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2019•Communications in Statistics - Simulation and ComputationRequires access

Quasi-maximum likelihood estimation of GARCH with student distributed noise

K. Djeddour-Djaballah, L. Kerar

Open publisher page 3 citations

Abstract

We establish the strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for a GARCH process with Student marginal distribution. We first give a necessary and sufficient condition for the existence of a strictly stationary solution for the GARCH equation. Numerical simulations elaboreted the confirmation of the consistency of estimate.

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What this paper is about

We establish the strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for a GARCH process with Student marginal distribution. We first give a necessary and sufficient condition for the existence of a strictly stationary solution for the GARCH equation. Numerical simulations elaboreted the confirmation of the consistency of estimate.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

We establish the strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for a GARCH process with Student marginal distribution. We first give a necessary and sufficient condition for the existence of a strictly stationary solution for the GARCH equation. Numerical simulations elaboreted the confirmation of the consistency of estimate.

Key concepts: Asymptotic distribution, Autoregressive conditional heteroskedasticity, Consistency (knowledge bases), Estimator, Strong consistency, Quasi-maximum likelihood, Mathematics, Maximum likelihood

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