Computational Management Science

, Volume 16, Issue 1–2, pp 345–369 | Cite as

On the construction of hourly price forward curves for electricity prices

  • Rüdiger Kiesel
  • Florentina Paraschiv
  • Audun SætherøEmail author
Original Paper


There are several approaches in the literature for the derivation of price forward curves (PFCs) which distinguish among each other by the procedure employed for the derivation of seasonality shapes, smoothing technique and by the design of the optimization procedure. However, a comparative study to highlight the strengths and weaknesses of different methods is missing. For the construction of PFCs we typically incorporate the information about market expectation from the observed futures prices and the deterministic seasonal effects of electricity prices. In most existing approaches, the seasonality shape is fitted to historically observed spot prices, and it is an exogenous input to the optimization procedure. As seasonal effects on electricity prices differ between markets, our model allows a more general and flexible definition of the seasonality shape. In this study, we propose an alternative calibration procedure for the seasonality shape, where the level of futures as well as historical spot prices are simultaneously taken into account in a joint optimization approach. We discuss comparatively the features of existing methods for PFCs, and highlight the advantages of our optimization procedure.


Electricity markets Hourly price forward curves Smoothing techniques Seasonality shapes 


  1. Benth FE, Koekkebakker S, Ollmar F (2007) Extracting and applying smooth forward curves from average-based commodity contracts with seasonal variation. J Deriv 15(1):52–66CrossRefGoogle Scholar
  2. Blöchlinger L (2008) Power prices—a regime-switching spot/forward price model with Kim filter estimation. Ph.D. thesis, CiteseerGoogle Scholar
  3. Caldana R, Fusai G, Roncoroni A (2017) Electricity forward curves with thin granularity: theory and empirical evidence in the hourly epexspot market. Eur J Oper Res 261(2):715–734CrossRefGoogle Scholar
  4. Erni D (2012) Day-ahead electricity spot prices-fundamental modelling and the role of expected wind electricity infeed at the European Energy Exchange. Ph.D. thesis, University of St. GallenGoogle Scholar
  5. Fleten S-E, Lemming J (2003) Constructing forward price curves in electricity markets. Energy Econ 25(5):409–424CrossRefGoogle Scholar
  6. Hagan PS, West G (2006) Interpolation methods for curve construction. Appl Math Finance 13(2):89–129CrossRefGoogle Scholar
  7. Hildmann M, Ulbig A, Andersson G (2013) Revisiting the merit-order effect of renewable energy sources. ArXiv preprint arXiv:1307.0444 Google Scholar
  8. Kiesel R, Paraschiv F (2017) Econometric analysis of 15-minute intraday electricity prices. Energy Econ 64:77–90CrossRefGoogle Scholar
  9. Paraschiv F, Erni D, Pietsch R (2014) The impact of renewable energies on eex day-ahead electricity prices. Energy Policy 73:196–210CrossRefGoogle Scholar
  10. Paraschiv F, Fleten S-E, Schürle M (2015) A spot-forward model for electricity prices with regime shifts. Energy Econ 47:142–153CrossRefGoogle Scholar
  11. Paraschiv F, Bunn DW, Westgaard S (2016) Estimation and application of fully parametric multifactor quantile regression with dynamic coefficients. Available at SSRN 2741692Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Chair of Energy Trading and Financial ServicesUniversity Duisburg-EssenEssenGermany
  2. 2.Department of MathematicsUniversity of OsloOsloNorway
  3. 3.NTNU Business SchoolNorwegian University of Science and TechnologyTrondheimNorway

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