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Temporal multifractal analysis of extreme events in the crude oil market

  • Original Paper - Cross-Disciplinary Physics and Related Areas of Science and Technology
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Abstract

The ‘crude oil price’ has been one of the integral components of rising globalization. This paper delves into the static and dynamic multifractal analysis of daily prices of Brent crude oil for the period of 33 years (1987–2020) using the multifractal detrended fluctuation analysis. We discuss the results and limitations of the static multifractal analysis of historical data of crude oil. The crude oil market contains much more complexity in its dynamics that needs to be examined to draw inferences. These are important for socio-economic and political policy formulation at the global level. Thus the temporal multifractal analysis of historical data of crude oil is performed to understand such complexity. Further, the effect of extreme events on the multifractal properties of the crude oil market is also investigated. It is shown that multifractal spectra evolve through different shapes with time and the multifractal asymmetry coefficient can be used as an indicator in the detection of extreme events.

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References

  1. B. Podobnik, H.E. Stanley, Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Phys. Rev. Lett. 100(8), 084102 (2008)

    Article  ADS  Google Scholar 

  2. J. Klamut, R. Kutner, T. Gubiec, Z.R. Struzik, Multibranch multifractality and the phase transitions in time series of mean interevent times. Phys. Rev. E 101, 063303 (2020). https://doi.org/10.1103/PhysRevE.101.063303

    Article  ADS  Google Scholar 

  3. R. Kutner, M. Ausloos, D. Grech, T. Di Matteo, C. Schinckus, H. Eugene Stanley, Econophysics and sociophysics: their milestones and challenges. Phys. A Stat. Mech. Appl. 516, 240–253 (2019). https://doi.org/10.1016/j.physa.2018.10.019

    Article  Google Scholar 

  4. S. Kumar, S. Kumar, P. Kumar, Diffusion entropy analysis and random matrix analysis of the Indian stock market. Phys. A Stat. Mech. Appl. 560, 125122 (2020)

    Article  Google Scholar 

  5. P. Devi, P. Kumar, S. Kumar, Multi-fractal detrended cross-correlation analysis (MFDCCA) approach to study effect of global crisis and demonetization on financial sector of India. Math. Eng. Sci. Aerosp. 12(2), 601–614 (2021)

    Google Scholar 

  6. V. Kukreti, H.K. Pharasi, P. Gupta, S. Kumar, A perspective on correlation-based financial networks and entropy measures. Front. Phys. 8, 323 (2020). https://doi.org/10.3389/fphy.2020.00323

    Article  Google Scholar 

  7. A. Samal, S. Kumar, Y. Yadav, A. Chakraborti, Network-centric indicators for fragility in global financial indices. Front. Phys. 8, 659 (2021). https://doi.org/10.3389/fphy.2020.624373

    Article  Google Scholar 

  8. S. Kumar, S. Kumar, P. Kumar, Diffusion entropy analysis and random matrix analysis of the Indian stock market. Phys. A Stat. Mech. Appl. 560, 125122 (2020). https://doi.org/10.1016/j.physa.2020.125122

    Article  Google Scholar 

  9. S. Drożdż, R. Kowalski, P. Oświȩcimka, R. Rak, R. Gȩbarowski, Dynamical variety of shapes in financial multifractality. Complexity 2018, 1–13 (2018)

    Article  Google Scholar 

  10. B.M. Tabak, D.O. Cajueiro, Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility. Energy Econ. 29(1), 28–36 (2007)

    Article  Google Scholar 

  11. J. Alvarez-Ramirez, J. Alvarez, E. Rodriguez, Short-term predictability of crude oil markets: a detrended fluctuation analysis approach. Energy Econ. 30(5), 2645–2656 (2008)

    Article  Google Scholar 

  12. C.-Z. Yao, C. Liu, W.-J. Ju, Multifractal analysis of the WTI crude oil market, US stock market and EPU. Phys. A Stat. Mech. Appl. 550, 124096 (2020)

    Article  Google Scholar 

  13. J.D. Hamilton, Understanding crude oil prices. Energy J. 30(2), 179–206 (2009)

    Article  Google Scholar 

  14. Y. Wang, Y. Wei, C. Wu, Auto-correlated behavior of WTI crude oil volatilities: a multiscale perspective. Phys. A Stat. Mech. Appl. 389(24), 5759–5768 (2010)

    Article  Google Scholar 

  15. R. Gu, H. Chen, Y. Wang, Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis. Phys. A Stat. Mech. Appl. 389(14), 2805–2815 (2010)

    Article  Google Scholar 

  16. Z.-Q. Jiang, W.-J. Xie, W.-X. Zhou, Testing the weak-form efficiency of the WTI crude oil futures market. Phys. A Stat. Mech. Appl. 405, 235–244 (2014)

    Article  Google Scholar 

  17. M. Pal, P.M. Rao, P. Manimaran, Multifractal detrended cross-correlation analysis on gold, crude oil and foreign exchange rate time series. Phys. A Stat. Mech. Appl. 416, 452–460 (2014)

    Article  Google Scholar 

  18. W. Mensi, S. Hammoudeh, S.-M. Yoon, How do OPEC news and structural breaks impact returns and volatility in crude oil markets? Further evidence from a long memory process. Energy Econ. 42, 343–354 (2014)

    Article  Google Scholar 

  19. C. Yuxin, R. Yongping, Multifractal detrended cross-correlations between WTI crude oil price fluctuations and investor fear gauges. Appl Econ Lett 26(7), 587–593 (2019)

    Article  Google Scholar 

  20. Historical Brent Spot Prices FOB. https://www.eia.gov/dnav/pet/hist/RBRTED.htm. Accessed 19 May 2020

  21. J.W. Kantelhardt, S.A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, H.E. Stanley, Multifractal detrended fluctuation analysis of nonstationary time series. Phys. A Stat. Mech. Appl. 316(1–4), 87–114 (2002)

    Article  Google Scholar 

  22. S. Kumar, N. Deo, Multifractal properties of the Indian financial market. Phys. A Stat. Mech. Appl. 388(8), 1593–1602 (2009). https://doi.org/10.1016/j.physa.2008.12.017

    Article  Google Scholar 

  23. S. Kumar, N. Deo, Analysing correlations after the financial crisis of 2008 and multifractality in global financial time series. Pramana 84(2), 317–325 (2015)

    Article  ADS  Google Scholar 

  24. S. Drożdż, Detecting and interpreting distortions in hierarchical organization of complex time series. Phys. Rev. E 91(3), 030902 (2015)

    Article  ADS  Google Scholar 

Download references

Acknowledgements

Sunil Kumar thanks the Science and Engineering Research Board, Department of Science and Technology, Government of India for providing the financial grant to project No. MTR/2020/000651 under the MATRICS scheme.

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Author notes

  1. Sunil Kumar and Pawan Kumar contributed equally to this study.

Authors and Affiliations

  1. School of Basic and Applied Sciences, K.R. Mangalam University, Gurugram, Haryana, 122001, India

    Pooja Devi, Sushil Kumar & Pawan Kumar

  2. Department of Physics, Ramjas College, University of Delhi, Delhi, Delhi, 110007, India

    Sunil Kumar & Imran Khan

  3. Department of Economics, Ramjas College, University of Delhi, Delhi, Delhi, 110007, India

    Pawan Kumar

  4. Department of Physics, Hansraj College, University of Delhi, Delhi, Delhi, 110007, India

    Sushil Kumar

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  1. Pooja Devi
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  2. Sunil Kumar
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  3. Pawan Kumar
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  4. Sushil Kumar
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Correspondence to Sunil Kumar or Pawan Kumar.

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Devi, P., Kumar, S., Kumar, P. et al. Temporal multifractal analysis of extreme events in the crude oil market. J. Korean Phys. Soc. 81, 354–360 (2022). https://doi.org/10.1007/s40042-022-00534-7

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  • DOI: https://doi.org/10.1007/s40042-022-00534-7

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