Early warning of critical transitions in crude oil price

Sufang An, Feng An, Xiangyun Gao and Anjian Wang

Energy, 2023, vol. 280, issue C

Abstract: Critical transitions in the crude oil price are important in market management. Previous methods have focused on generic early warning signals in complex systems if a critical transition is approaching and ignored the specific feature of the oil price. This paper proposes a heteroscedastic network model in which early warnings of critical transitions are identified based on the community structure of the network representing the dynamic process of a time series. Using WTI crude oil price data, we detect early warning signals of critical transitions. Our findings indicate that major switches exist between early warnings and critical transitions in different periods, and the corresponding features can be analyzed based on the fundamentals and expectations of traders. Importantly, based on the network indicators associated with early warnings, the fundamental features may be similar during certain periods, and the changes in fundamentals and expectations before and after critical transitions are not random. A new complex system perspective is used to explore early warnings for critical transitions, and useful implications for energy-related market investors and policy-makers are provided.

Keywords: Crude oil price; Complex network; Critical transition; Machine learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:280:y:2023:i:c:s0360544223014834

DOI: 10.1016/j.energy.2023.128089

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