IDEAS home Printed from https://ideas.repec.org/a/taf/sactxx/v2022y2022i9p749-774.html
   My bibliography  Save this article

Optimal reinsurance pricing with ambiguity aversion and relative performance concerns in the principal-agent model

Author

Listed:
  • Ailing Gu
  • Shumin Chen
  • Zhongfei Li
  • Frederi G. Viens

Abstract

This paper first studies the optimal reinsurance problems for two competitive insurers and then studies the optimal reinsurance premium pricing problem for their common reinsurer by using the dynamic programming technique. The two insurers are subject to common insurance systematic risk. Each purchases proportional or excess-of-loss reinsurance for risk control. They aim to maximize the expected utilities of their relative terminal wealth. With the insurers' optimal reinsurance strategies, the reinsurer decides the reinsurance premiums for each insurer, also aiming to maximize the expected utility of her terminal wealth. Thus, the optimal reinsurance pricing problem is formulated as a Stackelberg game between two competitive insurers and a reinsurer, where the reinsurer is the leader, and the insurers are followers. Besides, all three players take model ambiguity into account. We characterize the optimal strategies for the insurers and the reinsurer and provide some numerical examples to show the impact of competition and model ambiguity on the pricing of reinsurance contracts.

Suggested Citation

  • Ailing Gu & Shumin Chen & Zhongfei Li & Frederi G. Viens, 2022. "Optimal reinsurance pricing with ambiguity aversion and relative performance concerns in the principal-agent model," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2022(9), pages 749-774, October.
  • Handle: RePEc:taf:sactxx:v:2022:y:2022:i:9:p:749-774
    DOI: 10.1080/03461238.2022.2026459
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03461238.2022.2026459
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03461238.2022.2026459?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:sactxx:v:2022:y:2022:i:9:p:749-774. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/sact .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.