Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation

Jon Danielsson, L. de Haan, L. Peng and Casper de Vries

Journal of Multivariate Analysis, 2001, vol. 76, issue 2, 226-248

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; optimal extreme sample fraction (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (65)

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Working Paper: Using a bootstrap method to choose the sample fraction in tail index estimation (2000)
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