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The influence of global benchmark oil prices on the regional oil spot market in multi-period evolution

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  • Jiang, Meihui
  • An, Haizhong
  • Jia, Xiaoliang
  • Sun, Xiaoqi

Abstract

Crude benchmark oil prices play a crucial role in energy policy and investment management. Previous research confined itself to studying the static, uncertain, short- or long-term relationship between global benchmark oil prices, ignoring the time-varying, quantitative, dynamic nature of the relationship during various stages of oil price volatility. This paper proposes a novel approach combining grey relation analysis, optimization wavelet analysis, and Bayesian network modeling to explore the multi-period evolution of the dynamic relationship between global benchmark oil prices and regional oil spot price. We analyze the evolution of the most significant decision-making risk periods, as well as the combined strategy-making reference oil prices and the corresponding periods during various stages of volatility. Furthermore, we determine that the network evolution of the quantitative lead/lag relationship between different influences of global benchmark oil prices shows a multi-period evolution phenomenon. For policy makers and market investors, our combined model can provide decision-making periods with the lowest expected risk and decision-making target reference oil prices and corresponding weights for strategy adjustment and market arbitrage. This study provides further information regarding period weights of target reference oil prices, facilitating efforts to perform multi-agent energy policy and intertemporal market arbitrage.

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  • Jiang, Meihui & An, Haizhong & Jia, Xiaoliang & Sun, Xiaoqi, 2017. "The influence of global benchmark oil prices on the regional oil spot market in multi-period evolution," Energy, Elsevier, vol. 118(C), pages 742-752.
    • Handle: RePEc:eee:energy:v:118:y:2017:i:c:p:742-752
    DOI: 10.1016/j.energy.2016.10.104
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    3. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.
    4. Melike E. Bildirici & Memet Salman & Özgür Ömer Ersin, 2022. "Nonlinear Contagion and Causality Nexus between Oil, Gold, VIX Investor Sentiment, Exchange Rate and Stock Market Returns: The MS-GARCH Copula Causality Method," Mathematics, MDPI, vol. 10(21), pages 1-16, October.
    5. Fan He & Xuansen He, 2019. "A Continuous Differentiable Wavelet Shrinkage Function for Economic Data Denoising," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 729-761, August.
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    7. Li, Xiuming & Sun, Mei & Gao, Cuixia & He, Huizi, 2019. "The spillover effects between natural gas and crude oil markets: The correlation network analysis based on multi-scale approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 306-324.
    8. Zhang, Qi & Di, Peng & Farnoosh, Arash, 2021. "Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models," Energy, Elsevier, vol. 223(C).
    9. Ke Gong & Yi Peng & Yong Wang & Maozeng Xu, 2018. "Time series analysis for C2C conversion rate," Electronic Commerce Research, Springer, vol. 18(4), pages 763-789, December.
    10. Miljkovic, Dragan & Goetz, Cole, 2020. "The effects of futures markets on oil spot price volatility in regional US markets," Applied Energy, Elsevier, vol. 273(C).

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