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DTSTART:20070311T020000
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DTSTAMP:20260807T155844
DTSTART;TZID=America/Detroit:20261104T160000
DTEND;TZID=America/Detroit:20261104T170000
SUMMARY:Workshop / Seminar:Exploratory Optimal Reinsurance under the Mean-Variance Criterion
DESCRIPTION:This paper proposes a Reinforcement Learning (RL) approach to the optimal reinsurance problem\nwhen the insurer faces uncertainty about the insurance claim dynamics. To this end\, we first formulate\nan exploratory version of the problem as a relaxed stochastic control problem. Within a broad class of\nparametric retention functions and general risk loading functions\, we derive the closed-form equilibrium\npolicy under the continuous-time mean-variance criterion. This is achieved through a formal verification\ntheorem and solving classical solutions of a system of exploratory extended Hamilton-Jacobi-Bellman\n(EEHJB) equations. We then establish a policy iteration theorem\, showing that starting from any timeand state-homogeneous policy\, policy iteration converges to the derived equilibrium policy. Next\, we\ndevelop a martingale orthogonality theorem\, which serves as the foundation of our RL algorithm. The\nalgorithm is evaluated through simulation studies and real data from the U.S. National Flood Insurance\nProgram. Results demonstrate that the RL approach effectively learns unknown claim distributions\,\ntracks unobserved changes in claim dynamics and produces higher insurer surplus trajectories than the\nmaximum likelihood estimation (MLE) approach while simultaneously exhibiting greater robustness to\nthe choice of training window.
UID:150012-21907810@events.umich.edu
URL:https://events.umich.edu/event/150012
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 1360
CONTACT:
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