The authors are grateful to the insightful comments received on the first versions of this article from the Associate Editor and from two anonymous referees. We also thank Angel Leon, Craig Pirrong, Cristopher R. Knittel, Michael Roberts, Vicente Meneu and the participants at the I Workshop in Electricity Derivatives (Valencia, Spain), IX Foro Finanzas (Pamplona, Spain), EFA (Berlin), EARIE (Madrid), I Commodities Modelling Workshop (Birkbeck, London). Escribano acknowledges financial support provided by MICIN grant ECO2009-08308 and by the Bank of Spain Excellence Program and J.I. Peña by MCIN grant ECO2009-12551. P. Villaplana also acknowledges financial support provided by Energy Economics Laboratory, of the Universidad Carlos III de Madrid and Fundación Ramón Areces. The views expressed in this article are those of the authors, and not those of the Comisión Nacional de Energía.
Modelling Electricity Prices: International Evidence*
Article first published online: 19 APR 2011
© Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2011
Oxford Bulletin of Economics and Statistics
Volume 73, Issue 5, pages 622–650, October 2011
How to Cite
Escribano, A., Ignacio Peña, J. and Villaplana, P. (2011), Modelling Electricity Prices: International Evidence. Oxford Bulletin of Economics and Statistics, 73: 622–650. doi: 10.1111/j.1468-0084.2011.00632.x
- Issue published online: 14 SEP 2011
- Article first published online: 19 APR 2011
- Final Manuscript Received: October 2010
This article analyses the evolution of electricity prices in deregulated markets. We present a general class of models that simultaneously takes into account several factors: seasonality, mean reversion, GARCH behaviour and time-dependent jumps. The models are applied to daily equilibrium spot prices of eight electricity markets. Eight different nested models were estimated to compare the relative importance of each factor in each of the eight markets. We find strong evidence that electricity equilibrium prices are mean-reverting, with volatility clustering (GARCH) and with jumps of time-dependent intensity, even after adjusting for seasonality.