Estimating multivariate volatility: an approach based on mixed-frequency data

• Autores: Antonio Rubia Serrano
• Localización: Cuadernos aragoneses de economía, ISSN 0211-0865, ISSN-e 2603-929X, Vol. 19, Nº 1, 2009 (Ejemplar dedicado a: Operaciones especiales y enfoques singulares en los mercados financieros), págs. 85-97
• Idioma: inglés
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• Resumen

  • 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.