CMStatistics 2016: Start Registration
View Submission - CFE
Title: Cholesky-GARCH, theory and application to conditional beta Authors:  Serge Darolles - Paris Dauphine (France) [presenting]
Sebastien Laurent - AMU (France)
Christian Francq - CREST and University Lille III (France)
Abstract: The class of Cholesky-GARCH models, based on the Cholesky decomposition conditional variance matrix, are studied. We first consider the one-step and multi-step QML estimators. We prove the consistency and the asymptotic normality of the two estimators and derive the corresponding stationarity conditions. We then show that this class of models is useful to estimate conditional betas and compare it to other approaches proposed in the financial literature. Finally, we use real data to show that our model performs very well compared to other multivariate GARCH models.