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


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
Publication statusE-pub ahead of print - 9. Oct 2023


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


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