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Market Integration and Contagion in Asian Emerging Stock and Foreign Exchange Markets Chu-Sheng Tai* Department of Economics and Finance College of Business Administration Texas A&M University-Kingsville MSC 186, 700 University Blvd. Kingsville, TX 78363-8203, USA Abstract This paper examines whether Asian emerging stock markets have become integrated into world capital markets since their official liberalization dates by estimating and testing a dynamic international asset pricing model (ICAPM) in the absence of purchasing power parity (PPP) using an asymmetric multivariate GARCH-in-Mean (MGARCH-M) approach. Also examined in this paper is whether there are pure contagion effects between stock and foreign exchange markets for each Asian country during the 1997-98 Asian crisis. The empirical results show that first, both currency and world market risks are priced, suggesting that omitting currency risk in pricing international assets might give rise to model misspecification. Second, the stock markets for India, Korea, Malaysia, Philippines, and Thailand are segmented from the world capital markets before their liberalization dates, but have become fully integrated since then. Third, the market liberalization has reduced the cost of capital and price volatility for most of the markets. Finally, as for the contagion effects, data from Korea, Malaysia, and Philippines indicate strong feedback relations between stock prices and exchange rates. This feedback relation is consistent with the predictions of both the stock- and flow-oriented exchange rate models. Data from India and Taiwan show that exchange rates led stock prices, whereas that of Thailand fails to reveal any recognizable pattern during the crisis. JEL Classifications: C32, G12, G15 Key Words: Market Integration; Contagion; Currency Risk; Multivariate GARCH-M Market Integration and Contagion in Asian Emerging Stock and Foreign Exchange Markets Abstract This paper examines whether Asian emerging stock markets have become integrated into world capital markets since their official liberalization dates by estimating and testing a dynamic international asset pricing model (ICAPM) in the absence of purchasing power parity (PPP) using an asymmetric multivariate GARCH-in-Mean (MGARCH-M) approach. Also examined in this paper is whether there are pure contagion effects between stock and foreign exchange markets for each Asian country during the 1997-98 Asian crisis. The empirical results show that first, both currency and world market risks are priced, suggesting that omitting currency risk in pricing international assets might give rise to model misspecification. Second, the stock markets for India, Korea, Malaysia, Philippines, and Thailand are segmented from the world capital markets before their liberalization dates, but have become fully integrated since then. Third, the market liberalization has reduced the cost of capital and price volatility for most of the markets. Finally, as for the contagion effects, data from Korea, Malaysia, and Philippines indicate strong feedback relations between stock prices and exchange rates. This feedback relation is consistent with the predictions of both the stock- and flow-oriented exchange rate models. Data from India and Taiwan show that exchange rates led stock prices, whereas that of Thailand fails to reveal any recognizable pattern during the crisis. JEL Classifications: C32, G12, G15 Key Words: Market Integration; Contagion; Currency Risk; Multivariate GARCH-M 1 1. Introduction A large number of Asian emerging markets have embarked on a series of reforms in recent years, including liberalization of their national stock markets. As a result of these developments and the important implications of market integration on international capital budgeting and investment, market integration has emerged as an important body of literature. Two recent examples of this literature that study the impact of market liberalization on market integration for Asian emerging markets are Bekaert and Harvey (1995) and De Santis and Imrohoroglu (1997).1 Bekaert and Harvey (1995) propose a one-factor asset pricing model that allows the conditional expected returns of a country to be affected by their covariance with a world benchmark portfolio and by the variance of the country returns. They use a conditional regime-switching model to account for periods when national markets were segmented from world capital markets and when they became integrated later in the sample. In contrast to general perceptions that markets are becoming more integrated, their results suggest that some countries are becoming less integrated into the world market. However, based on specification tests, their model is rejected in most countries. They point out that one possible extension of their study is to consider currency risk as another potential priced factor. Instead of using the conditional regime-switching methodology, De Santis and Imrohoroglu (1997) utilize the multivariate GARCH-in-Mean (MGARCH-M) approach. They introduce a dynamic integration version of the classic CAPM framework that assumes full market segmentation until the official 1 Earlier theoretical and empirical papers on market integration include Solnik (1974), Stehle (1977), Stulz (1981), Errunza and Losq (1985), Eun and Janakiramanan (1986), Jorion and Schwartz (1986), Wheatley (1988), and Errunza, Losq and Padmandaban (1992). More recent papers include Hardouvelis, Malliaropulos, and Priestley (2000), Bekaert, Harvey and Lumsdaine (2001), and Carrieri, Errunza and Hogan (2001). However, these papers either focus on developed markets or do not consider how market liberalization affects market integration. Instead of focusing on how market liberalization affects market integration, Obstfeld (1994), Bekaert, Harvey and 2 liberalization date of each market, and full integration thereafter to capture the fact that the analyzed markets were legally segmented for part of the sample period. Their empirical results show that neither the country-specific risk, nor the world market risk is priced and thus no conclusion can be made regarding the impact of market liberalization on market integration. One possible common cause for the weak findings of De Santis and Imrohoroglu (1997) and the rejection of Bekaert and Harvey’s model is that both assume purchasing power parity (PPP) and thus ignore currency risk, which motivates the current research. Due to a series of financial crises in 1990s including the Exchange rate Mechanism (ERM) attacks of 1992, the Mexican peso collapse of 1994, the Asian crisis of 1997, the Russian collapse of 1998, and the Brazilian devaluation of 1999, the study of the transmission of financial shocks/crisis across markets/countries has also emerged as one of the most intensive research topics in international financial literature in recent years. Previous papers on this topic have failed to take into account an important distinction between the two concepts of interdependence and contagion except Forbes and Rigobon (2002).2 Masson (1998) argues that there are three main channels that financial markets turbulence can spread from one country to another: monsoonal effects, spillovers and pure contagion effects. ‘Monsoonal’ effects, or ‘contagions from common causes’ tend to occur when affected countries have similar economic fundamentals or face common external shocks. The second type of financial market interlinkages arises from spillover effects, which may be due to trade linkages or financial Lundblad (2000, 2001) and Henry (2000a) study the impact of market liberalization on the economic development of the underlying countries. 2 Forbes and Rigobon (2002) define contagion as a significant increase in cross-market linkages after a shock to one country or group of countries, and find that there was virtually no increase in unconditional correlation coefficients during the 1997 Asian crisis and thus conclude that there was no contagion but interdependence. However, they also point out that their definition of contagion is not universally accepted, and therefore it warrants another examination of whether contagion did occur during 1997 Asian crisis. 3 interdependence. The first two channels of financial crises can be categorized as fundamentalsdriven crises since the affected countries share some macroeconomic fundamentals, which implies that the transmission of financial crises is due to the interdependence among those countries and not necessarily due to contagion. The third transmission channel is the pure contagion effect. Contagion here refers to the cases where crisis in one country/market triggers a crisis elsewhere for reasons unexplained by macroeconomic fundamentals. For instance, a crisis in one country may lead creditors and investors to pull out from other countries over which they have a poor understanding resulting from information asymmetries. Given the facts that both market integration and contagion have important implications in international finance and that previous studies in these two topics are inconclusive and thus debatable, in this paper I attempt to provide new empirical evidence on theses two issues. Specifically, I develop a dynamic version of international capital asset pricing model (ICAPM) in the absence of PPP, and then test the model using data from six Asian emerging countries. This study contributes our understanding in the return dynamics of emerging markets with respect to market integration and contagion in several ways. First of all, unlike previous studies on market integration (e.g., Bekaert and Harvey (1995) and De Santis and Imrohoroglu (1997)), PPP is not assumed when testing market integration/segmentation hypothesis. Many empirical studies have documented that PPP does not hold, especially in short horizons.3 In the absence of PPP, international investors will face different real returns when holding the same assets. In this case, currency risk will emerge as another potential priced factor. Secondly, previous studies have failed to control for the economic fundamentals when testing contagion. In this paper, I rely on the developed ICAPM, which provides me a theoretical basis in selecting the economic 4 fundamentals. The economic fundamentals under ICAPM are the world market and currency risks, so the evidence of contagion is based on testing whether idiosyncratic risks - the part that cannot be explained by the world market and currency risks, are significant in describing the return dynamics of both stock and foreign exchange markets for each emerging country during the 1997-98 Asian crisis. Finally, a parsimonious parameterization of asymmetric trivariate GARCH-M process is employed to model the conditional covariance matrix of asset returns, which is very important in testing contagion.4 The advantage of this multivariate approach is that it utilizes the information in the entire variance-covariance matrix of the errors, which, in turn, leads to more precise estimates of the parameters of the model. The empirical results show that first, both currency and world market risks are priced, suggesting that omitting currency risk in pricing international assets might give rise to model misspecification. Second, the stock markets for India, Korea, Malaysia, Philippines, and Thailand are segmented from the world capital markets before their liberalization dates, but have become fully integrated since then. Third, consistent with Bekaert and Harvey (1997), Stulz (1999) and Henry (2000b), the market liberalization has reduced the cost of equity capital and price volatility for most of countries. Finally, as for the contagion effects, data from Korea, Malaysia, and Philippines indicate strong feedback relations between stock prices and exchange 3 4 Rogoff (1996) provides a detailed discussion on the issue of PPP. According to Forbes and Rigobon (1999), Dornbusch, Park and Claessens (1999), and Kaminsky and Reinhart (2000), previous empirical studies on contagion can be categorized by methodology into four groups: (1) the testing of significant increases in correlation (Calvo and Reinhart (1996), Baig and Goldfajn (1999), Forbes and Rigobon (1999, 2002) and Park and Song (1999)); (2) the testing of significance in innovation correlation (Baig and Goldfajn (1999)); (3) the testing of significant volatility spillover (Edwards (1998), Edwards and Susmel (1999)); (4) crisis prediction regression (Bae, Karolyi, and Stulz (2000), Eichengreen, Ross, and Wyplosz (1996), Kaminsky and Reinhart (2000), Van Rijckeghem and Weder (1999), Sachs, Tornell, and Velasco (1996)). None of the contagion studies mentioned above explicitly takes the time dependencies in the second moment into account. A recent paper by Bekaert, Harvey, and Ng (2002) applies three-stage univariate GARCH model to study contagion in equity markets by testing whether there is evidence of significant increase in cross market residual 5 rates. Data from India and Taiwan show that exchange rates led stock prices, whereas that of Thailand fails to reveal any recognizable pattern during the crisis. The remainder of this paper is organized as follows. Section 2 develops the dynamic version of ICAPM in the absence of PPP. Section 3 presents the econometric methodologies used to estimate and test the model. Section 4 discusses the data. Section 5 reports the empirical results. Summary and concluding remarks are offered in Section 6. 2. The dynamic ICAPM in the absence of PPP Since one of the purposes of this paper is to investigate whether Asian emerging stock markets have become fully integrated after their liberalization, it would be appropriate to consider a dynamic version of ICAPM. De Santis and Imrohoroglu (1997) introduce a structure that allows in a single model a situation of full market segmentation until the official liberalization date, and full integration thereafter. I modify their model by incorporating the currency risk.5 The dynamic ICAPM with currency risk applied to each emerging stock market index can be written as follows. correlation during a crisis. Although they model conditional second moments, they can not answer whether return shocks emanating from one market will significantly affect the other markets during the crisis. 5 Unlike the regime-switching model of Bekaert and Harvey (1995), De Santis and Imrohoroglu (1997) assume that the integration process is irreversible and recognize that their assumption has a drawback to detect the presence of the two regimes in the data if the official liberalization date is a poor indicator of when that market actually became accessible to foreign investors and allowed residents to invest abroad. This simplistic specification for a test on the relevance of liberalization would imply that an abrupt change on the pricing of risk happened on the day of the event, and thus is subject to criticism. Although interest rate differentials could be used as integration proxy to allow for time-varying integration (e.g., Hardouvelis, Malliaropulos, and Priestley (2000)), the interest rate data for most of Asian emerging countries are not available until 1990s, which is the period when most of these countries have liberalized their markets, thus making this kind of approach unfeasible. 6 ri ,t = (1 − LDi ,t )λ w,t −1 hiw ,t + λ c ,t −1hic ,t + LDi ,t λl ,t −1 hi ,t + ε i ,t (1) where ri ,t is the excess return from date t − 1 to date t on local stock market i ; LDi ,t is a liberalization dummy variable, which is equal to one before the opening date of stock market i to foreign investors and zero otherwise.6 λ w,t −1 , λ c ,t −1 and λl ,t −1 are time-varying prices of world market, currency, and local market risks, respectively. hiw ,t is the conditional covariance between the excess returns on the local market index and world market portfolio; hic ,t is the conditional covariance between the excess returns on the local market index and the bilateral U.S. dollar exchange rate changes, and hi ,t is the conditional variance of the excess returns on the local market index. Finally, ε i,t denotes the country-specific, idiosyncratic shock for market i . The dynamic ICAPM in the absence of PPP specified in equation (1) indicates that if a capital market is completely segmented, the expected returns on the local market index is only associated with the country-specific risk, which is proxied by the conditional variance of its stock index returns. However, if capital market is fully integrated, then the source of risk of the expected returns is affected by its covariance with the world market portfolio returns. Equation (1) requires not only the estimation of the conditional variance of each local stock market, but also the estimation of the conditional covariances between the local stock market and world market portfolio returns, and between the local stock market returns and exchange rate changes. Therefore, I need to generalize the process for the conditional second 6 Table 1 reports the International Finance Corporation (IFC) official liberalization dates for six Asian emerging countries under study. The IFC date is based on the Investibility Index, which represents the ratio of the market capitalization of stocks that foreigners can legally hold to total market capitalization. 7 moments to a trivariate framework to include equations representing the world market portfolio returns and exchange rate changes, which are estimated as follows: rc ,t = λ w,t −1 hcw,t + λc ,t −1 hc ,t + ε c ,t (2) rw,t = λ w,t −1 hw,t + λc ,t −1 hcw ,t + ε w,t (3) where hcw,t is the conditional covariance between exchange rate changes and world market portfolio returns. hw,t and hc ,t are conditional variances of world market portfolio returns and exchange rate changes, respectively. ε w,t is the unexpected return on the world market portfolio conditional on expectations based on the information available at t − 1 , and similarly ε c,t is the unexpected change on the bilateral exchange rate conditional on expectations based on the information available at t − 1 . To model the time-varying prices of world market and local market risk, their dynamics are chosen according to the theoretical asset-pricing model developed by Merton (1980). In his model, both the prices of world market and local market risk are the coefficients of risk aversion of risk-averse investors, and thus should be positive. Consequently, similar to Bekaert and Harvey (1995), De Santis and Gerard (1997, 1998), an exponential function is used to model both the dynamics of λ w,t −1 and λl ,t −1 . For the dynamic of λ c ,t −1 , a linear specification is adopted because the model does not restrict the price of currency risk to be positive.7 7 As pointed out by De Santis and Gerad (1997), the conditional ICAPM is only a partial equilibrium model and the theory does not help identify the state variables that affect the prices of world market, currency and local market risks, so inevitably any parameterization of the dynamics of λ w, t − 1 , λ c , t − 1 , and λl ,t −1 can be criticized for being ad hoc. 8 λ w,t −1 = exp(ϕ w' z t −1 ) (4) λ c ,t −1 = ϕ c' z t −1 (5) λl ,t −1 = exp(ϕ l' z l ,t −1 ) (6) where Z t −1 and Z l ,t −1 are vectors of information variables observed at the end of time t − 1 and ϕ ’s are time-invariant vectors of weights. Thus, the price of currency risk is assumed to be a linear function of the information variables in Z t −1 , and the price of world market risk is assumed to be an exponential function of information variables in Z t −1 . Similarly, the price of local market risk is also an exponential function of local information variables in Z l ,t −1 . Given the dynamics of price of risk, I can then test whether the prices of world market and currency risks are significantly priced and change over time by testing whether the information variables in Z t −1 are significant in addition to significant GARCH parameters. Similarly, the time-varying price of each local market risk can be tested whether the information variables in Z l ,t −1 are significant. Notice that the parameterization of the dynamic ICAPM differs from that of Bekaert and Harvey (1995). Bekaert and Harvey (1995) apply a two-step estimation procedure, in which they use an univariate GARCH-in-Mean approach to estimate the price of world market risk in the first step, and then in the second step the estimated price of world market risk is treated as data to estimate the country-specific risk associated with each local stock market returns. However, my parameterization not only allows a country’s stock returns to include country-specific risk ( λl ,t −1 ) 9 and world market risk ( λ w,t −1 ), but also requires the estimation of the price of currency risk ( λ c ,t −1 ). More importantly, all the prices of risk are allowed to change through time and are estimated simultaneously to overcome the potential efficiency loss in Bekaert and Harvey (1995). Another main purpose of the paper is to test whether there are any pure contagion effects between a country’s stock market and its foreign exchange market during the 1997-98 Asia crisis. Consequently, I modify equations (1) and (2) to allow the local stock market’s idiosyncratic shocks at t − 1 to affect the return of its foreign exchange market at t , and vice versa. Specifically, equations (1) and (2) can now be written as: ri ,t = (1 − LDi,t )λw,t −1hiw,t + λc,t −1hic,t + LDi ,t λl ,t −1hi ,t + (δ ii ε i ,t −1 + δ ciε c,t −1 ) + CDt (ωii ε i ,t −1 + ω ciε c,t −1 ) + ε i ,t (7) rc ,t = λ w,t −1 hcw,t + λ c ,t −1 hc ,t + (δ cc ε c ,t −1 + δ ic ε i ,t −1 ) + CDt (ω cc ε c ,t −1 + ω ic ε i ,t −1 ) + ε c ,t (8) where " CDt " is a crisis dummy variable, which is equal to one during the Asian crisis and zero otherwise.8 In testing the pure contagion effects, I allow the past market-specific shocks to affect current asset returns in the entire sample period (i.e., mean spillovers), and then test whether there are any incremental influences of past return shocks on these returns during the crisis period (i.e., contagion). Thus, the hypothesis of no contagion between stock and foreign exchange markets for each Asian emerging country can be examined by testing whether the contagion coefficients, ω ic or ω ci , are individually or jointly significant after the systematic risks and any mean spillovers have been accounted for. 10 3. Econometric Methodology To model the conditional second moments, several multivariate GARCH models have been proposed such as the diagonal VECH model of Bollerslev, Engle, and Wooldridge (1988), the constant correlation (CCORR) model of Bollerslev (1990), the factor ARCH (FARCH) model of Engle, Ng, and Rothschild (1990), and the BEKK model of Engle and Kroner (1995). Among these four popular MGARCH models, the diagonal BEKK model is selected and is modified to accommodate the asymmetric volatility effects in variances and covariances, which has been documented in recent papers by, among others, Kroner and Ng (1998) and Bekaert and Wu (2000). The diagonal BEKK model is preferred because it not only yields a positive definite covariance matrix for all values of ε t −1 , but also economizes on parameters relative to other MGARCH processes.9 Specifically, the dynamic process for the conditional variance-covariance matrix of the asset returns is specified as: H t = C ' C + A ' ⋅ H t −1 ⋅ A + B ' ⋅ ε t −1ε t' −1 ⋅ B + D ' ⋅ η t −1η t' −1 ⋅ D (9) where H t is 3× 3 time-varying variance-covariance matrix of asset returns; C is restricted to be a 3× 3 upper triangular matrix and A , B , and D are 3× 3 diagonal matrices. η t −1 is 3× 1 vector such η i ,t −1 = ε i ,t −1 if ε i ,t −1 < 0 , 0 otherwise which captures the asymmetric impact that the vector of past negative shocks has on the conditional covariance matrix in a manner similar to that of Glosten et al. (1993). In this model the conditional variance and covariance of each 8 9 I assume that Asian crisis began in July 1997 and ended in June 1998 in the sample. De Santis and Gerard (1997) find strong support for this parameterization of matrices 11 A and B . return are related to the past squared residuals and cross residuals, past squared asymmetric shocks and cross asymmetric shocks of all returns while they are only related to their own past conditional variance and covariance. Even with this diagonal BEKK parameterization, it still requires the estimation of 35 parameters in the conditional mean and variance equations. Under the assumption of conditional normality, the log-likelihood to be maximized can be written as: ln L(ϖ ) = − TN 1 T 1 T ln 2π − ∑ ln | H t (ϖ ) | − ∑ ε t (ϖ ) ' H t (ϖ ) −1 ε t (ϖ ) 2 2 t =1 2 t =1 (10) where ϖ is the vector of unknown parameters in the model. Since the normality assumption is often violated in financial time series, I use quasi-maximum likelihood estimation (QML) proposed by Bollerslev and Wooldridge (1992) which allows inference in the presence of departures from conditional normality. Under standard regularity conditions, the QML estimator is consistent and asymptotically normal and statistical inferences can be carried out by computing robust Wald statistics. The QML estimates can be obtained by maximizing equation (10), and calculating a robust estimate of the covariance of the parameter estimates using the matrix of second derivatives and the average of the period-by-period outer products of the gradient. Optimization is performed using the Broyden, Fletcher, Goldfarb, and Shanno (BFGS) algorithm.10 10 I have also tried the estimation by fitting a t-distribution on the vector of errors, with v degrees of freedom as recommended by Engle and Bollerslev (1986) and Bollerslev (1987). However, the t-distribution did not give good results. As noted by Bera and Higgings (1993) this could be due to the fact that although conditional tdistribution allows kurtosis to exceed 3, it assumes that it is constant since the estimated degrees of freedom v are time invariant. Therefore, I proceed by focusing on QML estimates. 12 4. Data and summary statistics The sample of Asian countries examined in the paper includes India, Korea, Malaysia, Philippines, Taiwan and Thailand. The data consists of end-of-month observations of stock market total (i.e. dividend adjusted) return indices, and of local bilateral spot exchange rates expressed as units of U.S. dollar against one unit of each Asian currency.11 The sample covers monthly observations for the period 1980:01 to 2002:03 for India, Korea and Thailand, and 1984:12 to 2001:03 for Malaysia and Taiwan, and 1986:01 to 2001:03 for Philippines. The MSCI world market total return index is used to proxy the world market portfolio. The USdollar denominated excess stock return is computed as: ri ,t = ln( pt +1 1 ) − ln(1 + Rtus $ ) where pt pt 12 is either the IFC emerging market total return index or MSCI world market total return index at time t , and RtUS $ is annualized one-month Eurodollar deposit rate. The currency return is computed as: rc ,t = ln(S i ,t ) − ln(S i ,t −1 ) where S i ,t is the spot exchange rate at time t expressed as U.S. dollar price of one unit of local currency of country i . The information variables selected in this paper to model the dynamics of λ w,t −1 and λ c ,t −1 are based on previous studies. Harvey (1991) shows that U.S. information variables are useful in predicting foreign stock returns. Giovannini and Jorion (1987) find that nominal interest rates have explanatory power for the time variation of currency returns. Thus, several information variables are chosen to be included in Z t −1 . They are dividend yield on the world market index 11 The International Finance Corporation (IFC) total return indices are used to study the market integration for six Asian emerging countries. They are India ( IFCIND ), Korea ( IFCKOR ), Malaysia ( IFCMAL ), Philippines ( IFCPHI ), Taiwan ( IFCTAI ), and Thailand ( IFCTHA ). 13 in excess of the one-month Eurodollar deposit rate ( DIV ), the change in the U.S. term premium, measured by the yield difference between 10-year Treasury constant maturity rate and 1-month Eurodollar rate ( ∆USTP ), the U.S. default premium, measured by the yield difference between Moody’s Baa-rated and Aaa-rated U.S. corporate bond ( USDP ), the lagged world excess returns ( WORLD ), and a constant ( CON ). Given the limited local information variables available for each Asian emerging country, the information variables contained in Z l ,t −1 only include lagged local market returns ( LOCAL ) and a constant ( CON ). All the data are extracted from Datastream. Table 2 presents summary statistics of the continuously compounded stock and currency returns for each emerging country.12 As can be seen from Panel A, IFCMAL has the highest mean returns (1.153%) with a standard deviation of 11.191%, while IFCTHA has the lowest mean returns (0.385%) with a standard deviation of 11.112%. Comparing the performance of six currency returns, Taiwan dollar ( TAIWDUS ) is the best one and the only one with a positive mean return of 0.098%, and a standard deviation of 1.376%. This is not surprising since Taiwan is one of the few countries that was not seriously affected by the Asian currency crisis. Table 2 also reports Bera-Jarque and Ljung-Box statistics. In eleven out of twelve cases, the Bera-Jarque test statistic strongly rejects the hypothesis of normally distributed stock and currency returns. The Ljung-Box test statistic for raw returns, LB (20) , is significant in six cases (three stock returns: IFCMAL , IFCPHI , and IFCTHA , and three currency returns: SKORWUS , TAIWDUS , and THAIBUS ), implying linear dependencies in these returns. For squared returns, 14 LB 2 (20) is significant for all stock returns except IFCPHI , and for all currency returns except INDNRUS and SKORWUS , indicating strong nonlinear dependencies in these returns. This is consistent with the volatility clustering observed in most stock and foreign exchange markets: Large (small) changes in prices tend to be followed by large (small) changes of either sign. The GARCH models used in this study are well known to capture this property. 5. Empirical results The quasi-maximum likelihood estimation of the dynamic ICAPM in the absence of PPP (equations (3)-(8)) is reported in Table 3. The parameter estimates of the time-varying prices of risk are shown in Panel A. The estimates of mean spillovers and contagion parameters are depicted in Panel B. Panel C reports the parameter estimates for the conditional variance process. The diagnostic test statistics for the standardized residuals are shown in Panel D. Finally, The hypothesis tests concerning the time-varying prices of risk and the summary statistics about the estimated risk premia and conditional volatility are shown in Panels E and F, respectively. 5.1. The dynamic ICAPM with currency risk Panel A of Table 2 presents the estimation results for the prices of risk for each emerging country. First considering the price of country-specific risk before liberalization, it is significant 12 Given the different sample size for each country, I do not report the descriptive statistics for the selected information variables and MSCI world market index in Table 2, but they are available upon request. 15 and changes over time in three cases (India, Korea, and Malaysia) since the lagged local stock market returns are significant in describing the price dynamic of the country-specific risk. For Philippines and Thailand, their prices of country-specific risk are significant but time-invariant. However, it is insignificant for Taiwan. These results can be confirmed by the hypothesis tests (#7) reported in Panel E of Table 3. As can be seen, the null hypothesis of zero price of countryspecific risk is significantly rejected at least at the 5% level in all cases except Taiwan. Next, considering the prices of world market and currency risk, the hypothesis tests reported in Panel E show that the price of world market risk not only is significant (#3) but also changes over time (#4) in all cases. As for the currency risk, it is also significantly priced (#5) and changes over time (#6) in all cases except India. These empirical results imply that India, Korea, Malaysia, Philippines, and Thailand were fully segmented before their liberalization dates, but have become fully integrated with the world capital markets thereafter. This result is in sharp contrast to De Santis and Imrohoroglu (1997), who find neither the country-specific risk nor the world market risk is significantly priced for the six Asian emerging countries examined in this paper, and consequently they can not draw any conclusion regarding how market liberalization affects the market integration for each country studied. The significant currency risk found in this paper indicates that an international asset pricing model under PPP such as the one used by De Santis and Imrohoroglu (1997) would be misspecified. 5.2 Evidence of Mean Spillover and Contagion After controlling the systematic market and currency risks, I can then test pure contagion effects between stock and foreign exchange markets for each country. However, before that, I 16 need to control for the overall mean spillovers in the entire sample period, so any incremental mean spillover effects can be tested during the crisis period. It can be seen from Panel B of Table 3 that the parameter estimates for mean spillovers are significant in three cases: India, Korea, and Malaysia. For example, the past return shocks originating from Indian currency market have significant and positive ( δ ci = 0.027) impact on its domestic stock market, suggesting that exchange rates lead stock prices. On the other hand, the past return shocks emanating from Malaysian stock market have significant and positive ( δ ic = 1.226) impact on the value of its domestic currency, implying that stock prices lead exchange rates. Finally, the past return shocks from Korean stock market also have significant and positive ( δ ic = 0.035) impact on domestic currency value and vice versa ( δ ci = 0.006), indicating a feedback relation between Korean stock and foreign exchange markets. The finding of significant positive impact of past return shocks from the stock market (foreign exchange market) on foreign exchange market (stock market) is consistent with Ajayj and Mougoue (1996) where they conclude that currency appreciation has a positive effect on domestic stock market. This finding can be explained based on stock-oriented model of exchange rates (Frankel (1983)). An increase in stock prices causes an increase in the wealth of domestic investors, which in turn leads to a higher demand for money with ensuing higher interest rates. The higher interest rates encourage capital inflows ceteris paribus, which in turn is the cause of currency appreciation. Since significant systematic risk premia have been founded and the overall mean spillovers have been controlled for the entire sample period, I can now test whether there are any pure contagion effects between domestic stock and foreign exchange markets and examine the dynamic relation between these two markets during the crisis. As shown in Panel B, the 17 contagion effects are significant in five cases for the stock markets, and three cases for the foreign exchange markets. Among the five cases where contagion effects are due to the shocks from the foreign exchange market to the stock market, two have the negative effects: India ( ω ci = -0.054) and Malaysia ( ω ci = -0.146), and the other three have positive effects: Korea ( ω ci = 0.132), Philippines ( ω ci = 1.047), and Taiwan ( ω ci = 0.100). The significantly negative impact of past foreign exchange shocks on the domestic stock market (India and Malaysia) can be explained by the flow-oriented exchange rate model (Dornbusch and Fischer (1980)). The model focuses on the current account or trade balance, which posit that currency movements affect international competitiveness and the trade balance, thereby influencing real income and output. As a country’s currency appreciates, it decreases her international competitiveness in good markets, which has a negative effect on a firm’s future cash flow. Consequently, returns on domestic stock market decrease. On the other hand, the significantly positive impact of past foreign exchange shocks on the domestic stock market (Korea, Philippines, and Taiwan) can be explained by portfolio balance approach. According to this approach, a rise in the value of domestic currency against the U.S. dollar raises the returns on domestic assets. Investors quickly shift funds from dollar assets to domestic assets such as stock due to higher returns. This shift of portfolio composition in favor of domestic stocks and against dollar assets results in decreases in stock supply and increases in stock demand, which then raises domestic stock prices and their returns. The portfolio balance model, thus, implies that currency appreciation tends to have a positive effect on the local stock market, which is the opposite of the flow-oriented exchange rate model discussed earlier. 18 As for the three cases where the contagion effects are due to the shocks from the stock market to foreign exchange market, the effects are negative for Philippines ( ω ic = -0.709), but positive for Korea ( ω ic = 1.034) and Malaysia ( ω ic = 2.170). The significantly negative impact of past stock return shocks on current spot exchange rates for Philippines can be explained by the stock market’s providing a barometer for the health of an economy (Solnik (1987)). During economic expansion, investors are bullish on stock market, and this tends to fuel inflation expectations, which exerts downward pressure on the value of domestic currency. On the other hand, the significantly positive impact of past stock return shocks on current spot exchange rates for Korea and Malaysia can again be explained by stock-oriented exchange rate model (Frankel (1983)) discussed earlier. Overall data from Korea, Malaysia, and Philippines indicate strong feedback relations between stock prices and exchange rates during the crisis. Data from India and Taiwan show that exchange rates led stock prices, whereas that of Thailand fails to reveal any recognizable pattern during the crisis. 13 5.3 Conditional Variance Process and Residual Diagnostics 13 Notice that although the dynamic ICAPM in the absence of PPP with trivariate GARCH-M parameterization may enable one to uncover some economic and/or statistical relations, it is sometimes difficult to interpret the underlying fundamental economic relation based on those results. It is likely that the results may be generated from other structure relations, i.e., via interest rate parity condition or IS-LM-related policies. For example, some recessionary shocks or unfavorable information on the country will cause a stock price decrease and an exchange rate depreciation. In this case the time relation between the stock price and foreign exchange rate will be generated from the relative efficiency of the stock market and foreign exchange market. Consequently, both stock- and flow-oriented exchange rate models may not play any role in generating the feedback relation found here. 19 Panel C presents the parameter estimates for the conditional variance process. As can be seen, most of elements in the vectors a and b are statistically significant at 1% level, implying that strong GARCH effect is present for all the return series. In addition, the estimates satisfy the stationarity conditions for all the variance and covariance processes.14 As for the asymmetric volatility parameter, d , it is much stronger in the stock market (four cases) than in the foreign exchange markets (two cases). To access the fit of the dynamic ICAPM in the absence of PPP with trivariate GARCH-M specification, Panel C reports the Ljung-Box statistics for 20th-order serial correlation in the level ( LB (20) ) and squared standardized residuals ( LB 2 (20) ). Under the multivariate framework, the standardized residuals at time t is computed as Z t = H t−1 / 2 ε t , where H t−1 / 2 is the inverse of the Cholesky factor of the estimated variance-covariance matrix. Panel D shows that none of the LB (20) statistics is significant except in three cases. Similarly, none of the LB 2 (20) statistics is significant except in one case. Overall, the dynamic ICAPM in the absence of PPP with trivariate GARCH-M parameterization effectively eliminates most of the linear and nonlinear dependencies found in the raw data. As for B − J test statistics, most of them are smaller than those found in the raw data but still significant in most cases, indicating departure from the normality, which justifies the use of robust standard errors computed from using the quasi-maximum likelihood method of Bollerslev and Wooldridge (1992). 5.4 The Cost of Capital and Conditional Volatility 14 For the process in H t to be covariance stationary, the condition a i a j + bi b j < 1 ∀i, j has to be satisfied (Bollerslev, 1986; De Santis and Gerard, 1997, 1998). 20 One important implication of market liberalization is the issue of cost of capital. The cost of capital is the discount rate used to discount the expected cash flows of a project that a firm intends to take on. This discount rate is the rate of return required by the firm’s shareholders and is determined by the CAPM. If a market is fully segmented from other capital markets, this required rate of return is equal to a risk-free rate plus a risk premium equal to the firm’s beta times a local market’s risk premium. If a country is opened up to foreign investors and lets its residents invest abroad, then the residents of the country no longer have to bear all the risks associated with the domestic economic activities because foreign investors will bear some of these risk when they invest in the country. On the other hand, the domestic investors will bear some foreign risks when they buy foreign securities. As a result, the domestic investors will benefit from the market liberalization through the process of diversification since some of the domestic and foreign risks will offset each other (e.g., Stulz (1999) and Henry (2000b)). Therefore, the required rate of return for domestic investors will be smaller due to the lower risk they are bearing. Consequently, one would expect that market liberalization would reduce the cost of capital for the country. Panel E of Table 3 confirms this conjecture. For example, the predicted average monthly total risk premium has decreased from 0.177% in the preliberalization sub-period to -0.166% in the post-liberalization sub period for India. This is also true for the other countries except Malaysia. To examine whether these decreases of cost of capital are really statistically significant, an OLS was run to see if the slope coefficient for the liberalization dummy variable for each emerging market is significantly negative. Panel A of Table 4 shows that the slope coefficients are negative in all cases except Malaysia, and are significant in three cases: Malaysia, Philippines, and Taiwan, which confirms the conclusion that the costs of capital for these markets have indeed decreased because of their liberalization. 21 Another important implication of market liberalization is the issue of excess price volatility induced by foreign capital movements. Government officials in emerging markets were concerned whether their stock markets would become more volatile when they decided to open up their markets to foreign investors. Panel E of Table 3 presents the conditional volatility calculated from before and after the liberalization date for each market. As can be seen, the conditional volatility has decreased from 9.705% to 8.907% for Malaysia, from 11.268% to 11.011% for Philippines, and from 14.345% to 11.465% for Taiwan since they were opened up to the foreign investors. For the other three markets, it appears that their conditional volatilities have increased somewhat, but these increases are due to the market turmoil during the 1997-98 Asian crisis. If the conditional volatility is calculated from the liberalization date up to the outbreak of the crisis in middle-1997 for each market, then we would expect to see a decrease in the conditional volatility for most of the markets. To officially test this hypothesis, an OLS was run on the predicted conditional volatility over the pre-crisis sample for each market. Panel B of Table 4 shows that the conditional volatility has indeed diminished in four of six markets (Korea, Malaysia, Philippines, and Taiwan) since their liberalization dates because the slope coefficients are statistically significant and negative for these markets. This result is consistent with Bekaert and Harvey (1997), and Henry (2000b), who also find that the volatilities for emerging markets have diminished through the process of liberalization. One explanation for this finding is that the liberalization process has expanded the base of the domestic stock market due to the influx of foreign institutional investors who make their investment decisions based more on a rational investment analysis focusing on fundamental values. 22 6. Summary and Concluding Remarks In this paper, I have developed a dynamic ICAPM in the absence of PPP in an attempt to examine market integration and contagion using data from six Asian emerging countries. In testing market integration, the dynamic ICAPM allows full market segmentation until the official liberalization date and full market integration thereafter. To test pure contagion effect between foreign exchange and stock markets, I first control for the economic fundamentals or systematic risks and overall mean spillovers. I then allow the past return shocks from one market to affect the current returns of the other market during the 1997-98 Asian crisis. This study contributes our understanding in the dynamics of asset returns in emerging markets in several ways. First of all, unlike previous studies on market integration (e.g., Bekaert and Harvey (1995) and De Santis and Imrohoroglu (1997)), PPP is not assumed when testing market integration/segmentation hypothesis. Secondly, previous studies have failed to control for the economic fundamentals when testing contagion. In this paper, I rely on the developed ICAPM, which provides me a theoretical basis in selecting the economic fundamentals. The economic fundamentals under ICAPM are the world market and currency risks, so the evidence of contagion is based on testing whether idiosyncratic risks - the part that cannot be explained by the world market and currency risks, are significant in describing the return dynamics of foreign exchange and stock markets for each emerging country during the crisis. Finally, a parsimonious parameterization of asymmetric trivariate GARCH-M process is employed to model the conditional covariance matrix of asset returns. Since the ICAPM is fully parameterized, several interesting statistics including estimated risk premia and conditional volatility can be recovered. The empirical results can be summarized as follows. 23 First, both currency and world market risks are priced, suggesting that omitting currency risk in pricing international assets might give rise to model misspecification as I have shown to be the case for De Santis and Imrohoroglu (1997). 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Working Paper, IMF. 33 Table 1: Regulation of Asian emerging markets Country India Korea Malaysia Philippines Taiwan Thailand IFC official liberalization date November 1992 January 1992 December 1988 a June 1991 January 1991 December 1988 Degree of openness 24% of issued share capital 10% of capital of listed companies; 25% after July 1992 30% for banks and institutions; 100% for remaining stocks 100% foreign ownership Investable up to 10% Investable up to 49% a The IFC official liberalization date for Philippines is October 1989, which is hard to justify according to Bekaert and Harvey (2000), who argue that a Foreign Investment Act was signed into law in June 1991 by Philippine government. Under this Act, all restrictions on foreign investments were removed over a period of three years. As a result, I chose June 1991 as the liberalization date for Philippines. 34 Table 2: Summary Statistics The six US-dollar denominated monthly emerging equity/currency returns are India ( IFCIND / INDNRUS ), Korea ( IFCKOR / SKORWUS ), Malaysia ( IFCMAL / MALAYUS ), Philippines ( IFCPHI / PHILPUS ), Taiwan ( IFCTAI / TAIWDUS ), and Thailand ( IFCTHA / THAIBUS ). 1-month Eurodollar deposit rate is used to calculate excess equity returns. The log first differences of bilateral exchange rates between the U.S. dollar and each of the six emerging currencies are used to proxy the currency risk. The Bera-Jarque ( B − J ) tests normality based on both skewness and excess kurtosis and is distributed χ 2 with two degrees of freedom. LB (20) and LB 2 (20) denote the Ljung-Box test statistics for up to the 20th order autocorrelation of the raw and squared returns, respectively. * and ** denote statistical significance at the 5% and 1% level, respectively. Sample period 1981:02 – 2001:03 Mean Std. Dev. Min Max B−J IFCIND 0.643 8.598 -27.948 30.210 4.677 LB (20) 30.000 2 1981:02 – 1985:01 – 1986:02 – 2001:03 2001:03 2001:03 Panel A: Stock returns (%) IFCKOR IFCMAL IFCPHI 0.545 1.153 0.185 10.996 11.191 10.238 -40.888 -34.672 -37.365 53.600 38.467 43.008 107.430** 91.088** 21.180** 20.905 43.525** 33.294* 1985:01 – 2001:03 1981:02 – 2001:03 IFCTAI 1.084 12.924 -43.888 42.749 22.954** IFCTHA 0.385 11.112 -41.281 38.454 86.193** 18.820 46.458** 58.874** 156.439** TAIWDUS 0.098 1.376 -6.827 4.581 204.351** THAIBUS -0.312 2.865 -21.023 14.161 5921** LB (20) 50.007** Mean Std. Dev. Min Max B−J INDNRUS -0.731 1.845 -19.561 5.911 22929** LB (20) 28.054 90.565** 25.742 20.652 79.009** 38.829** 9.492 15.522 149.830** 34.615* 48.652** 130.942** 2 LB (20) 141.580** 103.919** 14.846 Panel B: Currency returns (%) SKORWUS MALAYUS PHILPUS -0.268 -0.232 -0.525 2.974 2.376 3.102 -35.889 -15.117 -14.602 8.926 14.483 8.628 80759** 3268.913** 373.171** 35 Table 3: Quasi-Maximum Likelihood estimation of the dynamic ICAPM: Trivariate GARCH(1,1)-M ri ,t = (1 − LDi ,t )λ w,t −1 hiw ,t + λ c ,t −1 hic ,t + LDi ,t λl ,t −1 hi ,t + (δ ii ε i ,t −1 + δ ci ε c ,t −1 ) + CDt (ω ii ε i ,t −1 + ω ci ε c ,t −1 ) + ε i ,t rc ,t = λ w,t −1 hcw,t + λ c ,t −1 hc ,t + (δ cc ε c ,t −1 + δ ic ε i ,t −1 ) + CDt (ω cc ε c ,t −1 + ω ic ε i ,t −1 ) + ε c ,t rw,t = λ w,t −1 hw,t + λc ,t −1 hcw,t + ε w,t where λ w,t −1 = exp(φ w' z t −1 ) ; λc ,t −1 = φ c' z t −1 ; z t −1 = {CON , USDIV , ∆USTP, USDP, WORLD} λl ,t −1 = exp(φ l' z l ,t −1 ) ; z l ,t −1 = {CON , LOCAL} ε t | I t −1 ~ N (0, H t ) H t = C ' C + A ' ⋅ H t −1 ⋅ A + B ' ⋅ ε t −1ε t' −1 ⋅ B + D ' ⋅ η t −1η t' −1 ⋅ D “ LDi ,t ” is a liberalization dummy variable for emerging market i . “ CDt ” is a dummy variable for Asian crisis period. Robust t-statistics are given in parentheses. * and ** denote statistical significance at the 5% and 1% level, respectively. Panel A: Conditional mean process - prices of world market, currency, and local market risks CON IFCIND φw φc IFCKOR φw φc IFCMAL φw φc 1.926 (3.324)** -6.563 (-1.628) 21.309 (4.838)** -0.081 (-0.011) -0.873 (-0.513) -0.493 (-0.149) 63.534 (2.339)* -2.743 (-0.882) -5.069 (-0.251) IFCPHI φw φc (-0.474) -8.400 (-2.029)* 0.052 (1.150) 0.250 (1.886) -10.365 (-1.795) 2.992 (1.912) 3.845 (0.315) 1.487 (1033.86)** 0.364 (201.16)** -0.064 (-1.585) 0.220 (9.755)** 1.243 (2.408)* IFCTAI φw φc (-2.500)* -1.749 (-2.084)* 1.383 (2.171)* -4.037 (-1.515) 41.714 (5.487)** -1.660 (-0.447) -1.138 (-2.251)* 11.849 (5.629)** -28.995 (-2.206) 66.746 (2.814)** 59.760 (10.378)** IFCTHA φw φc -4.812 (-2.340)* 28.909 (3.136)** -5.054 (-1.995)* 39.822 (7.654)** -44.728 (-8.154)** 48.461 (3.433)** -7.216 (-1.307) (3.030)** 1670.390 (5.375)** 174.055 (4.860)** LOCAL (-12.254)** 9.338 (3.358)** 1.052 (1.380) 2.882 (5.439)** -4.718 (-8.670)** -1.698 (-3.051)** -34.437 (-10.686)** LOCAL 0.915 (2.696)** -0.380 (-0.420) -2.039 (-1.382) 1.784 (1.831) -165.663 (-5.277)** -3.709 -41.400 (-3.025)** 215.528 (-1.291) LOCAL -1.407 (-0.667) 2.085 (1.116) 0.290 (0.299) 2.537 (2.585)** -8.100 (-2.339)* 25.841 (3.220)** -7.598 (-0.837) -8.749 (-1.505) 3.130 (1.016) 78.593 (3.273)** 108.974 (1.214) 107.946 (3.241)** CON φl -17.320 (-5.062)** -9.509 CON φl LOCAL -1.572 CON φl LOCAL -0.775 CON φl WORLD (2.075)* CON φl USDP 0.666 CON φl ∆USDP USDIV -3.577 (-2.929)* LOCAL 1.896 (0.077) 36 Table 3 (continued) Panel B: Mean spillover and contagion Mean spillover Contagion δ ii δ ci δ cc δ ic ω ii ω ci ω cc ω ic 0.007 (0.094) 0.007 (0.586) 0.027 (9.967)** 0.006 (2.300)* 0.078 (0.548) 0.561 (779.581)** 0.275 (0.927) 0.035 (17.066)** -0.221 (-0.514) -1.530 (-15.919)** -0.054 (-3.311)** 0.132 (6.068)** 0.480 (1.940) -0.654 (-819.963)** 1.140 (0.640) 1.034 (535.050)** Malaysia 0.007 (0.254) -0.011 (-1.717) 0.397 (10.589)** 1.226 (4.159)** 0.057 (0.480) -0.146 (-9.947)** 1.019 (8.866)** 2.170 (4.928)** Philippines 0.517 (18.644)** 0.005 (1.523) 0.182 (4.807)** 0.200 (1.226) 0.504 (5.519)** 1.047 (16.086)** 0.098 (1.400) -0.709 (-4.512)** Taiwan -0.001 (-0.013) 0.007 (1.075) 0.662 (5.842)** 0.605 (0.581) 0.836 (5.623)** 0.100 (5.037)** -0.520 (-4.084)** -2.092 (-1.526) Thailand 0.179 (2.801)** -0.006 (-0.848) 0.273 (1.750) -0.249 (-0.706) -0.002 (-0.011) 0.050 (0.946) -0.393 (-3.486)** 1.358 (1.631) India Korea 37 Table 3 (continued) Panel C: Conditional variance process IFCIND INDNRUS WORLD ai 0.948 (67.873)** 0.102 (0.723) 0.540 (2.409)* bi di 0.219 (3.192)** 1.709 (7.805)** 0.023 (0.352) -0.048 (-0.254) -0.677 (-0.676) 1.739 (0.402) IFCKOR ai bi di SKORWUS WORLD 0.921 (170.515)** -0.543 (-121.048)** 0.335 (8.492)** 0.289 (11.373)** 0.966 (1678.70)** -0.056 (-1.114) 0.916 (2.087)* -0.225 (-1.717) 3.183 (0.868) IFCMAL MALAYUS WORLD ai 0.828 (55.878)** -0.467 (-9.720)** 0.622 (17.227)** bi di 0.420 (14.220)** 0.168 (1.846) 0.125 (1.691) 1.145 (3.292)** 39.982 (22.828)** 2.355 (2.623)** IFCPHI ai bi di PHILPUS WORLD 0.717 (17.808)** 0.112 (1.094) 0.754 (7.036)** 0.274 (4.849)** 1.953 (19.687)** 0.249 (4.646)** -1.329 (-2.976)** -3.789 (-1.885) -1.083 (-1.412) IFCTAI TAIWDUS WORLD ai 0.866 (61.816)** -0.015 (-0.123) 0.072 (0.187) bi di -0.212 (-0.985) 0.574 (5.987)** -0.084 (-0.314) -1.004 (-2.732)** -34.604 (-5.701)** -0.026 (-0.021) IFCTHA ai bi di THAIBUS WORLD 0.913 (38.849)** -0.228 (-5.470)** 0.909 (48.239)** -0.395 (-6.710)** 1.065 (8.852)** -0.231 (-2.928)** -0.001 (-0.009) -0.002 (-0.006) 0.001 (0.005) 38 Table 3 (continued) a Panel D: Diagnostics for the residuals IFCIND INDNRUS WORLD B−J 5.858 348.691** 61.967** LB (20) 25.461 13.626 24.905 26.808 31.960* 9.596 IFCKOR SKORWUS B−J WORLD 3.602 118.661** 82.316** LB (20) 13.542 77.891** 20.219 15.720 20.210 12.157 IFCMAL MALAYUS B−J WORLD 77.810** 340.193** 2.792 LB (20) 16.491 31.485* 23.990 2 LB (20) 2 LB (20) 2 LB (20) 10.014 10.456 15.143 IFCPHI PHILPUS B−J WORLD 10.283** 39.005** 30.808** LB (20) 24.383 36.637* 16.535 20.490 8.200 10.015 IFCTAI TAIWDUS B−J WORLD 40.738** 3.101 24.483** LB (20) 16.527 18.079 25.945 17.914 10.401 17.054 IFCTHA THAIBUS WORLD 2 LB (20) 2 LB (20) B−J LB (20) 2 LB (20) a 60.123** 44297.219** 28.517** 20.472 11.175 16.228 10.118 0.350 12.833 LB ( 20 ) and LB 2 ( 20) are the Ljung-Box test statistics of order 20 for serial correlation in the standardized residuals and standardized residuals squared. respectively. B−J is the Bera-Jarque test statistic for normality. * and ** denote statistical significance at the 5% and 1% level, 39 Table 3 (continued) a Panel E: Hypothesis testing concerning time-varying prices of world market, currency, and local market risk IFCIND IFCKOR IFCMAL IFCPHI IFCTAI IFCTHA Null hypothesis k k 260.242 4.13E+9 1296.648 1108.214 363.231 424.336 1. H0: φ = φ = 0 w c ∀k = CON , USDIV , ∆USTP , USDP, WORLD 2. H0: φ wk = φ ck = 0 ∀k = USDIV , ∆USTP , USDP, WORLD 3. H0: φ wk = 0 ∀k = CON , USDIV , ∆USTP , USDP, WORLD 4. H0: φ wk = 0 ∀k = USDIV , ∆USTP , USDP, WORLD 5. H0: [0.000] [0.000] [0.000] [0.000] 59.817 7.84E+8 968.011 704.474 72.452 64.795 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] 244.435 248.630 137.139 318.52 141.310 258.324 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] 49.647 14.750 61.209 40.843 17.844 11.591 [0.000] [0.005] [0.000] [0.000] [0.001] [0.020] 9.714 2.63E+9 693.274 449.080 60.763 20.028 [0.083] [0.000] [0.000] [0.000] [0.000] [0.001] 7.628 1.81E+8 693.064 449.019 45.936 19.600 [0.106] [0.000] [0.000] [0.000] [0.000] [0.000] 9.271 25.797 194.711 7.371 2.653 9.143 φ ck = 0 ∀k = USDIV , ∆USTP , USDP, WORLD 7. H0: [0.000] φ ck = 0 ∀k = CON , USDIV , ∆USTP , USDP, WORLD 6. H0: [0.000] φ lk = 0 [0.009] [0.000] [0.000] [0.025] [0.265] [0.010] ∀k = CON , LOCAL Panel F: Estimated time-varying risk premia and conditional volatility (%) Risk premium IFCIND IFCKOR IFCMAL IFCPHI IFCTAI IFCTHA Before the liberalization 0.177 0.442 -0.246 3.149 2.352 0.241 After the liberalization -0.116 -0.745 0.132 0.394 0.340 0.238 Conditional volatility Before the liberalization 8.276 9.205 9.705 11.268 14.345 6.603 After the liberalization 8.774 10.228 8.907 11.011 11.465 12.007 a Robust Wald statistics are in the first row, and the corresponding p-values are in the brackets. 40 Table 4: The impact of liberalization on the estimated risk premium and conditional volatility RPi ,t = β 0 + β 1 LDi ,t + u i ,t ∀i CVi ,t = β 0 + β 1 LDi ,t + u i ,t ∀i where “ RP ” and “ CV ” denote estimated risk premium and conditional volatility, respectively; “ LDi ,t ” is a liberalization dummy variable for each emerging market i . t-statistics are given in parentheses. * and ** denote statistical significance at the 5% and 1% level, respectively. Panel A: IFCIND IFCKOR IFCMAL IFCPHI IFCTAI IFCTHA intercept LD intercept LD intercept LD intercept LD intercept LD intercept LD 0.002 -0.002 0.004 -0.012 -0.002 0.004 0.031 -0.028 0.023 -0.020 0.004 -0.002 RP (2.188)* (-2.342)* (0.869) (-1.585) (-0.350) (0.470) (4.297)** (-3.035)** (6.892)** (-4.695)** (1.380) (-0.649) Panel B: Full sample 0.083 (100.67)** 0.005 (3.927)** 0.092 (59.073)** 0.010 (4.456)** 0.097 (23.340)** -0.008 (-1.674) 0.113 (68.665)** -0.003 (-1.261) 0.143 (38.224)** -0.029 (-6.110)** 0.078 (17.523)** 0.041 (7.230)** 41 CV Pre-crisis 0.083 (92.317)** 0.007 (4.097)** 0.092 (98.539)** -0.004 (-2.786)** 0.097 (30.278)** -0.019 (-5.017)** 0.113 (70.159)** -0.006 (-2.629)** 0.143 (33.604)** -0.027 (-4.660)** 0.078 (28.867)** 0.015 (3.952)**