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

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  • Haldrup, Niels
  • Nielsen, Frank S.
  • Nielsen, Morten Ørregaard

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.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:eneeco:v:32:y:2010:i:5:p:1044-1058
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    Keywords

    Cointegration Electricity prices Fractional integration Long memory Regime switching;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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