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Deregulated Wholesale Electricity Prices in Italy


  • Bruno Bosco
  • Lucia Parisio
  • Matteo Pelagatti


In this paper we analyze the time series of daily average prices generated in the Italian electricity market, which started to operate as a Pool in April 2004. The objective is to characterize the high degree of autocorrelation and multiple seasonalities in the electricity prices. We use periodic time series models with GARCH disturbances and leptokurtic distributions and compare their performance with more classical ARMA-GARCH processes. The within-year seasonal variation is modelled using the low frequencies components of physical quantities, which are very regular throughout the sample. Results reveal that much of the variability of the price series is explained by deterministic multiple seasonalities which interact with each other. Periodic AR-GARCH models seem to perform quite well in mimicking the features of the stochastic part of the price process.

Suggested Citation

  • Bruno Bosco & Lucia Parisio & Matteo Pelagatti, 2006. "Deregulated Wholesale Electricity Prices in Italy," Working Papers 20060301, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica, revised Apr 2006.
  • Handle: RePEc:mis:wpaper:20060301

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    References listed on IDEAS

    1. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    4. Fabra, Natalia & Toro, Juan, 2005. "Price wars and collusion in the Spanish electricity market," International Journal of Industrial Organization, Elsevier, vol. 23(3-4), pages 155-181, April.
    5. de Jong, C.M. & Huisman, R., 2002. "Option Formulas for Mean-Reverting Power Prices with Spikes," ERIM Report Series Research in Management ERS-2002-96-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Franses, Philip Hans & Paap, Richard, 2004. "Periodic Time Series Models," OUP Catalogue, Oxford University Press, number 9780199242030, June.
    7. Wilkinson, Louise & Winsen, Joseph, 2002. "What We Can Learn from a Statistical Analysis of Electricity Prices in New South Wales," The Electricity Journal, Elsevier, vol. 15(3), pages 60-69, April.
    8. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
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    Cited by:

    1. Sandro Sapio, 2012. "Modeling the distribution of day-ahead electricity returns: a comparison," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1935-1949, December.
    2. Florian Ziel & Rick Steinert & Sven Husmann, 2014. "Efficient Modeling and Forecasting of the Electricity Spot Price," Papers 1402.7027,, revised Oct 2014.
    3. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2007. "A robust multivariate long run analysis of European electricity prices," Working Papers 20070901, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
    4. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-22, August.
    5. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2010. "Long-run relations in european electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 805-832.
    6. Elbert Dijkgraaf & Maarten C.W. Janssen, 2009. "Defining European Wholesale Electricity Markets: An “And/Or” Approach," Tinbergen Institute Discussion Papers 09-079/3, Tinbergen Institute.
    7. Streimikiene, Dalia & Siksnelyte, Indre, 2016. "Sustainability assessment of electricity market models in selected developed world countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 72-82.
    8. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2006. "Deregulated Wholesale Electricity Prices in Europe," Working Papers 20061001, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
    9. G. Papaioannou & P. Papaioannou & N. Parliaris, 2014. "Modeling the stylized facts of wholesale system marginal price (SMP) and the impacts of regulatory reforms on the Greek Electricity Market," Papers 1401.5452,
    10. Di Cosmo, Valeria, 2015. "Forward Price, Renewables and the Electricity Price: The Case of Italy," Papers WP511, Economic and Social Research Institute (ESRI).
    11. Andrea Cervone & Ezio Santini & Sabrina Teodori & Donatella Zaccagnini Romito, 2014. "Electricity Price Forecast: a Comparison of Different Models to Evaluate the Single National Price in the Italian Energy Exchange Market," International Journal of Energy Economics and Policy, Econjournals, vol. 4(4), pages 744-758.

    More about this item


    Electricity auctions; Periodic Time Series; Conditional Heteroskedasticity; Multiple Seasonalities;

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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