Tail Index Estimation: Quantile-Driven Threshold Selection
Jon Danielsson (),
Laurens de Haan and
Casper de Vries ()
Staff Working Papers from Bank of Canada
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
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed
Downloads: (external link)
https://www.bankofcanada.ca/wp-content/uploads/2019/08/swp2019-28.pdf Full text (application/pdf)
Working Paper: Tail index estimation: quantile driven threshold selection (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:19-28
Access Statistics for this paper
More papers in Staff Working Papers from Bank of Canada 234 Wellington Street, Ottawa, Ontario, K1A 0G9, Canada. Contact information at EDIRC.
Bibliographic data for series maintained by ().