Forecasting Volatility Using High-Frequency Data

Abstract

Abstract This article focuses on some aspects of high-frequency data and their use in volatility forecasting. High-frequency data can be used to construct volatility forecasts. The article reviews two leading approaches to this. One approach is the reduced-form forecast, where the forecast is constructed from a time series model for realized measures, or a simple regression-based approach such as the heterogeneous autoregressive model. The other is based on more traditional discrete-time volatility models that include a modeling of returns. Such models can be generalized to utilize information provided by realized measures. The article also discusses how volatility forecasts, produced by complex volatility models, can benefit from high-frequency data in an indirect manner, through the use of realized measures to facilitate and improve the estimation of complex models.

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