Tail Index Estimation: Quantile-Driven Threshold Selection

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

Staff Working Papers from Bank of Canada

Abstract: The selection of upper order statistics in tail estimation is notoriously difficult. Methods that are based on asymptotic arguments, like minimizing the asymptotic MSE, 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 analyze the finite sample properties of the metric, we perform rigorous simulation studies. 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 economically relevant variation between the tail index estimates.

Keywords: Econometric and statistical methods; Financial stability (search for similar items in EconPapers)
JEL-codes: C01 C14 C58 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2019-08
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (10)

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Related works:
Working Paper: Tail index estimation: quantile driven threshold selection (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:19-28

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