Skip to main content
SpringerLink
Log in

COVID-19 and Fractal Characteristics in Energy Markets: Evidence from US Energy Price Time Series

  • Chapter
  • First Online:
Time and Fractals

Part of the book series: Contributions to Management Science ((MANAGEMENT SC.))

  • 109 Accesses

Abstract

The efficient market hypothesis (EMH) introduced by Fama (J Finance 25(2), 383–417, 1970) has been employed in energy market studies since the 2000s. Using the multifractal detrended fluctuation analysis (MF-DFA) approach, which allows for nonlinear analysis in the time series of energy prices, the deviation of EMH in energy markets can be measured especially in the aftermath of an event or a crisis. Given the outbreak of the COVID-19 pandemic, in late 2019 as the most consequential event influencing the global economy, we attempt to examine the efficiency of the most significant US energy markets in the pre- and post-pandemic era using the fractal approach in this chapter.

Results of the MF-DFA technique unveil that, firstly, the multifractal spectrum in all US energy markets has increased significantly post-pandemic, which in turn indicates the complexity and multi-scaling behaviors in such markets. Secondly, the electricity market always exhibits deviations from the efficiency present in the two previous periods due to the nature of the impossible storage. Finally, the results of the present study signify that COVID-19 has transformed the fractal structure pattern of all US selected energy markets. Moreover, the MF-DFA technique yields good power to analyze the time series of energy prices.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Crude oil data available in http://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm

    Natural gas data available in https://www.eia.gov/dnav/ng/ng_pri_fut_s1_d.htm

    Electricity data available in https://www.eia.gov/electricity/wholesale/

Abbreviations

DFA:

detrended Fluctuation Analysis

DID:

difference-in-differences

EMH:

efficient market hypothesis

FMH:

fractal market hypothesis

HH:

Henry Hub natural gas

MF-DFA:

multifractal detrended fluctuation analysis

NYH C Gasoline:

Conventional Gasoline

NYH H Oil:

Heating Oil

PJM:

PJM West (Electricity)

WTI:

West Texas Intermediate

References

  • Agbon, I. S., & Araque, J. C. (2003). Predicting oil and gas spot prices using chaos time series analysis and fuzzy neural network model. In SPE hydrocarbon economics and evaluation symposium (pp. 5–8). https://doi.org/10.2118/82014-MS

    Chapter  Google Scholar 

  • Ai, H., Zhong, T., & Zhao, Z. (2022). The real economic costs of COVID-19: Insights from electricity consumption data in Hunan Province, China. Energy Economics, 105, 1–12.

    Article  Google Scholar 

  • Alvarez-Ramirez, J., & Escarela-Perez, R. (2010). Time-dependent correlations in electricity markets. Energy Economics, 32, 269–277. https://doi.org/10.1016/j.eneco.2009.05.008

    Article  Google Scholar 

  • Alvarez-Ramirez, J., Cisneros, M., Ibarra-Valdez, C., & Soriano, A. (2002). Multifractal Hurst analysis of crude oil prices. Physica A, 313, 651–670. https://doi.org/10.1016/S0378-4371(02)00985-8

    Article  Google Scholar 

  • Alvarez-Ramirez, J., Alvarez, J., & Rodriguez, E. (2008). Short-term predictability of crude oil markets: A detrended fluctuation analysis approach. Energy Economics, 30, 2645–2656. https://doi.org/10.1016/j.eneco.2008.05.006

    Article  Google Scholar 

  • Bachelier, L. (1900). ‘Théorie de la spéculation’ [Ph.D. thesis in mathematics]. Annales Scientifiques de l’ Ecole Normale Supérieure, 17, 21–86.

    Article  Google Scholar 

  • Bianchi, S., De Bellis, I., & Pianese, A. (2010). Fractal properties of some European electricity markets. International Journal of Financial Markets and Derivatives, 1(4), 395–421. https://doi.org/10.1504/IJFMD.2010.035766

    Article  Google Scholar 

  • Cabedo, J. D., & Moya, I. (2003). Estimating oil price value at risk using the historical simulation approach. Energy Economics, 25, 239–253.

    Article  Google Scholar 

  • Cootner, P. H. (1964). The random character of stock market prices. MIT Press.

    Google Scholar 

  • Corazza, M., & Malliaris, A. G. (2002). Multi-Fractality in foreign currency markets. Multinational Finance Journal, 6(2), 65–98.

    Article  Google Scholar 

  • David, S. A., Inacio Jr, C. M. C., Quintino, D. D., & Machado, J. A. T. (2019). Measuring the Brazilian ethanol and gasoline market efficiency using DFA-Hurst and fractal dimension. Energy Economics, 85, 104614. https://doi.org/10.1016/j.eneco.2019.104614

    Article  Google Scholar 

  • Dong, X., Li, J., & Gao, J. (2009). Multi-fractal analysis of world crude oil prices. In International joint conference on computational sciences and optimization (pp. 489–493). https://doi.org/10.1109/CSO.2009.9

    Chapter  Google Scholar 

  • Fama, E. (1970). Efficient market hypothesis: A review of theory and empirical work. Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486

    Article  Google Scholar 

  • Feng, Z., Zou, L., & Wei, Y. (2011). Carbon price volatility: Evidence from EU ETS. Applied Energy, 88, 590–598. https://doi.org/10.1016/j.apenergy.2010.06.017

    Article  Google Scholar 

  • Ftiti, Z., Jawadi, F., Louhichi, W., & Arbi, M. M. (2019). On the relationship between energy returns and trading volume: A multifractal analysis. Applied Economics, 51(29). https://doi.org/10.1080/00036846.2018.1564122

  • Gerogiorgis, I. D. (2009). Fractal scaling in crude oil price evolution via time series analysis of historical data. Chemical Product and Process Modeling, 4(5), 1–12. https://doi.org/10.2202/1934-2659.1370

    Article  Google Scholar 

  • Ghosh, D., Dutta, S., & Chakraborty, S. (2016). Multifractal behavior of electricity bid price in Indian energy market. Electrical Power and Energy Systems, 74, 162–171. https://doi.org/10.1016/j.ijepes.2015.07.026

    Article  Google Scholar 

  • Giot, P., & Laurent, S. (2003). Market risk in commodity markets: A VaR approach. Energy Economics, 25, 435–457.

    Article  Google Scholar 

  • Grech, D., & Mazur, Z. (2004). Can one make any crash prediction in finance using the local Hurst exponent idea? Physica A: Statistical Mechanics and its Applications, 336, 133–145.

    Article  Google Scholar 

  • Hammoudeh, S., Mokni, K., Ben-Salha, O., & Ajmi, A. N. (2021). Distributional predictability between oil prices and renewable energy stocks: Is there a role for the COVID-19 pandemic? Energy Economics, 103, 1–12.

    Article  Google Scholar 

  • He, L., Fan, Y., & Wei, Y. (2007). The empirical analysis for fractal features and long-run memory mechanism in petroleum pricing systems. International Journal of Global Energy Issues, 27(4), 492–502. https://doi.org/10.1504/IJGEI.2007.014869

    Article  Google Scholar 

  • Huang, S., & Liu, H. (2021). Impact of COVID-19 on stock price crash risk: Evidence from Chinese energy firms. Energy Economics, 101, 1–10.

    Article  Google Scholar 

  • Jia, Z., Wen, S., & Lin, B. (2021). The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China. Applied Energy, 302, 1–21.

    Article  Google Scholar 

  • Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications, 316(1–4), 87–114.

    Article  Google Scholar 

  • Kristoufek, L., & Vosvrda, M. (2014). Commodity futures and market efficiency. Energy Economics, 42, 50–57. https://doi.org/10.1016/j.eneco.2013.12.001

    Article  Google Scholar 

  • Liu, X., Zhou, X., Zhu, B., & Wang, P. (2020). Measuring the efficiency of China’s carbon market: A comparison between efficient and fractal market hypotheses. Journal of Cleaner Production, 271. https://doi.org/10.1016/j.jclepro.2020.122885

  • Lo, A. W. (1991). Long-term memory in stock market prices. Econometrica, 59(5), 1279–1313. https://doi.org/10.2307/2938368

    Article  Google Scholar 

  • Mandelbrot, B., & Wallis, J. R. (1968). Noah, Joseph, and operational hydrology. Water Resources Research, 4, 909–918.

    Article  Google Scholar 

  • Norouzzadeh, P., & Jafari, G. R. (2005). Application of multifractal measures to Tehran price index. Physica A: Statistical Mechanics and its Applications, 356(2–4), 609–627.

    Article  Google Scholar 

  • Norouzzadeh, P., Dullaert, W., & Rahmani, B. (2007). Anti-correlation and multifractal features of Spain electricity spot market. Physica A, 380, 333–342. https://doi.org/10.1016/j.physa.2007.02.087

    Article  Google Scholar 

  • Panas, E., & Ninni, V. (2001). Are oil markets chaotic? A non-linear dynamic analysis. Energy Economics, 22, 549–568. https://doi.org/10.1016/S0140-9883(00)00049-9

    Article  Google Scholar 

  • Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., & Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Physical Review E, 49(2), 1685.

    Article  Google Scholar 

  • Peters, E. (1991). Chaos and order in the capital markets. Wiley.

    Google Scholar 

  • Plourde, A., & Watkins, G. C. (1998). Crude oil prices between 1985 and 1994: How volatile in relation to other commodities? Resource and Energy Economics, 20, 245–262.

    Article  Google Scholar 

  • Pradhan, B. K., & Ghosh, J. (2021). COVID-19 and the Paris agreement target: A CGE analysis of alternative economic recovery scenarios for India. Energy Economics, 103, 1–13.

    Article  Google Scholar 

  • Qian, B., & Rasheed, K. (2004). Hurst exponent and financial market predictability. In 2nd IASTED international conference on financial engineering and applications (FEA 2004) (pp. 203–209).

    Google Scholar 

  • Sadorsky, P. (2006). Modeling and forecasting petroleum future volatility. Energy Economics, 28, 467–488.

    Article  Google Scholar 

  • Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6(2). https://doi.org/10.1142/9789814566926_0002

  • Serletis, A., & Andreadis, I. (2004). Random fractal structures in North American energy markets. Energy Economics, 26, 389–399. https://doi.org/10.1016/j.eneco.2004.04.009

    Article  Google Scholar 

  • Serletis, A., & Bianchi, M. (2007). Informational efficiency and interchange transactions in Alberta’s electricity market. The Energy Journal, 28, 121–143.

    Article  Google Scholar 

  • Serletis, A., & Rosenberg, A. A. (2007). The Hurst exponent in energy futures prices. Physica A, 380, 325–332.

    Article  Google Scholar 

  • Short-Term Energy Outlook, (STEO). (2021). U.S. Energy Information Administration. EIA.

    Google Scholar 

  • Si, D. K., Li, X. L., & Xu, X. C. (2021). The risk spillover effect of the COVID-19 pandemic on energy sector: Evidence from China. Energy Economics, 102, 1–12.

    Article  Google Scholar 

  • Smith, L. V., Tarui, N., & Yamagata, T. (2021). Assessing the impact of COVID-19 on global fossil fuel consumption and CO2 emissions. Energy Economics, 97, 1–19.

    Article  Google Scholar 

  • Uritskaya, O. Y. (2005a). Forecasting of magnitude and duration of currency crises based on analysis of distortions of fractal scaling in exchange rate fluctuations. Noise and Fluctuations in Econophysics and Finance (Proc. SPIE), 5848, 17–26.

    Article  Google Scholar 

  • Uritskaya, O. Y. (2005b). Fractal methods for modeling and forecasting of currency crises. In Proceedings of the fourth International Conference on modeling and analysis of safety and risk in complex systems (pp. 210–215). SPbSU Press.

    Google Scholar 

  • Uritskaya, O. Y., & Serletis, A. (2008). Quantifying multi-scale inefficiency in electricity markets. Energy Economics, 30, 3109–3117. https://doi.org/10.1016/j.eneco.2008.03.009

    Article  Google Scholar 

  • Wang, L., He, K., & Zou, Y. (2014). Multiscale fractal analysis of electricity markets. In Seventh international joint conference on computational sciences and optimization (pp. 378–382). https://doi.org/10.1109/CSO.2014.79

    Chapter  Google Scholar 

  • Weiner, R. J. (2002). Sheep in wolves clothing? Speculators and price volatility in petroleum futures. Quarterly Review of Economics and Finance, 42, 391–400.

    Article  Google Scholar 

  • World Energy Outlook, (WEO). (2021). International Energy Agency, IEA. https://www.iea.org/reports/world-energy-outlook-2021; www.eia.gov

  • Zhang, J., & Wang, J. (2010). Fractal detrended fluctuation analysis of Chinese energy markets. International Journal of Bifurcation and Chaos, 20(11), 3753–3768. https://doi.org/10.1142/S0218127410028082

    Article  Google Scholar 

  • Zhao, Z., Zhu, J., & Xi, B. (2016). Multi-fractal fluctuation features of thermal power coal price in China. Energy, 117, 10e18. https://doi.org/10.1016/j.energy.2016.10.081

    Article  Google Scholar 

  • Zhuang, X., Wei, Y., & Ma, F. (2015). Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price. Physica A: Statistical Mechanics and its Applications, 430, 101–113.

    Article  Google Scholar 

Download references

Acknowledgments

Data availability: www.eia.gov

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this chapter.

Formatting of Funding Sources

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Economics, Meybod University, Meybod, Iran

    Mehdi Emami-Meybodi

  2. Department of Economics, Shiraz University, Shiraz, Iran

    Sakine Owjimehr & Ali Hussein Samadi

Authors
  1. Mehdi Emami-Meybodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sakine Owjimehr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Hussein Samadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sakine Owjimehr .

Editor information

Editors and Affiliations

  1. UNESCO Chair Professor Emeritus, Cambridge, MA, USA

    Nezameddin Faghih

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Emami-Meybodi, M., Owjimehr, S., Samadi, A.H. (2023). COVID-19 and Fractal Characteristics in Energy Markets: Evidence from US Energy Price Time Series. In: Faghih, N. (eds) Time and Fractals . Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-031-38188-1_7

Download citation

Publish with us

Policies and ethics

Search

Navigation