Exploratory Dividend Optimization with Entropy Regularization

Sang Hu () and Zihan Zhou
Additional contact information
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

JRFM, 2024, vol. 17, issue 1, 1-23

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.

Keywords: dividend optimization; entropy regularization; distributional control; exploratory HJB (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/17/1/25/pdf (application/pdf)
https://www.mdpi.com/1911-8074/17/1/25/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:17:y:2024:i:1:p:25-:d:1316599

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().