Research On Some Financial Risks Based On Copula Function

With the economic globalization, openness of financial market is increasing, floating capital has covered the global widely, the correlation between the financial markets is increasing significantly. Income distribution has obvious peak and fat-tailed features, there are a lot of nonlinear relationship, therefore, we need to establish a dynamic nonlinear model to describe the dynamic between things related structures. However Copulas function is a kind of research tools that commonly used in financial risk analysis of nonlinear correlation, it can be used to accurately describ the dependency structure between multiple random variables, it is the statistical properties of the flexible form and good flexibility to capture the nonlinear.Copulas functio is introduced into the analysis of financial risk in this article, the first two chapters mainly introduced the financial risk and the significance, research background, present situation at home and abroad of the Copulas function and the Copulas function of theory knowledge, and then introduced the VaR financial risk measurement methods and the VaR calculation method. Due to the financial risk of income distribution has obvious peak and fat-tailed features and many nonlinear relationship, the traditional VaR calculation method based on the normality assumption and used the linear correlation to calculate the correlation coefficient between the variables, but the linear correlation coefficient can’t accurately describe the correlation of non-normal distribution, therefore, this article use properties better rank correlation coefficient to instead to solve the VaR value. Monte carlo simulation method of using moving the window technology is used to calculate the VaR value.In empirical research, the closing price of the Shanghai and shenzhen index was selected as the sample data, he optimal Copulas connect function was selected based on the the advantages and disadvantages of inhomogeneity Copulas connect function, square Euclidean distance and K- S two indicators were chosen as a testing standard of goodness-of-fit test validate, the results show that Clayton Copulas function fitting degree is best, which is improved based on traditional VaR method, then the monte carlo simulation technique is used to the VaR calculation, the calculation resultsshow that the traditional VaR calculation underestimates the financial risks. Introduction of the Copulas connect function more fully considered the financial risk variables correlation between marginal distribution function, and not limited to normality assumption, thus it is able to consider financial risks deeper degree.