Multivariate high‐frequency‐based volatility (HEAVY) models
提出一类利用高频数据的新多元波动率模型（HEAVY），相比多元GARCH模型在短期预测方差和相关性上表现更优。
SUMMARY 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 generalized autoregressive conditional heteroskedasticity (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. Copyright © 2011 John Wiley & Sons, Ltd.