Crude oil price analysis and forecasting: A perspective of “new triangle”

• Published in: Energy economics Vol. 87; p. 104721
• Main Authors: Lu, Quanying, Li, Yuze, Chai, Jian, Wang, Shouyang
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.03.2020; Elsevier Science Ltd
• Subjects: Bayesian analysis; Bayesian model average; Crude oil; Crude oil prices; Data; Data search; Dynamic Bayesian structural time series model; Economic crisis; Energy economics; Financial market; Google trend; Kalman filtering; Oil; Petroleum; Predictions; Shale; Shale oil; Spike and slab prior; Supply & demand; Time series; Turning points; Google trend; C53; C11; C22; Q4; Crude oil; Dynamic Bayesian structural time series model; Bayesian model average; C15; Spike and slab prior; Kalman filtering; C2
• ISSN: 0140-9883; 1873-6181
• DOI: 10.1016/j.eneco.2020.104721

In this paper, the new structural characteristics and core influencing factors of the crude oil prices are summarized based on previous representative research results. Firstly, a newly dynamic Bayesian structural time series model (DBSTS) is developed to investigate the oil prices. In particular, Google trend is introduced as an indicator to reflect the impact of search data on the oil price. Secondly, the spike and slab method is employed to select core influence factors. Finally, the Bayesian model average (BMA) is utilized to predict the oil price. Experimental results confirm that the supply and demand of global crude oil and the financial market are still the main factors affecting the oil price. Furthermore, Google trend can reflect the changes in the crude oil price to a certain extent. Moreover, the impact of shale oil production on the oil price is gradually increasing, yet remains relatively small. In addition, the DBSTS model can identify turning points in historical data (such as the 2008 financial crisis). Finally, the findings suggest the DBSTS model has good predictive capabilities in short-term prediction, making it suitable for analyzing the crude oil prices. •A novel dynamic Bayesian structural time series model is developed.•415 explanatory variables are included, especially, Google trend search data.•Spike-slab regression is used to extract core factors and analyze new structural characteristics.•The impact of shale oil production on oil price is small relatively.•Turning points of historical oil price are identified and analyzed.