Portfolio Optimization Model Based On DCC-MGARCH-CVaR And Empirical Research

With the continuous development of the global financial and economic,and related financial theory gradually rich,financial investment risk measurement methods have become more complicated and comprehensive.Modern portfolio theory is one of the important research fields in the study of the finance,its purpose is to seek the optimal portfolio in order to achieve the expected return on the premise of minimize the investment risk,or on the premise of established fixed risk level to maximize investment return.Variance as the traditional method of risk measure method,which is questioned by many scholars,so the VaR risk measurement method gradually developed into the most popular international standard tool of financial risk control.In order to overcome the lack of VaR method,conditional VaR(CVaR)risk measurement techniques have been considered to be more reasonable and more effective than VaR method in the modern risk management practices.Since the correlation between assets is time-varying and the risk of static portfolio cannot characterize changes in the portfolio yield trends over a period of time.Dynamic Conditional Correlation multidimensional GARCH model(DCC-MGARCH)has a good computational advantage that was proposed by Engle,studying its algorithm and realizes them by MATLAB program.Based on the mean-variance portfolio optimization theory that the objective function is to minimize the variance,but we minimize CVaR as the objective function in the new portfolio optimization model.In order to improve the precision of the CVaR value and reducing portfolio risk,this article introduce the DCC-MGARCH model and carry on the empirical analysis by the use of actual data of China’s financial markets.Whether the DCC-MGARCH-CVaR portfolio optimization model is feasible in our country’s financial market can be tested form the empirical analysis.Besides,the empirical analysis provides a method of calculation for exploring portfolio optimization model in the China’s financial market.