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Semiparametric efficient adaptive estimation of the GJR-GARCH model

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  • Ciccarelli Nicola

    (Center and Department of Econometrics and Operations Research, Tilburg University, P.O. Box 90153, 5000 LETilburg, Netherlands)

Abstract

In this paper we derive a semiparametric efficient adaptive estimator for the GJR-GARCH(1,1){(1,1)} model. We first show that the quasi-maximum likelihood estimator is consistent and asymptotically normal for the model used in analysis, and we secondly derive a semiparametric estimator that is more efficient than the quasi-maximum likelihood estimator. Through Monte Carlo simulations, we show that the semiparametric estimator is adaptive for the parameters included in the conditional variance of the GJR-GARCH(1,1){(1,1)} model with respect to the unknown distribution of the innovation.

Suggested Citation

  • Ciccarelli Nicola, 2018. "Semiparametric efficient adaptive estimation of the GJR-GARCH model," Statistics & Risk Modeling, De Gruyter, vol. 35(3-4), pages 141-160, July.
    • Handle: RePEc:bpj:strimo:v:35:y:2018:i:3-4:p:141-160:n:3
    DOI: 10.1515/strm-2017-0015
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    References listed on IDEAS

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