How macro-variables drive crude oil volatility? Perspective from the STL-based iterated combination method

• Published in: Resources policy 2022-08, Vol.77, p.102656, Article 102656
• Main Authors: Zhang, Li, Wang, Lu, Wang, Xunxiao, Zhang, Yaojie, Pan, Zhigang
• Format: Article
• Language: English
• Subjects: Combination approaches; Crude oil; GARCH-MIDAS; Macroeconomic variables; STL decomposition
• ISSN: 0301-4207; 1873-7641
• DOI: 10.1016/j.resourpol.2022.102656

Based on the GARCH-MIDAS framework, this article mainly explores whether the Seasonal and Trend decomposition using Loess (STL decomposition) and the iterated combination approach can improve the prediction accuracy of crude oil price volatility by using macro variables. We introduce five macro variables, that is, consumer price index, interest rate, producer price index, industrial production index, and unemployment rate, into the long-term component in the GARCH-MIDAS model. Though the model including raw macro variables performs better than the model containing each STL-based single subsequence, we find that the iterated combination forecasts using the forecasts obtained by each subsequence are superior to the corresponding standard ones. Besides, when mean-variance investors perform asset allocation, they can obtain more certainty-equivalent-returns by applying our new iterated combination approach. Finally, various robustness checks can verify the above-mentioned findings. In short, this article may provide a new perspective for economic empirical applications and theoretical research. •This paper proposes a novel model under the framework of GARCH-MIDAS.•Future oil volatilities can be obtained by the iterated combination method.•The model including raw data beats other models in the in-sample estimation.•STL-based iterated combination method can improve predictive accuracy.