摘要
针对金融市场数据"厚尾""波动性聚集"和"杠杆效应"等特征,提出TGARCH和Copula模型组合,并使用VaR,CoVaR和ΔCoVaR方法定量测度了新冠疫情前后,股市和债市间的极端风险溢出效应和由此产生的不对称性,最后运用改进的向量自回归VAR研究新冠疫情对债股市场的动态冲击影响.实证结果表明:TGARCH与Copula的融合能较好地度量债股市场风险溢出的非对称性,改进的VAR模型能更好的探究新冠疫情对债股市场的动态冲击效应.所构建的模型丰富了债股市场极端风险溢出度量和突发事件影响的探究方法,为金融投资者提供了防范化解金融风险的工具.
In view of the characteristics of financial market data such as"thick tail","volatility aggregation"and"leverage effect"the combination of TGARCH and Copula model is proposed,and the extreme risk spillover effect and the resulting asymmetry between the stock market and the bond market before and after COVID-19 are quantitatively measured by VaR,CoVaR andΔCoVaR method.Finally,the dynamic impact of COVID-19 on the bond market is studied by using the improved vector autoregressor VAR.The empirical results show that the fusion of TGARCH and Copula can better measure the asymmetry of risk spillover in the debt-stock market,and the improved VAR model can better explore the dynamic impact effect of Covid-19 on the debt-stock market.The model established in this paper enriches the methods of measuring extreme risk spillover and exploring the impact of emergencies in the debt-stock market.
作者
文艺
郑蕾
蔡银琼
桂预风
WEN Yi;ZHENG Lei;CAI Yin-qiong;GUI Yu-feng(School of Science,Wuhan University of Technology,Wuhan 430070,China)
出处
《数学的实践与认识》
2021年第24期41-52,共12页
Mathematics in Practice and Theory
