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Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices

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Author Info
Siem Jan Koopman () (Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam)
Marius Ooms () (Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam)
M. Angeles Carnero () (Dpt. Fundamentos del Analisis Economico, University of Alicante)

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Abstract

Novel periodic extensions of dynamic long memory regression models with autoregressive conditional heteroskedastic errors are considered for the analysis of daily electricity spot prices. The parameters of the model with mean and variance specifications are estimated simultaneously by the method of approximate maximum likelihood. The methods are implemented for time series of 1, 200 to 4, 400 daily price observations. Apart from persistence, heteroskedasticity and extreme observations in prices, a novel empirical finding is the importance of day-of-the-week periodicity in the autocovariance function of electricity spot prices. In particular, daily log prices from the Nord Pool power exchange of Norway are modeled effectively by our framework, which is also extended with explanatory variables. For the daily log prices of three European emerging electricity markets (EEX in Germany, Powernext in France, APX in The Netherlands), which are less persistent, periodicity is also highly significant.

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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 05-091/4.

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Date of creation: 12 Oct 2005
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Handle: RePEc:dgr:uvatin:20050091

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

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188. [Downloadable!] (restricted)
  2. Doornik, Jurgen A. & Ooms, Marius, 2003. "Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 333-348, March. [Downloadable!] (restricted)
    Other versions:
  3. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July. [Downloadable!] (restricted)
  4. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb.. [Downloadable!] (restricted)
  5. Bollerslev, T. & Ghysels, E., 1994. "Periodic Autoregressive Conditional Heteroskedasticity," Cahiers de recherche 9408, Universite de Montreal, Departement de sciences economiques. [Downloadable!]
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  6. Bystrom, Hans N. E., 2005. "Extreme value theory and extremely large electricity price changes," International Review of Economics & Finance, Elsevier, vol. 14(1), pages 41-55. [Downloadable!] (restricted)
  7. 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. [Downloadable!]
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  8. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September. [Downloadable!] (restricted)
  9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
  10. H. Peter Boswijk & Franc Klaassen, 2005. "Why Frequency Matters for Unit Root Testing," Tinbergen Institute Discussion Papers 04-119/4, Tinbergen Institute. [Downloadable!]
  11. Ooms, M. & Franses, Ph.H.B.F., 1998. "A seasonal periodic long memory model for monthly river flows," Econometric Institute Report EI 9842 Revision_Date: 20, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
  12. Álvaro Escribano & Juan Ignacio Peña & Pablo Villaplana, 2002. "Modeling Electricity Prices: International Evidence," Economics Working Papers we022708, Universidad Carlos III, Departamento de Economía. [Downloadable!]
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Fell, Harrison, 2008. "EU-ETS and Nordic Electricity: A CVAR Approach," Discussion Papers dp-08-31, Resources For the Future. [Downloadable!]
  2. Abdou Kâ Diongue & Dominique Guegan & Bertrand Vignal, 2009. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00307606_v1, HAL. [Downloadable!]
    Other versions:
  3. Hipòlit Torró & Julio Lucia, 2008. "Short-term electricity futures prices: Evidence on the time-varying risk premium," Working Papers. Serie EC 2008-08, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie). [Downloadable!]
  4. andrés M. Alonso & Carolina Garcia-Martos & Julio Rodriguez & Maria Jesus Sanchez, 2008. "Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting," Statistics and Econometrics Working Papers ws081406, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
  5. Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2007. "The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts," Tinbergen Institute Discussion Papers 07-036/4, Tinbergen Institute. [Downloadable!]
  6. Isao Ishida & Toshiaki Watanabe, 2009. "Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model," Global COE Hi-Stat Discussion Paper Series gd08-032, Institute of Economic Research, Hitotsubashi University. [Downloadable!]
  7. Niels Haldrup & Frank S. Nielsen & Morten Ørregaard Nielsen, 2007. "A Vector Autoregressive Model for Electricity Prices Subject to Long Memory and Regime Switching," CREATES Research Papers 2007-29, School of Economics and Management, University of Aarhus. [Downloadable!]
    Other versions:
  8. repec:mop:credwp:08.09.77 is not listed on IDEAS
  9. Isao Ishida & Toshiaki Watanabe, 2009. "Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model," CIRJE F-Series CIRJE-F-608, CIRJE, Faculty of Economics, University of Tokyo. [Downloadable!]
  10. Bruno Bosco & Lucia Parisio & Matteo Pelagatti, 2007. "Deregulated Wholesale Electricity Prices in Italy: An Empirical Analysis," International Advances in Economic Research, Springer, vol. 13(4), pages 415-432, November. [Downloadable!] (restricted)
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