Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts
反驳了标准波动率模型预测能力差的观点，证明其对潜在波动率因子能做出惊人准确的日间预测，并讨论了基于高频日内数据改进波动率测量的新方法。
A voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using ARCH and stochastic volatility models. While most of these studies have documented highly significant in-sample parameter estimates and pronounced intertemporal volatility persistence, traditional ex-post forecast evaluation criteria suggest that the models provide seemingly poor volatility forecasts. Contrary to this contention, we show that volatility models produce strikingly accurate interdaily forecasts for the latent volatility factor that would be of interest in most financial applications. New methods for improved ex-post interdaily volatility measurements based on high-frequency intradaily data are also discussed.