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Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices

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  • Marius Ooms
  • M. Angeles Carnero
  • Siem Jan Koopman

Abstract

In this paper we consider different periodic extensions of regression models with autoregressive fractionally integrated moving average disturbances for the analysis of daily spot prices of electricity. We show that day-of-the-week periodicity and long memory are important determinants for the dynamic modelling of the conditional mean of electricity spot prices. Once an effective description of the conditional mean of spot prices is empirically identified, focus can be directed towards volatility features of the time series. For the older electricity market of Nord Pool in Norway, it is found that a long memory model with periodic coefficients is required to model daily spot prices effectively. Further, strong evidence of conditional heteroskedasticity is found in the mean corrected Nord Pool series. For daily prices at three emerging electricity markets that we consider (APX in The Netherlands, EEX in Germany and Powernext in France) periodicity in the autoregressive coefficients is also stablished, but evidence of long memory is not found and existence of dynamic behaviour in the variance of the spot prices is less pronounced. The novel findings in this paper can have important consequences for the modelling and forecasting of mean and variance functions of spot prices for electricity and associated contingent assets

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 158.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:ausm04:158

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Keywords: GARCH; Long Memory;

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Citations

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Cited by:
  1. Hans Andeweg, André Dorsman, Kees van Montfort, 2009. "Electricity Traffic over the Barriers of Networks: The Case of Germany and The Netherlands," Frontiers in Finance and Economics, SKEMA Business School, vol. 6(2), pages 120-139, October.
  2. Niels Haldrup & Morten O. Nielsen, 2004. "A Regime Switching Long Memory Model for Electricity Prices," Economics Working Papers 2004-2, School of Economics and Management, University of Aarhus.
  3. Massimiliano Caporin & Juliusz Pres' & Hipolit Torro, 2010. "Model Based Monte Carlo Pricing of Energy and Temperature Quanto Options," "Marco Fanno" Working Papers 0123, Dipartimento di Scienze Economiche "Marco Fanno".
  4. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
  5. Niels Haldrup & Morten Ø. Nielsen, 2006. "Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1.
  6. 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.
  7. Hipòlit Torró, 2007. "Forecasting Weekly Electricity Prices at Nord Pool," Working Papers 2007.88, Fondazione Eni Enrico Mattei.
  8. Kosater, Peter, 2006. "On the impact of weather on German hourly power prices," Discussion Papers in Statistics and Econometrics 1/06, University of Cologne, Department for Economic and Social Statistics.
  9. Kosater, Peter & Mosler, Karl, 2006. "Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices," Applied Energy, Elsevier, vol. 83(9), pages 943-958, September.
  10. 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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