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Long-term memory dynamics of crude oil price spread in non-dollar countries under the influence of exchange rates

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  • Liu, Siyao
  • Fang, Wei
  • Gao, Xiangyun
  • An, Feng
  • Jiang, Meihui
  • Li, Yang

Abstract

The non-US countries’ oil trade will be affected by the combined fluctuations in oil price and exchange rate. For China, the world’s largest oil importer, any slight fluctuations in oil price or exchange rate will significantly impact his oil import. Therefore, this paper firstly quantified the oil price spread between nominal price and real price corresponding to its domestic currency to show such impact. Secondly, we conducted statistical tests on the price spread under the circumstance when China imports from Russia, Oman, Dubai, WTI and Brent, and we introduced the fractal model to explore the nonlinear characteristics of the five price spread series. The five series all showed obvious long-term memory characteristics. Thirdly, time-varying memory series were constructed to further explore their dynamic features and the results showed they could forewarn the reversal trend of price spread series. Finally, the complex network is applied to analyze the dynamical mechanism of time-varying memory series and further explore the laws of the price spread. The algorithm could provide references for policymakers to anticipate the general trend of price spread and compare the price advantage of the crude oil.

Suggested Citation

  • Liu, Siyao & Fang, Wei & Gao, Xiangyun & An, Feng & Jiang, Meihui & Li, Yang, 2019. "Long-term memory dynamics of crude oil price spread in non-dollar countries under the influence of exchange rates," Energy, Elsevier, vol. 182(C), pages 753-764.
    • Handle: RePEc:eee:energy:v:182:y:2019:i:c:p:753-764
    DOI: 10.1016/j.energy.2019.06.072
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    Cited by:

    1. Huang, Xiaohong & Huang, Shupei, 2020. "Identifying the comovement of price between China's and international crude oil futures: A time-frequency perspective," International Review of Financial Analysis, Elsevier, vol. 72(C).
    2. Wang, Kai-Hua & Su, Chi-Wei & Xiao, Yidong & Liu, Lu, 2022. "Is the oil price a barometer of China's automobile market? From a wavelet-based quantile-on-quantile regression perspective," Energy, Elsevier, vol. 240(C).
    3. Wen, Danyan & Liu, Li & Ma, Chaoqun & Wang, Yudong, 2020. "Extreme risk spillovers between crude oil prices and the U.S. exchange rate: Evidence from oil-exporting and oil-importing countries," Energy, Elsevier, vol. 212(C).
    4. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
    5. Yuksel Haliloglu, Ebru & Sahin, Serkan & Berument, M. Hakan, 2021. "Brent–Dubai oil spread: Basic drivers," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 492-505.
    6. Lin, Boqiang & Su, Tong, 2021. "Do China's macro-financial factors determine the Shanghai crude oil futures market?," International Review of Financial Analysis, Elsevier, vol. 78(C).
    7. Jaromír Vrbka & Jakub Horák & Tomáš Krulický, 2022. "The influence of world oil prices on the Chinese Yuan exchange rate," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 9(4), pages 439-462, June.
    8. Tiwari, Aviral Kumar & Mishra, Bibhuti Ranjan & Solarin, Sakiru Adebola, 2021. "Analysing the spillovers between crude oil prices, stock prices and metal prices: The importance of frequency domain in USA," Energy, Elsevier, vol. 220(C).

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