Robust Optimal Reinsurance and Investment Problem Under Markov Switching via Actor–Critic Reinforcement Learning
Fang Jin (),
Kangyong Cheng,
Xiaoliang Xie and
Shubo Chen
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Fang Jin: College of Science, Hunan City University, Yiyang 413000, China
Kangyong Cheng: College of Science, Hunan City University, Yiyang 413000, China
Xiaoliang Xie: College of Mathematics and Statistics, Hunan University of Technology and Business, Changsha 410215, China
Shubo Chen: College of Science, Hunan City University, Yiyang 413000, China
Mathematics, 2025, vol. 13, issue 21, 1-21
Abstract:
This paper investigates a robust optimal reinsurance and investment problem for an insurance company operating in a Markov-modulated financial market. The insurer’s surplus process is modeled by a diffusion process with jumps, which is correlated with financial risky assets through a common shock structure. The economic regime switches according to a continuous-time Markov chain. To address model uncertainty concerning both diffusion and jump components, we formulate the problem within a robust optimal control framework. By applying the Girsanov theorem for semimartingales, we derive the dynamics of the wealth process under an equivalent martingale measure. We then establish the associated Hamilton–Jacobi–Bellman (HJB) equation, which constitutes a coupled system of nonlinear second-order integro-differential equations. An explicit form of the relative entropy penalty function is provided to quantify the cost of deviating from the reference model. The theoretical results furnish a foundation for numerical solutions using actor–critic reinforcement learning algorithms.
Keywords: robust optimal problem; reinsurance; investment; markov switching; actor–critic reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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