主题：数据驱动和分布不确定下的鲁棒投资组合选择

主讲人：李仲飞教授

时间：2019年6月3日（周一）下午4:00-5:30

地点：公司五楼会议室

个人简介：

李仲飞，男，中国科学院管理学博士，中山大学管理学院教授、博士生导师，广东省人文社科重点研究基地中山大学金融工程与风险管理研究中心主任，教育部长江学者特聘教授，国家创新研究群体项目获得者，国家杰出青年科学基金获得者，全国模范教师，国务院特殊津贴专家，全国百篇优秀博士学位论文获得者，广东省珠江学者特聘教授，广东省南粤优秀教师。

Abstract.

In this talk, I first present a computationally tractable optimization method for a robust mean-CVaR portfolio selection model under the condition of distribution ambiguity, where the Conditional Value-at-Risk (CVaR) is used to measure risk. I develop an extension that allows the model to capture a zero net adjustment via the linear constraint in the mean return, which can be cast as a tractable conic program. Also, I 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. The resulting robust portfolio is very well diversified and superior to its non-robust counterpart in terms of portfolio stability, expected returns and turnover.

Secondly, I develop alpha-robust mean-CVaR portfolio selection models, which allow the investor to distinguish ambiguity and ambiguity attitude with different levels of ambiguity aversion. For the case when there is a risk-free asset and short-selling is allowed, the analytic solution is obtained for the alpha-robust CVaR optimization model subject to a minimum mean return constraint. Moreover, a closed-form portfolio rule is derived for the alpha-robust mean-CVaR optimization problem in a market without the risk-less asset. The results obtained from solving the numerical example show that if an investor is more ambiguity-averse, his investment strategy will always be more conservative.