Copula Theory And Its Applications In Financial Analysis

With the increasingly volatility of financial market and the frequent financial crises, how to effectively monitor and reduce financial risk become the focus of the financial sector and investors. Traditional VaR method can quantify risk to an exact number, but it also has its limitations. So this paper introduces CVaR method with consistent characteristics to measure risk more completely.In order to diversify risk, investors tend to invest to portfolios with varieties of financial assets to hedge risk. It requires fully understand of the correlation between assets. However, financial market is time-varing、fluctuant and nonlinear, which makes the correlation between assets to be complicated and fickle. Copula theory simplifies this issue with separating the marginal distributions and the correlation structure between assets, which is described by a Copula function.In order to better prevent a crisis to financial market in extreme cases, this paper applies Extreme Value Theory to estimate the tail distribution. On this basis, we combine it with GARCH type models and Copula theory to built the multivarate time series models——EGARCH-POT-Copula model and GJR-POT-Copula model, and use different Copula functions to study on the open-end fund in China, then measure the risks of the portfolio consists with the elected fund key-stocks with the Monte Carlo simulation method and the historical simulation method. The results show that the investment portfolio can reduce risk; these models can measure risk effectively and provide reference for the investment strategies of investors; EGARCH-POT-Copula model is more conservative than GJR-POT-Copula model, investors can choose them by their own invest preferences and their levels of risk-taking.In the end, this paper makes an analysis of the research findings about using the Copula model to mesure the risk of financial market, such as Shanghai, Shenzhen and Hong Kong stock market and commercial banks, and then describes the extensive applications of the Copula models in financial analysis.