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Measuring the connectedness of European electricity markets using the network topology of variance decompositions

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  • Xiao, Binqing
  • Yang, Ye
  • Peng, Xuerong
  • Fang, Libing

Abstract

The United Kingdom’s 2016 decision to exit the European Union (Brexit)has emphasized the need to comprehend systemic risk in Europe. The liberalization of the European electricity markets has drawn much attention. This study uses the network topology of variance decompositions to research the connectedness of the European electricity markets. Our results show that the European electricity markets experienced relatively high connectedness during the sample period from 2013 to the end of 2017. The analysis of the network connectedness suggests that regulators could identify the markets that most threaten system stability.

Suggested Citation

  • Xiao, Binqing & Yang, Ye & Peng, Xuerong & Fang, Libing, 2019. "Measuring the connectedness of European electricity markets using the network topology of variance decompositions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119313172
    DOI: 10.1016/j.physa.2019.122279
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    Cited by:

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    2. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    3. Karkowska, Renata & Urjasz, Szczepan, 2021. "Connectedness structures of sovereign bond markets in Central and Eastern Europe," International Review of Financial Analysis, Elsevier, vol. 74(C).
    4. Muhammad Abubakr Naeem & Sitara Karim & Tooraj Jamasb & Rabindra Nepal, 2022. "Risk transmission between green markets and commodities," CAMA Working Papers 2022-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Tadahiro Nakajima & Yuki Toyoshima, 2020. "Examination of the Spillover Effects among Natural Gas and Wholesale Electricity Markets Using Their Futures with Different Maturities and Spot Prices," Energies, MDPI, vol. 13(7), pages 1-14, March.
    6. Naeem, Muhammad Abubakr & Karim, Sitara & Hasan, Mudassar & Lucey, Brian M. & Kang, Sang Hoon, 2022. "Nexus between oil shocks and agriculture commodities: Evidence from time and frequency domain," Energy Economics, Elsevier, vol. 112(C).
    7. Ma, Rufei & Liu, Zhenhua & Zhai, Pengxiang, 2022. "Does economic policy uncertainty drive volatility spillovers in electricity markets: Time and frequency evidence," Energy Economics, Elsevier, vol. 107(C).
    8. Mensi, Walid & Naeem, Muhammad Abubakr & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Dynamic and frequency spillovers between green bonds, oil and G7 stock markets: Implications for risk management," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 331-344.
    9. Sikorska-Pastuszka, Magdalena & Papież, Monika, 2023. "Dynamic volatility connectedness in the European electricity market," Energy Economics, Elsevier, vol. 127(PA).
    10. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2020. "Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).

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    More about this item

    Keywords

    Risk measurement; European electricity markets; Connectedness; Variance decompositions;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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