Modelling Volatility Cycles: The MF2‐GARCH Model

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.