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Forecasting electricity prices: The impact of fundamentals and time-varying coefficients

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  • Karakatsani, Nektaria V.
  • Bunn, Derek W.
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    Abstract

    This paper investigates the day-ahead forecasting performance of fundamental price models for electricity spot prices, intended to capture: (i) the impacts of economic, technical, strategic and risk factors on intra-day prices; and (ii) the dynamics of these effects over time. A time-varying parameter (TVP) regression model allows for a continuously adaptive price structure, due to agent learning, regulatory and market structure changes. A regime-switching regression model allows for discontinuities in pricing due to temporal irregularities and scarcity effects. The models that invoke market fundamentals and time-varying coefficients exhibit the best predictive performance among various alternatives, in the British market.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 24 (2008)
    Issue (Month): 4 ()
    Pages: 764-785

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    Handle: RePEc:eee:intfor:v:24:y:2008:i:4:p:764-785

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Electricity prices Forecasting Time-varying effects Regime-switching;

    References

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    Citations

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    Cited by:
    1. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," MPRA Paper 10428, University Library of Munich, Germany.
    2. Gianfreda, Angelica & Grossi, Luigi, 2012. "Forecasting Italian electricity zonal prices with exogenous variables," Energy Economics, Elsevier, vol. 34(6), pages 2228-2239.
    3. Liebl, Dominik, 2013. "Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective," MPRA Paper 50881, University Library of Munich, Germany.
    4. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity spot prices by incorporating intra-day relationships: Evidence form the UK power market," HSC Research Reports HSC/13/01, Hugo Steinhaus Center, Wroclaw University of Technology, revised 15 Apr 2013.
    5. Furió, Dolores, 2011. "A Survey on the Spanish Electricity Intraday Market/El mercado de electricidad español: el mercado intradiario," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 29, pages 657 (20 pag, Agosto.
    6. Lazarczyk, Ewa, 2013. "Market Specific News and Its Impact on Electricity Prices – Forward Premia," Working Paper Series 953, Research Institute of Industrial Economics, revised 20 Aug 2013.
    7. Bobinaite, Viktorija & Juozapaviciene, Aldona & Staniewski, Marcin & Szczepankowski, Piotr, 2013. "Comparative analysis of features of Polish and Lithuanian Day-ahead electricity market prices," Energy Policy, Elsevier, vol. 63(C), pages 181-196.
    8. Jónsson, Tryggvi & Pinson, Pierre & Madsen, Henrik, 2010. "On the market impact of wind energy forecasts," Energy Economics, Elsevier, vol. 32(2), pages 313-320, March.
    9. Kalantzis, Fotis & Sakellaris, Kostis, 2012. "Investigating the Impact of the Greek Electricity Market Reforms on its Day-Ahead Market Prices," MPRA Paper 37794, University Library of Munich, Germany.
    10. Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2010. "The power of weather," DNB Working Papers 236, Netherlands Central Bank, Research Department.
    11. Janczura, Joanna & Weron, Rafal, 2011. "Goodness-of-fit testing for the marginal distribution of regime-switching models," MPRA Paper 32532, University Library of Munich, Germany.
    12. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer, vol. 97(3), pages 239-270, July.
    13. Florian Ziel & Rick Steinert, 2014. "Efficient Modeling and Forecasting of the Electricity Spot Price," Papers 1402.7027, arXiv.org.
    14. Fouquau, Julien & Bessec, Marie & Méritet, Sophie, 2014. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Economics Papers from University Paris Dauphine 123456789/13532, Paris Dauphine University.

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