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Semiparametric Maximum Lickelihood Estimation of GARCH Models

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Abstract

We consider a semiparametric GARCH model where the functional form for the conditional density of the errors is unknown. Adaptive conditions of the parameters are examined. Semiparametric Maximum Likelihood (SML) estimators are constructed by maximizing the nonparametric pseudo log-likelihood function computed using the residuals from initial root-n consistent estimates. SML estimators are shown to be adaptive for the adaptively estimable parameters and consistent for all identifiable parameters. Monte Carlo results suggest that SML estimators outperform quasi maximum likelihood estimators and the adaptive maximum likelihood estimators in finite samples.

Suggested Citation

  • Jian Yang, 1998. "Semiparametric Maximum Lickelihood Estimation of GARCH Models," University of Western Ontario, Departmental Research Report Series 9816, University of Western Ontario, Department of Economics.
    • Handle: RePEc:uwo:uwowop:9816
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    File URL: https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1331&context=economicsresrpt
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    Keywords

    GARCH models; Adaptive estimation; Semiparametric estimation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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