Open Access: Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models

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  • Author: Statistics Views
  • Date: 04 May 2020

Each week, we select a recently published Open Access article to feature. This week's article comes from the Journal of Applied Econometrics and considers using multiplicative component GARCH‐MIDAS models in volatility forecasting.

The article's abstract is given below, with the full article available to read here.

thumbnail image: Open Access: Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models

Conrad, C, Kleen, O. Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models. J Appl Econ. 2020; 35: 19– 45. https://doi.org/10.1002/jae.2742

We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of generalized autoregressive conditional heteroskedasticity–mixed‐data sampling (GARCH‐MIDAS) models suggested in Engle, Ghysels, and Sohn (Review of Economics and Statistics, 2013, 95, 776–797). 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 R2 of a Mincer–Zarnowitz regression and evaluate the QMLE and forecast performance of 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 (heterogeneous autoregression), realized GARCH, HEAVY (high‐frequency‐based volatility) 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 2 and 3 months ahead.

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