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

Author

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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.

Suggested Citation

  • Zhu, Ke & Ling, Shiqing, 2013. "Global self-weighted and local quasi-maximum exponential likelihood estimators for ARMA-GARCH/IGARCH models," MPRA Paper 51509, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:51509
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    File URL: https://mpra.ub.uni-muenchen.de/51509/1/MPRA_paper_51509.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

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

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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