1 Introduction
As crude oil becomes increasingly influenced by market dynamics, fluctuations in its price have a significant effect on the global economic and financial landscape (Naser, 2016). Accurate forecasts of crude oil prices play a crucial role in providing scientific support for energy-intensive enterprises and helping investors optimize their portfolios to effectively manage risks (Tian et al., 2023; Zhang and Wang, 2022).
To develop precise projections, it is essential to uncover the underlying factors driving fluctuations in crude oil prices. Previous research has indicated that the long-term trend of oil prices is determined by market supply and demand fundamentals (Dées et al., 2007; Hamilton, 2009), whereas short-term volatility may be affected by external factors such as stock performance (Bouri et al., 2022), exchange rates (Sun et al., 2022), and investor sentiment (Dai et al., 2022a). The increasing occurrence of extreme weather events in recent years has increased the vulnerability of oil products to climate-related risks (Cruz and Krausmann, 2013; Wen et al., 2021; Tumala et al., 2023), including the impact of global warming caused by the greenhouse effect (Kweku et al., 2018; Zhang et al., 2024). The demand for crude oil and other fossil fuels tends to rise during periods of extreme hot weather (van Ruijven et al., 2019). Moreover, elevated temperatures can disrupt operations at drilling and refinery sites (Yalew et al., 2020; Qui et al., 2023) and pose challenges to the integrity of oil transportation infrastructure, such as pipelines (Izaguirre et al., 2021), potentially affecting the oil supply. Consequently, stakeholders in oil markets must consider extremely high temperatures when assessing market conditions and making pricing decisions. However, existing research on crude oil forecasting has not adequately considered extremely high weather conditions. Although recent studies have highlighted the importance of extreme weather information in predicting crude oil prices (Xu et al., 2023), their reliance on media reports introduces subjective bias. Microscale meteorological observations have the potential to provide oil market managers with precise weather information (Katopodis and Sfetsos, 2019). Our objective is to contribute additional empirical evidence regarding the relationship between extreme weather events and oil price forecasts by utilizing precise meteorological data from storage and supply locations in specific target oil markets.
There are two primary types of models used for forecasting oil prices. The first type consists of traditional statistical models, such as exponential smoothing (ES) (Azevedo and Campos, 2016; He, 2018), the autoregressive integrated moving average (ARIMA) (Xiang and Zhuang, 2013; Zhao and Wang, 2014), and vector autoregression (VAR) models (Baumeister and Kilian, 2014). However, these models face challenges in capturing the inherent nonlinearity in oil price dynamics (Gao and Lei, 2017). The second category of methodologies comprises emerging machine learning models (Zhao et al., 2017), which are primarily represented by support vector regression (SVR) (Wang et al., 2020), recurrent neural networks (RNNs) (Chaitanya Lahari et al., 2018), and long short-term memory (LSTM) models (Güleryüz and Özden, 2020). These models have advantages in characterizing the nonlinear relationship between influencing factors and crude oil prices, and they offer effective forecasting accuracy (Öztunç Kaymak and Kaymak, 2022). However, machine learning models are commonly perceived as enigmatic black boxes, as they present challenges in providing users with a comprehensive understanding of their predictive mechanisms.
The advent of explainable machine learning methods has offered a valuable tool for elucidating how factors drive predictive outcomes, and their application has expanded into research fields such as forecasting bitcoin prices (Goodell et al., 2023) and energy consumption (Aras and Hanifi Van, 2022). Nevertheless, these explainable methods have not yet been applied in crude oil price forecasting research.
Considering the aforementioned limitations, this research makes three significant contributions to the current literature. First, we develop an extremely high-temperature weather index (HTI) based on daily meteorological data specific to the crude oil production and storage sites of the China International Energy Exchange (INE). Unlike previous indices that broadly describe the frequency of extreme weather events on a global or national scale (Guo et al., 2023a) or those derived from textual reports on extreme weather attention (Xu et al., 2023), our HTI provides a finer scale to present extreme high-temperature weather information for INE crude oil price prediction.
Second, our study confirms that including the HTI as a predictive factor enhances the accuracy of INE crude oil futures price prediction in terms of errors and trend changes. In fact, the out-of-sample predictive contribution of the HTI even surpasses several common indicators, such as the stock market index, in most instances. As the HTI value increases, a corresponding rise in the predicted INE crude oil futures price is observed. Third, we introduce explainable methods to improve the credibility of machine learning models in the crude oil prediction process, overcoming the inherent deficiencies of previous black box models. This enables us to gain deeper insights into the correlation between varying degrees of extreme heat and crude oil price dynamics. The remainder of the paper is organized as follows: Section 2 introduces the methodology. Section 3 describes the data. Section 4 presents our results. Finally, Section 5 provides concluding remarks.
See the article:
Can extremely high-temperature weather forecast oil prices?
https://doi.org/10.1007/s42524-025-4075-5
