Research on financial market portfolio optimization based on high-dimensional R-vine Copula

被引：0

作者：

Lin Y. ^{[1
]}

Liang Z. ^{[1
]}

Lin Z. ^{[1
]}

Wu Q. ^{[1
]}

机构：

[1] Business College, Chengdu University of Technology, Chengdu

来源：

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2019年 / 39卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Dependent structure; Financial market; Portfolio optimization; R-vine Copula;

D O I：

10.12011/1000-6788-2019-0448-12

中图分类号：

学科分类号：

摘要：

In order to optimize the investment portfolio of the international financial market, this paper selects the important stock index of the seven major stock markets in the world. First, we use the more flexible APARCH model to describe the "stylized facts" of the stock index return sequence. In the portfolio optimization model, the R-vine Copula, which is selected by the maximum spanning tree (MST) algorithm is used to describe the interdependent structure of the seven stock markets, and then measure portfolio risk under R-vine Copula dependent structure CVaR. Finally, the Mean-CVaR portfolio model was established under the R-vine Copula dependent structure condition. And compare Mean-VaR, Mean- CVaR and Mean-CVaR model based on R-vine Copula dependency structure. The empirical results shows that the interdependence between assets can be used to optimize the portfolio effect, reducing the risk of portfolio risk while increasing the rate of return. The Mean-CVaR model based on the R-vine Copula dependent structure is better than the Mean-CVaR model, while the Mean-VaR model has a relatively poor performance. © 2019, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

引用

页码：3061 / 3072

页数：11

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