Using a bootstrap method to choose the sample fraction in tail index estimation

Jon Danielsson (), L. de Haan (), L. Peng and Casper G. de Vries ()

No 197, Econometric Institute Report from Erasmus University Rotterdam, Econometric Institute

Abstract: Tail index estimation depends for its accuracy on a precise choice of the sample fraction, i.e. the number of extreme order statistics on which the estimation is based. A complete solution to the sample fraction selection is given by means of a two step subsample bootstrap method. This method adaptively determines the sample fraction that minimizes the asymptotic mean squared error. Unlike previous methods, prior knowledge of the second order parameter is not required. In addition, we are able to dispense with the need for a prior estimate of the tail index which already converges roughly at the optimal rate. The only arbitrary choice of parameters is the number of Monte Carlo replications.

Keywords: tail; index; bootstrap; bias; mean; squared; error (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ias
Date: 2000
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Related works:
Working Paper: Using a Bootstrap Method to choose the Sample Fraction in Tail Index Estimation (1997)
Working Paper: Using a bootstrap method to choose the sample fraction in tail index estimation (2000)
Journal Article: Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation (2001)
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Persistent link: http://EconPapers.repec.org/RePEc:dgr:eureir:2000197

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