Dependence,Risk Spillover And Influence Factors Between The Chinese Mainland Stock Market And Major International Stock Markets

With the rapid development of China's economy and the acceleration of financial market integration,the economic,trade and financial links between China's mainland and other major economies are increasingly close.Their financial markets present complex dependence,such as nonlinearity,asymmetry,and tail dependence.At the same time,the transmission speed of risk among the international financial markets is also accelerating,followed by increased difficulty in risk measurement and management.Therefore,accurately describing the dependence among financial markets,effectively measuring financial risk,and reasonably examining risk spillover have become critical problems to be solved urgently in modern financial analysis.In order to promote the internationalization of China's financial market and maintain economic and financial security,it is necessary to investigate the dependence,risk spillover and influencing factors between the Chinese mainland stock market and major international stock markets.In the research and practice of financial market dependence and risk management,the traditional methods have been proved to fail to characterize the nonlinear dependence and tail dependence among financial markets.The emergence of the Copula theory and its successful application provides a good solution.This paper comprehensively uses methods such as the Copula function to describe the complex dependence among international financial markets,carrying out risk measurement and risk spillover tests more accurately.To be specific,the central objective of this paper is to estimate the dependence,risk spillover and influencing factors between the Chinese mainland stock market and major international stock markets after China joined the World Trade Organization.To this end,this paper firstly constructs four kinds of marginal distribution models.Thus,the optimal model describing the marginal distribution of sampled stock returns is determined,which lays a foundation for the correct use of the Copula function for the analysis in this paper.It is found that the non-parametric ARMA-GARCH-EVT model is the optimal model to describe the marginal distribution of the sampled stock markets.Secondly,in the stage of dependence modeling,this paper uses nine types of static Copula functions,three time-varying Copula functions and the DCC-GARCH model to characterize the dependence structure between the Shanghai Securities Composite Index and other sampled indices.The results show that the dependence is weak during the sample period while showing significantly time-varying and regional characteristics.Further,the diagnostic results of structural broken points show that the dependence between the Chinese mainland stock market and major international stock markets are affected by the financial crisis and other events,showing a prominent periodic feature.Thirdly,on the basis of the economic fundamentals hypothesis,capital flows hypothesis and market contagion hypothesis,this paper explores the influencing factors of the dependence from four aspects,i.e.,economic policy uncertainty,common shock,macroeconomic conditions and stock market characteristics.The panel regression results show that the economic policy uncertainty differentials and interest rate differentials significantly decrease the dependence between the Chinese mainland and overseas stock markets,while the global financial crisis and trade interdependence have positive impacts.In addition,based on examining the dependence among different countries or regions,this paper further discusses the dependence between stock assets and other financial assets.Specifically,this paper takes emerging cryptocurrency assets as representatives and uses the Copula function,DCC-TGARCH and DCC-MIDAS models to explore the cross-asset class dependence between the world stock market and cryptocurrency market,as well as the effect of economic policy uncertainty and the COVID-19 pandemic on them.The results show a weak dependence between the world stock market portfolio and the cryptocurrency market index CRIX,indicating that the cryptocurrency market has a specific risk hedging ability for the stock market.This paper also confirms the impact of economic policy uncertainty and the COVID-19 pandemic on such dependence.Finally,considering the advantages of the Vine Copula model in describing the complex dependence structure of multivariable,three types of Vine Copulas are employed for analysis and modeling to determine the optimal model.The results indicate that the R-Vine Copula is the optimal model to characterize the high-dimensional dependence structure between the Chinese mainland stock market and major international stock markets,suggesting noticeable structural differences.Based on the R-Vine Copula,this paper estimates the value at risk(Va R)of each stock index and their portfolio using the Monte-Carlo simulation and out-of-sample forecasting in a rolling time window.Then,the risk spillover between the Chinese mainland stock market and major international stock markets is detected using the Va R-Granger causality test and the Diebold & Yilmaz spillover index.The results indicate extreme risk spillovers from the Chinese mainland stock market to the United States,France,and Germany markets.Furthermore,the findings suggest that China's Taiwan,China's mainland,China's Hong Kong,the United States,Britain and Japan are net exporters of risk spillover,while Russia,Brazil,Germany,South Korea,India,France and Australia are net receivers.This paper mainly has three implications as follows.First,formulate relevant policies to guard against systemic risks from the international financial market and accelerate central bank digital currency promotion.Second,improve macro fundamentals,strengthen the development of the financial market,and steadily advance the opening-up and international cooperation.Third,fully consider the dependence and risk spillover among the world stock market and cryptocurrency market as well as the current economic policy uncertainty to predict the trend of international financial markets more accurately.