Abstract: In order to tap the direction and intensity of risk spillover between the international financial markets and China’s, in this paper, the jumps and other typical facts of single market return is primarily captured by ARJI-GARCH model; then the R-vine optimized by the maximum spanning tree algorithm is used to depicts the complex dependency structure of multi-dimensional financial assets; Finally, the risk spillover effects between the international crude oil market, the international gold market, the US stock market, Chinese stocks market and foreign exchange markets is measured by R-vine-copula-CoVaR model. The empirical results show that, the risk spillover effect between different markets is bidirectional, but the degrees vary widely, the international gold market is the largest source of risk spillover, the negative risk spillovers exist in China’s foreign exchange market to the stock market and the international gold market; the bidirectional risk spillover effect between different markets is asymmetric with the spillover effect of international crude oil market and gold market is much larger than that of Chinese stock market and foreign exchange market; The Rosenb-Latt test show that risk spillover measured by CoVaR based on R-vine is more flexible and effective; the back testing results show that the R-vine-copula-CoVaR model can effectively measure the risk spillover effects of international financial markets to China's financial markets, while the CoVaR measure of China’s financial market risk spillover maybe overvalued.

Key words: risk spillover, R-vine copula, maximum spanning tree, CoVaR, back testing

摘要： 为了挖掘国际金融市场与中国金融市场的风险溢出效应,本文首先通过ARJI-GARCH模型捕捉单个市场收益率的跳跃等典型事实特征,然后采用最大生成树(Maximum Spanning Tree,MST)算法优化的R-vine来刻画多维金融资产的复杂相依结构;最后构建R-vine-copula-CoVaR模型,测度了国际原油市场、国际黄金市场、美国股票市场与中国股票市场、外汇市场之间的风险溢出效应。实证结果表明:各市场之间均存在双向风险溢出效应,但溢出程度差别很大,国际黄金市场是风险溢出的最大爆发源,仅有中国外汇市场与中国股票市场、国际黄金市场间存在负向风险溢出;市场之间的双向风险溢出效应呈非对称性,国际原油市场与黄金市场的风险溢出效应远大于中国股票市场与外汇市场风险溢出效应;Rosenb-Latt检验表明基于R藤的CoVaR风险溢出测度更具有灵活性和有效性;后验测试结果表明R-vine-copula-CoVaR模型能有效地测度国际金融市场对中国金融市场风险溢出效应,而对中国金融市场风险溢出效应的CoVaR测度存在被高估的可能。

关键词: 风险溢出, R-vine copula, 最大生成树, CoVaR, 后验测试