6月3日礪儒講壇第66講：數據驅動和分布不確定下的魯棒投資組合選擇（李仲飛教授）
主題：數據驅動和分布不確定下的魯棒投資組合選擇
主講人：李仲飛教授
時間：2019年6月3日（周一）下午4:00-5:30
地點：學院五樓會議室
個人簡介：
李仲飛，男，中國科學院管理學博士，中山大學管理學院教授、博士生導師，廣東省人文社科重點研究基地中山大學金融工程與風險管理研究中心主任，教育部長江學者特聘教授，國家創新研究群體項目獲得者，國家杰出青年科學基金獲得者，全國模范教師，國務院特殊津貼專家，全國百篇優秀博士學位論文獲得者，廣東省珠江學者特聘教授，廣東省南粵優秀教師。
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.