Volatility Prediction Using a Realized-Measure-Based Component Model

Abstract

Abstract This article introduces a volatility model with a component structure allowing for a realized measure based on high-frequency data (e.g., realized variance) to drive the short-run volatility dynamics. In a joint model of the daily return and the realized measure, the conditional variance of the daily return has a multiplicative component structure: the first component traces long-run (secular) volatility trends, while the second component captures short-run (transitory) movements in volatility. Despite being a fixed-parameter model, its component structure implies time-varying parameters, which are “data-driven” to capture changing volatility regimes. We discuss the model dynamics and estimation by maximum likelihood. The empirical analysis reveals statistically significant out-of-sample gains compared to benchmark models, particularly for short forecast horizons and during the financial crisis.