Spillover Effect of the Financial Crisis on Stock Markets

Transcription

1 Global Stock and Bond Markets Spillover Effects: Evidence from the Financial Crisis 1 Xu Sun Khoa Nguyen 1 Contact Author: Xu Sun, Department of Economics & Finance, University of Texas - Pan American, 1201 West University Drive, Edinburg, Texas xnsun@utpa.edu Khoa Nguyen, Department of Economics & Finance, University of Texas - Pan American, 1201 West University Drive, Edinburg, Texas

2 Abstract This paper employs the Dynamic Conditional Correlation (DCC) GARCH model to study the spillover effect of the financial crisis from the U.S. stock and bond markets to markets in other five international countries. The empirical findings support a contagion effect. The conditional volatilities of returns on both assets suddenly increase upon the onset of crisis. In addition, the conditional correlations also increase from precrisis period to post-crisis period, which is consistent with the predictions of contagions and herding behaviors during market turmoil. 2

3 I. Introduction The subprime financial crisis starting from U.S. mortgage collapse in 2007 induced unexpected falling in financial assets prices, leaded to serious liquidity issues for large and small financial institutions, incurred higher stock volatility, and destroyed overall market confidence. As the leader of the world market, the negative impact of U.S. subprime crisis produced a spillover effect aboard, for both developed and emerging countries. Market spillovers could happen as a result of market connections through economic reasons like trades or foreign investments. When the correlation between two countries becomes tighten during market downturns, it is generally recognized as the contagion effect from the origin country of crisis to other related countries. Previous studies have yielded supportive evidence for the contagion effects both across assets and across countries. Connolly et al (2005), Baur and Lucey (2009), and Ehrmann et al (2011) documented contagions both within and across assets, like stocks, bonds, and foreign exchanges, among multiple countries. The consequences of contagions of financial crisis include financial instabilities, bank runs, economic recessions, and especially, loss of investor confidences. Increasing correlations across assets or across countries imply a reduction in portfolio diversification, especially during market turbulence. Thus, it is important to understand the time-varying channels of return and volatility spillovers among assets and countries over time. While studies on market spillovers have produced different opinions as to how correlations between two countries evolve throughout time. Ang and Bekaert (2002), Butler and Joaquin (2002), and Bekaert et al (2005) found the correlation between financial asset returns tends to increase during market downturns. While, some studies question on methodology like controls for heterescadesticilty or try to distinguish contagions from globalizations or market interdependence, like Forbes and Rigobon (2002), Briere et al (2012), and Campbell et al (2008). The existing literatures on testing contagions are faced with several limitations. First of all, when using correlation changes as measures of contagions, researchers have to control for the heteroscadesticity caused by varying volatilities during market turmoil. Second, the measure of contagions must incorporate the time-varying feature of dynamic 3

4 correlations. One way to overcome these limitations is to employ the Multivariate Dynamic Conditional Correlation (DCC) GARCH process, as proposed by Engle (2002), to estimate the dynamic changes in correlation between two countries or two assets. This study, using the financial crisis as a trigger, examines the spillover effects of U.S. equity market and bond market to other five international markets before, during, and after the financial crisis. Specifically, I study the bivariate contagion effects within equity markets and within bond markets. The studying period starts from January 2004 to fully capture the pre-crisis correlations between markets, and ends at December 2012 to track the post-crisis correlations. The countries under study include Canada, Germany, Spain, Hungary, and Mexico, all of which either have close economic ties with the U.S. or suffered severe financial stresses due to the crisis. The findings of this study provide strong evidence for the across-country return spillover effects and volatility contagions throughout the crisis period. The stock and bond returns of the U.S. have significant impact on the stock and bond returns of all five countries under study, except for Canadian bond. The conditional covariances of all five stocks and five bonds, given the influence of the financial crisis, suddenly shoot up at the beginning of the crisis. The volatilities transmission on stock markets faded after crisis but those on bonds lasted long. The correlations between the U.S. stock market and each of the five stock market experienced significant increases from pre-crisis time to postcrisis time. The correlations between bond markets increased for all five countries from pre-crisis to post-crisis period as well. The increases in market correlations and market conditional volatilities hence lend support for the contagion effect from the U.S. to other countries in both stock and bond markets. The following of this paper is structured as follows. In section II, previous literatures on market spillovers and contagions are presented to build the base of this study. Section III and IV discuss the data and methodology used in this study, respectively. Section V shows the empirical results and Section VI concludes. II. Literature Review 4

5 The question on how different financial markets across countries move together has been the research interest for many years. The early study by Becker et al. (1995) documented spillover effects between U.S. and U.K. stock markets, and between U.S. and Japanese stock markets, respectively, and show that information shocks of U.S. macroeconomic news could explain part of the stock markets spillovers, which implies market linkages among countries. Campbell and Ammer (1993) investigated the correlation of stock and bonds returns in U.S. postwar period employing a Vector Autoregressive (VAR) model to calculate variance and covariance of each component. They, though find some explanatory power of stock variables on bond returns, like Fama and French (1989), report that the correlations between returns of two assets were weak. Diebold and Yilmaz (2009) use the VAR model and show that return and volatility spillovers are significantly different across countries. More recently, Ehrmann et al (2011) studied the structural transmission among four financial markets, that is, money markets, equity markets, bond markets, and foreign exchange markets, and supported the dominance of U.S. financial markets over the Euro area markets both within and across assets. Shocks on U.S. stock market have significant impacts on the money and bond markets of euro markets, indicating an indirect linkage among financial markets across countries. If markets are linked economically or financially, then the shocks on one market should have an impact on other markets, and hence could result in market contagions during market downturns. Foreign investment in local bond market could exert significant impacts on local market stability and development. The benefit of large foreign participation could be found in capital financing and liquidity of local market. However, huge holdings of foreign debts also expose foreign investors to larger risks if default happens. Burger and Warnock (2007) study the ability of countries to attract foreign investors in local bond markets from the viewpoint of U.S. investors, and document that, U.S. investors tend to hold local currency foreign bonds issued by developed countries over emerging countries, and would avoid bond markets with high historical variances. The investing behaviors of domestic foreign investors hence build up the linkage across markets and across countries. Bekaert et al (2005), defining contagion as the excess correlation between markets over what is expected from economic fundamentals, argue that correlation of their model 5

6 residuals will be higher during periods of high volatility, like crisis. The correlation results from their GARCH model residuals indicate no evidence of contagion during the Mexican crisis time, but significant increases during the Asian crisis time. Connolly et al (2005) show that, bond returns move in the same direction with large changes in stock market uncertainty, which suggest the important transmission effect of stock markets on bond markets during market turnover periods. Stivers (2005) also find that stock market uncertainty drive correlation changes between stock and bond returns. Baur and Lucey (2009), using stock and bond data for nine developed countries, document cross-country and cross-asset contagions, and also similar findings in correlation changes between returns of two assets. Hence, those studies, unlike other studies that arguing decreasing diversification benefits during volatile market, imply an increase in stock-bond diversification benefits when uncertainty in stock markets are presented. While, some studies argue that the increasing correlation documented in some literatures are due to heteroskedasticity and hence question the existences of contagion effect during market turmoil. Forbes and Rigobon (2002), defining contagion as a significant increase in co-movement between two countries after a market shock, argue that, if using some adjustment, there is no evidence for increasing unconditional correlations during 1987 U.S. crisis, 1994 Mexican crisis, and 1997 Asian crisis. Briere et al (2012) address the distinction between globalization and contagion effect. By studying correlation changes across four assets in four geographic areas, they conclude their findings of instable correlations as a joint result of globalization phenomenon and flight to quality effect. Campbell et al (2008), challenging the assumption of multivariate distribution of conditional correlation, assume a conditional Student-t distribution rather than normal distribution and incorporate a GARCH model to capture the time-varying conditional variance of equity returns residuals. They then prove that it is the fat-tailed distribution of asset returns, rather than real increasing correlations, that lead to significant reduction of diversification benefits documented in earlier studies. Chiang et al (2007) stated that, the traditional empirical studies on market correlations suffer several limitations, especially the disturbance of volatility changes and the 6

7 distinction between contagion and market interdependence. More recently, some studies employ the Dynamic Conditional Correlation (DCC) -Garch process, as developed by Engle (2002), to account for heteroskedasticity when measuring correlation changes between two markets over time. Using this multivariate Garch model, Chiang et al (2007) documented an increasing in correlations among nine Asian stock markets, supporting a contagion effect during the 1997 Asian crisis period. Capinello et al (2006) proposed an Asymmetric Generalized Dynamic Conditional Correlation (AG-DCC) model on the basis of Engle (2002) by allowing for asymmetries in correlation changes, and find that, both stock and bond returns show evidence for asymmetry for both conditional volatility to bad news, but only the former shows correlation asymmetry. Corresponding tofindings in Chiang et al (2007), they also reported higher correlations during financial turmoil. III. Data Daily stock and bond prices of local market indices are used in this study to proxy for equity and debt market performances for each country and are obtained from DataStream 2. The stock indexes used in this studies include: U.S. Standard & Poor 500 Composite Price Index, Canada - Standard & Poor/TSX Composite Price Index, Germany DAX 30 Price Index, Spain IBEX 35 Price Index, Hungary Budapest (BUX) Price Index, and Mexico IPC (BOLSA) Price Index. This study uses the J.P. Morgan bond indexes to proxy the performances of international bond markets: the J.P. Morgan Government Bond Index (GBI) for the U.S., Canada, Germany, and Spain, and the J.P. Morgan Emerging Market Bond Index (EMBI) for Hungary and Mexico. Daily prices are denominated in local currencies to avoid exchange rate hedging behaviors. Canada and Mexico are chosen as both of these two countries have close trade and financial connections with U.S. 3. The mortgage-based securities, which resulted in the 2 Some studies, like Cappiello et al. (2003) and Burger & Warnock (2007), use weekly data instead of daily data to overcome the potential non-synchronous problem, but some other studies argue that, to capture the co-movement between two markets, daily data are required as investors always alter the composition of portfolios quickly. 3 For example, the North American Free Trade Agreement (NAFTA) treaty among Canada, Mexico, and the United States was implemented on January 1st 1994 to increase trade among the three countries by reducing tariff or eliminating other restrictions, such as limited import quotas. 7

8 subprime crisis in the U.S., have been common in Germany for over 200 years. The high-debt and high-risk investment of the U.S. hit the Germany banking sector and led local financial institutions to insolvency 4. The crisis also severely impacted the weakest economies in the European Union, such as Spain, which has high level of debt and account deficits 5. Last, in the case of Hungary, the explosion of U.S. financial crisis accelerated the depreciation of the currency, which in turn pushed the country to adopt the Euro to avoid further depreciation of its financial assets, like the large holding of foreign loans. This study focuses on how market turmoil on U.S. equity market affects the performances of other international equity and bond markets. Specifically, I use the 2007 financial crisis as the trigger, and study how market linkages have been changing over the crisis period, which covers from January 1st, 2004 to December 31, This time period is chosen to avoid the effect of Internet bubble burst at the beginning of century, and to track the U.S. market performance before the subprime financial crisis in The sample in this study is divided into three subsamples: before crisis period, during crisis period, and after crisis period. The first sample starts from January 1st, 2004 to December 31, The second subsample covers a period from January 1st, 2008 to June 30, The rest of whole sample then is the third subsample denoting the postcrisis period, which starts from July 1st, 2009 to December 31, Dam (2010) analyzed the subprime crisis and financial regulation, and has identified that German banks always use off-balance-sheet vehicles for their mortgage-based securities and they also bear higher leverage than banks in any other European countries. Furthermore, despite that Germany has the one of the largest financial markets in European, the Central Bank of Germany, the German Bundesbank, has lost its important role in leading domestic financial sectors and supervising financial service institutions that are responsible for banking, securities, and insurance regulations, due to the establish of the European Central Bank. 5 Reinhart and Rogoff (2008) identified Spain as the most catastrophic country affeted by largescale financial crisis from the post-war period. Taylor (2009) sees Spain as the country having the biggest housing bubble, as measured by the change in housing investment as a percentage of GDP. 6 The U.S. National Bureau of Economic Research (NBER) has identified December 2007 and June 2009 as the start and end of subprime crisis, respectively. Since this study uses daily data, it is necessary to determine the exact date of the beginning and end of financial crisis. This study, following Mollick and Assefa (2013), adopts January 1st of 2008 as the onset of financial crisis and July 1st of 2009 as the ending of crisis, assuming that all information became available to the broad market by then. 8

9 Table 1 shows the descriptive statistics of six international stock and bond markets, including the U.S. markets, in three periods around financial crisis, respectively. In each of those stocks, the average return during the crisis time is smaller, or even negative, than returns in the rest two periods. The lower returns across crisis time are accompanied with higher volatilities, as measured by the standard deviation, of the same period. In the crisis period, the statistic of skewness is positive for all six stocks, except for Canadian index. The right-skewed feature of stock returns during crisis therefore suggests mass concentration of return distributions and relative few high values of returns during this period, which is consistent with the general market performance during market turmoil. For the bond markets, the return differences are not obvious, but the volatilities of bond returns during crisis time are also higher than those during non-crisis periods. Another noteworthy characteristic of this table is the high value of kurtosis of stock and bond returns during the crisis. This hence suggests that, large positive or negative shocks of returns are more likely to happen during this period, and returns are not normally distributed. <Insert Table 1 here> In figure 1, the returns of each stock and bond are plotted to present a visual analysis. As expected, all six stocks show a clustering effect and large volatilities within the crisis period, from January 2008 to June, For the bond returns, the volatilities during crisis time are also higher than those of the rest periods. There are large fluctuations of bond returns of Spain during the post-crisis period, as a result of the recent Spain debt crisis, which was generated by long-term loans and led to bankruptcy runs. The volatile feature of stock returns necessitates an adoption of GARCH process to model the return volatilities as well in addition to model returns. <Insert Figure 1 here> IV. Methodology This paper employs the Multivariate Dynamic Conditional Correlation (DCC) - GARCH process, as proposed by Engle (2002), to estimate the dynamic changes in correlation 9

10 between the U.S. markets and each of the other international markets. Compared with the traditional methodologies in testing market correlations, such as cointegration test, the Vector Autoregressive (VAR) or Error Correction (ECM) model, and general multivariate GARCH model, the DCC-GARCH allows for the estimation of time-varying correlation coefficients of standardized residuals and hence controls for heteroscadasticity. Hence, the estimation results from the DCC-GARCH model enable us to track the conditional changes in correlations between two markets even when the markets suffer large shocks under the period of study. Since the research interest of this study is to test how the burst of the financial crisis affects the market linkage between the U.S. markets and other international markets before, during, and after the crisis, the DCC-GARCH model then is applied to a bivariate framework which consists of the U.S. stock (bond) index and each of the five international stock (bond) indexes. In the bivariate DCC-GARCH model, the system equation contains multiple mean equations and conditional variance equations. Let R! be a vector of stock (bond) returns of two countries, R!!,! = [R!!,!, R!!,! ]. A simple representation of the mean equation is a reduced-form of VAR (1) model: R!!,! = α!! + β!! R!!,!!! + β!" R!!,!!! + ε!" (1.a) where ε! = [ε!!, ε!! ] is a vector of two error terms with a conditional variancecovariance matrix H! = {h!" } where i=1 and 2. The variance equation takes the form of h!",! = c!" + γ!" h!",!!! + λ!" ε!",!!! (1.b) Or, the simply matrix form of variance equation can be written as H! = D! P! D! (1.c) where D! = Diag[ h!" ] is a diagonal matrix with the standard deviation of conditional covariance h!, and P! = ρ!",! is a correlation matrix of conditional correlation coefficients. In Engle (2002), the conditional correlation matrix P! is allowed for time varying, which hence gives the second step of the DCC-GARCH estimate with a DCC structure as 10

11 𝑃! = 𝑄!!! 𝑄! 𝑄!!! (1.d) where! 𝑄! = 1 𝛼 𝛽 𝑆 + 𝛼 𝑢!!! 𝑢!!! + 𝛽𝑄!!! (1.e) is the conditional variance-covariance matrix of residuals from the unconditional variance-covariance matrix, S. 𝑄! = 𝐷𝑖𝑎𝑔[ 𝑞!! ] is a diagonal matrix with the standard deviation of the elements on the diagonals of 𝑄!. 𝛼 𝑎𝑛𝑑 𝛽 are non-negative scalars that satisfy 𝛼 + 𝛽 < 1. In the second step of DCC model, the residuals of the first stage, 𝜀!", is transformed by ℎ!" as 𝑢!! = 𝜀!" / ℎ!", which then be used to estimate the parameters of the dynamic conditional correlation. While, in this paper, the conditional correlation between returns of the U.S. market and of another market is estimated by 𝜌!",! = 𝑞!",! / 𝑞!!,! 𝑞!!,!. V. Empirical Results A. Correlation Test Before starting the GARCH process, it is necessary to check the unconditional pair correlations between the U.S stock (bond) market and each of the other five stock (bond) market, as the correlation is widely used to measure the linkage between two financial markets. Table 2 presents the simple pair correlations between the U.S. stock market and international stock market, the U.S. bond market and international bond market, and the U.S. stock market and the bond market, respectively. The standard z-test is then applied to test for statistic significance of correlation changes, as suggested by Morrison (1983) 7. For the correlations between the U.S. stock market and another global stock market, as shown in panel A, there is a significant increase in correlations across the crisis period for all stock pairs, which then indicate a primary contagion effect among those stocks. The 7 The null hypothesis is no increase in correlation, and the test statistic is calculated as 𝑇 = (𝑍! 𝑍! )/!!!!! +!!!!!!!!!, where 𝑍! = ln [ 1 + 𝜌! 1 𝜌! ] and 𝑍! = ln [ 1 + 𝜌! 1 𝜌! ]. 𝑍! and 𝑍! are the Fisher transformation of correlation coefficients during period 1 and period 2, and 𝑁! and 𝑁! are the number of observations during period 1 and period 2, respectively. The Z- statistic is robust to the non- normality. 11

12 correlation changes for bonds decrease with the onset of financial crisis, or even became negative for the US-Mexico pair. In the post-crisis period, the correlations of the US- Canada, the US-Germany, and the US-Mexico pair rebound, but the increases are not significant. On the contrary, after the crisis, the correlations between the U.S. bond and Spain bond, and between the U.S. bond and Hungary bond continue to decrease. The test results shown in panel C suggests that shocks on the U.S. stock market can also be transferred to both domestic and global bond markets as well. The correlation between the U.S. stock market and its bond market significantly increased after the crisis. The market downturn on U.S. equity market also tightened its correlation with Canadian bond market as well, indicating a two-dimensional contagion effect across markets and across countries. <Insert Table 2 here> B. Unit Root Test Since this paper is using time-series data, I employ three Unit Root test to test the stationarity of data series: the Dickey-Fuller-GLS (DF-GLS) Unit Root test, Phillips- Perron test, and the KPSS test, respectively. Results of unit root test are shown in table 3. The DF-GLS test is know to have greater power than the standard Dickey-Fuller (DF) test, and the null hypothesis of the DF-GLS test is that the series is non-stationary. Rejection of the null hypothesis hence indicates a stationary trend. The Phillips-Perron test also rejects the null hypothesis of a unit root. not surprising as all series are expressed in returns already. Finally, the null hypothesis of the KPSS test is that the series under test is trend stationary. Consistent with previous two tests, the KPSS tests results also indicate stationarity. The results of unit root test are expected, as all of the series are expressed in returns already. <Insert Table 3 here> C. The DCC-GARCH model 12

13 The preliminary correlation tests in table 2 highlights the significant changes in correlations before, during, and after the financial crisis. In this section, the estimations of the dynamic conditional correlation (DCC) GARCH (1,1) model are discussed to analyze the time-varying information of the correlation matrix across the crisis. The coefficients of DCC-GARCH (1,1) estimation from equation (1.a) to (1.e) for each of the five stock pairs are shown in table 4. In panel A, the estimation of the mean equation shows that, overall, the effect between the U.S. stock and Canadian stock is onedirectional, from the U.S. market to the Canadian stock market. Focusing on each period, the AR(1) term of the U.S. stock still has a significant positive impact on the current Canadian stock return before and during the crisis time, and the impact is enlarged from pre-crisis to crisis time but disappears after crisis. The coefficients of the lagged variance terms and shocks in last period are also highly significant, which is consistent with the dynamic changes in volatilities. The persistent terms, which is the sum of the ARCH and GARCH term in variance equations, are all smaller than 1, suggesting the persistent effect of volatility across time. The estimated interest in contagion effect is to see how correlation changes over time. For the US-Canada stock pair, the conditional correlation is significant and increases from in the pre-crisis period to in the post-crisis period, indicating higher market linkage during market downturns. In panel B of the U.S Germany pair, the estimating results are similar to those in the U.S.-Canada pair, except that the effect between two stocks is two-directional. Both the past U.S. stock and the German stock have significantly positive effect on the current return of each other. Throughout the study period, the correlation between these two stocks kept on growing, indicating a contagion effect during crisis time as well. For Spain and Hungary, past returns of the U.S. stock significantly affect the current performance of their stocks, too. In addition, unexpected shocks from the U.S. and previous variances significantly and persistently increase the current stock volatilities in these two countries. Market correlations with the U.S. stock market significantly increase after the explosion of financial crisis as well. 13

14 The estimating results in the U.S.-Mexico stock pair are a little different from those of other pairs. The financial crisis did not affect the stock performance of Mexico stock market, but produced significantly higher volatilities to its stock returns, as all the coefficients in the variance equation across three periods are highly significant. The stock market correlation between the U.S and Mexico greatly increased with the onset of crisis but decreased during the post-crisis time. This result is expected, as Mexican banks were not highly involved in subprime mortgages and did not lead to large scales of bank run, like the one in the 1994 Peso crisis. <Insert Table 4 here> In table 5, the DCC estimation results on bonds are presented. The U.S. bond returns had no impact on the Canadian bond market returns before and after the crisis, but influenced the volatilities of Canadian bond market persistently. The correlation between two bond markets also increased throughout the entre period. For the U.S. and Germany pair in panel B, the results show that lagged U.S. bond returns have significantly positive effect on the current bond returns of German market. The ARCH and GARCH term in the variance equation are also highly significant, indicating volatilities transmission before and after the crisis period. As the U.S. and Canada pair, correlations between the U.S. bonds and Germany bonds are tightened after the crisis. Returns of bonds in the U.S. and Spain affect each other bivariately across the entire study period. The performances of two markets are also significantly impacted by timevarying volatilities, as shown in the variance equation. However, unlike previous two pairs, the U.S. and Spain bond pair suggests a decreasing correlation over time, from a significant correlation of in the pre-crisis time to an insignificant value of in the post-crisis time. Impact on the bond market of Hungary is also one-directional, from the U.S. to Hungary. While, the onset of financial crisis changed the direction of correlations between two bond markets. Before crisis, two bond markets are positively correlated, even though not significant, but as soon as the crisis exploded, two markets became significantly correlated. For Mexico, its bond market was significantly affected by the performance of the U.S. bond market before the financial crisis. However, the outburst of financial 14

15 crisis did not tighten the uni-directional impact. Correlations between two bond markets also show slight significance in the post-crisis period. Consistent with the results from stock markets, Mexican financial institutions protected themselves from bank runs and public fears, and hence did not suffer too much loss this time. <Insert Table 5 here> Figure 2 and figure 3 plot the time-varying correlations and variances/covariances for each stock and bond of all five countries, respectively. Looking at the correlation plots for stocks on the left side of figure 2, it is clear that, all five correlations experienced a drop on the onset of financial crisis, but then started to increase during the crisis time, a pattern indicating the contagion effect. Throughout the crisis period, the conditional correlations fluctuated a lot. For the variances plots on the right side, the conditional variances of all five-country stocks suddenly shoot up as soon as the financial crisis exploded, and then faded after crisis. In figure 3 of five bonds, the patterns of conditional correlations also have large fluctuations during the crisis period, especially for those in Hungary and Mexico. For the conditional variances, there are also higher volatilities right after the onset of financial crisis. However, the high conditional variances in bonds fluctuate between positive and negative values and lasted longer than those in stocks. Even in the post-crisis time, shocks still impact the contemporary volatilities of bond returns for all countries. <Insert Figure 2 and Figure 3 here> VI. Conclusion This paper uses the Dynamic Conditional Correlation (DCC) GARCH model to capture the time-varying spillover effects and volatility transmissions from the U.S. to other five countries (Canada, Germany, Spain, Hungary, and Mexico) before, during and after the financial crisis period. For stocks, the results in this study show transmission effect from the U.S. stock market to all five global markets, and a feedback impact from German stock as well. Throughout the crisis period, the conditional correlations and covariances between each international stock and the U.S. stock increased over time, 15

16 lending support for the contagion effect during market turmoil. The performance of the U.S. bond market could be transmitted to bond markets of Germany, Spain, and Mexico, but not to Canada and Hungary. However, the financial crisis indeed significantly affected the volatilities of bond returns of all five bond markets. The findings in this study hence provide investment suggestions for investment decisions and risk managements. When diversifying their portfolios and evaluating the risk positions, investors have to take into account changes in market correlations and volatility spillovers, especially during market turmoil time. 16

17 Reference: Ang, A., Bekaert, G., International asset allocation with regime shifts. The Review of Financial Studies Baur, Dirk G., Brian M. Lucey, Flights and contagion An empirical analysis of stock bond correlations. Journal of Financial Stability 5 (4), Becker KG, Finnerty JE, Friedman J Economic news and equity market linkages between the U.S. and U.K. Journal of Banking and Finance 19, Bekaert, Geert & Campbell R. Harvey & Angela Ng, Market Integration and Contagion," Journal of Business, University of Chicago Press, 78(1), Brière, Marie, Ariane Chapelle, and Ariane Szafarz, No contagion, only globalization and flight to quality. Journal of International Money and Finance 31(6), Burger, J. D. and Francis E. Warnock, Foreign participation in local currency bond markets. Review of Financial Economics 16, Butler, K.C., Joaquin, D.C., Are the gains from international portfolio diversification exaggerated? The influence of downside risk in bear markets. Journal of International Money and Finance 21 (7), Campbell, John Y., and John Ammer, What Moves the Stock and Bond Markets? A Variance Decomposition for Long-Term Asset Returns. The Journal of Finance 48 (1), Campbell, Rachel A.J., Catherine S. Forbes, Kees G. Koedijk, Paul Kofman, Increasing correlations or just fat tails? Journal of Empirical Finance 15 (2), Cappiello, L., Robert F. Engle, and Kevin Sheppard, Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns. Journal of Financial Econometrics 4 (4), Chen, L., Lesmond, D., Wei, J., Corporate yield spreads and bond liquidity. Journal of Finance 62,

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19 Reinhart, C.M., Default currency crises, and sovereign credit ratings. Worldbank Economic Review 16, Reinhart, C. M., and Rogoff, K. S., Is the 2007 US sub-prime financial crisis so different? An international historical comparison (No. w13761). National Bureau of Economic Research. Taylor, J. B Economic policy and the financial crisis: an empirical analysis of what went wrong. Critical Review, 21(2-3),

20 Table 1: Descriptive statistics of stock and bond returns. This table presents the descriptive statistics of global stock and bond returns from January 1st, 2004 to December 31, The "before" sample is the pre-crisis time, and starts from January 1st, 2004 to December 31, The "during" sample denotes the crisis time, and covers a period from January 1st, 2008 to June 30, The "after" sample represents the post-crisis period, which starts from July 1st, 2009 to December 31, N Mean Std Min Max Skewness Kurtosis Panel A: Returns of stocks US Before 1, During After Canada Before 1, During After Germany Before 1, During After Spain Before 1, During After Hungary Before 1, During After Mexico Before 1, During After Panel B: Returns of bonds US Before 1, During After Canada Before 1, During After Germany Before 1, During After Spain Before 1, During After Hungary Before 1, During After Mexico Before 1, During After

21 Table 2: Pair correlation changes before, during and after the financial crisis This table presents the unconditional pair correlations between the U.S. stock (bond) and international stock (bond) before, during and after the financial crisis. The "before" sample is the precrisis time, and starts from January 1st, 2004 to December 31, The "during" sample denotes the crisis time, and covers a period from January 1st, 2008 to June 30, The "after" sample represents the post-crisis period, which starts from July 1st, 2009 to December 31, Country Pair Before During After Z-Stat Z-Stat Z-Stat (Before- (During- (Before- During) After) After) Panel A: US Stock - Global Stock US - Canada 0.664*** 0.740*** 0.774*** *** * *** US - Germany 0.469*** 0.633*** 0.721*** *** *** *** US - Spain 0.433*** 0.553*** 0.621*** *** ** *** US - Hungary 0.152*** 0.408*** 0.471*** *** -1.29* *** US - Mexico 0.654*** 0.782*** 0.735*** *** 1.843** *** Panel B: US Bond - Global Bond US - Canada 0.834*** 0.779* 0.848*** 2.661*** *** US - Germany 0.601*** 0.512* 0.614*** 2.173*** *** US - Spain 0.598*** 0.484*** *** 2.720*** *** 17.57*** US - Hungary *** *** 2.525*** *** US - Mexico 0.174*** ** 0.117*** 4.760*** *** Panel C: US Stock - Global Bond US - US * ** ** *** US - Canada * ** 0.168** ** *** US - Germany US - Spain US - Hungary US - Mexico * ** 1.636* Note: The unadjusted Z-tests on correlation changes before, during and after financial crisis are calculated following the formula given in footnote 6. The null hypothesis is no increase in correlation. The 1%, 5%, and 10% critical values for a one-sided test of the null are 2.32, 1.64, and 1.28, respectively. "***", "**", "*" for significance level at 1%, 5%, and 10%, respectively. 21

22 Table 3: Unit root tests for stock returns and bond returns DF-GLS Phillips-Perron KPSS Variable (lags)a (lags)b (lags)c Panel A: Return of stocks US *** (26) *** (7) (23) Canada *** (26) *** (7) (31) Germany *** (26) *** (7) (32) Spain *** (26) *** (7) (34) Hungary *** (26) *** (7) (42) Mexico *** (26) *** (7) (52) Panel B: Returns of bonds US *** (26) *** (7) (23) Canada *** (26) *** (7) (28) Germany *** (26) *** (7) (67) Spain *** (26) *** (7) (70) Hungary * (26) *** (7) (13) Mexico *** (26) *** (7) (6) Note: "***", "**", "*" for significance level at 1%, 5%, and 10%, respectively. a: The null hypothesis of the Dickey-Fuller-GLS test is that the series is non-stationary. Maximum lags are chosen by Schwert criterion. b: The null hypothesis of Phillips-Perron test for unit root is that the series has a unit root. Lags in parentheses are the Newey-West lags. c: The null hypothesis of the KPSS test is that the series under test is trend stationary. Maximum lags are chosen by automatic bandwidth selection criterion. 22

23 Table 4: DCC - GARCH (1,1) estimations for stock pairs This table presents the estimation results for the Dynamic Conditional Correlation (DCC) - GARCH (1,1) estimation results for five pairs of stocks. The DCC-GARCH (1,1) estimates the following systematic equations, 𝑅!!,! = 𝛼!! + 𝛽!! 𝑅!!,!!! + 𝛽!" 𝑅!!,!!! + 𝜀!" ℎ!",! = 𝑐!" + 𝛾!" ℎ!",!!! + 𝜆!" 𝜀!",!!! (1.a) (1.b) 𝐻! = 𝐷! 𝑃! 𝐷! (1.c) 𝑃! = 𝑄!!! 𝑄! 𝑄!!! (1.d) where 𝑄! = 1 𝛼 𝛽 𝑆 + 𝛼! 𝑢!!! 𝑢!!! + 𝛽𝑄!!! (1.e) and i, j=1,2, representing assets in the U.S. and each of the five countries. The persistence is calculated as the sum of the ARCH term and GARCH term. The values in parentheses are the t-statistics. Panel A: US - Canada Before During After Overall Stock US Canada US Canada US Canada US Canada Mean 𝛼!! 0.000** 0.001*** *** 0.001** 0.001*** 0.001*** (2.176) (3.148) (0.151) (0.835) (3.469) (2.187) (3.712) (3.699) 𝑅!",!!! * ** ** 0.091*** (-1.372) (1.954) (-0.799) (2.325) (-1.231) (1.617) (-1.991) (3.536) 𝑅!",!!! (0.043) * *** ** (-0.517) (-1.060) (-1.938) (-2.919) (0.318) (-0.716) (-2.195) Variance 𝑐!! 0.000*** 0.000* 0.000** 0.000** 0.000*** 0.000*** 0.000*** 0.000*** (2.816) (1.794) (2.184) (2.305) (4.184) (2.699) (5.509) (4.247) ℎ!",!!! 0.046*** 0.043*** 0.103*** 0.105*** 0.092*** 0.073*** 0.084*** 0.078*** (4.616) (3.063) (4.518) (3.142) (6.173) (5.269) (10.121) (8.665) 𝜀!",!!! 0.924*** 0.915*** 0.863*** 0.858*** 0.872*** 0.902*** 0.900*** 0.906*** (50.657) (26.843) (33.246) (24.706) (48.345) (49.873) (96.371) (84.755) Persistence Correlation 0.656*** 0.731*** 0.777*** 0.723*** (18.669) (22.287) (26.473) (26.678) lambda *** ** 0.030*** (3.179) (1.283) (2.504) (5.459) lambda *** *** 0.954*** (56.269) (0.363) (53.537) ( ) N Log LH AIC BIC Q (5) Mean Stock 𝛼!! 𝑅!",!!! Before US Germany 0.000** 0.001*** (2.262) (3.830) ** 0.387*** (-2.449) (9.360) ** ** ** *** *** Panel B: US - Germany During After US Germany US Germany *** 0.001*** (0.005) (-0.060) (3.161) (2.600) *** 0.377*** ** 0.304*** (-2.803) (6.544) (-2.048) (5.511) Overall US Germany 0.001*** 0.001*** (3.817) (4.510) *** 0.364*** (-4.047) (12.649) 23

24 R!",!!! *** *** 0.071** *** 0.056*** *** (1.643) (-4.554) (0.479) (-4.912) (2.013) (-3.072) (2.823) (-7.577) Variance c!! 0.000*** 0.000** 0.000** 0.000* 0.000*** 0.000*** 0.000*** 0.000*** (2.657) (2.498) (2.315) (1.959) (4.244) (2.802) (5.451) (4.441) h!",!!! 0.050*** 0.075*** 0.097*** 0.068*** 0.089*** 0.069*** 0.082*** 0.082*** (4.540) (3.897) (4.255) (4.362) (5.807) (5.560) (9.676) (9.099) ε!",!!! 0.922*** 0.872*** 0.877*** 0.915*** 0.875*** 0.911*** 0.902*** 0.905*** (48.616) (24.593) (34.227) (51.229) (47.050) (59.141) (95.784) (91.805) Persistence Correlation 0.526*** 0.679*** 0.746*** 0.674*** (21.384) (22.457) (48.627) (20.109) lambda *** (1.304) (1.251) (1.083) (4.806) lambda *** 0.555* *** (2.765) (1.690) (0.180) ( ) N Log LH AIC BIC Q (5) * ** *** ** Panel C: US - Spain Before During After Overall Stock US Spain US Spain US Spain US Spain Mean α!! 0.000** 0.001*** *** *** 0.001*** (2.225) (4.185) (0.047) (0.293) (3.568) (1.022) (4.282) (4.143) R!",!!! ** 0.310*** *** 0.399*** *** *** 0.316*** (-2.145) (8.545) (-2.761) (7.013) (-1.206) (2.691) (-2.826) (11.290) R!",!!! *** *** *** (1.148) (-3.317) (-0.024) (-4.751) (1.104) (-0.745) (1.212) (-5.372) Variance c!! 0.000** 0.000*** 0.000** 0.000* 0.000*** 0.000*** 0.000*** 0.000*** (2.488) (3.302) (2.202) (1.959) (3.987) (2.791) (5.433) (4.804) h!",!!! 0.047*** 0.130*** 0.098*** 0.083*** 0.095*** 0.106*** 0.084*** 0.114*** (4.347) (4.364) (4.613) (3.226) (6.060) (6.239) (9.577) (8.920) ε!",!!! 0.927*** 0.763*** 0.883*** 0.879*** 0.871*** 0.874*** 0.899*** 0.878*** (48.965) (14.182) (39.741) (24.566) (46.761) (45.850) (91.418) (70.404) Persistence Correlation 0.469*** 0.635*** 0.631*** 0.577*** (17.873) (19.254) (22.032) (32.385) lambda ** 0.033** (0.876) (0.967) (2.008) (2.328) lambda *** 0.582** 0.846*** 0.848*** (5.394) (2.479) (9.027) (8.865) N Log LH AIC BIC

25 Q (5) ** * 9.793* ** ** * *** *** Panel D: US - Hungary Before During After Overall Stock US Hungary US Hungary US Hungary US Hungary Mean α!! 0.000** 0.001*** *** *** 0.001*** (2.287) (3.529) (-0.407) (-0.097) (2.934) (1.058) (3.441) (3.446) R!",!!! * 0.404*** *** 0.338*** *** *** 0.288*** (-1.935) (7.993) (-3.435) (6.205) (-1.075) (3.170) (-2.974) (9.589) R!",!!! (-0.414) (1.147) (-0.063) (-1.167) (0.760) (-1.463) (0.322) (-1.277) Variance c!! 0.000*** 0.000*** 0.000* *** 0.000*** 0.000*** 0.000*** (2.623) (2.736) (1.869) (1.495) (3.759) (2.722) (4.806) (4.317) h!",!!! 0.050*** 0.083*** 0.101*** 0.152*** 0.095*** 0.096*** 0.078*** 0.105*** (4.312) (4.300) (4.486) (3.672) (5.449) (4.402) (8.839) (7.755) ε!",!!! 0.920*** 0.864*** 0.884*** 0.841*** 0.870*** 0.870*** 0.907*** 0.870*** (45.420) (27.029) (38.268) (20.582) (41.818) (30.044) (91.445) (53.522) Persistence Correlation 0.169*** 0.447*** 0.450*** 0.323*** (4.584) (6.190) (15.611) (8.044) lambda * 0.085** *** (1.899) (2.469) (1.415) (4.089) lambda *** 0.810*** *** (8.516) (12.728) (0.771) ( ) N Log LH AIC ( ) BIC ( ) Q (5) 9.888* ** * 9.861* 19.53*** ** * ** *** *** Panel E: US - Mexico Before During After Overall Stock US Mexico US Mexico US Mexico US Mexico Mean α!! 0.000** 0.002*** *** 0.001*** 0.000*** 0.001*** (2.426) (4.721) (-0.647) (-0.358) (3.122) (3.003) (2.843) (4.435) R!",!!! ** *** ** *** (-1.621) (2.464) (-3.254) (-0.829) (-2.110) (0.141) (-3.904) (0.584) R!",!!! *** * (0.190) (0.316) (1.191) (1.299) (2.923) (1.267) (1.839) (1.600) Variance c!! 0.000*** 0.000*** 0.000** 0.000* 0.000*** 0.000*** 0.000*** 0.000*** (2.844) (4.098) (2.501) (1.877) (4.152) (3.221) (5.221) (4.613) h!",!!! 0.051*** 0.102*** 0.095*** 0.080*** 0.105*** 0.070*** 0.088*** 0.082*** (4.701) (5.005) (4.698) (4.887) (6.206) (4.948) (10.039) (9.192) ε!",!!! 0.917*** 0.813*** 0.884*** 0.911*** 0.855*** 0.899*** 0.898*** 0.902*** (47.142) (23.280) (40.947) (61.245) (42.688) (46.271) (93.709) (87.552) 25

26 Persistence Correlation 0.672*** 0.795*** 0.708*** 0.700*** (13.532) (35.287) (31.972) (36.963) lambda *** 0.094** 0.076*** 0.038*** (3.342) (2.488) (3.249) (3.436) lambda *** 0.501* 0.713*** 0.918*** ( ) (1.944) (7.659) (31.986) N Log LH AIC ( ) BIC ( ) ** Q (5) ** * ** *** ** Note: "***", "**", "*" for significance level at 1%, 5%, and 10%, respectively. 26