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Global self-weighted and local quasi-maximum exponential likelihood estimators for ARMA-GARCH/IGARCH models

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  • Zhu, Ke
  • Ling, Shiqing

Abstract

This paper investigates the asymptotic theory of the quasi-maximum exponential likelihood estimators (QMELE) for ARMA–GARCH models. Under only a fractional moment condition, the strong consistency and the asymptotic normality of the global self-weighted QMELE are obtained. Based on this self-weighted QMELE, the local QMELE is showed to be asymptotically normal for the ARMA model with GARCH (finite variance) and IGARCH errors. A formal comparison of two estimators is given for some cases. A simulation study is carried out to assess the performance of these estimators, and a real example on the world crude oil price is given.

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File URL: http://mpra.ub.uni-muenchen.de/51509/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 51509.

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Date of creation: 17 Nov 2013
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Publication status: Published in Annals of Statistics 4.39(2011): pp. 2131-2163
Handle: RePEc:pra:mprapa:51509

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Keywords: ARMA–GARCH/IGARCH model; asymptotic normality; global selfweighted/local quasi-maximum exponential likelihood estimator; strong consistency.;

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