A Class of Reduced Bias Estimators of Distortion Risk Measures under Dependence Serials with Heavy-Tailed Marginals

Authors

  • El Hadji Deme LERSTAD, UFR SAT, Universit´e Gaston Berger, BP 234, Saint-Louis, S´en´egal
  • Aminetou Agbrabatt FST, Universit´e de Nouachott Al Aasriya, BP 2373, Nouakchott, Mauritanie
  • Mamadou Aliou Barry LERSTAD, UFR SAT, Universit´e Gaston Berger, BP 234, Saint-Louis, S´en´egal
  • Sidiya Ahmedou LERSTAD, UFR SAT, Universit´e Gaston Berger, BP 234, Saint-Louis, S´en´egal
  • Khalil El Waled FST, Universit´e de Nouachott Al Aasriya, BP 2373, Nouakchott, Mauritanie

DOI:

https://doi.org/10.29020/nybg.ejpam.v18i2.6024

Keywords:

Risk Premiums, Heavy-Tailed, Insurance, Bias reduction, Kernel Estimation, Extreme value,, Dependence serials

Abstract

In this paper, we introduce a class of semi-parametric estimators of the  distortion risk premiums for dependent insurance losses with  heavy-tailed marginals. Our approach is based on the kernel estimation of the tail index and extreme quantiles under the first and second orders regularly varying assumptions for  stationary insured risks with heavy-tailed distribution under dependence serials.  Moreover, we illustrate the behaviour of our proposed estimator and give a comparison between  this estimator and the classical one in terms of the absolute bias and the root median squared error.

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Published

2025-05-01

Issue

Vol. 18 No. 2: (April 2025)

Section

Mathematical Statistics

License

Copyright (c) 2025 El Hadji Deme, Aminetou Agbrabatt, Mamadou Aliou Barry, Sidiya Ahmedou, Khalil El Waled

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How to Cite

A Class of Reduced Bias Estimators of Distortion Risk Measures under Dependence Serials with Heavy-Tailed Marginals. (2025). European Journal of Pure and Applied Mathematics, 18(2), 6024. https://doi.org/10.29020/nybg.ejpam.v18i2.6024