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Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices

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  • Haldrup; Niels
  • Morten Oerregaard Nielsen

    ()
    (Department of Economics, University of Aarhus, Denmark)

Abstract

The functioning of electricity markets has experienced increasing complexity as a result of deregulation in recent years. Consequently this affects the multilateral price behaviour across regions with physical exchange of power. It has been documented elsewhere that features such aslong memory and regime switching reflecting congestion and non-congestion periods are empirically relevant and hence are features that need to be taken into account when modeling price behavior. In the present paper we further elaborate on the co-existence of long memory and regime switches by focusing on the effect that the direction of possible congestion episodes has on the price dynamics. Under non-congestion prices are identical. The direction of possible congestion is identified by the region with excess demand of power through the sign of price differences and hence three different states can be considered: Non-congestion and congestion periods with excess demand in the one or the other region. Using data from the Nordic power exchange, Nord Pool, we find that the price dynamics and long memory features of the price series generally are rather different across the different states. Also, there is evidence of fractional cointegration at some grid points when conditioning on the states.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series Economics Working Papers with number 2005-18.

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Length: 23
Date of creation: 17 Oct 2005
Date of revision:
Handle: RePEc:aah:aarhec:2005-18

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Cointegration; electricity prices; forecasting; fractional integration and cointegration; long memory; Markov switching;

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Cited by:
  1. Erik Lindström & Fredric Regland, 2012. "Independent Spike Models: Estimation and Validation," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(2), pages 180-196, May.
  2. Luc Bauwens & Christian M. Hafner & Diane Pierret, 2013. "Multivariate Volatility Modeling Of Electricity Futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 743-761, 08.
  3. Haldrup, Niels & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2010. "A vector autoregressive model for electricity prices subject to long memory and regime switching," Energy Economics, Elsevier, vol. 32(5), pages 1044-1058, September.
  4. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2010. "Long-run relations in european electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 805-832.
  5. Marckhoff, Jan & Wimschulte, Jens, 2009. "Locational price spreads and the pricing of contracts for difference: Evidence from the Nordic market," Energy Economics, Elsevier, vol. 31(2), pages 257-268, March.
  6. Lindström, Erik & Regland, Fredrik, 2012. "Modeling extreme dependence between European electricity markets," Energy Economics, Elsevier, vol. 34(4), pages 899-904.
  7. Matteo Manera & Massimiliano Serati & Michele Plotegher, 2008. "Modeling Electricity Prices: From the State of the Art to a Draft of a New Proposal," Working Papers 2008.9, Fondazione Eni Enrico Mattei.

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