Emerging Bond Market Volatility: Dynamic Interdependence and Volatility Transmission between the US and Emerging Bond Markets Chih-Ling Lin Central Taiwan University of Science and Technology Ming-Chieh Wang National Chi Nan University Yin-Feng Gau∗ National Chi Nan University
Abstract Using the GJR-GARCH(1,1)-M model with time-varying conditional correlation, this paper explores the integration between the US and fifteen emerging bond markets. The empirical results show the magnitude of return spillover between the US and emerging bond markets is weaker than that of volatility spillovers. Furthermore, emerging bond markets exhibit volatility asymmetric effects, showing negative shocks have a greater impact on the volatility than positive innovations. We also find the US bond market leads several emerging bond markets in the expected return and volatility, including Brazil, Colombia, Venezuela, China, Malaysia, South Korea, Poland, and Russia. The slope of yield curve of US bond market and the Eurodollar interest rate have a better explanatory ability for most of emerging market bond returns. Overall, most emerging bond markets appear partially integrated with the US bond market, and the benefit of international diversification is not diminished sharply during the period with extremely high volatility in both the US and local emerging bond markets. Keywords: Emerging bond market; Volatility spillover; Time-varying conditional correlation; Diversification benefit. ∗
Address correspondence to Yin-Feng Gau, Department of International Business, National Chi Nan University, 1 University Rd., Puli, Nantou 545, Taiwan, E-Mail:
[email protected]. We thank M. Hua, S. Hsu, S. Liao, and S. Yang for helpful comments.
1
1. INTRODUCTION The emerging markets have become one of the asset allocation classes for the international portfolio allocation (Harvey, 1995). There are many international capital inflows from developed countries to emerging bond markets and most capital inflow was concentrated in the Asia and Latin America during 1990s after the debt crisis of the mid-1980’s (López-Mejía, 1999; Bekaert and Harvey, 2003).1 In addition, as the lowering of trade barriers between countries, the international trade has increased substantially in recent years. Therefore, it is important for US investors to understand the transmission mechanism between markets and the correlation structure between emerging bond markets for their portfolio risk management. Emerging bond markets appear to exhibit relatively low correlations with the developed capital markets. Although emerging markets have long been characterized by their high volatility and with different sources of risks and returns from developed countries, most emerging market countries in Eastern Europe, Asia and Latin America currently are more financially sound than several years ago owing to the liberalization of their trade and financial systems and macroeconomic stabilization (Bekaert and Harvey, 2003). However, much research focuses on the issue of emerging equity markets, but seldom on emerging bond markets.2 The study of the volatility of bond market returns is not only important for predicting market returns or bond yields (Cai, Jiang, and Kumar, 2004; Fleming, Kirby, and Ostdiek, 2001), but also helps investors understand the behavior and source of cross-market volatility transmission for the purpose of international diversification, risk management, asset pricing and making asset allocation decision. Hence, it is natural to raise the question of measurement of risk and the volatility of returns in emerging market assets and the interdependence across countries. Of more specific interest is the degree of integration of emerging markets. Because of the globalization and liberalization of world economy, investors can assess to the global financial markets more rapidly via electronic trading technologies, also the transmission of news becomes more easily. As a result, the formation of economy zone, trade organization and financial liberalization may change the relationship of financial capital market presented in the previously researches, especially, in the emerging market economy. This paper examines the dynamic interrelationships between the US and fifteen 1
According to United States Department of the Treasury, in the early 2000s, many US investors still have their money go to emerging markets because of the high rate of returns in the emerging markets. 2 Most literature has focused on the issue of interdependence of international equity and bond markets in developed countries (including the US, Germany, United Kingdom, Japan and so on) and emerging equity markets in, for example, Asia and Eastern Europe, but little research exists on emerging bond markets (Skintzi and Refenes, 2006; Cifarelli and Paladino, 2006, Lin, Wang, and Gau., 2007).
2
emerging bond markets in terms of return and volatility transmission mechanism and time-varying conditional correlation. We use the GJR-GARCH (1,1)-in-mean model to allow for the time-varying conditional covariance structure and the asymmetry in volatility. We also consider the impact of global and local factors on emerging bond market returns to explore the integration between the US and selected emerging bond markets. Following Hunter and Simon (2005), we also investigate the benefit of international bond diversification during the extremely high return volatility periods. The sample countries cover three main geographical areas, including fifteen emerging bond markets for Argentina, Brazil, Chile, Colombia, Mexico, Venezuela, China, Indonesia, Philippine, Malaysia, South Korea, Thailand, Hungary, Poland and Russia. This paper contributes to expanding the literature on increasing interdependence between world bond markets in several ways. First, we focus on the interdependence between US and emerging bond markets including Asia, Latin American and East Europe that, compared to equity markets, are less studied in the literature. Second, we model the return and volatility spillovers allowing for the time-varying conditional correlation and asymmetric volatility effect.3 Third, the study obtains the influence of market factors by using global factors and local factors on return and volatility of emerging bond markets. This paper is organized as follows. Section 2 discusses the characteristics and the financial evolution of the emerging bond markets, and reviews the literature on empirical tests of relationships between international financial markets. Section 3 presents methodologies used to study the dynamic interrelationship. We extend the bivariate GARCH model to allow for both time-varying conditional correlation structure and asymmetric volatility effect to study the return and volatility transmission. Section 4 describes the data and discusses the empirical results. Section 5 is the conclusion.
2. LITERATURE REVIEW The risk involved in emerging market assets is higher than in major developed capital markets. Erb, Harvey, and Viskanta. (1999) characterized emerging market bonds as having small market capitalization, high volatility, and negative skewness. The extreme risk from several crises such as the Mexican peso crisis of 1994, the Asian currency crisis in 1997/1998, the Russian and the Brazilian bond market bursts of 1998/1999, the crises in Turkey and Argentina of 2000/2001 and so on, raises the importance of an adequate measure of extreme risks. In particular, how the crisis spills over geographical boundaries 3
Although some research on the dynamic interdependence between equity markets has found the evidence of asymmetric volatility and the time-varying conditional correlation, previous research in the bond market returns pays little attention on the impact of asymmetric volatility in the bond returns.
3
or how the financial turmoil transmits from one market to another is crucial for people to understand the risk in emerging market assets (Bekaert and Harvey, 2003). Moreover, the gauging of time-varying conditional correlation between various assets is essential for understanding gains of international diversification depending on the correlation structure of different assets (Eun and Resnick, 2002).
2.1 The Development of Emerging Bond Markets The emerging-market bond consists primarily of sovereign or government debts. Emerging markets cover more than thirty developing countries including Latin America, Eastern Europe, Russia, the Middle East, Africa and Asia excluding Japan. The strong commodity demand and domestic economic growth make developing economies attractive. According to Eichengreen (2006), the extraordinary stockpile of international reserve in emerging economies helps them to shift from the external deficit to surplus. The damage caused from financial crisis has raised the problem of the lack of well-functioning domestic bond market.4 Asian bond markets have increasingly grown after the 1997 Asian financial crisis. The crises induced the central banks in the region to consider the need for developing more liquid, deep and matured bond markets. After the first launch of Asian Bond Fund (ABF 1) in 2003,5 the launch of the second Asian Bond Fund (ABF2) by the Executives' Meeting of East Asia and Pacific Central Banks (EMEAP) in May 2005 is the most important development of bond market in Asia. The ABF 2 provides the retail and institutional investors with the flexibility to invest in the Asian bond markets. At the same time, it offers investors to diversify their exposure to bond markets in Asia within one instrument. The high economic growth in Asia also makes investments in this area attractive. According to Hong-Kong Monetary Authority, the total market capitalization of domestic bond markets in eight Asian economies (Hong Kong, Indonesia, South Korea, Malaysia, Philippines, Singapore, Taiwan and Thailand) was increasing significantly from the 20% at the end of 1995 to 47% of the combined GDP of the economies at the end of 2003. Although the total financing grew at only about 7% over the average of the last few years and still far below compared with the mature economies in the world, the Asian bond markets are expected to accelerate in the next ten years, driven by China and India. In a survey from foreign investors in emerging Asian and Latin American bond markets in the year of 2003, Burger and Warnock (2006) showed the biggest bond market is the US 4
The emerging bond markets are linked to the financial liberalization. The financial reforms have dramatically transformed the financial environment in emerging markets. The financial liberalization includes bank sectors reforms, privatizations as well as the openness to the capital inward and outward (Gelos and Werner, 2001, Beim and Calomiris, 2001). 5 ABF 1 is the close-ended fund and only confined to the investment of (the Executives' Meeting of East Asia and Pacific Central Banks) EMEAP central banks. http://www.info.gov.hk/hkma/cindex.htm.
4
market which comprises the 42% of the global bond outstanding. The emerging market for Asian bond is 3.7% and for Latin America is 1.4%. Furthermore, from the survey of holding percentage of bonds issued by different countries for international investors, Burger and Warnock (2006) showed international investors tend to hold around 20% of all industrial country bonds, and 21% of all bonds issued by Latin American countries, whereas, emerging Asia is different. Among emerging Asia bond markets, the Philippines appears much more attractive to global investors with 22%, and the next is Malaysia with 12%. They also pointed out that the Philippine bond market tends to issue foreign-currency-denominated bonds is the reason for high holding percentage, since investors are reluctant to bear the currency risk. McCauley and Jiang (2004) addressed that the Asian bonds offer a favorable risk-return trade-off relative to the US Treasury bonds. In total, the emerging Asian bonds become one of the attractive and important asset classes for global investors. Hoti (2004) evaluated the nature of international capital flow for developing countries. Capital inflows with regard to portfolio investment (PI) in Indonesia dramatically increase in 1994, however, down to negative (outflow) promptly in 1997-1998, owing to the Asian financial crisis during the period. Reflow occurs again after 1998 but becomes more volatile than before. Since the financial liberalization in international capital inflow is a pulling factor to allow for foreign capital flowing to the domestic security markets, and this tends to cause several impacts on economic factors, as stated in Hoti (2004) that “the large capital inflows tend to cause rapid monetary expansion, inflationary pressure, real exchange rate appreciation, and widening current account deficits in the recipient countries”.
2.2 Volatility Transmission Mechanism across Markets Focusing on the issue of interdependence among international financial markets in terms of return and volatility spillover after extreme financial events,6 Hamao et al. (1990), In, Kim, Yoon, and Viney (2001), Forbes and Rigobon (2002), and Lin, Engle, and Ito (1994) examined the international transmission mechanism between the US stock market and its counterpart from industrial countries.7 Several have documented the leading of the US stock market for foreign markets and a significant volatility spillover from the US to 6
Most studies on the relationship between markets have used terms such as “co-movement”, “contagion”, “interdependence”, “linkages”, “spillover”, and “transmission” to describe the phenomenon of interactions or transmission between markets. Forbes and Rigobon (2002) used the restrictive definition about the contagion and presented an overview of the literature on contagion. He argued that there is no contagion only interdependence if there are continuously strong linkages. 7 A major issue about international returns is the lead-lag effect across national market. For example, Eun and Shim (1989) used the VAR approach to examine the transmission of returns from the US to selected European markets and showed the US stock market is the most influential one in the world.
5
foreign countries. Bekaert and Harvey (1997) measured the volatility spillover by dividing the volatility into two components, local and world factors, while Ng (2000) considered the regional, local, and world factors. Lane and Milesi-Ferretti (2005) pointed out that emerging markets’ exposure to currency risks is higher than that of developed markets as they highly rely on external funds. Many theories have been proposed to explain how innovations are transmitted between markets. 8 In summary, the contagion in the recent literature defined as a significant change in the strength of cross-market after a crisis. Therefore, the terms such like linkages, links, co-movement and interdependence are used to describe the financial market relations to each other, while the spillover and transmission refer to the general transmission of shocks between markets. The term of “interdependence” refers to the markets appearing continue level of high correlation during stability period or after the crisis/shocks; hence the interdependence represents a more general type of market linkages. In this paper, we do not use the concept of contagion; instead, we examine the dynamic interrelationships between markets using the concept of “interdependence”. We analyze both market relationships and shocks transmission between US and individual emerging bond market. Most studies on bond markets focused on the relationship between mature bond markets. Hunter and Simon (2005) studied the lead-lag relations and the conditional correlations between 10-year US government bond returns and their counterparts from the UK, Germany, and Japan, and found the mean spillover effect is not significant in all countries, implying the contemporary relationships between US and individual industrial country. They also found the US bond market led both German and Japanese bond markets. Steely (2006) found volatility spillover between the stock, bond and interest rate returns in the UK. Using the bivariate EGARCH model, Skintzi and Refenes (2006) investigated the dynamic linkage among the European bond markets and found that volatility interdependence is stronger than the return interdependence, and that an asymmetric effect from local and regional shocks to the bond market volatility process exists. Studies on the volatility spillover among emerging bond markets conclude the US bond market leads the emerging bond markets and local factors still play an import role in pricing the emerging bond market returns. However, the results relating to the co-movement are quite mixed. Christiansen (2003) found strong volatility spillover effects
8
For an overview, see Forbes and Rigobon (2002).
6
from both the US and European into the individual bond markets. But the volatility spillover effect from aggregate European bond markets is stronger, compared to that from the US bond market, suggesting the formation of economic zone enhances the interdependence between asset returns. However, Christiansen did not consider the possibility of time varying conditional correlation and asymmetric effects in volatility. Extreme events could affect the interdependence of emerging bond markets. Cifarelli and Paladino (2006) studied sovereign bond spread issued by ten emerging countries, and found the increasing covariance between spreads in periods of stress but not diminishing benefit of international portfolio diversification. They also found the September 11 shock from the US affected both Latin America and Asian spreads, while Argentina crisis only turbulent the volatility within the region. Most studies on determinants of sovereign bond spread agreed the US interest rate affects emerging bond markets significantly. By examining the spillover effects between emerging equity market and Brady bond in Asian and Latin America and cycles in capital flows to Latin American economies, Calvo and Reinhart (1996) argued the falling US interest rate is associate the capital flow inflow to Latin America, and vice versa. In general, US short-term interest rate reflects the global liquidity, thus a fall in short-term interest rates in developed markets make investor seek for higher yields in emerging markets. The slope of the US yield curve reflects the expected economic growth of US, the emerging market benefit from the US economic growth by increasing the creditworthiness and lower emerging market spread (Sy, 2002).9 By investigating yield spread between the benchmark US Treasury bond and the sovereign bonds of China, Korea, Malaysia, Philippines and Thailand, Battern, Fetherston, and Hoontrakul (2006) showed the credit spread of Asia-Pacific sovereign bond is negative to the changes in interest rates on US benchmark bond and only Philippines’ bond spread significantly relates to asset and exchange rate variables. Country risk is also an important determinant of emerging bond returns. Arora and Cerisola (2001) examined the impact of US monetary policy by using sovereign bond spreads as the proxy for the country risk and the US federal fund target rate as the proxy for the US monetary policy, and found domestic fundamentals are the most important factor affecting the country risk, and the US monetary policy influences the sovereign bond spread. Min (1998) studied determinants of bond spread denominated by dollar in eleven emerging markets and concluded macroeconomic variables are more influential than the international interest rates.
9
Sy (2002) examined the impact of various US interest rate on the emerging bond spread and found that the US interest rate affects the emerging bond market spread significantly.
7
2.3 Approaches to Modeling the Volatility Transmission According to Claessens, Dornbusch, and Park (2001), four approaches to measuring market comovement are cross-market correlation coefficients, ARCH and GARCH models, cointegration techniques, and the direct estimation of specific transmission mechanisms. Besides, a variety of other methods includes the VAR, probit, regime-switching models, and GMM estimation. Among these methods, the VAR and cointegration analysis gives a measure of long-run linkages, but the correlation coefficient is useful for understanding the short-run dynamics between markets. Hunter and Simon (2005) used the bivariate GARCH to study the lead-lag relations and the conditional correlations between 10-year US government bond returns and their counterparts from the UK, Germany, and Japan. For the correlation between the bond markets, they found the US and other major bond markets are time varying and are driven by changing macroeconomic and market condition. The lower conditional bond return correlations may be caused by the different business cycle conditions between countries since the greater absolute differences in yields curve slopes or absolute interest rate differentials. They also found that during high-stress periods in U.S. or other foreign bond markets do not higher the bond return correlations, implying the benefits of international bond diversification are not diminished during periods of weakness or increased volatility in either the US or foreign bond markets.
3. ECONOMETRIC MODELS Stock volatility exhibits persistence or a tendency to cluster and leverage effect that refers to a larger impact on volatility from negative shocks than positive shocks of equal magnitude (Hamao et al., 1990; Koutmos and Booth, 1995; Bekaert and Wu, 2000).10 The volatility asymmetry effect in bond markets is not the same as the leverage effect in stock market. Cappiello, Engle, and Sheppard (2006) suggested that the possible explanation for asymmetries in return volatility is time-varying risk premia (volatility feedback). De Goeij and Marquering (2006) argued the asymmetry effect in bond return volatility can be caused by news announcements, since investors anticipate news before announcements released and it could cause over- or under-reaction once announcements are released.
10
There are two theories explaining asymmetric volatility of equities: the leverage effect and the volatility feedback hypothesis. Black (1976) and Christie (1982) attributed that the asymmetric volatility to change of financial leverage, or the debt-to-equity ratio. The other possible explanation for the volatility asymmetries rests on time-varying risk premia, or volatility feedback, as originally suggested by French, Schwert, and Stambaugh (1987) and Campbell and Hentschel (1992). If the volatility will be higher in the future, investors should be compensated for bearing the risk by raising the required rate of return. Hence the stock price decline immediately in order to allow for higher future returns.
8
However, this volatility asymmetry is not found across major world bond markets (Cappiello et al., 2006; Hunter and Simon, 2005). We examine the volatility asymmetry in emerging bond markets by using the GARCH framework to specify the return and volatility transmission between the US bond market and fifteen emerging bond markets. Two mostly used models for capturing the asymmetry effect are the EGARCH (exponential GARCH; Nelson, 1991) and GJR GARCH (Glosten, Jagannathan, and Runkle; 1993). Engle and Ng (1993) tested the asymmetry in volatility, and suggested that the GJR GARCH model is the better model for modeling asymmetry. In this paper, we use the GJR-GARCH specification to capture the volatility asymmetry.
3.1 The GJR-GARCH(1,1)-in-Mean Specification The GARCH-in-mean model is specified for considering the tradeoff between risk and returns. Glosten et al. (1993) pointed out that the standard GARCH-M model appears positive but insignificant relationship between conditional mean and conditional volatility of stock excess return. They proposed a modified GARCH in Mean (GARCH-M) model to examine the relation between return and risk by considering the positive and negative unanticipated returns to have different impacts on the conditional variance into the model. In this study, we use the GJR-GARCH-M model to examine the return and volatility spillover as well as the dynamic interrelationships between bond market returns including the impact of high stress volatility on the international diversification similar to that used by Hunter and Simon (2005). Since the US market has a much bigger capitalization than emerging bond markets, it is expected that emerging bond market will be influenced by the bigger US market.
3.2 Time-Varying Conditional Correlation 3.2.1 The Impact of US Exogenous Variables on Conditional Correlation In the traditional GARCH model, the constant correlation specification is assumed for convenient. Bollerslev (1990) proposed the bivariate constant conditional correlation (CCC) model that assumes the conditional correlation is constant through time. u us ,t uˆ t ξ t −1 = ~ N(0, u EM ,t
hus ,t Ht = hEM ,US ,t
Ht)
(1)
hEM ,US ,t hEM ,t
(2) where Ri,t is the weekly log return of bond from a particular market i at time t. ui ,t denotes shocks to asset i’s returns at time t and ξ t −1 includes all available information at time t -1.
9
Error terms are conditionally distributed as bivariate normal distribution with zero-mean and variance-covariance Ht. The constant correlation specification is
hEM ,US ,t = ρ EM _ US , 0 ×
[
hEM ,t hUS ,t
]
(3)
where ρ EM _ US ,0 is the conditional correlation coefficient between the US and individual country i’s bond returns. However, the specification is too simple to capture the inter-dynamics between different asset returns. Longin and Solnik (1995) examined the interdependence between stock markets and the results reject the hypothesis of constant conditional correlation between international equity markets. The evolution of international interactions between markets could attribute to the volatility of national markets change over time or the change of interdependence across markets (Longin and Solnik, 1995). To consider the time-varying conditional correlation between international bond markets, we follow Hunter and Simon (2005) to use a time-varying conditional covariance model as follows. Time-varying conditional correlation specification with instrumental variables
hEM ,US ,t = ( ρ 0 + ρ1 × z t −1 + ρ 2 × trend )
[
hEM ,t hUS ,t
]
(4)
If ρ1 coefficient is significantly different from zero, it suggests the conditional correlation between markets varies over time. The lagged instrument z t −1 we use is the US 3-month Eurodollar interest rate, which is a proxy for the global liquidity condition (economic condition),11 and trend denotes the time trend covering the sample period. 3.2.2 The Effect of Extremely High Volatility on Correlation Specification
We also measure the impact of extremely high variance in US bond returns and emerging bond returns to understand whether the benefits of international diversification in bonds diminished during high-stress period. Vj,t (j = EM, US) is equal to 1 if the conditional variance of market j is greater than its average volatility plus two standard deviation, otherwise zero. The estimated model is as follows: Time varying correlation specification with the extremely high variance 11
Here, we apply US 3-month Eurodollar interest rate as proxy of the global liquidity condition instead of US 3-month Treasury Bill yield, since this interest rate reflects the investor expectation on global liquidity condition As pointed out by Arora and Cerisola (2001), the emerging market bonds are more risky than developed markets and the rise in the U.S. interest rate may cause an increase in emerging bond spreads. They used the yield on the three-month U.S. treasury Bill as the proxy the changes in global liquidity and economic conditions for the reason that the yield on the three-month U.S. treasury Bill often to be considered a short-term risk-free rate in the literature and as a benchmark for pricing other high-yield assets in world capital market..
10
h EM ,US ,t = ( ρ 0 + ρ 1 z t −1 + ρ 2 × trend + ρ 3 × V EM ,t h EM ,t −1 + ρ 4 × VUS ,t hUS ,t −1 )
where
1 if h j , t −1 > mean volatility + 2std V j ,t = othewise 0
,
[
h EM ,t
hUS ,t
]
j = EM, US.
If the coefficients ρ 3 and ρ 4 are positive and statistically significantly different from zero, it implies the conditional correlation between bond markets increases in period t as hEM,t-1 and hUS,t-1 are extremely high in period t-1, vise versa. The modern portfolio theory presented by Markowitz (1952) indicated two criteria for generating a proper portfolio:(1) An investor is assumed to seek the highest rate of return for a given level of risk and (2) the lowest level of risk for a given rate of return (risk aversion assumption). A portfolio having this characteristic is known as an efficient portfolio and has generally been accepted as the paradigm of optimal portfolio construction.
3.3 The GJR-GARCH(1,1)-M Model with Time-Varying Conditional Correlation Several factors that affect the bond returns are incorporated into the model (Barr and Priestley, 2004; Hunter and Simon, 2005; Batten et al., 2006). Barr and Priestley (2004) divided factors into world factors and local factors that affect the bond market returns. The world factors we consider include the US Treasury Bill return (USTB), the slope of the yield curve calculating by the difference in yield between the 30 year and the 2 year US Treasury Bill (USYC), S&P 500 stock returns (USEQT), the change in the risk free interest rate as interest rate factor (USIR). The local factors are, the emerging bond market return (EM), the emerging stock market return (EMEQT), the change in the interest rate (EMIR) and the credit spread between emerging bond market and US bond market (EMCRD). The selected independent variables are used to examine the possibility of mean spillover between markets and the effect of the selected dependent variables on the US bond returns as well. To investigate the volatility spillover within market and across market, our model consider past innovation of own and the other country’s bond returns, lagged own variance, and the asymmetry effect suggested by the Cappiello et al. (2006). All independent variable are specified in first lag expect for the risk premium from market and error term. The first lag of the independent variable is for the purpose that avoiding a spurious finding of lead-lag relationships across markets as suggested by Hunter and Simon (2005). The bivariate GARCH (1,1) model is as follows:
11
(5)
The US market = θ 0 + θ 1USTB
USTB
t
+ θ6
hUS ,t + θ 7
h EM
t −1 ,t
+ θ 2 EM
t −1
+ θ 3USYC
t −1
+ θ 4 USEQT
t −1
+ θ 5USIR
t −1
+ u us ,t
where uUS ,t ~ N (0, hUS ,t )
(6)
and
EM US 2 2 2 2 hUS ,t = α US ,0 + α US ,1uUS ,t −1 + α us , 2 hUS ,t −1 + α us , 3 u EM ,t −1 + γ us ,1u EM ,t −1 I t −1 + γ US , 2 uUS ,t −1 Ι t −1
(7)
Individual emerging market p
EM t = b EM ,0 +
∑b
EM , n EM t − n
+ θ EM ,1USTB t −1 + θ EM , 2USYC t −1 + θ EM ,3USEQT t −1
n =1
+ θ EM , 4USIR t −1 + θ EM ,5 EMEQT t −1 + θ EM , 6 EMIR t −1 + θ EM , 7 EMCRD t −1
(8)
+ θ EM ,8 hUS ,t + θ EM ,9 h EM ,t + u EM ,t
where u EM ,t ~ N (0, h EM ,t ) 2 2 2 2 EM us hEM,t =αEM,0 +αEM,1 uEM ,t −1 +αEM,2 hEM,t −1 +αEM,3 uus,t −1 + γ EM,1 uEM,t −1It −1 + γ EM,2 uus,t −1 Ιt −1
where
I t −1 = 1 if ut −1 < 0 I t −1 = 0 otherwise
(9) (10)
where the uUS ,t and u EM ,t is the innovations of the US bond market and emerging bond market, respectively. Equations (6) and (8) are the conditional mean models for the US and individual emerging bond returns, respectively. Suppose USTBt and EMt represent the weekly bond returns of US market and the particular emerging market. In Eq. (6) and Eq. (8), both the conditional mean of US and emerging bond returns depend on lagged US Treasury Bill ( USTBt −1 ), lagged US yield curve as the difference in yield between US 30-year minus US 2-year Treasury Bill ( USYCt −1 ), lagged S&P 500 stock returns ( USEQTt −1 ), the first lag of the change in the risk free interest rate ( USIRt −1 ). Individual emerging bond returns’ conditional mean is also related to lagged own market return ( EM i,t − n ),the first lag of own stock market return ( EMEQTti−1 ), lagged of the change in the interest rate ( EMIRt −1 ), lagged of credit spread calculated the difference between yields on sovereign bonds of developing countries and US treasury securities of comparable maturities ( EMCRDt −1 ).
hUS ,t and
hEM ,t represent the risk premium at time t, for the US and
emerging bonds, respectively. In Equations (7) and (9), the conditional variance of the US and emerging bond returns is related to own past squared innovations and past conditional variances. In
12
addition, to examine the volatility spillover effect across the US and emerging markets, we test for the impact of shocks from both bond markets. In Eq. (7), α US ,1 measures the lagged volatility shocks spillover effect and α US , 2 measures the degree of persistence and the idiosyncratic conditional variance, while the α US ,3 measures the lagged volatility shocks from individual emerging bond market. In Eq. (9), the conditional volatility for emerging bond returns is affected by its lagged idiosyncratic shock, lagged volatility, and lagged US idiosyncratic shocks. In this model, α EM ,1 measures the own market shock volatility spillover effect, α EM ,2 measures the degree of persistence and the idiosyncratic conditional variance, and the α EM ,3 measures the volatility spillover effect from the US bond market. EM 2 To capture the volatility transmission effect we add γ US ,1u EM to Eq. (7) and ,t −1 I t −1 US 2 γ EM , 2 uUS ,t −1 Ι t −1
to Eq. (9). The dummy variable Ι tj−1 (j = EM, US) is used to capture the
asymmetric impacts of lagged positive and negative innovations. Ι tj−1 equals 1 if
u j ,t −1 < 0 , 0 otherwise.
4. EMPIRICAL RESULTS 4.1 Data Description The data is obtained from the DataStream, based on weekly frequency (Friday to Friday),12 covering the period from January, 31 December 1994 through 27 March 2007.13 The emerging bond return data used in this study are the Emerging Market Bond Index Global (EMBI Global) provided by JP Morgan from DataStream. All return data are converted to US dollars–denominated returns as the foreign-currency-denominated bonds attract the foreign investors who are not willing to bear currency risk. The EMBI Global total return indices consist of Eurobonds, Traded Bonds and local market debt instruments issued by sovereign and quasi-sovereign entities. Since the global bond market provide well-diversified base for investors that can be expected to share a common understanding of the fundamental forces driving the market. The selected countries consist of the major emerging markets of Latin America, Asia and Eastern Europe including Argentina, Brazil, Chile, Colombia, Mexico, Venezuela, China, Indonesia, Philippine, Malaysia, South Korea, Thailand, Hungary, Poland, and Russia. The
12
The reason for using weekly returns proposed in the literature is to minimize the non-synchronous trading period and day-of-the-week effects (see Cappiello et al., 2006; Skintzi and Refenes, 2006). 13 Since the data limitation, the bond returns data of emerging countries are available in different data range.
13
US bond return is the US benchmark ten-year government bond return index (USTB). Returns are calculated as the natural logarithm difference of the total return index relative, Ri,t = ln( Pt / Pt −1 ) , using Friday to Friday closing total return indices. In order to explore the impact of world factor and local factor on return of emerging bond markets, the world instrumental variables are used including the US Treasury Bill return (USTB), the slope of the yield curve calculating by the difference in yield between the 30 and the 2 year US Treasury bond (USYC), S&P 500 stock returns (USEQT), the change in the risk free interest rate (USIR). The local factors are the emerging bond market return (EM), the emerging stock market return (EMEQT), the change in the interest rate (EMIR), and the credit spread between emerging bond market, and US bond market (EMCRD). US bond returns (USTB): representing US 10-year bond return index. Many researches pointed out the ten-years bond yields are more liquid than other particular maturities of bond yield and appropriate for the benchmark of bond return analysis (McCauley and Jiang, 2004; Mohanty, 2001; Alonso et al. 2004; Hunter and Simon, 2005). The lagged 10-year US Treasury total return index is used in this paper to examine the transmission mechanism of return to emerging bond market returns. Some authors apply this variable as the proxy for global liquidity condition (Eichengreen and Mody, 1998a and 1998b). USEQT: representing S&P 500 return index. This variable is used to detect the US stock effect due to the evidence of the correlation between the stock and bond market in the literature (Fleming, Kirby, and Ostdiek, 1998; Ilmanen, 2003; Christiansen, 2003). Fleming et al. (1998) found that the volatility linkage between US stock and bond markets are strongly caused by the information spillover. It is well known that the stock and bond market tend to move negatively, in other words, if the stock market strength means the bond market weakness. Ilmanen (2003) find the correlation between stock and bond market appeared positive to negative since 1998. This may imply that the increasing hedging potential of bond markets in recent years (Steeley, 2006). USYC: representing the slope of the yield curve calculating by the difference in yield between the 30-year and the 2-year US Treasury bill. The yield curve describes the relationship between the particular yield and a bond’s maturity. The slope of yield curve measures the degree of tightness of government monetary policy. An upward sloping curve is often thought as the feature that the loose monetary policy which is provided by a period of higher inflation and higher bond yield and leads to the economic environment of cheap money and the vice versa. The shape of the slope of yield curve can used to express the global economic condition that is heating or recession. For instance, if investor expects a recession, then
14
they may expect the falling inflation, consequently causing the falling in long term bonds yield relative to short term bonds. Investors may anticipate the loose monetary policy increasing the money supply or by reducing the base interest rate in the near future. Therefore, the shape of yield curve contains important information for investors for their policy-making and investment advice. The slope of yield curve measures the expected future short rate and the potential level of future inflation and economic volatility proposed by Batten et al. (2006). We use this variable to proxy for global economic condition as well. Besides, the steeper yield curve means the higher return in the longer bond that is evidenced in the literature (Campbell and Shiller, 1991; Ilmanen, 1996; Hunter and Simon, 2005). USIR: representing US 1-month Eurodollar rate as proxy for interest rate factor. This variable is used to capture the US short term interest rate effect on each emerging bond market in the mean equation as well. In general, short-term US interest rate reflect the global liquidity which means falls in short-term interest rates in the mature market make investor seek for higher yields in emerging markets. The positive sign may caused by the different business cycle. USER3M: representing US 3-month Eurodollar rate. The changes in US short term Eurodollar rates as proxy of the global liquidity condition and the economic condition. Arora and Cerisola (2001) pointed out that the emerging market bond are more risky than the industrial market and the rise in the U.S. policy interest rate may cause the increase in emerging bond spread. They use the yield on the three-month U.S. treasury Bill as the proxy the changes in global liquidity and economic conditions for the reason that the yield on the three-month U.S. treasury Bill often to be considered a short-term risk-free rate in the literature and as a benchmark for pricing other high-yield assets in world capital market. We specify the time-varying conditional correlation is related to the Eurodollar rate that could be an appropriate variable for capturing the global liquidity and economic condition. EMEQT: representing the stock index of each emerging market. This variable is used to capture each emerging market stock effect on its own bond returns and to examine if the stock market leads the bond market or not. EMIR: representing 1-month interest rate of emerging countries as proxy for domestic interest rate factor. This variable is used to capture the own market short term interest rate effect on each emerging bond market in the mean equation as well. EMCRD: representing the yield spread between the US and each emerging market bond. Several literatures have evidence that the country risk play important role for bond pricing in emerging bond market. Arora and Cerisola (2001) investigate the impact of U.S. monetary policy on the emerging country risk and conclude that the fluctuations in country risk can be explained by the country-specific fundamentals most. The country risk proxies
15
by the interest rate spread calculated the difference between yields on sovereign bonds of developing countries and U.S. treasury securities of comparable maturities.
4.2 Preliminary Findings of Bond Return Series Table 1 reports the summary statistic for the weekly return series in each of the markets as well as the US bond market. The average returns over the sample period are ranging from the lowest -0.001% (in US) to highest 0.311% (in Brazil). While the standard deviations range from the lowest 0.004 (in US) to highest 0.05 (in Russia). The estimates reflect that the greatest risk in Russia and lowest in US. All return series distributions are skewed to the left except Chile and Poland, and the kurtosis statistics are greater than 3. From both skewness and kurtosis statistics, it suggests that the negative shocks are more common than positive shocks in emerging bond markets. In general, the distributions for all series exhibit leptokurtosis, except that US return series is more symmetric and less leptokurtic than emerging bond markets. This is also confirmed by the Jarque-Bera statistics, the rejection of the null hypothesis of zero and excess kurtosis for all returns reflects non-normal distribution properties of all returns. The correlation structure is probably the most important feature for the investor and portfolio managers to hedging and diversifying their portfolio. To obtain the idea of the linkage between US and emerging market, Table 2a reports the unconditional correlation for each of the 16 bond markets separated to different periods. The sample period is divided to two subsample periods, year 1994 to 1998 and year 1998 to 2007, and one full period. Since the Asian currency crisis occurred around 1997 to 1998 and the Russian financial crisis in 1998, many countries bond return fall sharply during these period (as shown in Figure 1). Thus, the correlation in the period prior to and after the crisis may be informative to examine the change of the correlation between bond markets during high stress period. The correlation in the period prior to the crisis, 1994 to 1998, between each emerging bond market and the US bond market without Chile, Indonesia and Hungary are presented in Table 2a.14 Russia has the lowest correlation with US (-0.942) and China has the highest correlation with US (0.701). The average correlation between US and emerging bond markets is around 0.075. Overall the bond markets are lower correlated during this period. In the sub-period from 1998 to 2007 shown in the second row of Table 2a, it shows that the lowest correlation coefficient between US and emerging bond markets is Brazil (-0.037) and the highest correlation coefficient with US is China (0.804). Several countries such as Hungary, Chile and Poland also appear increasing correlation with US. Overall, the 14
The data of Chile bond returns available only from 1999:06, Indonesia is from 2004:05 and Hungary is from 1999:01.
16
average correlation between US and emerging bond markets is around 0.270 appearing slightly increasing during the volatile period. In the third row of Table 2a reports the correlation between US and other emerging bond markets covering the full sample period from 1994 to 2007 ranging from -0.007 (Russia) to 0.778 (China). The average correlation between US and emerging bond markets is around 0.252. In general, emerging bond market reveal low correlated with US bond market; however, China appears high correlated with US bond market. The correlations between dependent variables used in this paper are reported in Table 3. It appears low correlated between variables. The cross-correlation of bond returns in the Table 4 shows the unconditional contemporaneous autocorrelation of US bond returns with fifteen emerging bond markets: Argentina (0.101), Chile (0.563), Mexico (0.315), Venezuela (0.154), China (0.772), Indonesia (0.113), Malaysia (0.342), Philippine (0.111), South Korea (0.324), Thailand (0.143), Hungary (0.559), Poland (0.304). However, the contemporaneous correlations of US bond returns with Brazil, Colombia, and Russia are not statistically significant. Overall the cross-correlations between US and each of emerging bond markets are not statistically significant, and it means we may not find any significant mean spillover between the bond markets. Table 5 lists the Augmented Dickey-Fuller (ADF) unit root test results. All return series are stationary. For the conditional mean specification in the GARCH model, we use the Akaike Information Criterion (AIC) and Schwarz Criterion (SC) to choose the appropriate ARMA(p,q) model for each return series. The Ljung-Box (Q) statistics for serial correlation in residuals up to 5 and 10 lags are statistically insignificant for all series, suggesting no autocorrelations in residuals. However, the Ljung-Box statistics for the squared residuals (Q2) are statistically significant for all series, showing all squared residuals from ARMA(p,q) models are strongly serial correlated. This suggests the possibility of the autoregressive conditional heteroskedasticity in residuals. We use the GARCH specification to capture the autocorrelation in squared residuals. Moreover, the ARCH Lagrange multiplier (LM) test confirms the existence of ARCH effects in the US and emerging bond markets. The Jarque-Bera (JB) normality test reveals the non-normal distribution of all returns series.
4.3 Empirical Results We estimate parameters by the Quasi Maximum Likelihood Estimation (QMLE) method and calculate robust variance-covariance proposed by Bollerslev and Wooldridge (1992).15 Tables 6 reports the estimation results of the GJR-GARCH-M model with time-varying conditional correlation, where sample countries are divided into Panels A, B and C for 15
The maximum likelihood estimation may yield inappropriate standard errors when the innovations are non-normally distributed. This quasi-maximum likelihood approach with Bollerslev-Wooldridge standard errors is more appropriate for inference.
17
Latin America, Asia, and Eastern Europe, respectively. The Ljung-Box statistic Q2(11) show that, except for South Korea, the serial correlations in squared standardized residuals up to 11 lags for most series are not significantly different from zero at the 5% level of significance. This indicates the GJR-GARCH-M with time-varying conditional correlation is successful in capturing the time variation in volatility for most countries, except for South Korea. 4.3.1 The Level of Financial Integration The results also indicate the presence of market spillover effect. The return spillover effect from the lagged US bond returns to the emerging bond returns is negatively significant for some countries. The own market spillover effect is significant in some countries, and the Asian bond market appears the strong significant return spillover other than that in Latin America and Eastern European bond market. Under the market efficient hypothesis, the Asian bond market reflects less efficient than other regions. The US Treasury Bill and the slope of the yield curve capture the bond returns for emerging markets. The lagged US bond returns show statistically and economically significant in Latin American bond markets for Brazil, Colombia, Venezuela, in Asian bond markets for China, Malaysia and South Korea and in Eastern European bond market for Poland. The US bond return moves negative with emerging bond market but not for China. This may reflect the different economic condition between US and bond market. The lagged slope of yield curve measures the expected future short rate and the potential level of future inflation and economic condition. The slope of yield curve appears important factor to predict the emerging bond returns. It is positively significant in bond market for Brazil, Mexico, Venezuela, Malaysia, South Korea and Thailand as well as in Eastern European bond markets for Hungary and Poland. The positive coefficient estimate indicates that the steeper of yield curve increasing the future bond return in those bond markets consistent in the literature where the steeper yield curves leads to the higher bond returns in the long term bond except for Hungary which have shorter bond life comparing to other emerging countries. Also the negative sign inconsistent with the literature may caused by the long term rate falls lead to the flatted yield curve in the later years. The bond returns for Argentina, Venezuela and Malaysia can be explained by S&P 500 stock index. The result is consistent with literature recent year that the stock market strength implies the weakness of bond returns. The interest rate factor affect bond returns positively significant Chile, Mexico, Venezuela, Hungary and Russia but insignificant in Asian bond market. In general, short-term US interest rate reflect the global liquidity which means falls in short-term interest rates in the mature market make investor seek for higher yields in emerging markets. The positive sign may caused by the different business
18
cycle. Overall, the world factors appear to be important in explaining the bond returns in emerging markets over sample period, particularly in Brazil, Mexico, Venezuela, Malaysia, South Korea, Thailand, Hungary and Poland. Consequently, we found an important feature that Brazil, Mexico, Venezuela, Malaysia, South Korea are among the top 15 trading partners of US, thus the trade dependence between market may be proper to explain why those markets are more affected by the US market. Although, China is the most important trading partner for the US and has the highest portfolio holding of US investor, it is more affected by local market factors than by world factors. The results indicate that the trading and portfolio holding play an important role to explain the return and volatility spillover between US and individual emerging bond market through macroeconomic effect. Consequently, most emerging bond markets appear partially integrated into the US bond market, which is consistent with Lin et al. (2007). 4.3.2 Own Market versus Cross Market Volatility Spillover Effects The across market volatility spillover effects and own market volatility spillover effects are discussed by geographically. We give some details as follows geographical are of Latin America, Asia and Europe. Latin America Panel A in Table 6 shows that all bond markets in Latin America have positively significant own market volatility spillover effects. The results indicate the strong volatility clustering and persistence in emerging bond markets and so as US market. There exists across volatility spillover effect from US market to some emerging bond markets in Latin America. The estimate coefficients present statistically and economically significant around -0.675 (Chile), 0.644 (Colombia) and 0.183 (Mexico) and 0.471 (Venezuela) for the first lagged squared volatility shock from US bond returns. The results provide statistically evidence of US bond returns positive spillover to Colombia, Mexico and Venezuela but negatively to Chile. While increasing volatility from US market lead to the increasing volatility in Latin American bond markets, but lower the volatility in Chile. The results indicate there is lead-lag relationship between US and these bond markets. We also found that the evidence of volatility independence across US and emerging bond market in Latin America. However, we found there is no significant lead-lag relation between emerging bond markets for Argentina and Brazil with US. Given the results from mean equation and variance equations, it shows that Colombia, Mexico, and Venezuela are more integrated to US bond markets. Asia In Asia bond markets, Indonesia shows no any significant evidence of volatility spillover
19
effect from own market or from US market. Other countries appear statistically significant spillover effect from both markets. The volatility spillover effect from US market to Asian bond market is significant in China, Malaysia, Philippine, South Korea, and Thailand. In particular, the portfolio holding of US investor for China increase sharply in the year 2006, 60% of all portfolio holding countries and also the increasing trade dependency may enhance the relationship of markets between US and China. However, the squared volatility shock from US bond market affects the five Asian bond market volatilities in different directions. While the rising volatility in US markets increases the volatility in the bond market for China, Philippine, and Malaysia but decreasing the volatility in South Korea and Thailand. Eastern Europe Panel C of Table 6 shows the own market volatility spillover effect is significant on all Eastern European markets. For the cross market volatility spillover effect, Hungary bond market appears insignificant estimation. However, it is significant Poland and Russia. The estimated coefficient is -0.811 in Poland. The result indicates the strong volatility spillover from US bond market to the Poland bond market but vice versa. On contrary, the estimated coefficient (0.735) of Russia bond market shows that the rising volatility in US bond market increasing the bond market volatility in Russia bond market. Overall, the Poland and Russia bond returns appear strong across market return and volatility spillover effect from US bond market. In summary, there is lead-lag relationship on volatility between US bond market returns and emerging bond market except for the Argentina, Brazil, Indonesia and Hungary. Comparing the magnitudes of volatility spillover effects, the own market volatility spillover effects are greater than cross volatility effects in emerging bond returns. The US bond market exhibits the leading position for those emerging countries. Besides, from the results in mean and variance equation, it reflects that the trading and portfolio holding play an important role to explain the return and volatility spillover between US and individual emerging bond market for Brazil, Mexico, Venezuela, China, South Korea, and Malaysia through macroeconomic effect. Consequently, most emerging bond market appears partially integration into US bond market consistent with Lin et al. (2007). Besides, our results support that the volatility interdependencies across US and emerging bond market which consistent with the emerging bond market literature. 4.3.3 Asymmetry Effect from Own Bond Market and the US Bond Market Kroner and Ng (1998) proposed that the change in information flow such as bad news may lead to the asymmetric effect in volatility. The covariance between asset returns will be affected by the change of the information flow. Cappiello et al. (2006) concluded that the
20
asymmetric effect bond market. It has been argued that the native shock affect the financial time series more than the positive shocks do. Since there is no leverage effect in bond market, several studies have suggested that the asymmetric effect in bond market by the macroeconomic announcement effect or new effect. Table 6 shows there exists asymmetric effect in volatility on the own market shocks for Argentina, Chile, Colombia, Mexico and Venezuela, China, Malaysia, Philippine, South Korea, Thailand and Russia. In addition, the shocks in the US bond returns have asymmetric impact in volatility on several bond market returns for Argentina, Colombia, Mexico, Venezuela, South Korea, China, Malaysia and Russia. However, Chile and Indonesia appear negatively significant coefficient. Brazil, Thailand, Philippine, Hungary and Poland exhibit the insignificant asymmetric impact from US market shocks, while Philippine exhibits the weakest asymmetric impact from US market shocks. In other word, the negative innovations in own market have greater impact in the volatility than positive innovations. 4.3.4 Effects of US Exogenous Variable and High Volatility on Conditional Correlation
The estimated coefficients show that the highly positive interdependence between US and each emerging market including China (0.784), Chile (0.715) and Hungary (0.630). The US bond market present significant positive and moderate correlated with Malaysia, South Korea, Thailand and Poland. While the Argentina, Colombia, Mexico, Philippine and Russia shows the slightly low correlation between US bond market return. However, Brazil and Indonesia appears negatively comovement with US market. Numerous literature have evidence the benefit of international portfolio diversification (Levy and Lerman, 1988; Cappiello et al., 2006; Hunter and Simon, 2004). In the portfolio theory, the low correlation between markets decreasing the portfolio risk. In conclude, investor can take those bond markets with low correlation of US bond market into their portfolio investment. We consider the possibility of time-varying conditional correlation in investigating the impact of the global liquidity condition (economic condition) on individual emerging bond market return. The estimation results indicate that the change of global liquidity condition affect the conditional correlation between the bond market returns for Chile, Mexico, Indonesia, Malaysia, Philippine, and Poland with statistically and positive significant value raging from 0.032 (Malaysia) to 0.685 (Indonesia). The change of global liquidity condition increase the correlation for those bond markets with US markets, therefore, investor should be aware of the increase of US short rate to manage their portfolio holding and risk hedge. The benefit of international bond diversification diminished or not during the high stress period is examined by using the method similar Hunter and Simon (2005). To estimate the effect of the high volatility on correlation, we set the threshold in the
21
covariance equation to capture high volatility series. The threshold is calculated by sample mean volatility plus two standard deviations. We consider both US and individual conditional bond return volatility to see the impact of across market return volatility on correlation. As shown in Table 6, during the extremely volatility period of US bond market, the conditional correlation between US and individual emerging bond market increases in Chile, Mexico, Venezuela (0.392), Indonesia, South Korea, and Poland. While the sharply rising in volatility of US bond market decreases the correlation between US and emerging bond market in Argentina, Brazil, Colombia, Malaysia, Hungary and Russia. Since estimated values of ρ 4 coefficient are very small, the benefit of international bond diversification is not diminished during the high volatility period of US market. Turning to the effect of extreme high volatility of individual emerging bond return on the conditional correlation between US market and each emerging market, Table 6 indicates estimated values of ρ 4 are statistically but not economically significant on the bond returns for most countries. During the high volatility periods of emerging market, the correlation between markets is increased in Argentina (0.009), Philippine (0.00008) and Poland (0.017). And the correlation between markets appears decreases in Brazil (-0.005), Chile (-0.00047), Malaysia (-0.000016), and Hungary (-0.00005) during the high volatility period of emerging bond market. These findings indicate the benefits of international diversification do not be diminished by the extreme high volatility period of both US and emerging market bond returns.
5. CONCLUSIONS In view of the globalization and liberalization of the world economy, global capital markets offer more investment opportunities for international investors. As a result, the study of the return and volatility transmission mechanism and time-varying conditional correlation among markets is necessary for portfolio decision, asset pricing and risk hedging. This paper focuses on the linkage between the US and emerging bond markets. In exploring the impact of global factor and local factor on return and volatility of emerging bond markets, the world instrumental variables include the slope of yield curve of US Treasury Bill, US Eurodollar rate for one and three month, S&P 500 return index, and local instrumental variables are credit spread, associated stock index and interest rate of countries studied. The empirical results indicate the return spillover effect is significant in emerging countries, and the Asian bond market appears the strong significant return spillover other than that in Latin America and Eastern European bond market. The return spillovers between the US and emerging bond markets are weaker than the volatility spillovers between the US and emerging bond markets. Furthermore, the US bond market leads emerging bond markets, except for Argentina, Brazil, Indonesia and Hungary. Comparing
22
the magnitudes of volatility spillover effects, the own market volatility spillover effects are greater than cross market volatility effects in emerging bond returns. The US bond market leads those emerging countries in the volatility transmission. The bond market returns for the most emerging countries are significantly influenced by the world factor, thus most emerging bond markets appear partially integrated into the US bond market. The trade dependence between the US and emerging bond markets could explain for the strong return and volatility spillover effects from the US market. Moreover, the trading and portfolio holding could also explain the return and volatility spillover between the US and individual emerging bond market through macroeconomic effect for Brazil, Mexico, Venezuela, China, South Korea, and Malaysia. Asymmetric effect from the own market shocks is significant for most emerging bond markets including Argentina, Chile, Colombia, Mexico and Venezuela, China, Malaysia, Philippine, South Korea, Thailand and Russia. In addition, the shocks in the US bond returns have asymmetric impact in volatility on several bond market returns for Argentina, Colombia, Mexico, Venezuela, South Korea, China, Malaysia and Russia. However, the negative innovations in own market have greater impact in the volatility than positive innovations. The change of global liquidity condition significantly affects the time-varying conditional correlation between the bond market returns for Chile, Mexico, Indonesia, Malaysia, Philippine, and Poland. The change of global liquidity condition increases the conditional correlation for those bond markets with the US markets, therefore, investor should be aware of the increase of US short rate to manage their portfolio holding and risk hedging. Our findings indicate the benefits of international diversification do not be diminished by the extreme high volatility period of both US and emerging market bond returns.
REFERENCES Alonso, F., Blanco, R., Del Rio, A., and Sanchis, A. (2004) “Estimating liquidity premia in the Spanish government securities market,” European Journal of Finance, 10, 453-474. Arora, V., and Cerisola, M. (2001) “How does U.S. monetary policy influence sovereign spreads in emerging markets?,” IMF Staff Papers,48, 474-498. Barr, D.O., and Priestly, R. (2004) “Expected returns, risk and the integration of international bond markets,” Journal of International Money and Finance, 23, 71-97. Batten, J., Fetherston, T., and Hoontrakul, P. (2006) “Factors affecting the yields of emerging market issuers in international bond markets: Evidence from the Asia Pacific Region,” Journal of International Financial Markets, Institutions & Money, 16, 57-70. Beim, D.O., and Calomiris, C.W. (2001), Emerging Financial Markets. McGraw Hill Irwin, New York.
23
Bekaert, G., and Harvey, C.R. (1997) “Emerging equity market volatility,” Journal of Financial Economics, 43, 29-78. Bekaert, G., and Harvey, C.R. (2003) “Emerging market finance,” Journal of Empirical Finance, 10, 3-55. Bekaert, G., and Wu, G. (2000) “Asymmetric volatility and risk in equity market,” Review of Financial Studies, 13, 131-142. Black, F. (1976). “Studies of stock prices volatility changes,” Proceeding from the American Statistical Association, Business and Economics Statistics Section, 177-181. Bollerslev, T. (1990) “Modelling the coherence in short-term nominal exchange rates: A multivariate generalized ARCH approach,” Review of Economics and Statistics, 72, 121-131. Bollerslev, T., and Wooldridge, J.M. (1992) “Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariance,” Econometric Reviews, 11, 143-172. Burger, J. D., and Warnock, F.E. (2006) “Foreign participation in emerging Asian bond markets,” The Korea Economic Institute (2006 Korea’s economy), 24-28. Cai, K., Jiang, X., and Kumar, P. (2004) “Time-varying corporate bond volatility and corporate bond returns,” Proceedings of 2004 FMA Annual Meeting held by Financial Management Association International in New Orleans, Louisiana. Calvo, S. and Reinhart, C.M. (1996) “Capital flow to Latin America: is there evidence of contagion effects?” in: Private Capital Flows to Emerging Markets, Guillermo Calvo, Morris Goldstein, and Eduard Hochreiter, eds., (Washington, DC: Institute for International Economic). Campbell, J.Y., and Hentschel, L. (1992). “No news is good news”, Journal of Financial Economics, 31, 281-318. Campbell, J.Y., and Shiller, R. (1991) “Yield spreads and interest rate movements: A bird’s eye view,” Review of Economic Studies, 58, 1991, 495-514. Cappiello, L., Engle, R.F., and Sheppard, K. (2006) “Asymmetric dynamics in the correlations of global equity and bond returns,” Journal of Financial Econometrics, 2006, 4, 537-572. Christiansen, C. (2003).”Volatility-spillover effects in European bond markets,” Working paper. Cifarelli, G. and Paladino, G. (2006) “Volatility co-movement between emerging sovereign bonds: Is there segmentation between geographical area?” Global Finance Journal, 16, 254-263. Claessens, S., Dornbusch, R and Park, Y.C. (2001) “Contagion: How it spreads and how it can be stopped,” In Claessens, S. & Forbes, K.J. (eds); International Financial Contagion, (Kluwer Academic Publishers, Norwell, MA). De Goeij, P., and Marquering, W. (2006) “Macroeconomic announcements and asymmetric volatility in bond returns,” Journal of Banking and Finance, 30, 2659-2680. Dickey, D.A. and Fuller, W.A. (1979) “Distribution of estimations for time series regressions with a unit root,” Journal of the American Statistical Association, 74, 427-431. Eichengreen, B., and Mody, A. (1998a) “Interest rate in the north and capital flows to the south: is there a missing link?,” International Finance, 1, 35-57. Eichengreen, B., and Mody, A. (1998b) “What explains changing spreads on emerging-market debt: fundamentals or market sentiments?” NBER working paper 6408(Cambridge, Massachusetts: National Bureau of Economic Research ,February.)
24
Eichengreen, B. (2006) “Global imbalances: The blind men and the elephant,” Brooking Policy Brief 1. Engle, R.F., and Ng, V.K. (1993) “Measuring and testing the impact of news on volatility,” Journal of Finance, 48, 1749-1778. Erb, C.B., Harvey, C.R., and Viskanta, T.E. (1999) “Understanding emerging market bonds,” Emerging Markets Quarterly, 4, 7-23. Eun, C.S. and Shim, S. (1989) “International transmission of stock market movements,” Journal of Financial and Quantitative Analysis, 24, 241-256. Eun, C.S. and Resnick, B.G.. (2002) “International diversification of investment portfolios, US and Japanese Perspective.” Management Science, 40, 140-160. Fleming, J., Kirby, C. and Ostdiek, B. (1998) “Information and volatility linkages in the stock, bond, and money markets,” Journal of Financial Economics, 49, 111-137. Fleming, J., Kirby, C. and Ostdiek, B. (2001) “The economic value of volatility timing,” Journal of Finance, 56(1), 329-352. Forbes, K.J., and Rigobon, R. (2002) “No contagion, only interdependence: Measuring stock market comovements,” Journal of Finance, 57, 2223-2261. French, K.R., Schwert, G..W., and Stambaugh, R.F. (1987). “Expected stock returns and volatility”, Journal of Financial Economics, 3-31. Gelos, R.G., and Werner, A.M. (2001) “Financial liberalization, credit constraints, and collateral: investment in Mexican manufacturing sector,” Journal of Development Economics 67, 1-27. Glosten, L.R., Jagannathan, R. and Runkle, D. (1993) “On the relation between the expected value and the volatility of the nominal excess return on stocks,” Journal of Finance, 48, 1779-1801. Hamao, Y., Masulis, R.W., and Ng, V.K. (1990) “Correlations in price changes and volatility across international stock markets,” Review of Financial Studies 3, 281-307. Harvey, C.R. (1995) “Predictable risk and returns in emerging markets,” Review of Financial Studies, 8, 773-816. Hoti, S. (2004) “An empirical evaluation of international capital flows for developing countries,” Mathematics and Computers in Simulation, 64, 143-160. Hunter, D. M., and Simon, D.P. (2004) “Benefits of international bond diversification.” Journal of Fixed Income, 54-72. Hunter, D. M. and Simon, D.P. (2005) “A conditional assessment of the relationships between the major world bond markets,” European Financial Management, 11, 463-482. Ilmanen, A. (1996) “Market rate expectations and forward rates,” Journal of Fixed income, 8-12. Ilmanen, A. (2003), “Stock-bond correlations,” Journal of Fixed Income, 13, 55-66. In, F., Kim, S., Yoon, J.H., and Viney, C. (2001) “Dynamic interdependence and volatility transmission of Asian stock markets: Evidence from the Asian crisis,” International Review of Financial Analysis, 10, 87-96. Kanas, A. (1998) “Volatility spillovers across equity markets: European evidence,” Applied Financial Economics, 8, 245-256. Koutmos, G., and Booth, G.. (1995) “Asymmetric volatility transmission in international stock markets,” Journal of International Money and Finance, 14, 747-762. Levy, H. and Lerman, S. (1988) “The benefits of international diversification in bonds,” Financial Analysts Journal, 44, 56-64. Lin, W., Engle, R. and Ito, T. (1994) “Do bulls and bears move across borders?
25
International transmission of stock returns and volatility,” Review of Financial Studies, 7, 507-538. Lin, C.L., Wang, M.C., and Gau, Y.F. (2007) “Expected risk and excess returns predictability in emerging bond markets,” Applied Economics, 39, 1511-1529. Ljung, G.M., and Box, G.E.P. (1978) “On a measure of lack of fit in time series models,” Biometrika, 65, 297-303. Longin, F. and Solnik, B. (1995) “Is the correlation in international equity returns constant:1960-1990?,” Journal of International Money and Finance, 14, 3-26. López-Mejía, A. (1999) “Large capital flows causes, consequences, and policy responses,” Finance and Development, 36, 3. Markowitz, H.M. (1952) “Portfolio selection,” Journal of Finance, 7, pp77-91. McCauley, R and Jiang, G. (2004) “Diversifying with Asian local currency bonds,” BIS Quarterly Review, September, 51–66. Min, H.G. (1998) “Determinants of emerging bond market bond spread: Do economic fundamentals matter?,” Policy Research Working Paper 1899, World Bank. Mohanty, M.S. (2001) “Improving liquidity in government bond markets: What can be done?,” BIS Paper No.11, Bank for International Settlements, December, 49-80. Nelson, D.B. (1991) “Conditional heteroskedasticity in asset returns: A new approach,” Econometrica, 59, 347-370. Ng, A. (2000) “Volatility spillover effects from Japan and the US to the Pacific Basin,” Journal of International Money and Finance, 19, 207-233. Skintzi, V. D., and Refenes, A. N., (2006) “Volatility spillovers and dynamic correlation in European bond markets,” Journal of International Financial Markets, Institutions & Money, 16, 23-40. Steeley, J.M. (2006) “Volatility transmission between stock and bond markets,” Journal of international financial markets, institutions & money, 16, 71-86. Sy, A. (2002) “Emerging market bond spreads and severing credit ratings: Reconciling market views with economic fundamental,” Emerging Market Review, 3, 380-408.
26
Figure 1 Total Bond Return Index (1994 to 2007)
27
Table 1 Description Statistics of Bond Returns Mean
Max.
Min.
0.000
0.057
-0.097
Argentina
0.020
4.795
-13.331
1.621
-1.931
Brazil
0.162
6.589
-9.101
1.403
-1.102
Chile
0.081
5.035
-4.666
0.539
0.351
34.425
16796.720
0.000
408
Colombia
0.106
5.648
-6.465
0.962
-0.808
13.907
2659.601
0.000
525
Mexico
0.096
3.426
-5.896
0.844
-0.954
10.996
1942.588
0.000
690
Venezuela
0.135
7.049
-15.653
1.343
-2.817
41.199
33111.160
0.000
533
China
0.084
1.852
-1.370
0.366
-0.018
4.608
68.658
0.000
637
Indonesia
0.122
1.566
-2.898
0.475
-1.884
14.701
831.117
0.000
132
Malaysia
0.079
4.110
-6.727
0.683
-1.609
27.848
14177.330
0.000
542
Philippine
0.117
4.254
-3.949
0.749
-0.710
10.781
1253.888
0.000
481
South Korea
0.058
3.568
-5.993
0.510
-2.950
46.427
55219.450
0.000
539
Thailand
0.067
4.319
-7.105
0.832
-2.402
27.632
13435.810
0.000
512
Hungary
0.055
0.862
-0.723
0.238
-0.269
4.192
30.277
0.000
425
Poland
0.114
5.731
-3.874
0.671
0.843
17.504
5738.485
0.000
646
Russia
0.126
19.373
-28.101
2.617
-2.811
41.893
30950.040
0.000
481
USTB
Std. Dev. Skewness Kurtosis Jarque-Bera p-value 0.022
-0.450
3.793
obs.
41.38
0.000
690
14.894
4496.220
0.000
690
10.230
1602.140
0.000
673
Latin American
Asia
Europe
Notes: The bond returns are calculated from weekly observations on the US and fifteen emerging bonds covering from January 1, 1994 to March 23, 2007. The values of mean, maximum and minimum returns are transferred to annualized returns.
28
Table 2a Unconditional Correlation of Returns: The US and Individual Emerging Markets The unconditional correlation of bond returns series with sample period separated by three subsamples. Note that Chile, Indonesia, and Hungary lack of data from year 1994 to 1998. We use pair-wise sample to calculate the correlation between markets. 1994-1998 1998-2007 1994-2007
Argentina 0.374 0.011 0.101
Brazil 0.238 -0.037 0.033
Chile NA
0.563 0.563
Colombia Mexico Venezuela 0.178 0.404 0.141 0.047 0.271 0.075 0.052 0.315 0.079
China 0.701 0.804 0.778
Table 2b Unconditional Correlation of Returns: The US and Regional Markets US
Latin America Asia 0.291684 0.59044
Eastern Europe 0.856181
Table 3 Unconditional Correlation between US Related Independent Variables USYC USYC 1 USEQT 0.001 USIR -0.019
USEQT 1 0.084
USIR
1
29
Indonisia Malaysia Philippine S.Korea Thailand Hungary Poland NA NA 0.223 -0.904 0.388 -0.289 0.391 0.025 0.354 0.073 0.292 0.255 0.559 0.489 0.025 0.342 0.063 0.324 0.143 0.559 0.416
Russia -0.942 -0.002 -0.007
Table 4 Cross Autocorrelation between US and Emerging Bond Returns lag Latin American
-3
-2
Argentina
0.009
Brazil
0.039
-1
0
1
2
3
0.047
-0.011 0.101*** 0.019
0.063
0.016
0.020
-0.009 0.049
0.049
0.012
0.023
Chile
-0.016 -0.006 -0.073 0.563*** 0.044
0.005
-0.002
Colombia
0.031
0.039
0.007
-0.029
0.019
-0.092
Mexico
0.043
0.012
-0.005 0.315*** -0.041
0.011
0.020
Venezuela
-0.073 -0.007 -0.020 0.1536*** 0.032
0.047
0.056
China
0.059
0.035
0.014
0.772*** -0.054
0.016
-0.024
Indonesia
0.051
0.018
0.191
0.113*** -0.016
-0.033
0.131
Malaysia
0.013
-0.032 -0.046 0.342*** -0.076
-0.033
-0.139
Philippine
0.030
0.008
0.084
0.111*** -0.004
0.007
-0.032
South Korea
0.120
-0.028 0.029
0.324*** -0.010
-0.049
-0.029
Thailand
0.079
0.014
0.031
0.143*** -0.023
-0.040
-0.062
Hungary
0.070
0.055
0.105
0.559*** 0.050
0.063
0.006
Poland
0.112
0.015
0.070
0.304*** 0.008
0.079
0.003
Russia
-0.009 -0.079 0.034
0.004
-0.039
-0.032
0.052
Asia
Europe
-0.034
Notes: *** indicates significance at the 1% level. This table reports the cross-correlations between the US and emerging bond returns.
30
Table 5 Diagnostic Tests for the Residuals of Chosen ARMA (p,q) Model for Bond Returns 2 2 JB ARCH-LM(7) Q(5) Q(10) Q (10) Q (5) 6.44 10.623 136.68 160.33 59.2206 10.58*** Argentina -25.21*** [0.265] [0.388] [0.000] [0.000] [0.000] 2.1218 5.0783 141.55 201.12 1400.918 -25.57*** 12.76*** Brazil [0.548] [0.749] [0.000] [0.000] [0.000] 5.3713 8,6704 92.119 92.736 6654.865 -26.73*** 20.47*** Chile [0.372] [0.564] [0.000] [0.000] [0.000] 7.0759 11.499 207.83 220.87 1887.935 29.33*** Colombia -18.38*** [0.215] [0.32] [0.000] [0.000] [0.000] 5.2587 139.76 194.25 1659.866 1.7211 -23.53*** 13.32*** Mexico [0.632] [0.73] [0.000] [0.000] [0.000] 0.3347 10.431 41.43 48.174 9696.487 5.32*** Venezuela -13.10*** [0.953] [0.236] [0.000] [0.000] [0.000] 6.7129 13.014 83.909 95.313 4969.832 -25.74*** 12.94*** China [0.152] [0.162] [0.000] [0.000] [0.000] 1.8726 16.458 17.876 411.5322 1.1129 2.42** Indonisia -12.33*** [0.892] [0.993] [0.002] [0.037] [0.000] 3.0549 9.8272 211.96 258.53 8221.269 36.31*** Malaysia -12.20*** [0.383] [0.277] [0.000] [0.000] [0.000] 1.6112 9.1378 66.149 71.746 1819.552 7.46*** Philippine -22.49*** [0.447] [0.243] [0.000] [0.000] [0.000] 10.321 38.986 73.25 41344.27 3.9436 -13.30*** 7.07*** S.Korea [0.268] [0.243] [0.000] [0.000] [0.000] 11.174 206 313.53 7768.687 2.4649 26.75*** Thailand -12.23*** [0.782] [0.344] [0.000] [0.000] [0.000] 11.131 25.429 60.699 30.275 5.1928 4.23*** Hungary -19.96*** [0.393] [0.347] [0.000] [0.000] [0.000] 11.632 399.82 513.59 5374.485 2.4782 18.56*** -25.98*** Poland [0.601] [0.235] [0.000] [0.000] [0.000] 12.58 101.16 163.01 17533.54 2.5522 -19.94*** 10.34*** Russia [0.769] [0.248] [0.000] [0.000] [0.000] 8.7425 13.046 24.091 35.553 38.45732 -28.31*** 2.95*** US [0.12] [0.221] [0.000] [0.000] [0.000] Notes: ARMA(p,q) is chosen based on the AIC and SC criteria. The Ljung-Box test statistics, Q(5) and Q(10), test for autocorrelation in the residual for up to 5 and 10 lags, respectively. The Q2(5) and Q2(10) are for autocorrelation in squared residuals for up to 5 and 10 lags, respectively. JB denotes the Jarque-Bera (JB) statistic for the normality test. The ADF is the augmented Dickey-Fuller unit root test. ARCH-LM test is used to examine the ARCH effect for up to 7 lags. *, **, *** indicate significance at the 10%, 5%, and 1% levels.
ADF
31
Table 6 Estimation Results of Bivariate GARCH (1,1)-M Model with Time-Varying Conditional Correlation USTB
t
= θ 0 + θ 1USTB
t −1
+ θ 2 EM
+ θ 3USYC
t −1
t −1
+ θ 4 USEQT
t −1
+ θ 5USIR
t −1
+θ6
hUS ,t + θ 7
h EM , t + u US , t , where uUS ,t ~ N (0, hUS ,t )
and
EM US 2 2 2 2 hUS ,t = α US ,0 + α US ,1uUS ,t −1 + α US , 2 hUS ,t −1 + α US , 3u EM ,t −1 + γ US ,1u EM ,t −1 I t −1 + γ US , 2 uUS ,t −1 Ι t −1 p
EM t = bEM , 0 + ∑ bEM , n EM t − n + θ EM ,1USTB t −1 + θ EM , 2USYC t −1 + θ EM , 3USEQT t −1 + θ EM , 4USIR t −1 + θ EM , 5 EMEQT t −1 + θ EM , 6 EMIR t −1θ EM , 7 EMCRD t −1 n =1
+ θ EM ,8 hUS , t + θ EM , 9 hEM , t + u EM ,t ,
hEM,t = αEM,0 +α u
2 EM,1 EM,t −1
where
u EM ,t ~ N (0, hEM , t )
EM US j j j 2 2 +αEM,2hEM,t−1 +αEM,3uus2 ,t −1 +γ EM,1uEM ,t −1It −1 +γ EM,2 uUS,t −1It −1, where It −1 =1 if ut −1 < 0, It −1 = 0
h EM ,US ,t = ( ρ 0 + ρ 1 z t −1 + ρ 2 × trend + ρ 3 × V EM ,t h EM ,t −1 + ρ 4 × VUS ,t hUS ,t −1 )
[
h EM ,t
]
hUS ,t , where
otherwise, j = EM, US.
1 if h j , t −1 > mean volatility + 2 std V j ,t = othewise 0
,
j = EM, US.
Dependent variables used are the weekly data includes US 10-year Treasury Bill (USTB), the term spread between the yield of the US 30-year Treasury Bill and 2-year Treasury Bill (USYC), S&P 500 index (USEQT), and US 1-month Eurodollar rate (USIR), emerging bond returns (EM), stock index of each emerging market (EMEQT), the yield spread between the emerging market and US market (EMCRD), and the emerging country interest rate (EMIR). The instrument zt-1 in conditional correlation equation is the US 3-month Eurodollar interest rate, which is a proxy for the global liquidity condition (economic condition) and trend denotes the time trend covering the sample period.
Panel A：Latin American Countries Conditional mean
US
Argentina
US
Brazil
US
Chile
US
Colombia
US
Mexico
US
Venezuela
Constant
0.164***
1.645***
0.023
0.569***
0.047***
0.140***
0.005
0.117**
0.002
0.148***
0.061***
0.064***
USTBt-1
-0.067
0.452
-0.083*
-0.605***
0.095***
0.071
0.038
-0.335**
-0.034
0.116
0.079***
-0.237***
EMt-1
-0.002
0.135***
-0.003
0.301***
0.023*
-0.151***
0.0002
1.7e-03
-0.01
EMt-2
0.02
-0.0135*** 0.088***
0.126***
USYCt-1
0.010
-0.274
3.9e-05
0.679***
-0.118***
-0.001
-0.083***
-0.147
-0.026
0.421***
-0.102***
0.240**
USEQTt-1
-0.006
0.094***
-0.014
-0.049
-0.001
0.001
-0.001
-0.006
-0.007
0.005
-0.007
-0.046
USIRt-1
0.025*
0.029
0.039**
0.077
0.027**
0.057***
0.040***
-0.047
0.038**
0.059**
0.038***
0.104***
EMEQTt-1
0.029
-6.4e-06***
0.011
0.015***
-0.009
-0.004***
EMIRt-1
-0.028**
0.027
0.004
0.027
-0.015
0.009
EMCRDt-1
0.017**
0.117***
-0.004
0.048**
-0.010
-0.003***
hUS , t
-0.543***
-4.315***
0.001
0.001
0.203***
0.136
h EM , t
0.021***
0.72
-0.038
0.067***
0.020***
0.077***
32
Conditional variance Constant
0.005***
0.186***
0.058***
0.174***
0.007***
0.415***
0.003**
0.120**
0.003**
0.015
0.049***
0.094***
2 US ,t −1
0.038***
-0.556
0.045***
0.098
0.024***
-0.675***
0.111***
0.644***
0.072***
0.183***
0.141***
0.471***
hUS ,t −1
0.914***
2 u EM , t −1
0.001***
u
0.609*** 0.143***
hEM ,t −1
0.002**
0.710***
0.959*** 0.292***
0.001***
0.743***
0.926*** 0.613***
-0.001
0.206***
0.94*** -0.017
1.5e-04
0.71***
0.580*** 0.096**
0.003***
0.706***
0.149*** 0.728***
2 Asymmetry u EM ,t −1
-0.001***
2.998**
-0.001
-0.442
-0.039***
0.038***
-0.111***
0.612**
-0.064***
0.091
-0.069***
1.102***
Asymmetry u
-0.004
0.0337***
-0.002
0.028
-5.8e-04
-0.289***
0.002*
0.404***
5.09e-04
0.535***
-0.003***
0.159***
2 US ,t −1
Time-varying conditional correlation Constant
0.42***
0.103***
0.594***
-0.117**
0.217***
-0.140***
USER3Mt-1
-0.017
0.0153
0.036***
0.015
0.062***
-0.011
Time Trend
-0.001***
-0.0003***
0.001***
0.001***
0.0003**
0.002***
High hEM ,t −1
-0.0002
-0.005***
-0.0005***
0.001
-0.007**
-4.9e-04
High hUS ,t −1
-3.693***
-1.656***
0.0002***
-3.2e-05
0.0006**
0.392**
Q2(11)
16.51
Panel B: Asian Countries US Conditional mean
5.82
China
15.59
US
9.26
Indonesia
14.25
US
4.58
Malaysia
6.79
US
8.04
Philippine
13.99
US
3.88
S. Korea
9.30
US
7.59
Thailand
Constant
0.062***
0.001
0.072
0.278***
0.068***
-0.118***
-0.005
0.162***
0.063***
0.119**
0.014
0115
USTBt-1
0.047
0.447***
-0.029
-0.368
0.092***
-0.267***
-0.019
0.126
-0.102
-0.324***
-0.019
0.329***
EMt-1
-0.044
-0.246***
0.0164
0.670***
-0.023***
0.134***
-0.006
0.178**
-0.014 0.314***
t.4e-04
0.180
EMt-2
0.011
-0.373*
0.041
USYCt-1
-0.028
-0.114***
-0.117
0.540***
-0.081***
-0.049**
-0.043
0.266
0.036
0.276***
-0.028
-0.322**
USEQTt-1
-0.003
0.005
-0.036
0.131**
-0.004
0.014***
-0.009
0.013
-0.014***
0.006
-0.016
-0.007
USIRt-1
0.029***
0.029***
0.029
-0.087***
0.041***
0.042***
0.036
0.027
0.038**
0.027
0.033
-0.028
EMEQTt-1
0.002
-0.012*
-0.001
0.015
-0.0002
-0.037
EMIRt-1
-0.007
0.061***
0.001
0.042
-0.008
0.010
33
EMCRDt-1
0.007
0.157*
0.041***
0.058***
-0.005
hUS ,t
-0.537***
0.248***
-0.154
0.408*
-0.398***
0.243***
-0.127***
0.077
h EM , t
0.278***
0.134**
-0.169
-0.126
0.107***
0.198***
0.016
-0.077
Conditional variance Constant
0.011***
0.067***
0.054*
0.211***
0.005***
0.015***
0.002
-0.004
0.018***
0.016***
0.039***
0.002
2 US ,t −1
0.038***
0.262***
-0.189
0.592***
0.072***
0.314***
0.098***
0.729**
0.063***
-0.033**
0.046
-0.002
hUS ,t −1
0.895***
u
u
2 EM , t − 1
0.015***
hEM ,t −1
Asymmetry
0.222 0.032***
0.013
0.750*** u
2 EM ,t −1
Asymmetry u
2 US ,t −1
0.931*** 1.480***
0.001***
-0.334***
0.948*** -0.043
-0.003
0.901***
0.085*** -0.029
0.005***
0.777***
0.656*** 0.261***
0.001
0.748***
0.074*** 0.929***
-0.016**
0.121***
0.068
0.335**
0.001***
-0.559***
-0.004*
0.401***
-0.005**
-0.082***
0.0015
0.002
-0.025
-0.429***
0.081
0.356
-0.085***
0.196***
-0.096***
0.126
-0.045
0.187***
0.017
-0.003
Time-varying conditional correlation Constant
0.656***
-0.053***
0.442***
-0.169*
0.561***
0.419***
USER3Mt-1
0.013***
0.722***
0.032***
0.039**
0.002
0.023
Time Trend
0.001***
-0.010***
0.001***
0.001
-0.0001***
0.001***
High hEM ,t −1
-0.062*
0.803***
-1.6e-05***
8.3e-05***
-0.021
-0.008
High hUS ,t −1
-0.0001***
0.001***
*-0.028***
0.009
0.001***
1.192
Q2(11)
15.33
16.69
12.27
Panel C: Eastern European Countries US Hungary Conditional mean
8.74
8.89
US
11.38
7.45
Poland
7.71
US
Russia
Constant
0.431***
0.462***
0.015
0.176***
0.0003
0.125***
USTBt-1
0.164**
0.396***
-0.044
0.113**
0.036
-0.054
EMt-1
0.075**
-0.002
-0.002
-0.056
-0.005
0.121***
EMt-2
0.128***
USYCt-1
-0.201***
-0.246***
-0.059*
0.196***
-0.068
-0.010
USEQTt-1
-0.009
0.001
-0.004
0.003
-0.001
0.013
34
14.98
21.40
10.03
13.47
USIRt-1
0.081***
0.012*
0.021
-0.024
0.040***
0.032***
EMEQTt-1
-0.001
0.003
0.014
EMIRt-1
0.004
0.020
0.0002
EMCRDt-1
0.003
0.035**
0.029
hUS ,t
-0.480***
-0.336***
-0.057*
0.064
-0.025
0.024*
hEM ,t
-0.560***
-0.497***
0.051
0.236
0.0001
0.023
Constant
0.008***
0.012***
0.001***
0.097***
0.004***
-0.030*
2 US ,t −1
0.132***
0.051***
0.099***
-0.811**
0.099***
0.735***
hUS ,t −1
0.916***
Conditional variance u
u
2 EM , t − 1
-0.022***
hEM ,t −1
0.875*** 0.027***
0.001
0.927***
0.936*** 0.370***
-4.2e-05
0.728***
0.0340 0.864***
Asymmetry u
2 EM ,t −1
0.018***
0.026**
0.001
0.016
0.0001*
0.226***
Asymmetry u
2 US , t −1
-0.165***
-0.139***
-0.056
0.038
-0.105***
-0.568**
Time-varying conditional correlation Constant
0.315***
0.071
-0.279***
USER3Mt-1
0.026*
0.046***
0.0.23
Time Trend
0.001***
0.001***
0.002***
High hEM ,t −1
-0.0001***
0.087***
0.0003
High hUS ,t −1
-0.0001***
0.017***
-0.005***
Q2(11)
8.66
10.92
7.93
3.24
6.59
10.54
Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, based on the Bollerslev-Wooldridge robust t-statistics. Q2(11) denotes the Ljung-Box Q statistic for serial correlation in squared standardized residuals up to 11 lags.
35
