Realising the future: forecasting with high‐frequency‐based volatility (HEAVY) models
详细研究了一类基于高频数据构建的HEAVY波动率模型，发现其具有动量和均值回复效应，能快速适应波动率水平的结构性变化，并通过信用紧缩期间的表现与传统GARCH模型比较，还分析了基于模型的预测分布估计和缺失数据处理。
Abstract This paper studies in some detail a class of high‐frequency‐based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realised measures constructed from high‐frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model‐based bootstrap which allows us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models. Copyright © 2010 John Wiley & Sons, Ltd.