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Fundamental and speculative shocks, what drives electricity prices?

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  • Katarzyna Maciejowska

Abstract

In the paper, Structural Vector Autoregressive models (SVAR) are used to identify fundamental and speculative shocks, in the UK electricity market. The structural shocks are identified via short run restrictions, which are imposed on the matrix of instantaneous effects. In the research, two main types of shocks are considered: fundamental shocks, which result from unexpected changes of demand, supply and generation costs and speculative shocks, which are associated solely with electricity prices. The results indicate that speculative shocks play an important role in the price setting process. Although they account for a significant part (from 30% to 95%) of the price volatility, I do not find evidence that the influence differs between peak and off-peak hours. When fundamental shocks are considered, some dependence between their effects on electricity prices and periods of the day is confirmed. For example, the impact of wind supply shocks on electricity prices is significantly stronger during the peak hours than during the off-peak hours. Moreover, they become a major source of electricity price volatility during the peak hours. Finally, it is confirmed that shocks associated with generation costs (prices of fuels) don’t have any instantaneous effect on the electricity prices.

Suggested Citation

  • Katarzyna Maciejowska, 2014. "Fundamental and speculative shocks, what drives electricity prices?," HSC Research Reports HSC/14/05, Hugo Steinhaus Center, Wroclaw University of Technology.
  • Handle: RePEc:wuu:wpaper:hsc1405
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    References listed on IDEAS

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    1. Martin Eichenbaum & Charles L. Evans, 1995. "Some Empirical Evidence on the Effects of Shocks to Monetary Policy on Exchange Rates," The Quarterly Journal of Economics, Oxford University Press, vol. 110(4), pages 975-1009.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Weron, Rafał & Zator, Michał, 2015. "A note on using the Hodrick–Prescott filter in electricity markets," Energy Economics, Elsevier, vol. 48(C), pages 1-6.
    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. Paschen, Marius, 2016. "Dynamic analysis of the German day-ahead electricity spot market," Energy Economics, Elsevier, vol. 59(C), pages 118-128.
    4. Di Cosmo, Valeria & Malaguzzi Valeri, Laura, 2018. "Wind, storage, interconnection and the cost of electricity generation," Energy Economics, Elsevier, vol. 69(C), pages 1-18.
    5. Weron, Rafał & Zator, Michał, 2014. "Revisiting the relationship between spot and futures prices in the Nord Pool electricity market," Energy Economics, Elsevier, vol. 44(C), pages 178-190.
    6. Nowotarski, Jakub & Weron, Rafał, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 57(C), pages 228-235.
    7. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    8. Keles, Dogan & Dehler-Holland, Joris & Densing, Martin & Panos, Evangelos & Hack, Felix, 2020. "Cross-border effects in interconnected electricity markets - an analysis of the Swiss electricity prices," Energy Economics, Elsevier, vol. 90(C).
    9. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.

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    More about this item

    Keywords

    Electricity spot prices; Structural analysis; Vector autoregression;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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