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 language | English |
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Pages (from-to) | 262-275 |
Number of pages | 14 |
Journal | Insurance: Mathematics and Economics |
Volume | 96 |
DOIs | |
Publication status | Published - Jan 2021 |
Externally published | Yes |
Keywords
- Convergence
- Deep learning
- Dividend management
- Investment
- Markov chain approximation
- Neural network
- Reinsurance
- Stochastic approximation