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Beta kernel quantile estimators of heavy-tailed loss distributions

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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.

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Authors and Affiliations

  1. CREM-Université Rennes 7, Place Hoche, 35065, Rennes Cedex, France

    Arthur Charpentier

  2. IMA, 44 Rue Rabelais, BP 808, 49008, Angers Cedex 01, France

    Abder Oulidi

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  1. Arthur Charpentier
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  2. Abder Oulidi
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Correspondence to Abder Oulidi.

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Charpentier, A., Oulidi, A. Beta kernel quantile estimators of heavy-tailed loss distributions. Stat Comput 20, 35–55 (2010). https://doi.org/10.1007/s11222-009-9114-2

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