Title: Statistical inference for mixture GARCH models
Authors: Maddalena Cavicchioli - University of Modena and Reggio Emilia - Dipartimento di Economia Marco Biagi (Italy) [presenting]
Abstract: Mixture generalized autoregressive conditional heteroskedastic models are considered. A new iteration algorithm of type EM is proposed for the estimation of model parameters. The maximum likelihood estimates are shown to be consistent, and their asymptotic properties are investigated. More precisely, we derive simple expressions in closed form for the asymptotic covariance matrix and the expected Fisher information matrix of the ML estimator. Finally, we study the model selection and propose testing procedures. Applications and examples illustrate the results.