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.