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

J. L. Geluk and Liang Peng

Statistics & Probability Letters, 2000, vol. 46, issue 3, 217-227

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. (Stochastic Process. Appl. 69 (1997) 138-159). 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: Regular; variation; Tail; index (search for similar items in EconPapers)
Date: 2000
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