Title: DCC-HEAVY: A multivariate GARCH model with realized measures of variance and correlation
Authors: Yongdeng Xu - Cardiff University (United Kingdom) [presenting]
Abstract: A new class of multivariate volatility models is proposed that utilising high-frequency data. We call this model the DCC-HEAVY model as key ingredients are the DCC model and the HEAVY model. We discuss the model's dynamics and highlight their differences from DCC-GARCH models. Specifically, the dynamics of conditional variances are driven by the lagged realized variances, while the dynamics of conditional correlations are driven by the lagged realized correlations in the DCC-HEAVY model. The new model removes well known asymptotic bias in DCC-GARCH model estimation and has more desirable asymptotic properties. We also derive a Quasi-maximum likelihood estimation and provide closed-form formulas for multi- step forecasts. Empirical results suggest that the DCC-HEAVY model outperforms the DCC-GARCH model in and out-of-sample.