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Modelling power spot prices in deregulated European energy markets: a dual long memory approach


  • Najeh Chaâbane
  • Foued Saâdaoui
  • Saloua Benammou


In the last decade, with deregulation and introduction of competition in power markets, prices forecasting have become a real challenge for all market participants. However, forecasting is a rather complex task since electricity prices involve many features comparably with financial ones. Electricity markets have a highly volatile nature. They are indeed a more unpredictable than that of other commodities referred to as extreme volatile. In this paper, the two most emerging European electricity markets are considered. A preliminary analysis of the time series attests to the presence of a long range dependance behaviour. Therefore, prices processes are modelled using ARFIMA-FIGARCH under Gaussian and non-Gaussian distributions. Such models are sufficiently flexible to handle the long memory phenomena often encountered in both conditional mean and conditional variance in electricity spot prices. Forecasting is subsequently performed on the basis of adequate models.

Suggested Citation

  • Najeh Chaâbane & Foued Saâdaoui & Saloua Benammou, 2012. "Modelling power spot prices in deregulated European energy markets: a dual long memory approach," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 14(4), pages 338-361.
  • Handle: RePEc:ids:gbusec:v:14:y:2012:i:4:p:338-361

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    Cited by:

    1. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "A Dual Generalized Long Memory Modelling for Forecasting Electricity Spot Price: Neural Network and Wavelet Estimate," Papers 2204.08289,
    2. Saâdaoui, Foued, 2018. "Testing for multifractality of Islamic stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 263-273.


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