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Symmetry/anti-symmetry phase transitions in crude oil markets

Author

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  • Alvarez-Ramirez, Jose
  • Soriano, Angel
  • Cisneros, Myriam
  • Suarez, Rodolfo
Abstract
Daily records of international crude oil prices are studied by means of a Zipf-type analysis. By using simple up-down information on price fluctuations, we have observed phase transitions between symmetric states, where occurrence of up and down fluctuations are almost equal, to anti-symmetric states, where there is a significant imbalance between up and down occurrences. Symmetric phase provides a time-scale domain for risk-neutral transactions. On the other hand, anti-symmetric phase can be exploited by speculators and consumers to accommodate contracts that take advantage of the spread between up and down occurrences.

Suggested Citation

  • Alvarez-Ramirez, Jose & Soriano, Angel & Cisneros, Myriam & Suarez, Rodolfo, 2003. "Symmetry/anti-symmetry phase transitions in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 322(C), pages 583-596.
    • Handle: RePEc:eee:phsmap:v:322:y:2003:i:c:p:583-596
    DOI: 10.1016/S0378-4371(02)01831-9
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    References listed on IDEAS

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    1. Vandewalle, N. & Ausloos, M., 1999. "The n-Zipf analysis of financial data series and biased data series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 268(1), pages 240-249.
    2. Alvarez-Ramirez, Jose & Cisneros, Myriam & Ibarra-Valdez, Carlos & Soriano, Angel, 2002. "Multifractal Hurst analysis of crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 651-670.
    3. Ausloos, M., 2000. "Statistical physics in foreign exchange currency and stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 285(1), pages 48-65.
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      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Yang, Tianle & Zhou, Fangxing & Du, Min & Du, Qunyang & Zhou, Shirong, 2023. "Fluctuation in the global oil market, stock market volatility, and economic policy uncertainty: A study of the US and China," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 377-387.
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    8. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    9. He, Ling-Yun & Chen, Shu-Peng, 2010. "Are crude oil markets multifractal? Evidence from MF-DFA and MF-SSA perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3218-3229.
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    11. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
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    13. Öztunç Kaymak, Öznur & Kaymak, Yiğit, 2022. "Prediction of crude oil prices in COVID-19 outbreak using real data," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    14. Tao Yin & Yiming Wang, 2019. "Predicting the Price of WTI Crude Oil Using ANN and Chaos," Sustainability, MDPI, vol. 11(21), pages 1-14, October.
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