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Forecasting electricity spot prices using time-series models with a double temporal segmentation

Listed author(s):
  • Marie Bessec

    (LEDa-CGEMP, Université Paris-Dauphine - Université Paris-Dauphine, PSL Research University)

  • Julien Fouquau

    (ESCP Europe)

  • Sophie Meritet

    (LEDa-CGEMP, Université Paris-Dauphine - Université Paris-Dauphine, PSL Research University)

The French wholesale market is set to expand in the next few years under European pressure and national decisions. In this article, we assess the forecasting ability of several classes of time-series models for electricity wholesale spot prices at a day-ahead horizon in France. Electricity spot prices display a strong seasonal pattern, particularly in France, given the high share of electric heating in housing during winter time. To deal with this pattern, we implement a double temporal segmentation of the data. For each trading period and season, we use a large number of specifications based on market fundamentals: linear regressions, Markov-switching (MS) models and threshold models with a smooth transition. An extensive evaluation on French data shows that modelling each season independently leads to better results. Among nonlinear models, MS models designed to capture the sudden and fast-reverting spikes in the price dynamics yield more accurate forecasts. Finally, pooling forecasts give more reliable results.

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Paper provided by HAL in its series Post-Print with number hal-01276807.

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Date of creation: 2016
Publication status: Published in Applied Economics, 2016, 48 (5), <10.1080/00036846.2015.1080801>
Handle: RePEc:hal:journl:hal-01276807
DOI: 10.1080/00036846.2015.1080801
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01276807
Contact details of provider: Web page: https://hal.archives-ouvertes.fr/

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