High-Frequency Data, Frequency Domain Inference, and Volatility Forecasting
提出一种利用高频数据建模金融市场波动率的简单方法，通过拟合长自回归模型来刻画波动率序列，并基于频域估计构建预测，实证表明其预测效果优于主流模型。
Although it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high-frequency data. The method avoids using a tight parametric model by instead simply fitting a long autoregression to log-squared, squared, or absolute high-frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared, or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise. © 2001 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology