Can extremely high-temperature weather forecast oil prices?

Abstract

Participants in oil markets are increasingly aware of the climate risks posed by frequent extreme weather. This paper examines the role of extremely high-temperature weather information in predicting oil futures prices on the China International Energy Exchange (INE). An extreme high-temperature weather index (HTI) is developed on the basis of meteorological data at INE’s crude oil production and storage sites. The local interpretable model-agnostic explanations (LIME) and accumulated local effects (ALE) methods are used to compare the predictive contribution of the HTI with that of 15 common predictors. The results indicate that the HTI enhances the out-of-sample accuracy of five classical prediction models for INE oil prices. The recurrent neural network (RNN) model exhibits superior out-of-sample forecast performance, with an MAE of 14.379, an RMSE of 19.624, and a DS of 66.67%. The predictive importance of the HTI in the best RNN model ranks third in most test instances, surpassing conventional oil price predictors such as stock market indicators. The ALE analysis reveals a positive correlation between extremely high-temperature weather and INE oil prices. These findings can help investors and oil market regulators improve oil price forecast accuracy while also providing new evidence about the relationship between climate risk and oil prices.

Graphical abstract

Keywords

crude oil futures / climate risks / explainable machine learning

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