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Asymptotic Theory for the QMLE in GARCH-X Models with Stationary and Non-Stationary Covariates

  • Heejoon Han

    ()

    (National University of Singapore)

  • Dennis Kristensen

    ()

    (University College London and CREATES)

This paper investigates the asymptotic properties of the Gaussian quasi-maximum-likelihood estimators (QMLE?s) of the GARCH model augmented by including an additional explanatory variable - the so-called GARCH-X model. The additional covariate is allowed to exhibit any degree of persistence as captured by its long-memory parameter dx; in particular, we allow for both stationary and non-stationary covariates. We show that the QMLE'?s of the regression coefficients entering the volatility equation are consistent and normally distributed in large samples independently of the degree of persistence. This implies that standard inferential tools, such as t-statistics, do not have to be adjusted to the level of persistence. On the other hand, the intercept in the volatility equation is not identifi?ed when the covariate is non-stationary which is akin to the results of Jensen and Rahbek (2004, Econometric Theory 20) who develop similar results for the pure GARCH model with explosive volatility.

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File URL: ftp://ftp.econ.au.dk/creates/rp/12/rp12_25.pdf
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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2012-25.

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Length: 28 Heejoon Han and Dennis Kristensen
Date of creation: 18 May 2012
Date of revision:
Handle: RePEc:aah:create:2012-25
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  1. Peter Reinhard Hansen & Zhuo (Albert) Huang & Howard Howan Shek, . "Realized GARCH: A Complete Model of Returns and Realized Measures of Volatility," CREATES Research Papers 2010-13, School of Economics and Management, University of Aarhus.
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  10. Escanciano, Juan Carlos, 2009. "Quasi-Maximum Likelihood Estimation Of Semi-Strong Garch Models," Econometric Theory, Cambridge University Press, vol. 25(02), pages 561-570, April.
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