基于pair-copula情景生成和广义熵约束的CVaR投资组合模型研究

Analysis of Portfolio CVaR Based on Pair-Copula Scenario Generation and the Constraint of Generalized Entropy

通过GARCH模型对收益率序列的边缘分布建模,结合copula构建收益率的联合分布函数,并由蒙特卡洛模拟生成收益率的情景,得到的结果代入广义熵约束的CVaR模型中,由此得到最优的投资权重.实证表明,在考虑不同资产之间的相依结构基础上得到的最优化结果相比传统的M-V模型具有明显的优势,在分散化和收益性上的到很好的效果.

At first this paper presents a method to find the optimal portfolio weights, which uses GARCH model and copula method to construct the marginal distribution and joint distribution of the rate of return. Then Monte Carlo simulation technique is used to generate scenario of the rate of return, which is regarded as the input variable in the model of CVaR with constraint of generalized entropy. The empirical result indicates that the optimal portfolio weights we get perform better than the M-V model in diversification and profitability in consideration of dependency structure between the assets in the portfolio.