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

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Date
2016
Dewey
Economie industrielle
Sujet
Forecasting; electricity spot prices; seasonality; regime-switching; combinations
JEL code
C.C2.C22; C.C5.C53; L.L9.L94
Journal issue
Applied Economics
Volume
48
Number
5
Publication date
2016
Article pages
361-378
Publisher
Chapman and Hall
DOI
http://dx.doi.org/10.1080/00036846.2015.1080801
URI
https://basepub.dauphine.fr/handle/123456789/15386
Collections
  • LEDa : Publications
Metadata
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Author
Bessec, Marie
status unknown
Fouquau, Julien
82248 ESCP-EAP
Méritet, Sophie
status unknown
Type
Article accepté pour publication ou publié
Abstract (EN)
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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