Research-Optimal Investment Under Non-Markovian Models via BSPDEs and Deep Learning

Research

Financial Math Seminar

Optimal Investment Under Non-Markovian Models via BSPDEs and Deep Learning

Speaker: Jingtang MA（Southwestern University of Finance and Economics）
Time: Nov 22, 2025, 16:00-17:00
Location: Room M616, College of Science Building

Abstract

We study optimal investment (utility maximization problems) in non-Markovian settings, where the dynamic programming principle (DPP) fails and Hamilton-Jacobi-Bellman (HIB) equations are inapplicable. Instead, backward stochastic partial differential equations (BSPDEs) can characterize the random field values of such problems. We propose iterative deep learning algorithms to solve these fully nonlinear BSPDEs and establish their convergence. Numerical experiments on rough volatility models validate the theoretical convergence of our methods. (This is joint work with Haofei Wu and Harry Zheng.)