Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: A stochastic recurrence equations approach

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dc.creatorStraumann, Daniel
dc.creatorMikosch, Thomas
dc.date2007-02-23
dc.date.accessioned2026-07-07T08:08:46Z
dc.date.available2026-07-07T08:08:46Z
dc.descriptionThis paper studies the quasi-maximum-likelihood estimator (QMLE) in a general conditionally heteroscedastic time series model of multiplicative form $X_t=σ_tZ_t$, where the unobservable volatility $σ_t$ is a parametric function of $(X_{t-1},...,X_{t-p},σ_{t-1},... ,σ_{t-q})$ for some $p,q\ge0$, and $(Z_t)$ is standardized i.i.d. noise. We assume that these models are solutions to stochastic recurrence equations which satisfy a contraction (random Lipschitz coefficient) property. These assumptions are satisfied for the popular GARCH, asymmetric GARCH and exponential GARCH processes. Exploiting the contraction property, we give conditions for the existence and uniqueness of a strictly stationary solution $(X_t)$ to the stochastic recurrence equation and establish consistency and asymptotic normality of the QMLE. We also discuss the problem of invertibility of such time series models.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053606000000803 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0702692
dc.identifierhttp://arxiv.org/abs/math/0702692
dc.identifierAnnals of Statistics 2006, Vol. 34, No. 5, 2449-2495
dc.identifierdoi:10.1214/009053606000000803
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131373
dc.subjectStatistics Theory
dc.subject60H25 (Primary) 62F10, 62F12, 62M05, 62M10, 91B84 (Secondary)
dc.titleQuasi-maximum-likelihood estimation in conditionally heteroscedastic time series: A stochastic recurrence equations approach
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