Austin Riis-Due will be speaking on "Exploratory Optimal Reinsurance under the Mean-Variance Criterion" on Wednesday, October 22, 2025 at 3:30pm in HSSB 1173.
Wednesday Oct 22, 3:30 pm - 4:30 pm, PSTAT Seminar, HSSB 1173 Speaker: Austin Riis-Due, University of Waterloo, Fellow of the Society of Actuaries
Title: Exploratory Optimal Reinsurance under the Mean-Variance Criterion
Abstract:
This paper proposes a Reinforcement Learning (RL) approach to the optimal reinsurance problem when the insurer faces uncertainty about the claim frequency or severity distributions. To this end, we first formulate an exploratory version of the problem as a relaxed stochastic control problem. Within a broad class of parametric retention functions and general risk loading functions, we derive the closed-form optimal policy under the continuous-time mean–variance criterion. This is achieved through a formal verification theorem and solving classical solutions of a system of exploratory extended Hamilton-Jacobi-Bellman (EEHJB) equations. We then establish a policy iteration theorem, showing that starting from any time- and state-homogeneous policy, policy iteration converges to the derived optimal policy. Next, we develop a martingale orthogonality theorem, which serves as the foundation of our RL algorithm. Finally, we demonstrate the convergence of the algorithm through numerical studies.
Bio:
Austin Riis-Due is a Ph.D. Candidate in Actuarial Science and Quantitative Finance at The University of Waterloo. He holds a Bachelor's in Mathematics from The University of Texas at Austin and is a Fellow of the Society of Actuaries. His primary research interests involve reinforcement learning for insurance economics problems, risk sharing, and projects with practitioners.