A hybrid deep learning method for optimal insurance strategies: Algorithms and convergence analysis

Zhuo Jin, Hailiang Yang and G. Yin

Insurance: Mathematics and Economics, 2021, vol. 96, issue C, 262-275

Abstract: This paper develops a hybrid deep learning approach to find optimal reinsurance, investment, and dividend strategies for an insurance company in a complex stochastic system. A jump–diffusion regime-switching model with infinite horizon subject to ruin is formulated for the surplus process. A Markov chain approximation and stochastic approximation-based iterative deep learning algorithm is developed to study this type of infinite-horizon optimal control problems. Approximations of the optimal controls are obtained by using deep neural networks. The framework of Markov chain approximation plays a key role in building iterative algorithms and finding initial values. Stochastic approximation is used to search for the optimal parameters of neural networks in a bounded region determined by the Markov chain approximation method. The convergence of the algorithm is proved and the rate of convergence is provided.

Keywords: Neural network; Deep learning; Markov chain approximation; Stochastic approximation; Investment; Reinsurance; Dividend management; Convergence (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:96:y:2021:i:c:p:262-275

DOI: 10.1016/j.insmatheco.2020.11.012

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