Multivariate Volatility: An approach based on Mixed-Frequency Data "
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.
Palabras clave: Volatility Forecasting, mixed-data sampling, MIDAS.