Forecasting crude oil price volatility with uncertainty: New modeling with multivariate selection

Yunyi Zhang, Ting Hu and Shuang Xiao

Finance Research Letters, 2025, vol. 80, issue C

Abstract: This study proposes an approach for the multivariate selection of the GARCH-MIDAS model by integrating genetic algorithm, which overcomes the problems of unidentified weight and parameter hopping encountered in the previous Lasso-based method. Our approach effectively identifies the optimal combination of uncertainty indices for volatility forecasting. Empirical analyses demonstrate that the GARCH-MIDAS model incorporating four uncertainty indices, namely economic policy uncertainty, world uncertainty, energy-related uncertainty, and monetary policy uncertainty, outperforms alternative models in oil price volatility prediction. Out-of-sample forecasts further validate the superior predictive performance of the multivariate model selected through our approach.

Keywords: Crude oil volatility; GARCH-MIDAS; Uncertainty index; Variable selection; Volatility forecasting (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:80:y:2025:i:c:s1544612325007020

DOI: 10.1016/j.frl.2025.107442

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