Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jeconom.2011.10.004
Title: ARCH/GARCH with persistent covariate: Asymptotic theory of MLE
Authors: Han, H. 
Park, J.Y.
Keywords: ARCH
Asymptotic distribution theory
GARCH
Maximum likelihood estimator
Persistent covariate
Issue Date: Mar-2012
Source: Han, H., Park, J.Y. (2012-03). ARCH/GARCH with persistent covariate: Asymptotic theory of MLE. Journal of Econometrics 167 (1) : 95-112. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jeconom.2011.10.004
Abstract: The paper considers a volatility model which introduces a persistent, integrated or near-integrated, covariate to the standard GARCH(1, 1) model. For such a model, we derive the asymptotic theory of the quasi-maximum likelihood estimator. In particular, we establish consistency and obtain limit distribution. The limit distribution is generally non-Gaussian and represented as a functional of Brownian motions. However, it becomes Gaussian if the covariate has innovation uncorrelated with the squared innovation of the model or the volatility function is linear in parameter. We provide a simulation study to demonstrate the relevance and usefulness of our asymptotic theory. © 2011 Elsevier B.V. All rights reserved.
Source Title: Journal of Econometrics
URI: http://scholarbank.nus.edu.sg/handle/10635/52120
ISSN: 03044076
DOI: 10.1016/j.jeconom.2011.10.004
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