A1362
Title: Two step estimation of multivariate GARCH and stochastic correlation models
Authors: Christian Francq - CREST and University Lille III (France)
Jean-Michel Zakoian - CREST (France) [presenting]
Abstract: The estimation of a wide class of multivariate volatility models is investigated. Instead of estimating an $m-$multivariate volatility model, a much simpler and numerically efficient method consists in estimating $m$ univariate GARCH-type models Equation by Equation (EbE) in the first step, and a correlation matrix in the second step. Strong consistency and asymptotic normality (CAN) of the EbE estimator are established in a general framework, including Dynamic Conditional Correlation models. The EbE estimator can be used to test the restrictions imposed by a particular MGARCH specification. For general Constant Conditional Correlation models, we obtain the CAN of the two-step estimator. Comparisons with the global method, in which the model parameters are estimated in one step, are provided. Monte Carlo experiments and applications to financial series illustrate the interest of the approach.
