0.308 0.051 0.200 0.061 0.882 0.030 0.976 0.038

0.424 0.028 0.216 0.050 0.818 0.030 0.973 0.028

0.294 0.076

0.252 0.035

0.812 0.033

0.729 0.247

χ2

df

p value

426.40

7

0.0000

20.25

3

0.0002

5422.10

7

0.0000

1347.96

3

0.0000

Panel C: Specification tests Are the a coefficients jointly equal to zero? H0 : a = 0 Are the a 0 coefficients jointly equal to zero? H 0 : a0 = 0 Are the b coefficients jointly equal to zero? H0 : b = 0 Are the b 0 coefficients jointly equal to zero? H 0 : b0 = 0 Are a and b jointly equal to a0 and b0 ? H0 : a = a0 , b = b0 Panel D: Residual summary statistics Diagonal GARCH(1,1) Average Skewness Kurtosis B–J Q(z) Q(z2 ) ENLM

df

24.74

6

0.0004

US

Japan

Korea

Thailand

Malaysia

Hong Kong

World

1.01 −1.49∗ 6.19∗ 128.48∗ 8.56 2.94 2.50

1.02 −0.05 0.56 41.60∗ 10.26 33.71 6.62

0.86 −0.01 1.15∗ 23.79∗ 7.56 16.37 9.57

0.92 −0.44∗ 1.93∗ 13.28 23.18 12.76 4.21

0.96 −0.99∗ 4.31∗ 38.40∗ 17.32 9.62 5.80

1.01 −1.68∗ 10.40∗ 433.70∗ 26.05 0.82 1.22

1.00 −0.97∗ 3.16∗ 26.00∗ 15.07 3.14 2.48

−0.01 −1.46∗ 5.95∗ 116.30∗ 6.72 3.23 4.55

−0.09 −0.06 0.17 56.10∗ 10.87 32.73 2.30

−0.02 0.02 1.15∗ 23.80∗ 8.08 19.32 12.83

−0.78 −0.47∗ 1.86∗ 15.11∗ 23.71 12.22 3.64

−0.01 −1.01∗ 4.38∗ 40.80∗ 16.70 10.24 5.98

−0.00 −1.75∗ 10.68∗ 466.29∗ 25.37 1.22 1.25

−0.02 −1.00∗ 3.39∗ 28.47∗ 14.37 3.34 2.64

We estimate the conditional International CAPM with time-varying risk (Eq. (4)) using monthly dollar-denominated returns from January 1985 to December 1998. Data for country equity indices and the world portfolio are from MSCI. The model relates the asset excess return rit to its world covariance 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, m ) include a constant, the world index dividend yield in excess of the Zt −1, included in the investor’s information set. The world instruments (Zt−1 one-month Eurodollar rate (XDPRW), the change in the US term premium (USTP), the change in the one-month Eurodollar rate (Euro$), and di the US default premium (USDP). The country specific instruments (Zt−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−1 hiNt + δdi ,t−1 ∗ qit + εit , εt |It−1 ∼ N(0, Ht ) d

i m where δm,t−1 = exp(Km Zt−1 ), δdi ,t−1 = exp(Kd Zt−1 ), and qt = D(Ht ) − (hNt ∗ hNt )/ hNNt . i The model is estimated with two alternative specifications of the covariance process. Diagonal GARCH(1,1): Ht = H0 ∗ (ii − aa − bb ) + aa ∗ εt−1 ε t−1 + bb ∗ Ht−1 Bi-diagonal GARCH(1,1) Ht = C C + [aa ∗ I + (a0 a0 ) ∗ (1 − I)] ∗ εt−1 ε t−1 + [bb ∗ I + (b0 b0 ) ∗ (1 − I)] ∗ Ht−1 where CC = H0 ∗ (ii − [aa ∗ I + (a0 a0 ) ∗ (1 − I)] − [bb ∗ I + (b0 b0 ) ∗ (1 − I)]), H0 is the unconditional covariance matrix of residuals, i is a (N × 1) unit vector, I is the identity matrix, 1 is a matrix of ones and a, a0 , b and b0 are (N × 1) vectors of unknown parameters. Robust standard errors are reported in italics. The specification tests are performed as robust Wald on the estimated coefficients. B–J is the Bera–Jacque test statistic for normality, while Q(z) and Q(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.

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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 aversion of all national investors. Since evidence suggests that most investors are risk averse the price of market risk must be positive. Therefore, we model the dynamics of the price of market risk m (δm ) as a positive function of common information variable, δm,t−1 = exp(Km Zt−1 ), where m Zt−1 is a set of global information variables observed at the end of time t − 1. The instruments include a constant, lagged excess world dividend price ratio (XPRW), the change in the US term premium (USTP), change in one-month Euro US$ deposit rate (Euro$), and the US default premium: Baa–Aaa (USDP). Since instrument set includes a constant, a test of whether the price of risk is constant can be performed by testing the hypothesis whether the coefficients of all the time-varying variables in the price of risk are jointly equal to zero. To estimate the price of domestic risk in a time-varying fashion it is necessary to allow di di δdi ,t−1 = exp(Kd i Zt−1 ), where Zt−1 is a set of local information variables for country i observed at the end of time t − 1. In the model of Errunza and Losq (1985), the price of domestic risk must be positive. For parsimony, we include a constant, the excess local dividend price ratio (XPRL) and the changes in the level of short-term interest rate (SI) in each country local information set. Clearly these local information variables will not fully reflect local economic conditions; hence tests based on their ability to predict the price of domestic risk may be biased. Table 3, Panel A reports the mean equation parameter estimates while Panel B reports the results of specification tests. Turn first to the price of market risk. The parameter estimates are reported in the last column of Panel A. The point estimates of the constant and the coefficients of XDPRW, USTP and USDP are statistically significant while the coefficient of Euro$ is not. Moreover the robust Wald tests reported in Panel B indicate that all parameters of the price of market risk are jointly different from zero. This suggests that the price of market risk is different from zero. Second the tests show that the coefficients of the time-varying variables conditioning the price of market risk are also jointly different from zero. This indicates that the price of market risk vary significantly over time. Turning next to the price of local residual risk, we find that none of the coefficient point estimates are significant. The joint tests reported in Panel B confirm these results. First the hypothesis that the estimated coefficients of the price of local risk are equal to zero jointly for all markets cannot be rejected any level of significance. This suggest that domestic risk is not a priced factor. This results is confirmed by the single country tests: for none of the countries included in the sample was the local risk priced, whether we considered developed or emerging markets. In brief, no evidence of segmentation is detected by our formal statistical tests of the partially segmented international CAPM. This suggests that over the sample period the East-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 computed as the ratio of the estimated model sum of squares to the total sum of squares, and are used to measure the success of the model in fitting returns in sample. R2M measures the explanatory power of the estimated market risk factor only, while R2M+C measures the explanatory power of both global and local factors jointly. Including the domestic risk factor worsens the model’s fit for four out of six equity markets. Only for Korea and Thailand does the inclusion of the domestic risk factor improves the model fit, even though, based on the Wald, local risk is not statistically for these two countries. For Korea, the pseudo-R2 more

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Table 3 Quasi-maximum likelihood estimates of the segmented conditional International CAPM with time-varying prices of risk Kdi US Panel A: Mean equation parameters estimates Constant 0.003 SE 0.045 SI 0.007 SE 0.211 XDPRL(W) −0.018 SE 0.010 USTP SE Euro$ SE USDP SE R2M R2M+D

−1.37 −1.85

Km Japan

Korea

Thailand

Malaysia

Hong Kong

World

0.008 0.030 0.009 0.220 −0.002 0.004

0.000 0.010 0.020 0.030 −0.000 0.001

0.012 0.010 −0.013 0.024 −0.001 0.002

0.008 0.010 0.020 0.060 0.001 0.002

−0.005 0.014 −0.009 0.040 0.015 0.030

−5.66 1.43

7.84 3.74

2.41 4.91

0.49 3.46

Panel B: Specification tests (a) Price of world market risk Is the price of market risk equal to zero? H0 : Km ,k = 0, ∀ k Is the price of market risk constant? H0 : Km ,k = 0, ∀ k > 1 (b) Price of local market residual risk—joint test Are all the coefficients of country specific risk jointly equal to zero? H0 : Kdi ,l = 0, ∀ i,l (c) Price of local market residual risk—single country tests Is the price of US domestic risk equal to zero? H0 : KUS,l = 0, ∀ l Is the price of Japan domestic risk equal to zero? H0 : Kjapan,l = 0, ∀ l Is the price of Korea domestic risk equal to zero? H0 : Kkorea,l = 0, ∀ l Is the price of Thai domestic risk equal to zero? H0 : Kthai,l = 0, ∀ l Is the price of Malaysia domestic risk equal to zero? H0 : Kmalay,l = 0, ∀ l Is the price of Hong Kong domestic risk equal to zero? H0 : KH.K.,l = 0, ∀ l

−2.56 −4.17

−1.98 −2.23

χ2

df

p value

60.638

5

0.000

13.449

4

0.000

0.070

18

1.000

0.000

3

1.000

0.000

3

1.000

0.029

3

0.998

0.031

3

0.998

0.001

3

0.998

0.000

3

1.000

3.66 1.89 −2.08 0.78 1.31 1.98 3.44 1.27 3.53

We estimate the segmented conditional International CAPM with time-varying risk using monthly dollar-denominated returns from January 1985 to December 1998. Data for country equity indices and the world portfolio are from MSCI. The model relates the asset excess return rit to its world covariance 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, m Zt −1, included in the investor’s information set. The world instruments (Zt−1 ) 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 di the US default premium (USDP). The country specific instruments (Zt−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−1 hiNt + δdi ,t−1 ∗ qit + εit , εt |It−1 ∼ N(0, Ht ) d

i m where δm,t−1 = exp(Km Zt−1 ), δdi ,t−1 = exp(Kd Zt−1 ) and qt = D(Ht ) − (hNt ∗ hNt )/ hNNt . i The conditional covariance process is parameterised as bi-diagonal GARCH(1,1): Ht = C C + [aa ∗ I + (a0 a0 ) ∗ (1 − I)] ∗ εt−1 ε t−1 + [bb ∗ I + (b0 b0 ) ∗ (1 − I)] ∗ Ht−1 where CC = H0 ∗ (ii − [aa ∗ I + (a0 a0 ) ∗ (1 − I)] − [bb ∗ I + (b0 b0 ) ∗ (1 − I)]), H0 is the unconditional covariance matrix of residuals, i is a (N × 1) unit vector, I is the identity matrix, 1 is a matrix of ones and a, a0 , b and b0 are (N × 1) vectors of unknown parameters. Robust standard errors are reported in italics. R2M is the pseudo-R2 when world market risk is the only priced factor, and R2M+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.

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than doubles, from 2.41% to 4.91% when the local factor is included. For Thailand the increase is even more substantial, from 0.49% for the world market factor alone to 3.46% for the world plus the local factor. These results suggests that local factors may be important in explaining some emerging markets returns, even tough our statistical tests may lack power to detect it. To further investigate the ability of the model to explain country specific risk, we perform a robust generalized least square regression (GLS) of the model’s estimated residuals on the local information variables. The estimated GARCH covariance matrix is used as the weighting matrix 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 returns for developed markets. However, we find that local variables have some power, albeit limited in explaining emerging market residual returns. This finding, coupled with the earlier finding of the importance of local factor in terms of pseudo-R2 , suggests that our model may lack the flexibility to fully detect the importance of local risk, possibly due to the time-varying nature of 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 addition to world market risk, currency risk is priced in international equity returns. To investigate whether this is the case in our sample, we expand our pricing Eq. (3) to include, in addition to market and domestic risk, a set of exchange rate related information variables: Xi E(Rit |It−1 )−Rft = φdi Zt−1 +δm,t−1 cov(Rit , Rmt |It−1 )+δdi ,t−1 var(Resit |It−1 ),

∀i

(7)

Xi where Zt−1 is country i exchange rate fluctuation information variables. Panel A of Table 4 reports the estimates of the parameters of the mean equations. The results for the price of market risk are similar to those obtained in Table 3, when exchange rate variables were not included. The coefficients on XDPRW, USTP and USDP are significant at the 5% level while the coefficient on Euro$ is not. In contrast to the previous table, for the price of domestic risk, the coefficient of XDPRL is significant in four out of six countries at the 5% level. Turning to exchange rate risk related variables, the coefficient estimates of XRATE and LCFRI are statistically significant at any standard level for every country,4 except for the coefficient of the real interest rate differential in Japan. These results must be taken with caution however, for Thailand and Hong Kong as their exchange rates were pegged to the dollar prior to the crisis. However, the evidence suggests that, for these two countries, at least after the inception of the crisis, equity returns were significantly related to exchange rate changes. Panel B of Table 4 report specification tests for Eq. (7). As in Table 3, the tests indicate that even when exchange risk is explicitly included, the coefficients of the price of world market risk are jointly highly significantly different from zero and that the price of market risk is time-varying. In this specification as well, we cannot reject the hypothesis that domestic residual risk is not priced. Although the evidence suggests that the equity markets in our sample are fully integrated, it is important to point out that our results maybe partially biased because currency risk is not included as a pricing factor in the estimated model, even though exchange

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Table 4 Currency risk and the segmented conditional International CAPM US

Japan

Panel A: Mean equation parameters estimates Coefficients of the price of local residual risk, Kdi Constant 0.009 0.007 SE 0.006 0.030 SI 0.007 0.110 SE 0.330 0.210 XDPRL −0.022 0.003 SE 0.010 0.004 Currency risk factor proxy variables coefficients, φdi XRATE – 1.71 SE – 0.33 LCFRI 0.10 0.22 SE 0.01 0.03 Const Coefficients of the price of world market risk, Km Km −5.78 SE 1.87

R2M+X R2M+D+X

Korea

0.000 0.010 0.022 0.060 0.007 0.001

Thailand

0.014 0.010 −0.010 0.020 −0.003 0.001

Malaysia

0.010 0.010 0.012 0.059 0.001 0.002

Hong Kong

0.003 0.011 −0.013 0.040 0.020 0.003

−9.76 0.01 0.53 0.00

53.43 0.06 0.55 0.01

XDPRW

USTP

Euro$

USDP

3.83 2.04

−2.14 0.71

−1.01 2.22

3.51 1.56

15.87 0.00 0.13 0.21

World

381.90 0.01 0.11 0.03

US

Japan

Korea

Thailand

Malaysia

Hong Kong

World

0.46 0.36

7.85 3.83

0.85 8.74

−0.05 3.28

1.05 0.04

−1.63 −1.97

3.88

Panel B: Specification tests (a) Price of world market risk Is the price of market risk equal to zero? H0 : Km ,k = 0, ∀ k Is the price of market risk constant? H0 : Km ,k = 0, ∀ k > 1 (b) Price of local market residual risk—joint test Are all the coefficients of country specific risk jointly equal to zero? H0 : Kdi ,l = 0, ∀ i,l

χ2

df

p value

50.430

5

0.000

12.802

4

0.012

0.204

18

1.000

11

0.000

5

0.025

6

0.000

(c) Local currency risk factor Are all the coefficients of the local currency risk proxy variables jointly equal to zero? 43.001 H0 : φdi ,q = 0, ∀ i,q Are the coefficients of the local exchange rate change jointly equal to zero? 13.251 H0 : φdi ,XRATE = 0, ∀ I Are the coefficients of the local real risk free differential jointly equal to zero? 34.155 H0 : φdi ,LCFRI = 0, ∀ i

We estimate an augmented version of the segmented conditional International CAPM which relates the asset excess return rit to its world covariance risk 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 global m ) which include a constant, the world index dividend yield in excess of the one-month Eurodollar rate risk is a functions of global instruments (Zt−1 (XDPRW), the change in the US term premium (USTP), the change in the one-month Eurodollar rate (Euro$), and the US default premium di ), the month-to-month change in the short-term interest rate (SI) and the (USDP). The price of country risk is conditioned on local instruments (Zt−1 local dividend price ratio in excess of short-term interest rate (XDPRL). The currency risk variables are the change in the local exchange rate to the US$ (XRATE) and the difference between local and US real risk free interest rates (LCFRI): Xi + δm,t−1 hiNt + δdi ,t−1 ∗ qit + εit , εt |It−1 ∼ N(0, Ht ) rit = φd Zt−1 i

d

i m Zt−1 ), δdi ,t−1 = exp(Kd Zt−1 ), and qt = D(Ht ) − (hNt ∗ hNt )/ hNNt . where δm,t−1 = exp(Km i The conditional covariance process is parameterized as bi-diagonal GARCH(1,1): Ht = C C + [aa ∗ I + (a0 a0 ) ∗ (1 − I)] ∗ εt−1 ε t−1 + [bb ∗ I + (b0 b0 ) ∗ (1 − I)] ∗ Ht−1 where CC = H0 ∗ (ii − [aa ∗ I + (a0 a0 ) ∗ (1 − I)] − [bb ∗ I + (b0 b0 ) ∗ (1 − I)]), H0 is the unconditional covariance matrix of residuals, i is a (N × 1) unit vector, I is the identity matrix, 1 is a matrix of ones and a, a0 , b and b0 are (N × 1) vectors of unknown parameters. Robust standard errors are reported in italics. R2M+X is the pseudo-R2 when world market currency risk are the priced factor, and R2M+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.

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rate changes are included as explanatory variable. As De Santis and Gerard (1998), among others, have found, currency risk premiums are significantly different from zero and, therefore, models of international asset pricing that only include market risk are misspecified. Although we found it impossible with current technology to estimate a model similar to De Santis and Gerard (1998) in which exposure to currency risk is explicitly included as a time-varying pricing factor, including exchange rate variables as conditioning variables in the mean equations provide evidence of the importance of currency risk for equity returns. The Wald tests show that the two exchange rate related conditioning variables are both jointly and individually highly significant across markets. Furthermore, including these variables improves the Table 5 Test of intercepts in the conditional International CAPM US

Japan

Korea

Thailand

Malaysia

Hong Kong

World

Panel A: Mean equation parameters estimate Country specific intercepts αi 0.499 0.330 SE 0.462 0.689

0.240 0.732

1.264 1.101

0.561 0.831

1.130 0.950

0.441 0.450

USTP

Euro$

USDP

−2.03 2.54

4.93 1.69

Const

XDPRW

Coefficients of the price of world market risk, Km Kmi −7.95 3.92 −2.35 SE 2.81 2.45 1.12

R2M

US

Japan

Korea

Thailand

Malaysia

Hong Kong

World

−0.085

7.41

2.11

−0.08

−2.38

−1.77

3.89

χ

2

df

p value

2.757

7

0.907

32.612

5

0.000

12.141

4

0.016

Panel B: Hypothesis tests Are the intercepts jointly equal to zero? H0 : αi = 0, ∀ i Is the price of market risk equal to zero? H0 : Km,k = 0, ∀ k Is the price of market risk constant? H0 : Km,k = 0, ∀ k > 1

We estimate a conditional version of the traditional International CAPM which relates the asset excess return rit to its world covariance risk covt−1 (rit ,rmt ) = hiNt , only. We include an intercept in the mean equation to proxy for m omitted local variables. The price of global risk is a functions of global instruments (Zt−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 term premium (USTP), the change in the one-month Eurodollar rate (Euro$), and the US default premium (USDP): rit = αi + δm,t−1 hiNt + εit , εt |It−1 ∼ N(0, Ht ) m where δm,t−1 = exp(Km Zt−1 ). The conditional covariance process is parametrized as bi-diagonal GARCH(1,1): Ht = C C + [aa ∗ I + (a0 a0 ) ∗ (1 − I)] ∗ εt−1 ε t−1 + [bb ∗ I + (b0 b0 ) ∗ (1 − I)] ∗ Ht−1 where CC = H0 ∗ (ii − [aa ∗ I + (a0 a0 ) ∗ (1 − I)] − [bb ∗ I + (b0 b0 ) ∗ (1 − I)]), H0 is the unconditional covariance matrix of residuals, i is a (N × 1) unit vector, I is the identity matrix, 1 is a matrix of ones and a, a0 , b and b0 are (N × 1) vectors of unknown parameters. Robust standard errors are reported in italics. R2M is the pseudo-R2 when the world market is the only priced factor.

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model’s pseudo-R2 for all markets except Thailand. The results for Thailand are not surprising as the bath was pegged to the dollar for most of the sample period. Overall these results underscore the significant impact of exchange rate risk on international equity returns and the importance of explicitly including currency risk in test of international asset pricing models. As a further robustness test, we estimate a simple conditional version of the international CAPM in which world market risk exposure is the single common pricing factor of equity returns but which includes as well a country specific constant. Formally, the model can be written as follows: E(Rit |It−1 ) − Rft = αi + δm,t−1 cov(Rit , Rmt |It−1 ),

∀i

(8)

where αi 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 in tax treatment, information or transaction costs across countries. A finding of significant country specific constants can be interpreted as a rejection of the international CAPM and/or of the hypothesis of market integration. Table 5 contains the estimation results for this version of the model. Panel A reports parameters estimates for the mean equation. Panel B reports the results of joint hypothesis tests. As far as the price of market risk is concerned, both the coefficients point estimates and the results joint tests are similar to those reported in Tables 3 and 4 where residual country risk was also considered. The evidence confirms the importance of world market risk in 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 different from 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 world equity markets and that the world covariance risk is a significant pricing factor across all these markets. 6. Conclusion This study tests a conditional version of the international CAPM where the world market and domestic risk are explicitly parameterized as independent pricing factors. We model conditional second moments using a novel bi-diagonal multivariate GARCH(1,1) process. We document that this novel GARCH specification provides a significantly better fit of the covariance process of emerging market returns than a standard diagonal specification. Our methodology is fully parametric enabling us to use the model estimates to investigate the relative magnitude and the dynamics of both the domestic and world risk premiums. Surprisingly, little or no evidence of market segmentation in South East Asia is uncovered over the period from 1985 to 1998, although 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 a capital market becomes more integrated, the firm has more opportunities to attract investment funds and inefficient operations should be abandoned. This likely decreases the cost of capital

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and risk in the firm. In the context of an international CAPM this will be experienced as a decline in price of domestic risk and asset volatility. However, when a market is opened to international investors, it may become more sensitive to world events, which drives the covariance of asset’s return and the world portfolio. For these reasons, changes in correlations between emerging markets and the world market may shed some light on the issues of capital market liberalization and integration. Fortunately, our model provides estimates of the time series cross-border correlation. Fig. 1 contains plots of the estimated correlations between the world market and the six equity markets included in the study. To filter out some of the high frequency estimation error in the point estimates of correlation, we also plot the H–P filtered correlations (Hodrick & Prescott, 1997). Despite the previous finding of market integration in emerging market returns, the estimated correlations suggests a low level of relatedness of these markets with the rest of the developed markets. Estimated correlations between developed countries and the world market often exceed 0.7 while for most emerging markets, these correlations are on average lower than 0.5. In our study, by far the highest estimated correlation is observed for the US (0.9 on average), as would be expected given the high portion of US capitalization in the world index. Japan also exhibits correlations over 0.7 in most periods. Hong Kong and Malaysia have an average estimated correlation of about 0.5, while for Korea and Thailand the averages are 0.3 and 0.4, respectively. Despite the fact that several financial liberalization programs were implemented in South East Asia emerging markets during our sample period, there is no evidence of increased relatedness with developed countries in the period prior to the onset of the Asian crisis. In fact the filtered series in the plot reveals that the correlations are relatively unchanged in most of emerging market countries. This evidence suggests that although our asset pricing tests do not detect segmentation, East Asian emerging markets may not be fully integrated. The evidence of some segmentation in these markets is consistent with the investment environment. For example, despite its recent liberalization, the Korean market is still not fully accessible to foreign investors. Foreign ownership is limited to only 10% in so-called unlimited industries and 8% in limited industries (which includes communications and defense). Recently, the 10% ceiling was raised to 25% for 45 firms that had hit the 10% cap. In the case of Thailand, most Thai stocks still have foreign ownership limits (see Bailey & Jagtiani, 1994 for details on foreign investment restrictions in Thailand prior to 1993). Among emerging markets in the sample, Malaysia appears to have enjoyed the most liberalized investment climate over most of the sample period. Although foreign investment was limited by the Foreign Investment Committee to 30% of equity, it appears that foreigners still played a large role in the Malaysian market. By the end of 1992, foreign participation in Malaysian equities was 27%. However, this liberalization process has come to an abrupt halt in September 1998, when severe restrictions to foreign investment were imposed. All this suggests that East Asian emerging markets may not yet be fully integrated. The scope of our conclusions may be limited for several reasons. First currency-risk is not specifically included as a pricing factor in the model, although we provide some evidence of the importance of exchange risk in explaining the cross-section of expect equity returns. Second, the selected local information variables may not capture adequately expectations about local economic conditions in each emerging market. Hence, special care must be taken in interpreting

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US

0.8 0.7

Hong Kong 0.6 0.5 0.4 0.3 0.2

Korea

0.1 0.0 Jan-85

Jan-87

Jan-89

Jan-91

Jan-93

Jan-95

Jan-97

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Jan-91

Jan-93

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1.0 0.9 0.8

Japan

0.7 0.6

Malaysia

0.5 0.4 0.3

Thailand 0.2 0.1 0.0 Jan-85

Jan-87

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

our findings. Our implementation has also ignored dynamic interactions between changes in barriers to investment and market returns, or time variation in the degree of market segmentation. 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

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World market portfolio in relation to the set of assets investigated. As Harvey (1991) states, the MSCI world market portfolio is really an industrial world market portfolio. Lastly, the IFC indices reflect the local prices of (investable) equities included in the index. Bailey, Chung and Kang (1999) provide evidence that emerging market stocks that are traded internationally may command a vastly different price when traded among foreign investors than on their domestic markets. This suggests that returns we use in our tests may not reflect all the pricing effects of barriers 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 countries also accounted for US $38.3 billion of new equity issues in the period from 1996 to 2000 compared 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 that investors have log utility. 3. Equation (2), although correct for country index portfolios, is not strictly valid for individual risky securities. However, it can be generalized for any asset by substituting to the variance of the nondiversifiable local market risk, the covariance between the asset returns and the component of its local market portfolio returns orthogonal to the world market returns. 4. This result is unchanged with the substitution of SI to RI. References Adler, M., & Dumas, B. (1983). International portfolio choice and corporation finance: A synthesis. Journal of Finance, 38(3), 925–984. Bailey, W., Chung, Y. P., & Kang, J.-K. (1999). Foreign ownership restrictions and equity price premiums: What drives cross-border investments? Journal of Financial and Quantitative Analysis, 34(4), 489–511. Bailey, W., & Jagtiani, J. (1994). Foreign ownership restrictions and stock prices in the Thai capital market. Journal of Financial Economics, 36(1), 57–87. Bekaert, G. (1999). Is there a free lunch in emerging market equities? Journal of Portfolio Management, 25(3), 83–95. Bekaert, G., Erb, C. B., Harvey, C. R., & Viskanta, T. E. (1998). Distributional characteristics of emerging market returns and asset allocation. Journal of Portfolio Management, 24(2), 102–116. Bekaert, G., & Harvey, C. R. (1995). Time-varying world market integration. Journal of Finance, 50(2), 403–444. Bekaert, G., & Harvey, C. R. (1997). Emerging equity market volatility. Journal of Financial Economics, 43(1), 29–77. Bekaert, G., & Harvey, C. R. (2000). Foreign speculators and emerging equity markets. Journal of Finance, 55(2), 565–613. Bekaert, G., & Hodrick, R. J. (1992). Characterizing predictable components in excess returns on equity and foreign exchange markets. Journal of Finance, 47(2), 467–510. Bekaert, G., & Urias, M. C. (1996). Diversification, integration and emerging market closed-end funds. Journal of Finance, 51(3), 835–869. Bollerslev, T., Engle, R. F., & Wooldridge, J. M. (1988). A Capital Asset Pricing Model with time-varying covariances. Journal of Political Economy, 96(1), 116–131.

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