报告主题：Portfolio Optimization under Probabilistic Risk Measure
报告人：Kok Lay Teo 教授 (Curtin University)
报告时间：2016年12月7日（周三）15:30
报告地点：校本部G507
邀请人：白延琴教授
主办部门：永利数学系
报告摘要：Portfolio selection models are of great practical significance to investors around the world. The way risk is defined and measured will lead to different optimal portfolios. Markowitz laid the foundation for this line of research with the well-known mean-variance (M-V) model in a single period case. In Markowitz's model, the portfolio variance was used as a measure of risk. Since then, many other risk definitions have been proposed. One such measure in a single period case is the mean absolute deviation. Another form of risk measure in a single period case is in terms of minimizing the maximum of individual risk which is measured using the mean absolute deviation. In the first part of this presentation, we introduce a probabilistic risk measure in a single period case, with allowance to cater for investors with different degree of risk aversion. The portfolio selection problem is formulated as a bi-criteria optimization problem to maximize the expected portfolio return and minimize the maximum individual risk of the assets in the portfolio. This bi-criteria optimization problem is shown to be equivalent to a linear programming problem. A simple analytical solution is derived. In the second part of this presentation, the probabilistic risk measure is extended for a multi-period portfolio selection problem. Like the single period case, an analytical solution is obtained for the corresponding bi-criteria optimization problem.
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