A Hybrid Deep Learning Method for Optimal Insurance Strategies: Algorithms and Convergence Analysis

Zhuo Jin*, Hailiang Yang, George Yin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

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.
Original languageEnglish
Article number19
Pages (from-to)262-275
Number of pages14
JournalInsurance: Mathematics and Economics
Volume96
DOIs
Publication statusPublished - 15 Jan 2021
Externally publishedYes

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