Robust optimal dynamic reinsurance policies under the mean-RVaR premium principle

Wanlu Zhang and Hui Meng

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 1, 113-143

Abstract: This work investigates a robust optimal dynamic reinsurance problem for an ambiguity averse insurer (AAI) concerned about potential ambiguity of claim intensity. The study aims to determine a robust optimal reinsurance contract to minimize the discounted ruin probability imposed a penalization owing to model ambiguity, including discounting for the time of ruin. We suppose that the surplus process is modeled by a diffusion model. The AAI purchases reinsurance from the reinsurer to manage risk, subject to the incentive compatibility constraint, which rules out moral hazard. Moreover, the reinsurance premium is calculated based on the mean-RVaR premium principle, which generalizes the expected value premium principle and the mean-CVaR premium principle and reflects the different risk preferences of reinsurers. Based on the dynamic programming approach, we obtain the value function and optimal reinsurance policies (the dual excess-of-loss reinsurance). Finally, we show a numerical example to illustrate the effects of premium and ambiguity averse on the discounted ruin probability and the robust optimal contract.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2022.2076121 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:53:y:2024:i:1:p:113-143

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2022.2076121

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().