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Modeling the UK electricity price distributions using quantile regression

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  • Hagfors, Lars Ivar
  • Bunn, Derek
  • Kristoffersen, Eline
  • Staver, Tiril Toftdahl
  • Westgaard, Sjur

Abstract

In this paper we develop fundamental quantile regression models for the UK electricity price in each trading period. Intraday properties of price risk, as represented by the predictive distribution rather than expected values, have previously not been fully analyzed. The sample covers half hourly data from 2005 to 2012. From our analysis we are able to show how the sensitivity towards different fundamental factors changes across quantiles and time of day. In the UK the supply of electricity is to a large extent generated from coal and gas plants, thus the price of gas and coal, as well as the carbon emission price, are included as fundamental factors in our model. We also include the electricity price lagged by one day, as well as demand and margin forecasts. We find that the sensitivities vary across the price distribution. Our findings also suggest that the sensitivity to fundamental factors exhibit intraday variation. We find that the sensitivity to gas relative to coal is higher in high quantiles and lower in low quantiles, as well as some indications of market power being exercised during peak hours. We have demonstrated a scenario analysis based on the quantile regression models, showing how changes in the values of the fundamentals influence the electricity price distribution.

Suggested Citation

  • Hagfors, Lars Ivar & Bunn, Derek & Kristoffersen, Eline & Staver, Tiril Toftdahl & Westgaard, Sjur, 2016. "Modeling the UK electricity price distributions using quantile regression," Energy, Elsevier, vol. 102(C), pages 231-243.
    • Handle: RePEc:eee:energy:v:102:y:2016:i:c:p:231-243
    DOI: 10.1016/j.energy.2016.02.025
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    References listed on IDEAS

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    1. Gerlach, Richard H. & Chen, Cathy W. S. & Chan, Nancy Y. C., 2011. "Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 481-492.
    2. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, January.
    3. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    4. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
    5. Arvesen, Ø. & Medbø, V. & Fleten, S.-E. & Tomasgard, A. & Westgaard, S., 2013. "Linepack storage valuation under price uncertainty," Energy, Elsevier, vol. 52(C), pages 155-164.
    6. Guermat, Cherif & Harris, Richard D. F., 2002. "Forecasting value at risk allowing for time variation in the variance and kurtosis of portfolio returns," International Journal of Forecasting, Elsevier, vol. 18(3), pages 409-419.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. Rothkopf, Michael H., 1999. "Daily Repetition: A Neglected Factor in the Analysis of Electricity Auctions," The Electricity Journal, Elsevier, vol. 12(3), pages 60-70, April.
    9. Paraschiv, Florentina & Erni, David & Pietsch, Ralf, 2014. "The impact of renewable energies on EEX day-ahead electricity prices," Energy Policy, Elsevier, vol. 73(C), pages 196-210.
    10. Dias, José G. & Ramos, Sofia B., 2014. "Energy price dynamics in the U.S. market. Insights from a heterogeneous multi-regime framework," Energy, Elsevier, vol. 68(C), pages 327-336.
    11. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Intra-day and regime-switching dynamics in electricity price formation," Energy Economics, Elsevier, vol. 30(4), pages 1776-1797, July.
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