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Regression estimators for the tail index

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Abstract

We propose a class of weighted least squares estimators for the tail index of a distribution function with a regularly varying tail. Our approach is based on the method developed by Holan and McElroy (2010) for the Parzen tail index. We prove asymptotic normality and consistency for the estimators under suitable assumptions. These and earlier estimators are compared in various models through a simulation study using the mean squared error as criterion. The results show that the weighted least squares estimator has good performance.

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Authors and Affiliations

  1. Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, 6720, Szeged, Hungary

    Amenah AL-Najafi, László L. Stachó & László Viharos

Authors
  1. Amenah AL-Najafi
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  2. László L. Stachó
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  3. László Viharos
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Corresponding author

Correspondence to Amenah AL-Najafi.

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Communicated by L. Molnár

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AL-Najafi, A., Stachó, L.L. & Viharos, L. Regression estimators for the tail index. ActaSci.Math. 87, 649–678 (2021). https://doi.org/10.14232/actasm-020-361-6

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  • DOI: https://doi.org/10.14232/actasm-020-361-6

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