Researchers from University of Melbourne Provide Details of New Studies and Findings in the Area of Insurance Economics (A Hybrid Deep Learning Method for Optimal Insurance Strategies: Algorithms and Convergence Analysis)

2021 FEB 25 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Current study results on Economics - Insurance Economics have been published. According to news reporting originating from Melbourne, Australia, by NewsRx correspondents, research stated, “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.”

Financial supporters for this research include Hong Kong Research Grants Council, University of Melbourne, National Science Foundation (NSF).

Our news editors obtained a quote from the research from the University of Melbourne, “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.”

According to the news editors, the research concluded: “The convergence of the algorithm is proved and the rate of convergence is provided.”

This research has been peer-reviewed.

For more information on this research see: A Hybrid Deep Learning Method for Optimal Insurance Strategies: Algorithms and Convergence Analysis. Insurance Mathematics and Economics, 2021;96:262-275. Insurance Mathematics and Economics can be contacted at: Elsevier, Radarweg 29, 1043 Nx Amsterdam, Netherlands.

The news editors report that additional information may be obtained by contacting Zhuo Jin, University of Melbourne, Ctr Actuarial Studies, Dept. of Economics, Melbourne, Vic 3010, Australia. Additional authors for this research include Hailiang Yang and G. Yin.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.insmatheco.2020.11.012. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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