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A vector autoregressive model for electricity prices subject to long memory and regime switching

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Author Info

  • Niels Haldrup

    ()
    (Aarhus University and CREATES)

  • Frank S. Nielsen

    ()
    (Aarhus University and CREATES)

  • Morten Ørregaard Nielsen

    ()
    (Queen's University and CREATES)

Abstract

A regime dependent VAR model is suggested that allows long memory (fractional integration) in each of the observed regime states as well as the possibility of fractional cointegration. The model is motivated by the dynamics of electricity prices where the transmission of power is subject to occasional congestion periods. For a system of bilateral prices non-congestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between a univariate price process under non-congestion and a bivariate price process under congestion. At the same time, it is an empirical regularity that electricity prices tend to show a high degree of long memory, and thus that prices may be fractionally cointegrated. Analysis of Nord Pool data shows that even though the prices are identical under non-congestion, the prices are not, in general, fractionally cointegrated in the congestion state. Hence, in most cases price convergence is a property following from regime switching rather than a conventional error correction mechanism. Finally, the suggested model is shown to deliver forecasts that are more precise compared to competing models.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1211.pdf
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Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1211.

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Length: 29 pages
Date of creation: Aug 2009
Date of revision:
Handle: RePEc:qed:wpaper:1211

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Keywords: Cointegration; electricity prices; fractional integration; long memory; regime switching;

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Cited by:
  1. Sun, Qi & Xu, Weijun & Xiao, Weilin, 2013. "An empirical estimation for mean-reverting coal prices with long memory," Economic Modelling, Elsevier, vol. 33(C), pages 174-181.
  2. Xu, Weijun & Sun, Qi & Xiao, Weilin, 2012. "A new energy model to capture the behavior of energy price processes," Economic Modelling, Elsevier, vol. 29(5), pages 1585-1591.
  3. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafal, 2012. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," MPRA Paper 42563, University Library of Munich, Germany.
  4. Janczura, Joanna & Trueck, Stefan & Weron, Rafal & Wolff, Rodney, 2012. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," MPRA Paper 39277, University Library of Munich, Germany.
  5. Angelica Gianfreda & Luigi Grossi, 2011. "Forecasting Italian Electricity Zonal Prices with Exogenous Variables," Working Papers 01/2011, University of Verona, Department of Economics.
  6. Eichler, M. & Türk, D., 2013. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Energy Economics, Elsevier, vol. 36(C), pages 614-624.
  7. Joanna Janczura, 2014. "Pricing electricity derivatives within a Markov regime-switching model: a risk premium approach," Computational Statistics, Springer, vol. 79(1), pages 1-30, February.

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