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

Author

Listed:
  • Geluk, J.L.
  • Peng, L.

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.

Suggested Citation

  • Geluk, J.L. & Peng, L., 1999. "An adaptive optimal estimate of the tail index for MA(1) time series," Econometric Institute Research Papers EI 9910-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1564
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    Cited by:

    1. Jaap Geluk & Liang Peng & Casper G. de Vries, 1999. "Convolutions of Heavy Tailed Random Variables and Applications to Portfolio Diversification and MA(1) Time Series," Tinbergen Institute Discussion Papers 99-088/2, Tinbergen Institute.

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