Optimal reinsurance for Gerber–Shiu functions in the Cramér–Lundberg model

Michael Julius Preischl, Stefan Thonhauser

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Complementing existing results on minimal ruin probabilities, we minimize expected discounted penalty functions (or Gerber–Shiu functions)in a Cramér–Lundberg model by choosing optimal reinsurance. Reinsurance strategies are modeled as time dependent control functions, which lead to a setting from the theory of optimal stochastic control and ultimately to the problem's Hamilton–Jacobi–Bellman equation. We show existence and uniqueness of the solution found by this method and provide numerical examples involving light and heavy tailed claims and also give a remark on the asymptotics.

Translated title of the contributionOptimale Rückversicherung für Gerber Shiu Funktionen im Cramér-Lundberg Model
Original languageEnglish
Pages (from-to)82-91
Number of pages10
JournalInsurance / Mathematics & economics
Volume87
Issue number87
Early online dateApr 2019
DOIs
Publication statusPublished - 2019

Fingerprint

Gerber-Shiu Function
Reinsurance
Optimal Stochastic Control
Ruin Probability
Control Function
Penalty Function
Existence and Uniqueness
Minimise
Numerical Examples
Model
Ruin probability
Expected discounted penalty function
Optimal reinsurance
Stochastic optimal control
Control function
Uniqueness
Gerber-Shiu function
Strategy

Keywords

  • Cramér-Lundberg model
  • Dynamic reinsurance
  • Gerber–Shiu functions
  • Optimal stochastic control
  • Policy iteration

ASJC Scopus subject areas

  • Economics and Econometrics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fields of Expertise

  • Information, Communication & Computing

Cite this

Optimal reinsurance for Gerber–Shiu functions in the Cramér–Lundberg model. / Preischl, Michael Julius; Thonhauser, Stefan.

In: Insurance / Mathematics & economics, Vol. 87, No. 87, 2019, p. 82-91.

Research output: Contribution to journalArticleResearchpeer-review

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