Conditional tail moment and reinsurance premium estimation under random right censoring

Yuri Goegebeur*, Armelle Guillou, Jing Qin

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

We propose an estimator of the conditional tail moment (CTM) when the data are subject to random censorship. The variable of main interest and the censoring variable both follow a Pareto-type distribution. We establish the asymptotic properties of our estimator and discuss bias-reduction. Then, the CTM is used to estimate, in case of censorship, the premium principle for excess-of-loss reinsurance. The finite sample properties of the proposed estimators are investigated with a simulation study and we illustrate their practical applicability on a dataset of motor third party liability insurance.

Original languageEnglish
JournalTEST
ISSN1133-0686
DOIs
Publication statusE-pub ahead of print - 9. Oct 2023

Keywords

  • Bias-reduction
  • Conditional tail moment
  • Excess-of-loss reinsurance
  • Pareto-type distribution
  • Random censorship

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