Data-driven robust mean-CVaR portfolio selection under distribution ambiguity
Data-driven robust mean-CVaR portfolio selection under distribution ambiguity
复制标题
分布模糊下数据驱动的鲁棒均值CVaR投资组合选择
DOI：
10.1080/14697688.2018.1466057
复制
发表时间：
2019-01-02
影响因子：
1.3
通讯作者：
Zhu, Shushang
中科院分区：
文献类型：
--
作者：
Kang, Zhilin;Li, Xun;Zhu, Shushang
In this paper, we present a computationally tractable optimization method for a robust mean-CVaR portfolio selection model under the condition of distribution ambiguity. We develop an extension that allows the model to capture a zero net adjustment via a linear constraint in the mean return, which can be cast as a tractable conic programme. Also, we adopt a nonparametric bootstrap approach to calibrate the levels of ambiguity and show that the portfolio strategies are relatively immune to variations in input values. Finally, we show that the resulting robust portfolio is very well diversified and superior to its non-robust counterpart in terms of portfolio stability, expected returns and turnover. The results of numerical experiments with simulated and real market data shed light on the established behaviour of our distributionally robust optimization model.
