Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323894 
Year of Publication: 
2025
Citation: 
[Journal:] Journal of Applied Econometrics [ISSN:] 1099-1255 [Volume:] 40 [Issue:] 4 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2025 [Pages:] 438-454
Publisher: 
Wiley, Hoboken, NJ
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.
Subjects: 
long‐ and short‐term volatility
long‐term forecasting
mixed frequency data
volatility component models
volatility forecasting
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.