Reduced-Bias Estimator of the Conditional Tail Expectation of Heavy-Tailed Distributions

El Hadji Deme (), Stéphane Girard () and Armelle Guillou ()
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El Hadji Deme: Université Gaston Berger de Saint-Louis, LERSTAD
Stéphane Girard: Inria Grenoble Rhône-Alpes & Laboratoire Jean Kuntzmann, Team Mistis
Armelle Guillou: Université de Strasbourg & CNRS, IRMA, UMR 7501

A chapter in Mathematical Statistics and Limit Theorems, 2015, pp 105-123 from Springer

Abstract: Abstract Several risk measures have been proposed in the literature. In this paper, we focus on the estimation of the Conditional Tail Expectation (CTE). Its asymptotic normality has been first established in the literature under the classical assumption that the second moment of the loss variable is finite, this condition being very restrictive in practical applications. Such a result has been extended by Necir et al., (Journal of Probability and Statistics 596839:17 2010) in the case of infinite second moment. In this framework, we propose a reduced-bias estimator of the CTE. We illustrate the efficiency of our approach on a small simulation study and a real data analysis.

Keywords: Bias-reduced Estimators; Conditional Tail Expectation (CTE); Small Simulation Study; Extreme Value Index; Beirlant (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-12442-1_7

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DOI: 10.1007/978-3-319-12442-1_7

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