Augmented GARCH sequences: Dependence structure and asymptotics

Abstract

The augmented GARCH model is a unification of numerous extensions of the popular and widely used ARCH process. It was introduced by Duan and besides ordinary (linear) GARCH processes, it contains exponential GARCH, power GARCH, threshold GARCH, asymmetric GARCH, etc. In this paper, we study the probabilistic structure of augmented $\mathrm{GARCH}(1,1)$ sequences and the asymptotic distribution of various functionals of the process occurring in problems of statistical inference. Instead of using the Markov structure of the model and implied mixing properties, we utilize independence properties of perturbed GARCH sequences to directly reduce their asymptotic behavior to the case of independent random variables. This method applies for a very large class of functionals and eliminates the fairly restrictive moment and smoothness conditions assumed in the earlier theory. In particular, we derive functional CLTs for powers of the augmented GARCH variables, derive the error rate in the CLT and obtain asymptotic results for their empirical processes under nearly optimal conditions.

Published in at http://dx.doi.org/10.3150/07-BEJ120 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

References in corpus (1)

• An invariance principle for weakly dependent stationary general models

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• Estimation of the time of change in panel data
• Hybrid quantile estimation for asymmetric power GARCH models
• Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH($p$,$q$) processes
• Joint FCLT for Sample Quantile and Measures of Dispersion for Functionals of Mixing Processes
• Robust change-point detection in panel data