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Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices

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  • M. Angeles Carnero

    ()
    (University of Alicante)

  • Siem Jan Koopman

    ()
    (Vrije Universiteit Amsterdam)

  • Marius Ooms

    ()
    (Vrije Universiteit Amsterdam)

Abstract

Although the main interest in the modelling of electricity prices is often on volatility aspects, we argue that stochastic heteroskedastic behaviour in prices can only be modelled correctly when the conditional mean of the time series is properly modelled. 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 spotprices. 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 established, 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 Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 03-071/4.

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Date of creation: 11 Sep 2003
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Handle: RePEc:dgr:uvatin:20030071

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Keywords: Autoregressive fractionally integrated moving average model; Generalised autoregressive conditional heteroskedasticity model; Long memory process; Periodic autoregressive model; Volatility.;

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  1. Byström, Hans, 2001. "Extreme Value Theory and Extremely Large Electricity Price Changes," Working Papers 2001:19, Lund University, Department of Economics.
  2. Jurgen A. Doornik & Marius Ooms, 2001. "Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models," Economics Papers 2001-W27, Economics Group, Nuffield College, University of Oxford.
  3. Alvaro Escribano & J. Ignacio Peña & Pablo Villaplana, 2011. "Modelling Electricity Prices: International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(5), pages 622-650, October.
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Citations

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Cited by:
  1. Hipòlit Torró, 2007. "Forecasting Weekly Electricity Prices at Nord Pool," Working Papers 2007.88, Fondazione Eni Enrico Mattei.
  2. Haldrup Niels & Nielsen Morten Ø., 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 1-24, September.
  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. Koopman, Siem Jan & Ooms, Marius, 2006. "Forecasting daily time series using periodic unobserved components time series models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 885-903, November.
  5. 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.
  6. 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.
  7. 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.
  8. Sandro Sapio, 2004. "Market Design, Bidding Rules, and Long Memory in Electricity Prices," LEM Papers Series 2004/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  9. Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376.
  10. Malo, Pekka, 2009. "Modeling electricity spot and futures price dependence: A multifrequency approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(22), pages 4763-4779.

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