Modelling Volatility Cycles: The MF2‐GARCH Model

Christian Conrad and Robert Engle

Journal of Applied Econometrics, 2025, vol. 40, issue 4, 438-454

Abstract: We propose a novel multiplicative factor multi‐frequency GARCH (MF2‐GARCH) model, which exploits the empirical fact that the daily standardized forecast errors of one‐component GARCH models are predictable by a moving average of past standardized forecast errors. In contrast to other multiplicative component GARCH models, the MF2‐GARCH features stationary returns, and long‐term volatility forecasts are mean‐reverting. When applied to the S&P 500, the new component model significantly outperforms the one‐component GJR‐GARCH, the GARCH‐MIDAS‐RV, and the log‐HAR model in long‐term out‐of‐sample forecasting. We illustrate the MF2‐GARCH's scalability by applying the new model to more than 2100 individual stocks in the Volatility Lab at NYU Stern.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/jae.3118

Related works:
Working Paper: Modelling Volatility Cycles: The (MF)2 GARCH Model (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:40:y:2025:i:4:p:438-454

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is currently edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().