Two are better than one: Volatility forecasting using multiplicative component GARCH-MIDAS models


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.

Journal of Applied Econometrics, 35(1)

A preliminary version of this paper circulated unter the title “On the Statistical Properties of Multiplicative GARCH Models” (University of Heidelberg, Department of Economics, Discussion Paper No. 613, see

Onno Kleen
Onno Kleen

I am an Assistant Professor at the Erasmus University Rotterdam. My focus in research is upon time series analysis and its applications in macro-finance and distribution forecasting.