IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i17p2743-d1470556.html
   My bibliography  Save this article

Exploring the Dynamic Behavior of Crude Oil Prices in Times of Crisis: Quantifying the Aftershock Sequence of the COVID-19 Pandemic

Author

Listed:
  • Fotios M. Siokis

    (School of Economics and Regional Studies, University of Macedonia, 546 36 Thessaloniki, Greece)

Abstract

Crude oil prices crashed and dropped into negative territory at the onset of the COVID-19 pandemic. This extreme event triggered a series of great-magnitude aftershocks. We seek to investigate the cascading dynamics and the characteristics of the series immediately following the oil market crash. Utilizing a robust method named the Omori law, we quantify the correlations of these events. This research presents empirical regularity concerning the number of times that the absolute value of the percentage change in the oil index exceeds a given threshold value. During the COVID-19 crisis, the West Texas Intermediate (WTI) oil prices exhibit greater volatility compared to the Brent oil prices, with higher relaxation values at all threshold levels. This indicates that larger aftershocks decay more rapidly, and the period of turbulence for the WTI is shorter than that of Brent and the stock market indices. We also demonstrate that the power law’s exponent value increases with the threshold value’s magnitude. By proposing this alternative method of modeling extreme events, we add to the current body of literature, and the findings demonstrate its practical use for decision-making authorities—particularly financial traders who model high-volatility products like derivatives.

Suggested Citation

  • Fotios M. Siokis, 2024. "Exploring the Dynamic Behavior of Crude Oil Prices in Times of Crisis: Quantifying the Aftershock Sequence of the COVID-19 Pandemic," Mathematics, MDPI, vol. 12(17), pages 1-13, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2743-:d:1470556
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/17/2743/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/17/2743/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Salma Ommi & Milad Janalipour, 2022. "Selection of shelters after earthquake using probabilistic seismic aftershock hazard analysis and remote sensing," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 345-363, August.
    2. Alexander M. Petersen & Fengzhong Wang & Shlomo Havlin & H. Eugene Stanley, 2010. "Market dynamics immediately before and after financial shocks: quantifying the Omori, productivity and Bath laws," Papers 1006.1882, arXiv.org, revised Oct 2010.
    3. Mu, Guo-Hua & Zhou, Wei-Xing, 2008. "Relaxation dynamics of aftershocks after large volatility shocks in the SSEC index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5211-5218.
    4. Fabrizio Lillo & Rosario N. Mantegna, 2001. "Power law relaxation in a complex system: Omori law after a financial market crash," Papers cond-mat/0111257, arXiv.org, revised Jun 2003.
    5. Focardi, Sergio & Cincotti, Silvano & Marchesi, Michele, 2002. "Self-organization and market crashes," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 241-267, October.
    6. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    7. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    8. Sornette, Didier & Johansen, Anders, 1998. "A hierarchical model of financial crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 261(3), pages 581-598.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Siokis, Fotios M., 2012. "Stock market dynamics: Before and after stock market crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1315-1322.
    2. Jiang, X.F. & Chen, T.T. & Zheng, B., 2013. "Time-reversal asymmetry in financial systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5369-5375.
    3. Siokis, Fotios M., 2012. "The dynamics of a complex system: The exchange rate crisis in Southeast Asia," Economics Letters, Elsevier, vol. 114(1), pages 98-101.
    4. X. F. Jiang & T. T. Chen & B. Zheng, 2013. "Time-reversal asymmetry in financial systems," Papers 1308.0669, arXiv.org.
    5. Negrea, Bogdan, 2014. "A statistical measure of financial crises magnitude," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 397(C), pages 54-75.
    6. Pagnottoni, Paolo & Spelta, Alessandro & Pecora, Nicolò & Flori, Andrea & Pammolli, Fabio, 2021. "Financial earthquakes: SARS-CoV-2 news shock propagation in stock and sovereign bond markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    7. Xu, Hai-Chuan & Zhang, Wei & Liu, Yi-Fang, 2014. "Short-term market reaction after trading halts in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 103-111.
    8. Hai-Chuan Xu & Wei Zhang & Yi-Fang Liu, 2013. "Short-term Market Reaction after Trading Halts in Chinese Stock Market," Papers 1309.1138, arXiv.org, revised Jun 2014.
    9. Javier Morales & V'ictor Tercero & Fernando Camacho & Eduardo Cordero & Luis L'opez & F-Javier Almaguer, 2014. "Trend and Fractality Assessment of Mexico's Stock Exchange," Papers 1411.3399, arXiv.org.
    10. Gao, Jianbo & Hu, Jing, 2014. "Financial crisis, Omori's law, and negative entropy flow," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 79-86.
    11. Xavier Gabaix, 2009. "Power Laws in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 255-294, May.
    12. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    13. Federico Botta & Helen Susannah Moat & H Eugene Stanley & Tobias Preis, 2015. "Quantifying Stock Return Distributions in Financial Markets," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-10, September.
    14. Christopher M Wray & Steven R Bishop, 2016. "A Financial Market Model Incorporating Herd Behaviour," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-28, March.
    15. da Cunha, C.R. & da Silva, R., 2020. "Relevant stylized facts about bitcoin: Fluctuations, first return probability, and natural phenomena," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    16. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822, arXiv.org.
    17. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    18. Zhi-Qiang Jiang & Gang-Jin Wang & Askery Canabarro & Boris Podobnik & Chi Xie & H. Eugene Stanley & Wei-Xing Zhou, 2018. "Short term prediction of extreme returns based on the recurrence interval analysis," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 353-370, March.
    19. Coronel-Brizio, H.F. & Hernández-Montoya, A.R., 2010. "The Anderson–Darling test of fit for the power-law distribution from left-censored samples," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3508-3515.
    20. Yan Li & Bo Zheng & Ting-Ting Chen & Xiong-Fei Jiang, 2017. "Fluctuation-driven price dynamics and investment strategies," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-15, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2743-:d:1470556. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.