数学学科2020系列学术报告之一

题目：Calibrating Equilibrium Mean Variance Strategy with Reinforcement Learning
报告人：董玉超
时间：2020年1月6日（周一）上午9:30-10:30
地点：1-301
报告人简介：董玉超，2016年博士毕业于复旦大学数学科学学院，现为新加坡国立大学研究员，研究兴趣为数学金融和随机最优控制理论。
报告摘要：In this talk, we consider the mean-variance problem for terminal log-return under incomplete market. In additional, an entropy term is included in the objective functional to encourage exploration of the strategy. As the problem is time-inconsistent, we characterize the equilibrium strategy with the help of extended HJB equation. Finally, we propose a learning process to obtain the strategy through the interaction with the market.