Two Are Better Than One: Volatility Forecasting Using Multiplicative Component GARCH-MIDAS Models
Journal of Applied Econometrics, Forthcoming
68 Pages Posted: 21 Mar 2016 Last revised: 21 Aug 2019
Date Written: August 14, 2019
We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of GARCH-MIDAS models suggested in Engle et al. (2013). In those models volatility is decomposed into a short-term GARCH component and a long-term component that is driven by an explanatory variable. We derive the kurtosis of returns, the autocorrelation function of squared returns, and the R^2 of a Mincer-Zarnowitz regression and evaluate these models in a Monte-Carlo simulation. For S&P 500 data, we compare the forecast performance of GARCH-MIDAS models with a wide range of competitor models such as HAR, Realized GARCH, HEAVY and Markov-Switching GARCH. Our results show that the GARCH-MIDAS based on housing starts as an explanatory variable significantly outperforms all competitor models at forecast horizons of two and three months ahead.
Keywords: Forecast Evaluation, GARCH-MIDAS, Mincer-Zarnowitz Regression, Volatility Persistence, Volatility Component Model, Long-Term Volatility, Model Confidence Set
JEL Classification: C53, C58, G12
Suggested Citation: Suggested Citation