Hu, S.; Zhou, Z. Exploratory Dividend Optimization with Entropy Regularization. Journal of Risk and Financial Management 2024, 17, 25, doi:10.3390/jrfm17010025.
Hu, S.; Zhou, Z. Exploratory Dividend Optimization with Entropy Regularization. Journal of Risk and Financial Management 2024, 17, 25, doi:10.3390/jrfm17010025.
Hu, S.; Zhou, Z. Exploratory Dividend Optimization with Entropy Regularization. Journal of Risk and Financial Management 2024, 17, 25, doi:10.3390/jrfm17010025.
Hu, S.; Zhou, Z. Exploratory Dividend Optimization with Entropy Regularization. Journal of Risk and Financial Management 2024, 17, 25, doi:10.3390/jrfm17010025.
Abstract
This paper studies the dividend optimization problem in the entropy regularization framework by following the same continuous-time reinforcement learning setting as in Wang et al. (2020). 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 could be significantly different from the value function in the classical dividend optimization problem. In particular, the value function of the exploratory dividend optimization problem could be classified into three cases based on its monotonicity. Numerical examples are also presented to show the impact of temperature parameter on the solution.
Copyright:
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