How Much Is Optimal Reinsurance Degraded by Error?

Yinzhi Wang and Erik Bølviken

North American Actuarial Journal, 2022, vol. 26, issue 2, 283-297

Abstract: Estimation error reduces reinsurance optimality under a fitted model to suboptimality under the true one. A mathematical formulation of this issue of degradation is offered and examined through asymptotics as the sample size n of the historical observations becoming infinite. Assuming economic or distortion pricing of reinsurance it is shown that the rate of degradation is either O(1/n) or O(1n) depending on smoothness properties of the risk measure employed. Examples are conditional Value at Risk criteria, which tend to be O(1/n), and Value at Risk, which is O(1/n). A numerical study investigates the issue for smaller n and suggests a need for developing more robust optimal reinsurance techniques that can with stand model errors better.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10920277.2021.1956974 (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:uaajxx:v:26:y:2022:i:2:p:283-297

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uaaj20

DOI: 10.1080/10920277.2021.1956974

Access Statistics for this article

North American Actuarial Journal is currently edited by Kathryn Baker

More articles in North American Actuarial Journal from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().