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Bias Correction of ML and QML Estimators in the EGARCH(1,1) Model

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  • Antonis Demos

    ()
    (www.aueb.gr/users/demos)

  • Dimitra Kyriakopoulou

    ()

Abstract

n this paper we derive the bias approximations of the Maximum Likelihood (ML) and Quasi-Maximum Likelihood (QML) Estimators of the EGARCH(1,1) parameters and we check our theoretical results through simulations. With the approximate bias expressions up to O(1/T), we are then able to correct the bias of all estimators. To this end, a Monte Carlo exercise is conducted and the results are presented and discussed. We conclude that, for given sets of parameters values, the bias correction works satisfactory for all parameters. The results for the bias expressions can be used in order to formulate the approximate Edgeworth distribution of the estimators.

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File URL: http://wpa.deos.aueb.gr/docs/paper-bias-correction-Oct.pdf
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Bibliographic Info

Paper provided by Athens University of Economics and Business in its series DEOS Working Papers with number 1108.

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Date of creation: 10 Jun 2010
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Handle: RePEc:aue:wpaper:1108

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  1. Lee, Sang-Won & Hansen, Bruce E., 1994. "Asymptotic Theory for the Garch(1,1) Quasi-Maximum Likelihood Estimator," Econometric Theory, Cambridge University Press, vol. 10(01), pages 29-52, March.
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Cited by:
  1. Wintenberger, Olivier, 2013. "Continuous invertibility and stable QML estimation of the EGARCH(1,1) model," MPRA Paper 46027, University Library of Munich, Germany.

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