Modelling Volatility Cycles: The (MF)2 GARCH Model

Christian Conrad and Robert Engle

Working Paper series from Rimini Centre for Economic Analysis

Abstract: We suggest a multiplicative factor multi frequency component GARCH model which exploits the empirical fact that the daily standardized forecast errors of standard GARCH models behave counter-cyclical when averaged at a lower frequency. For the new model, we derive the unconditional variance of the returns, the news impact function and multi-step-ahead volatility forecasts. We apply the model to the S&P 500, the FTSE 100 and the Hang Seng Index. We show that the long-term component of stock market volatility is driven by news about the macroeconomic outlook and monetary policy as well as policy-related news. The new component model significantly outperforms the nested one-component (GJR) GARCH and several HAR-type models in terms of out-of-sample forecasting.

Keywords: Volatility forecasting; long- and short-term volatility; mixed frequency data; volatility cycles (search for similar items in EconPapers)
JEL-codes: C53 C58 G12 (search for similar items in EconPapers)
Date: 2021-03
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:21-05

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