A0754
Title: Modelling volatility cycles
Authors: Christian Conrad - Heidelberg University (Germany) [presenting]
Robert Engle - NYU Stern (United States)
Abstract: A multiplicative factor multi-frequency component GARCH model is proposed 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 more than 5,000 assets. 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.
