Copula Based Risk Measurement On Tail Dependence Of Financial Time Series

Economic globalization and financial market internationalization have made the linkage between financial markets increasingly close and more complicated.Financial risk is conductive between markets.By analyzing and studying the risk-dependent structure between financial markets,the accuracy of decision-making is improved while the risk of decision-making is reduced.In this paper,Copula function is used to measure the dependence structure between random variables,especially the tail dependence.A large number of empirical studies have shown that in the analysis of financial risk,financial assets usually show an asymmetric tail correlation,that is,when the financial market falls sharply,it shows a strong tail correlation,but it rises sharply in the financial market.Time,showing weak tail correlation.Therefore,some scholars introduced the tail correlation measure in order to better characterize the dependence structure of the tail between financial markets,especially between financial assets.Usually tail dependence is used in the stock market to measure the probability of simultaneous extremes of two cities,that is,skyrocketing and falling,especially the dependence structure of the left tail,which reflects the risk spillover effect between markets when extreme events occur.At the time of the financial crisis,the extreme value risk between financial markets attracted people's attention.For example,one stock market crash caused another stock market crash.In many cases,the correlation coefficient and the tail dependence are not completely consistent.If the correlation coefficient is used as the standard for risk measurement,the risk between financial markets may be estimated more or less.In order to effectively prevent the occurrence of a chain reaction between financial markets when the financial crisis occurs,and at the same time to monitor the spillover effect of risks among various stock markets,it is very important to study the tail dependence characteristics of various stock markets in my country under extreme circumstances.This paper uses the correlation function to model the relevant structure of the financial market.From the perspective of financial risk management and investment portfolio,the financial risk management methods based on VaR and ES are systematically studied.Through different Copula models and GARCH models for the research of SME stocks,the portfolio risks under the same dependency structure and different dependency structures are compared,and the effective boundaries under different Copula structures and different confidence levels are compared.The results show that the study of financial asset risk At this time,it is necessary to consider the nonlinear structure of the financial time series,especially the asymmetric tail structure.Copula's research on stocks in the real estate and financial industry through frequently correlated and time-varying related structures shows that the dynamic time-varying correlated Copula can better describe its dependent structure,and the real estate and financial industry stock yields show when the market is in a downturn Obvious correlation.Finally,the Copula function combined with non-parametric kernel density function and semi-parametric POT model is used to estimate the risk value and correlation of the Shanghai and Shenzhen stock markets.At the same time,it is proved that the choice of the edge distribution function has nothing to do with the dependent structure,but the risk value estimated by the semi-parametric POT model is higher than that estimated by the kernel density function.