Mathematical Finance & Financial Data Science Seminar
Reinforcement Learning with Dynamic Convex Risk Measures
Speaker: Sebastian Jaimungal, University of Toronto, Department of Statistical Sciences
Location: Online Zoom access provided to registrants
Date: Tuesday, May 10, 2022, 5:30 p.m.
Synopsis:
We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time-consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules that aid in obtaining optimal policies. We further develop an actor-critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to three optimization problems: statistical arbitrage trading strategies, obstacle avoidance robot control, and financial hedging.
Bio:
Sebastian Jaimungal is a full Professor of Mathematical Finance at the University of Toronto’s Department of Statistical Sciences. He is a fellow of the Fields Institute for Mathematical Sciences, Associate Member of University of Oxford’s Man Institute, a former chair of the SIAM activity group in Financial Mathematics & Engineering, and is on the editorial board of SIAM J. on Financial Mathematics and Quantitative Finance, among others. His current research interests span stochastic control and games, machine learning, and algorithmic trading.
Notes:
This event is free, but requires registration. Please click here to register. You will then receive the Zoom link by email about a day or so before the event.