On moment conditions for quasi-maximum likelihood estimation of multivariate ARCH models
Marco Avarucci (),
Eric Beutner and
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Eric Beutner: Maastricht University
Paolo Zaffaroni: Imperial College London and Università di Roma "La Sapienza"
No 2012/1, DSS Empirical Economics and Econometrics Working Papers Series from Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome
This paper questions whether it is possible to derive consistency and asymptotic normality of the Gaussian quasi-maximum likelihood estimator (QMLE) for possibly the simplest VEC-GARCH model, namely the multivariate ARCH(1) model of the BEKK form, under weak moment conditions similar to the univariate case. In contrast to the univariate specification, we show that the expectation of the loglikelihood function is unbounded, away from the true parameter value, if (and only if) the observable has unbounded second moment. Despite this non-standard feature, consistency of the Gaussian QMLE is still warranted. The same moment condition proves to be necessary and sucient for the stationarity of the score, when evaluated at the true parameter value. This explains why high moment conditions, typically bounded sixth moment and above, have been used hitherto in the literature to establish the asymptotic normality of the QMLE in the multivariate framework.
Keywords: multivariate; ARCH; models.; moment; conditions.; VEC-GARCH. (search for similar items in EconPapers)
Pages: 21 pages
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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http://www.dss.uniroma1.it/RePec/sas/wpaper/20121_ABZ.pdf First version, 2012 (application/pdf)
Journal Article: ON MOMENT CONDITIONS FOR QUASI-MAXIMUM LIKELIHOOD ESTIMATION OF MULTIVARIATE ARCH MODELS (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:sas:wpaper:20121
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