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Kernel-type estimators for the distortion risk premiums of heavy-tailed distributions

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  • Lazhar Benkhelifa

Abstract

A new kernel-type estimator for the distortion risk premiums of heavy-tailed losses is introduced. Using a least-squares approach, a bias-reduced version of this estimator is proposed. Under suitable assumptions, the asymptotic normality of the given estimators is established. A small simulation study, to illustrate the performance of our method, is carried out.

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  • Lazhar Benkhelifa, 2016. "Kernel-type estimators for the distortion risk premiums of heavy-tailed distributions," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2016(3), pages 262-278, March.
    • Handle: RePEc:taf:sactxx:v:2016:y:2016:i:3:p:262-278
    DOI: 10.1080/03461238.2014.924434
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