Estimating multivariate volatilityan approach based on mixed-frequency data
ISSN: 0211-0865
Année de publication: 2009
Titre de la publication: Operaciones especiales y enfoques singulares en los mercados financieros
Volumen: 19
Número: 1
Pages: 85-97
Type: Article
D'autres publications dans: Cuadernos aragoneses de economía
Résumé
The Mixed Data Sampling variance estimator is a novel procedure able to overperform the univariate GARCH models and other conventional methods in empirical applications. We propose a suitable estimator for the multivariate context which is easily computable and tractable even in large-scale problems. We address the one-step-ahed forecasting accuracy at the monthly frequency over alternative models. These include the unconditional sample estimator, the rolling-window (or realized) estimator, and a constrained multivariate GARCH model. The MIDAS multivariate procedure provides a signi.cant reduction in the out-of- sample bias over these alternatives.