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

Author's Department

Economics Department

All Authors

Diaa Noureldin

Document Type

Research Article

Publication Date



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


Last Page


This document is currently not available here.