Conditional tail moment and reinsurance premium estimation under random right censoring

Yuri Goegebeur (), Armelle Guillou () and Jing Qin ()
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Yuri Goegebeur: University of Southern Denmark
Armelle Guillou: Université de Strasbourg et CNRS
Jing Qin: University of Southern Denmark

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2024, vol. 33, issue 1, No 14, 230-250

Abstract: 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.

Keywords: Bias-reduction; Conditional tail moment; Excess-of-loss reinsurance; Pareto-type distribution; Random censorship; 62E20; 62G30; 62G32; 62N01 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11749-023-00890-x

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