Improved estimators of extreme Wang distortion risk measures for very heavy-tailed distributions

El Methni, Jonathan and Stupfler, Gilles (2017) Improved estimators of extreme Wang distortion risk measures for very heavy-tailed distributions. Econometrics and Statistics . ISSN 2452-3062 (In Press)

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A general way to study the extremes of a random variable is to consider the family of its Wang distortion risk measures. This class of risk measures encompasses several indicators such as the classical quantile/Value-at-Risk, the Tail-Value-at-Risk and Conditional Tail Moments. Trimmed and winsorised versions of the empirical counterparts of extreme analogues of Wang distortion risk measures are considered. Their asymptotic properties are analysed, and it is shown that it is possible to construct corrected versions of trimmed or winsorised estimators of extreme Wang distortion risk measures who appear to perform overall better than their standard empirical counterparts in difficult finite-sample situations when the underlying distribution has a very heavy right tail. This technique is showcased on a set of real fire insurance data.

Item Type: Article
Keywords: Asymptotic normality, Extreme value statistics, Heavy-tailed distribution, Trimming, Wang distortion risk measure, Winsorising
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: 10.1016/j.ecosta.2017.03.002
Depositing User: Eprints, Support
Date Deposited: 09 Mar 2017 11:51
Last Modified: 13 Oct 2017 01:31

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