A new algorithm based on copulas for financial risk calculation with applications to Chinese stock markets

This paper concerns the application of copula functions in calculating financial market risk. The copula function is used to model the dependence structure of multivariate financial assets. After introducing the traditional Monte Carlo simulation method and the pure copula method we present a new algorithm named mixture method based on copula's properties and the dependence measure, Spearman's rho. This new method is used to simulate daily returns of two stock market indices in China, Shanghai Stock Composite Index and Shenzhen Stock Composite Index and then calculate six risk measures including VaR and conditional VaR. The results are compared with that derived from the traditional Monte Carlo method and the pure copula method. From the comparison we show that for lower confidence level, the traditional Monte Carlo and pure copula method perform better than mixture method, while for higher confidence level, the mixture method is a better choice.