Publication detail

Monte Carlo-Based Tail Exponent Estimator

Author(s): doc. PhDr. Jozef Baruník Ph.D.,
Mgr. Lukáš Vácha Ph.D.,
Type: IES Working Papers
Year: 2010
Number: 6
ISSN / ISBN:
Published in: IES Working Papers 6/2010
Publishing place: Prague
Keywords: Hill estimator, α-stable distributions, tail exponent estimation
JEL codes: C1, C13, C15, G0
Suggested Citation: Barunik, J., Vacha, L. (2010). “ Monte Carlo-Based Tail Exponent Estimator ” IES Working Paper 6/2010. IES FSV. Charles University.
Grants: 402/09/0965: New Approaches for monitoring and prediction of capital markets 402/09/H045 - Nelineární dynamika v peněžní ekonomii a financích. Teorie a empirické modely
Abstract: In this paper we study the finite sample behavior of the Hill estimator under α-stable distributions. Using large Monte Carlo simulations we show that the Hill estimator overestimates the true tail exponent and can hardly be used on samples with small length. Utilizing our results, we introduce a Monte Carlo-based method of estimation for the tail exponent. Our method is not sensitive to the choice
of k and works well also on small samples. The new estimator gives unbiased results with symmetrical con_dence intervals. Finally, we demonstrate the power of our estimator on the main world stock market indices. On the two separate periods of 2002-2005 and 2006-2009 we estimate the tail exponent.
Downloadable: WP 2010_06_Barunik, Vacha
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