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A New Regression-Based Tail Index Estimator

Author

Listed:
  • João Nicolau

    (ISEG-Universidade de Lisboa and REM/CEMAPRE)

  • Paulo M. M. Rodrigues

    (Banco de Portugal and NovaSBE, Universidade Nova de Lisboa)

Abstract

A new regression-based approach for the estimation of the tail index of heavy-tailed distributions with several important properties is introduced. First, it provides a bias reduction when compared to available regression-based methods; second, it is resilient to the choice of the tail length used for the estimation of the tail index; third, when the effect of the slowly varying function at infinity of the Pareto distribution vanishes slowly, it continues to perform satisfactorily; and fourth, it performs well under dependence of unknown form. An approach to compute the asymptotic variance under time dependence and conditional heteroskcedasticity is also provided.

Suggested Citation

  • João Nicolau & Paulo M. M. Rodrigues, 2019. "A New Regression-Based Tail Index Estimator," The Review of Economics and Statistics, MIT Press, vol. 101(4), pages 667-680, October.
    • Handle: RePEc:tpr:restat:v:101:y:2019:i:4:p:667-680
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    References listed on IDEAS

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    9. Zsurkis, Gabriel & Nicolau, João & Rodrigues, Paulo M.M., 2024. "First passage times in portfolio optimization: A novel nonparametric approach," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1074-1085.

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