A new regression-based tail index estimator

João Nicolau, Paulo M. M. Rodrigues

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)667-680
JournalReview of Economics and Statistics
Volume101
Issue number4
DOIs
Publication statusPublished - 1 Oct 2019

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