Financial Risk Spillover Effects Research Based On Copula-GARCH-MIDAS Model

With the influence of economic globalization,the relationship between various financial markets becomes closer,and the risk contagion among markets occurs frequently.Due to the late start of the Chinese financial market,China will suffer greater risk spillover effects in the process of financial market opening.Therefore,it is very important for venture capitalists and financial managers to accurately measure the risk spillovers of other countries on the Chinese financial market.To date,Co Va R has become an important tool to measure the risk spillovers among financial markets,which depends heavily on the joint distribution modeling.Regarding this,copula-GARCH model provides an important tool.However,the conventional copula-GARCH model has two defects: the neglect of low frequency macroeconomic variables and the problem of dimension disaster.To this end,this disertation improves the conventional copulaGARCH model with mixed frequency data,and proposes the Co Va R measure to detect the financial risk spillovers.The study devotes to the following two aspects.First,in order to accurately measure the risk spillovers between two markets in a “oneto-one” pattern,this disertation proposes a bivariate copula-GARCH-MIDAS model.A GARCH-MIDAS framework with long-run volatility component driven by low frequency macroeconomic fundamentals is applied to fit the marginal distribution of a single market.Then,the copula technique is used to model dependence structure between two markets and derive the joint distribution.Finally,the Co Va R-type risk measures are calculated with the estimated joint distribution.In empirical research,the bivariate copula-GARCH-MIDAS model is applied to estimate risk spillovers from international crude oil market to the Chinese financial market.The empirical results show that bivariate copula-GARCH-MIDAS model outperforms the bivariate copula-GARCH model in terms of Co Va R measure,which means incorporating low-frequency macroeconomic fundamentals in long-run volatility component do help improve the accuracy of measuring risk spillovers.Second,in order to solve the problem that bivariate copula-GARCH-MIDAS is unable to measure risk spillovers among multiple markets,vine-copula technique is used to construct a vine-copula-GARCH-MIDAS model,which enables measure risk spillovers among financial markets in a “multiple-to-one” pattern.Through the empirical study on investigating risk spillovers from multiple developed stock markets to the Chinese stock market,the results show that vine-copula-GARCH-MIDAS model outperforms the vine-copula-GARCH model and the DCC-GARCH model,which once again confirms incorporating low-frequency macroeconomic fundamentals in long-run volatility component do help improve the accuracy of Co Va R measure.What's more,there is a significant risk spillovers on the Chinese stock market when multiple stock markets fall into crisis at the same time.Regulators should pay attention to the impact of multiple markets rather than that of a single market on the Chinese market.Theoretically,this disertation constructs a copula-GARCH-MIDAS model,which improves the goodness-of-fit on the marginal distribution.Additionally,copulae is used to model the dependence structure between two markets and derive the joint distribution.Practically,incorporating low-frequency macroeconomic fundamentals improves the accuracy of measuring systematic risk spillovers,which is beneficial for relevant decisions.