Portfolio selection via D-vine copula-quantile regression method
Xu Qifa;Wang Xiaying;Jiang Cuixia;Li Huiyan;School of Management, Hefei University of Technology;Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education;
In classical portfolio selection models, variance is an important parameter. However, it is not suitable for risk measure. To address this issue, this paper develops a generalized Omega ratio-based portfolio selection model by using a D-vine copula-quantile regression to estimate complete joint probability distribution of portfolio returns. The efficacy of the new method is illustrated through empirical studies on global energy markets and Chinese stock market. Two practical portfolios are constructed on three kinds of commodities and five stocks respectively. The empirical results show that the generalized Omega ratio-based portfolio selection model via D-vine copula-quantile regression is able to investigate and simulate the dynamic features of financial assets returns. Consequently it outperforms the others in terms of producing higher values of Sharpe ratio and generalized Omega ratio.