Abstract
In order to examine the risk spillovers among multiple stock markets efficiently, we develop a vine-copula-GARCH-MIDAS model to estimate the multivariate joint distribution, and then derive CoVaR-type risk measures. Our empirical results on international stock markets show that the vine-copula-GARCH-MIDAS model is promising and is superior to several popular models. One the one hand, it exploits macroeconomic fundamentals to improve the accuracy of CoVaR measure. On the other hand, it is able to measure risk spillovers in a “multiple-to-one” pattern and solve the problem of underestimation in conventional “one-to-one” methods. What’s more, we find that there are significant risk spillovers from multiple developed stock markets including the US, Japan, and Britain, to China, which is necessary for regulators to be concerned with multiple markets simultaneously instead of a single market.
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