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.
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