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Long-term relationships between electricity and oil, gas and coal future prices—evidence from Nordic countries, Continental Europe and the United Kingdom

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  • Stein Frydenberg
  • Joseph I. Onochie
  • Sjur Westgaard
  • Nora Midtsund
  • Hanna Ueland

Abstract

This paper investigates the relationship between futures prices of electricity, on the one hand and crude oil, natural gas and coal on the other hand, in the United Kingdom, German and Nordic energy markets. Energy traders and market participants use statistical models in their trading activities. We seek to establish robust cointegration based models as decision tools for energy commodity spread trading. We look at three different markets with different input mixtures, for electricity generation. Using daily futures data from period 2006 to 2012, we find cointegration between UK electricity prices and Coal and Gas, and between Nordic electricity prices and Coal. The spread between German electricity prices and Gas, Oil and Coal prices are also stationary. The consequence of this finding could be a possible trading strategy which involves buying electricity futures and selling gas or coal when the spread is above the mean and reversing the strategy when the spread is below the mean. The suggested strategy assumes a reversion to the mean, which we show takes some time, as displayed by the slow speed of adjustment.

Suggested Citation

  • Stein Frydenberg & Joseph I. Onochie & Sjur Westgaard & Nora Midtsund & Hanna Ueland, 2014. "Long-term relationships between electricity and oil, gas and coal future prices—evidence from Nordic countries, Continental Europe and the United Kingdom," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 38(2), pages 216-242, June.
    • Handle: RePEc:bla:opecrv:v:38:y:2014:i:2:p:216-242
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    File URL: http://hdl.handle.net/10.1111/opec.12025
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    Cited by:

    1. Cagli, Efe Caglar & Taskin, Dilvin & Evrim Mandaci, Pınar, 2019. "The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models," Energy Economics, Elsevier, vol. 84(C).
    2. Su, Chi-Wei & Yuan, Xi & Umar, Muhammad & Chang, Tsangyao, 2022. "Dynamic price linkage of energies in transformation: Evidence from quantile connectedness," Resources Policy, Elsevier, vol. 78(C).
    3. Zhao, Jing, 2022. "Exploring the influence of the main factors on the crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach," Resources Policy, Elsevier, vol. 79(C).
    4. Naeem, Muhammad Abubakr & Arfaoui, Nadia, 2023. "Exploring downside risk dependence across energy markets: Electricity, conventional energy, carbon, and clean energy during episodes of market crises," Energy Economics, Elsevier, vol. 127(PB).
    5. Onishi, Viviani C. & Antunes, Carlos H. & Fraga, Eric S. & Cabezas, Heriberto, 2019. "Stochastic optimization of trigeneration systems for decision-making under long-term uncertainty in energy demands and prices," Energy, Elsevier, vol. 175(C), pages 781-797.
    6. Rios, Daniel & Blanco, Gerardo & Olsina, Fernando, 2019. "Integrating Real Options Analysis with long-term electricity market models," Energy Economics, Elsevier, vol. 80(C), pages 188-205.

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