Volatility Prediction Using a Realized-Measure-Based Component Model*

Modelling Volatility by Variance Decomposition

Diaa Noureldin

Journal of Financial Econometrics, 2022, vol. 20, issue 1, 76-104

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.

Keywords: RMBC; HEAVY; component volatility; predictive ability tests; time-varying-parameter models (search for similar items in EconPapers)
JEL-codes: C32 C53 C58 (search for similar items in EconPapers)
Date: 2022
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