Data-driven or Alpha Robust Mean-CVaR Portfolio Selection under Distribution Ambiguity 在分布不确定下数据驱动或者阿尔法鲁棒的均值-CVaR投资组合选择

讲座名称: Data-driven or Alpha Robust Mean-CVaR Portfolio Selection under Distribution Ambiguity 在分布不确定下数据驱动或者阿尔法鲁棒的均值-CVaR投资组合选择
讲座时间: 2019-05-17
讲座人: 李仲飞
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校区: 兴庆校区
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讲座内容: 讲座题目:Data-driven or Alpha Robust Mean-CVaR Portfolio Selection under Distribution Ambiguity(在分布不确定下数据驱动或者阿尔法鲁棒的均值-CVaR投资组合选择) 讲座时间:2019年5月17日(星期五)上午10:00-11:00            讲座地点:经济与金融学院八楼国际学术交流厅 讲座人:李仲飞 报告摘要: 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.  
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