An adaptive optimal estimate of the tail index for MA(1) time series

Jaap Geluk and Liang Peng

No EI 9910-/A, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: For samples of random variables with a regularly varying tail estimating the tail index has received much attention recently. For the proof of asymptotic normality of the tail index estimator second order regular variation is needed. In this paper we first supplement earlier results on convolution given by Geluk et al. (1997). Secondly we propose a simple estimator of the tail index for finite moving average time series. We also give a subsampling procedure in order to estimate the optimal sample fraction in the sense of minimal mean squared error.

Keywords: asymptotic normality; minimal mean squared error; optimal sample fraction; regular variation; tail index (search for similar items in EconPapers)
Date: 1999-03-30
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Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:1564

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