Asymptotic Theory for QMLE for Real-Time GARCH(1,1) Model

Abstract

We investigate the asymptotic properties of the Gaussian Quasi-Maximum-Likelihood estimator (QMLE) for the Real-time GARCH(1,1) model of Smetanina (2017). The developed theory relies on the new dependence measure developed in Wu (2005) and is substantially different to the standard asymptotic theory for GARCH models. We prove consistency and asymptotic normality for the parameter vector at the usual √T rate. Finally, as part of the developed theory we also demonstrate how convergence rates of uniform laws of large numbers can be established via the powerful maximal inequalities for high-dimensional heavy-tailed time series using uniform functional dependence measure.