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Exploratory Dividend Optimization with Entropy Regularization

Author

Listed:
  • Sang Hu

    (School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China)

  • Zihan Zhou

    (School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China)

Abstract

This study investigates the dividend optimization problem in the entropy regularization framework in the continuous-time reinforcement learning setting. The exploratory HJB is established, and the optimal exploratory dividend policy is a truncated exponential distribution. We show that, for suitable choices of the maximal dividend-paying rate and the temperature parameter, the value function of the exploratory dividend optimization problem can be significantly different from the value function in the classical dividend optimization problem. In particular, the value function of the exploratory dividend optimization problem can be classified into three cases based on its monotonicity. Additionally, numerical examples are presented to show the effect of the temperature parameter on the solution. Our results suggest that insurance companies can adopt new exploratory dividend payout strategies in unknown market environments.

Suggested Citation

  • Sang Hu & Zihan Zhou, 2024. "Exploratory Dividend Optimization with Entropy Regularization," JRFM, MDPI, vol. 17(1), pages 1-23, January.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:1:p:25-:d:1316599
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    References listed on IDEAS

    as
    1. Haoran Wang & Xun Yu Zhou, 2020. "Continuous‐time mean–variance portfolio selection: A reinforcement learning framework," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1273-1308, October.
    2. Hans Gerber & Elias Shiu, 2006. "On Optimal Dividend Strategies In The Compound Poisson Model," North American Actuarial Journal, Taylor & Francis Journals, vol. 10(2), pages 76-93.
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