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Analysis of entropy on the European markets of energy and energy commodities prices

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  • Daniel Papla
  • Rafał Siedlecki

Abstract

The paper analyzes the problem of entropy in the moments of transition from a normal economic situation (2015–2019) to the Pandemic period (2020–2021) and the period of Russia’s attack on Ukraine (2022–2023). The research in the article is based on the analysis of electricity, oil, coal, and gas prices in 27 countries of the European Union and Norway. The daily data cover the period from January 1, 2015, to March 30, 2023, and were analyzed using two-dimensional sets of electricity and commodity prices. The work uses the time dependent James-Stein estimator of the Shannon informational entropy.

Suggested Citation

  • Daniel Papla & Rafał Siedlecki, 2025. "Analysis of entropy on the European markets of energy and energy commodities prices," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-11, January.
    • Handle: RePEc:plo:pone00:0315348
    DOI: 10.1371/journal.pone.0315348
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    References listed on IDEAS

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    1. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Alvarez, Jesus, 2012. "A multiscale entropy approach for market efficiency," International Review of Financial Analysis, Elsevier, vol. 21(C), pages 64-69.
    2. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    3. Asl, Mahdi Ghaemi & Canarella, Giorgio & Miller, Stephen M., 2021. "Dynamic asymmetric optimal portfolio allocation between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies," Resources Policy, Elsevier, vol. 71(C).
    4. Contreras-Reyes, Javier E., 2015. "Rényi entropy and complexity measure for skew-gaussian distributions and related families," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 84-91.
    5. Lin, Boqiang & Bai, Rui, 2021. "Oil prices and economic policy uncertainty: Evidence from global, oil importers, and exporters’ perspective," Research in International Business and Finance, Elsevier, vol. 56(C).
    6. Martina, Esteban & Rodriguez, Eduardo & Escarela-Perez, Rafael & Alvarez-Ramirez, Jose, 2011. "Multiscale entropy analysis of crude oil price dynamics," Energy Economics, Elsevier, vol. 33(5), pages 936-947, September.
    7. Adrian Bejan & George Tsatsaronis, 2021. "Purpose in Thermodynamics," Energies, MDPI, vol. 14(2), pages 1-25, January.
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