More is better? The impact of predictor choice on the INE oil futures volatility forecasting

Tong Fu, Dasen Huang, Lingbing Feng and Xiaoping Tang

Energy Economics, 2024, vol. 134, issue C

Abstract: This paper aims to address the predictor choice issue in forecasting volatility of INE oil futures by a comprehensive comparative study with a large number of predictive variables and applying machine learning models along with their interpretability tools. The main finding is that the selection of predictors is crucial for improving volatility forecasting accuracy, but it is not always the case that including more predictive variables leads to better forecasting results, even for machine learning models. Specifically, this paper has five major findings: (1) A few variables can significantly improve forecasting accuracy independently, but their contribution is limited. (2) Increasing the number of predictors from specific categories (market sentiment indicators, crude oil futures prices from other exchanges, and energy market indicators) helps to enhance forecasting accuracy. (3) Low-frequency variables have a weak effect on improving the daily volatility. (4) Ensemble tree models perform better than traditional machine learning models based on variable selection with dynamic parameter optimization, even without much parameter tuning. The above findings still hold true under a series of robustness tests and economic value assessments. These findings provide substantial evidence for addressing the issues of model and variable choice in crude oil futures volatility forecasting.

Keywords: INE oil futures market; Volatility forecasting; Factor selection; Ensemble tree model (search for similar items in EconPapers)
JEL-codes: E44 G17 Q47 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:134:y:2024:i:c:s0140988324002482

DOI: 10.1016/j.eneco.2024.107540

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