Beta kernel quantile estimators of heavy-tailed loss distributions

Arthur Charpentier and Abder Oulidi ()
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Abder Oulidi: MAI - Mathématiques Appliquées et Informatique - UCO - Université Catholique de l'Ouest

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Abstract: In this paper we suggest several nonparametric quantile estimators based on Beta kernel. They are applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte Carlo based study has shown that those estimators improve the efficiency of the traditional ones, not only for light tailed distributions, but also for heavy tailed, when the probability level is close to 1. We also compare these estimators with the Extreme Value Theory Quantile applied to Danish data on large fire insurance losses.

Keywords: Beta kernels; Champernowne distribution; Loss distributions; Quantile estimation; Transformed kernel; Value-at-risk (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (8)

Published in Statistics and Computing, 2010, 20 (1), pp.35-55. ⟨10.1007/s11222-009-9114-2⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-00425566

DOI: 10.1007/s11222-009-9114-2

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