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math.ST updates on arXiv.org

What is Learnable in Valiant's Theory of the Learnable? 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Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: A stochastic recurrence equations approach
Daniel Straumann, Thomas Mikosch · 2007-02-23 · via math.ST updates on arXiv.org

This 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.

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