Modelling Volatility Cycles
The MF2‐GARCH Model
Bibliographic Data
| ID | 21653598 |
|---|---|
| Authors | Christian Conrad (0000-0001-7981-2615, Alfred‐Weber‐Institute Heidelberg University Heidelberg Germany, corresponding author), Robert F Engle (0009-0009-9491-1510, Stern School of Business New York University New York USA) |
| Year | 2025 |
| Volume | 40 |
| Issue | 4 |
| Pages | 438-454 |
| Publication date | 2025-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.3118 |
| OpenAlex | W3156270897 |
| Language | EN |
| Citations received | 1 |
| References cited | 43 |
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
Autoregressive conditional heteroskedasticity · Econometrics · Economics · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Monetary Policy and Economic Impact
Conditional Heteroskedasticity in Asset Returns
Midas Regressions
Tests of Conditional Predictive Ability
Two are better than one
Comparing Predictive Accuracy
Testing for an Omitted Multiplicative Long-Term Component in GARCH Models
Comparing Possibly Misspecified Forecasts
Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling
How Relevant is Volatility Forecasting for Financial Risk Management
Stock Market Volatility and Macroeconomic Fundamentals
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |