Volatility Prediction Using a Realized-Measure-Based Component Model

Author's Department

Economics Department

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https://doi.org/10.1093/jjfinec/nbz041

Document Type

Research Article

Publication Title

Journal of Financial Econometrics

Publication Date

4-13-2020

doi

10.1093/jjfinec/nbz041

Abstract

This paper 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 Örst component traces long-run (secular) volatility trends, while the second component captures short-run (transitory) movements in volatility. Despite being a Öxed-parameter model, its component structure implies time-varying parameters, which are data-driven to capture changing volatility regimes. We discuss the model properties and estimation by maximum likelihood. The empirical analysis reveals strong out-of-sample performance compared to benchmark models. This is demonstrated using unconditional and conditional predictive ability tests, and also using the model conÖdence set.

First Page

76

Last Page

104

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