Business & Management Studies

Abstract

This paper aims to study the volatility spillover effects as well as the dynamic conditional correlation between stock market returns in China and the U.S. Firstly, the analysis uses a vector autoregression with a bivariate BEKK-GARCH model to capture the asymmetric volatility transmissions between the two markets during the sample of 1996-2019. Then a VAR-DCC-GARCH model is employed to estimate the dynamic conditional correlation between these two market returns. Finally, linear regression and Granger Causality test are conducted to further explore the effect of the U.S policy rates on such correlation. In order to account for the U.S monetary stances during the unconventional period, a combination of Fed fund rates and Shadow rates developed by Wu and Xia (2016) is used as policy rates. The main empirical results suggest (1) evidence of unidirectional volatility spillover from the U.S. to China market; but no spillover from China to U.S; (2) the dynamics of the conditional correlations from the VAR-DCC-GARCH model exhibit increases in correlation between the stock returns of China and U.S after 2008 financial crisis and recent trade war; (3) a linear regression shows that there is negative relationship between U.S policy rates and the dynamic conditional correlation, with the correlation coefficient r=-0.62. Granger Causality test suggests that the U.S policy rates do cause the change of the conditional correlation but not the other way around.