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Tail index estimation: quantile driven threshold selection

Danielsson, Jon, Ergun, Lerby M., Haan, Laurens de and Vries, Casper G. de (2016) Tail index estimation: quantile driven threshold selection. Systemic Risk Centre Discussion Papers (58). Systemic Risk Centre, The London School of Economics and Political Science, London, UK.

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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.

Item Type: Monograph (Discussion Paper)
Official URL:
Additional Information: © 2016 The Authors
Divisions: Finance
Systemic Risk Centre
Financial Markets Group
Subjects: H Social Sciences > HB Economic Theory
Date Deposited: 21 Apr 2016 08:05
Last Modified: 18 Nov 2022 00:05
Projects: ES/K002309/1
Funders: Economic and Social Research Council

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