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

Quanying Lu, Yuze Li, Jian Chai and Shouyang Wang

Energy Economics, 2020, vol. 87, issue C

Abstract: 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.

Keywords: Crude oil; Dynamic Bayesian structural time series model; Google trend; Kalman filtering; Spike and slab prior; Bayesian model average (search for similar items in EconPapers)
JEL-codes: C11 C15 C2 C22 C53 Q4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:87:y:2020:i:c:s0140988320300608

DOI: 10.1016/j.eneco.2020.104721

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