Conditional tail moment and reinsurance premium estimation under random right censoring

Yuri Goegebeur; Armelle Guillou; Jing Qin

doi:10.1007/s11749-023-00890-x

Conditional tail moment and reinsurance premium estimation under random right censoring

Yuri Goegebeur^*, Armelle Guillou, Jing Qin

^*Kontaktforfatter

University of Strasbourg

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

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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.

Originalsprog	Engelsk
Tidsskrift	TEST
Vol/bind	33
Sider (fra-til)	230-250
ISSN	1133-0686
DOI	https://doi.org/10.1007/s11749-023-00890-x
Status	Udgivet - mar. 2024

Bibliografisk note

Funding Information:
The authors sincerely thank the referees, the Associate Editor and Editor for their helpful comments on our paper. This work was supported by the French National Research Agency under Grant ANR-19-CE40-0013-01/ExtremReg project and by the CNRS International Research Network MaDeF.

Dokumenter og links

10.1007/s11749-023-00890-xLicens: CC BY

Open Access VersionForlagets udgivne version, 688 KBLicens: CC BY

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Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Citationsformater

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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.",

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author = "Yuri Goegebeur and Armelle Guillou and Jing Qin",

note = "Funding Information: The authors sincerely thank the referees, the Associate Editor and Editor for their helpful comments on our paper. This work was supported by the French National Research Agency under Grant ANR-19-CE40-0013-01/ExtremReg project and by the CNRS International Research Network MaDeF. ",

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T1 - Conditional tail moment and reinsurance premium estimation under random right censoring

AU - Goegebeur, Yuri

AU - Guillou, Armelle

AU - Qin, Jing

N1 - Funding Information: The authors sincerely thank the referees, the Associate Editor and Editor for their helpful comments on our paper. This work was supported by the French National Research Agency under Grant ANR-19-CE40-0013-01/ExtremReg project and by the CNRS International Research Network MaDeF.

PY - 2024/3

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N2 - 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.

AB - 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.

KW - Bias-reduction

KW - Conditional tail moment

KW - Excess-of-loss reinsurance

KW - Pareto-type distribution

KW - Random censorship

KW - 62N01

KW - 62E20

KW - 62G30

KW - 62G32

U2 - 10.1007/s11749-023-00890-x

DO - 10.1007/s11749-023-00890-x

M3 - Journal article

SN - 1133-0686

VL - 33

SP - 230

EP - 250

JO - TEST

JF - TEST

ER -

Conditional tail moment and reinsurance premium estimation under random right censoring

Abstract

Bibliografisk note

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SDU link

Fingeraftryk

Citationsformater