Tail index estimation: quantile driven threshold selection

Jon Danielsson, Lerby M. Ergun, Laurens de Haan and Casper de Vries

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: The selection of upper order statistics in tail estimation is notoriously difficult. Most methods are based on asymptotic arguments, like minimizing the asymptotic mse, that do not perform well in finite samples. Here we advance a data driven method that minimizes the maximum distance between the fitted Pareto type tail and the observed quantile. To analyse the finite sample properties of the metric we organize a horse race between the other methods. In most cases the finite sample based methods perform best. To demonstrate the economic relevance of choosing the proper methodology we use daily equity return data from the CRSP database and find economic relevant variation between the tail index estimates.

JEL-codes: C10 (search for similar items in EconPapers)
Pages: 77 pages
Date: 2016-03-09
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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http://eprints.lse.ac.uk/66193/ Open access version. (application/pdf)

Related works:
Working Paper: Tail Index Estimation: Quantile-Driven Threshold Selection (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:66193

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