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Electricity Market Dynamics and Regional Interdependence in the Face of Pandemic Restrictions and the Russian–Ukrainian Conflict

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  • András Szeberényi

    (Institute of Marketing and Communication, Budapest Metropolitan University, 1148 Budapest, Hungary)

  • Ferenc Bakó

    (Department of International and Applied Economics, Széchenyi István University, 9026 Győr, Hungary)

Abstract

Electricity constitutes a significant part of the consumption basket of European households and companies. Since energy products are essential components of almost all products and services, any change in energy prices directly impacts the general price level of those products and services. Therefore, this study aims to conduct a comprehensive analysis of power exchange data between 2019 and 2022. For the analysis, we examined the data of 15 countries. In the research, we compared electricity prices in European power exchanges using the Jaccard similarity index and the overlap coefficient, using the DAM hourly prices between 1 January 2019 and 31 December 2022. We transformed the time series into networks using the visibility graph procedure and compared the networks of the studied countries using the two comparison methods with the degree distribution functions. Our aim is to examine how the market anomalies caused by the COVID-19 pandemic and the Russian–Ukrainian conflict affect European electricity markets and how quickly the repercussions spread across the studied countries’ exchanges, and whether they show persistent or anti-persistent characteristics. The results support that similar market effects significantly influence the pattern of price changes among the countries. The methods forming the basis of the research can provide significant assistance in analyzing market trends and contribute to a better understanding of market processes.

Suggested Citation

  • András Szeberényi & Ferenc Bakó, 2023. "Electricity Market Dynamics and Regional Interdependence in the Face of Pandemic Restrictions and the Russian–Ukrainian Conflict," Energies, MDPI, vol. 16(18), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6515-:d:1236647
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    References listed on IDEAS

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    1. Fan, Xinghua & Li, Xuxia & Yin, Jiuli & Tian, Lixin & Liang, Jiaochen, 2019. "Similarity and heterogeneity of price dynamics across China’s regional carbon markets: A visibility graph network approach," Applied Energy, Elsevier, vol. 235(C), pages 739-746.
    2. András Szeberényi & Tomasz Rokicki & Árpád Papp-Váry, 2022. "Examining the Relationship between Renewable Energy and Environmental Awareness," Energies, MDPI, vol. 15(19), pages 1-25, September.
    3. Anna Cretì & Fulvio Fontini, 2019. "Economics of Electricity. Markets, Competition and Rules," Post-Print hal-02304345, HAL.
    4. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    5. Brugger, Heike & Eichhammer, Wolfgang & Mikova, Nadezhda & Dönitz, Ewa, 2021. "Energy Efficiency Vision 2050: How will new societal trends influence future energy demand in the European countries?," Energy Policy, Elsevier, vol. 152(C).
    6. Ocker, Fabian & Jaenisch, Vincent, 2020. "The way towards European electricity intraday auctions – Status quo and future developments," Energy Policy, Elsevier, vol. 145(C).
    7. Han, Mengjiao & Fan, Qingju & Ling, Guang, 2022. "Multiscale online-horizontal-visibility-graph correlation analysis of financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    8. Hesamzadeh, M.R. & Biggar, D.R. & Bunn, D.W. & Moiseeva, E., 2020. "The impact of generator market power on the electricity hedge market," Energy Economics, Elsevier, vol. 86(C).
    9. Thomas, Samuel & Rosenow, Jan, 2020. "Drivers of increasing energy consumption in Europe and policy implications," Energy Policy, Elsevier, vol. 137(C).
    10. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    11. Szőke, Tamás & Hortay, Olivér & Balogh, Eszter, 2019. "Asymmetric price transmission in the Hungarian retail electricity market," Energy Policy, Elsevier, vol. 133(C).
    12. Tomasz Rokicki & Radosław Jadczak & Adam Kucharski & Piotr Bórawski & Aneta Bełdycka-Bórawska & András Szeberényi & Aleksandra Perkowska, 2022. "Changes in Energy Consumption and Energy Intensity in EU Countries as a Result of the COVID-19 Pandemic by Sector and Area Economy," Energies, MDPI, vol. 15(17), pages 1-26, August.
    13. Roxana Săvescu & Ștefania Kifor & Raluca Dănuț & Raluca Rusu, 2022. "Transition from Office to Home Office: Lessons from Romania during COVID-19 Pandemic," Sustainability, MDPI, vol. 14(10), pages 1-14, May.
    14. Grau-Carles, Pilar, 2001. "Long-range power-law correlations in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(3), pages 521-527.
    15. Norbert Bozsik & András Szeberényi & Nándor Bozsik, 2023. "Examination of the Hungarian Electricity Industry Structure with Special Regard to Renewables," Energies, MDPI, vol. 16(9), pages 1-23, April.
    16. Hortay, Olivér & Víg, Attila A., 2020. "Potential effects of market power in Hungarian solar boom," Energy, Elsevier, vol. 213(C).
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