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Christian Conrad
;
Robert F Engle

modelling volatility cycles: the mf2-garch model (replication data)

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 2,100 individual stocks in the Volatility Lab at NYU Stern.

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Conrad, Christian; Engle, Robert F (2025): Modelling volatility cycles: the MF2-GARCH model (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2025013.1232487362

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