Multivariate High-Frequency-Based Volatility (HEAVY) Models

This paper introduces a new class of multivariate volatility models that utilizes high-frequency data.  We discuss the models' dynamics and highlight their differences from multivariate GARCH models.  We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts.  Estimation and inference strategies are outlined.  Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons.  Forecast gains are obtained for both forecast variances and correlations.