时 间：2026年3月27 日（周五）10：00 – 11：30
地 点：普陀校区理科大楼A1514室
报告人：贾颜玮 香港中文大学助理教授
主持人：危佳钦 华东师范大学教授
摘 要：
We study Merton’s expected utility maximization problem in an incomplete market, characterized by a factor process in addition to the stock price process, where the functional forms of the model primitives are unknown. The agent under consideration is a price taker who has access only to the stock and factor value processes and the instantaneous volatility. We propose an auxiliary problem in which the agent can invoke policy randomization according to a specific class of Gaussian distributions, and prove that the mean of its optimal Gaussian policy solves the original Merton problem. With randomized policies, we are in the realm of continuous-time reinforcement learning (RL) recently developed in Wang et al. (2020) and Jia and Zhou (2022a,b, 2023), enabling us to solve the auxiliary problem in a data-driven way without having to estimate the model primitives. Specifically, we establish a policy improvement theorem based on which we design both online and offline actor-critic RL algorithms for learning Merton’s strategies. A key insight from this study is that RL in general and policy randomization in particular are useful beyond the purpose for exploration—they can be employed as a technical tool to solve a problem that cannot be otherwise solved by mere deterministic policies. Finally, we present simulation and empirical studies in a stochastic volatility environment to demonstrate the decisive outperformance of the devised RL algorithms in comparison to the conventional model-based, plug-in method. This is a joint work with Min Dai, Yuchao Dong, and Xunyu Zhou.
报告人简介：
Dr. Yanwei Jia obtained his Ph.D. degree from the National University of Singapore in 2020, and B.Sc. from Tsinghua University in 2016. Prior joining the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong in 2023, he was an associate research scientist and adjunct assistant professor in the Department of Industrial Engineering and Operations Research at Columbia University. His research interest falls broadly into financial engineering and decision making problems, focusing on FinTech and data analytics. His recent research aims to develop fundamental theory on continuous-time reinforcement learning, and to solve problems in financial engineering, such as asset allocation and algorithmic trading. He also uses the structural estimation approach to study the information aggregation mechanism and the wisdom of the crowd.