Title
Volatility Prediction Using a Realized-Measure-Based Component Model*
Author's Department
Economics Department
Document Type
Research Article
Publication Date
5-2-2017
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
1
Last Page
38
Recommended Citation
APA Citation
Noureldin, D.
(2017). Volatility Prediction Using a Realized-Measure-Based Component Model*. 1–38.
https://fount.aucegypt.edu/faculty_journal_articles/4771
MLA Citation
Noureldin, Diaa
"Volatility Prediction Using a Realized-Measure-Based Component Model*." 2017, pp. 1–38.
https://fount.aucegypt.edu/faculty_journal_articles/4771