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Modeling Electricity Prices: From the State of the Art to a Draft of a New Proposal

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Author Info
Matteo Manera (University of Milano-Bicocca)
Massimiliano Serati (Institute of Economics, Cattaneo University – LIUC, Castellanza)
Michele Plotegher (ENI SPA)

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Abstract

In the last decades a liberalization of the electric market has started; prices are now determined on the basis of contracts on regular markets and their behaviour is mainly driven by usual supply and demand forces. A large body of literature has been developed in order to analyze and forecast their evolution: it includes works with different aims and methodologies depending on the temporal horizon being studied. In this survey we depict the actual state of the art focusing only on the recent papers oriented to the determination of trends in electricity spot prices and to the forecast of these prices in the short run. Structural methods of analysis, which result appropriate for the determination of forward and future values are left behind. Studies have been divided into three broad classes: Autoregressive models, Regime switching models, Volatility models. Six fundamental points arise: the peculiarities of electricity market, the complex statistical properties of prices, the lack of economic foundations of statistical models used for price analysis, the primacy of uniequational approaches, the crucial role played by demand and supply in prices determination, the lack of clearcut evidence in favour of a specific framework of analysis. To take into account the previous stylized issues, we propose the adoption of a methodological framework not yet used to model and forecast electricity prices: a time varying parameters Dynamic Factor Model (DFM). Such an eclectic approach, introduced in the late ‘70s for macroeconomic analysis, enables the identification of the unobservable dynamics of demand and supply driving electricity prices, the coexistence of short term and long term determinants, the creation of forecasts on future trends. Moreover, we have the possibility of simulating the impact that mismatches between demand and supply have over the price variable. This way it is possible to evaluate whether congestions in the network (eventually leading black out phenomena) trigger price reactions that can be considered as warning mechanisms.

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Paper provided by Fondazione Eni Enrico Mattei in its series Working Papers with number 2008.9.

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Date of creation: Feb 2008
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Handle: RePEc:fem:femwpa:2008.9

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Related research
Keywords: Electricity Spot Prices; Autoregressive Models; GARCH Models; Regime Switching Models; Dynamic Factor Models;

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Find related papers by JEL classification:
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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  1. Michael Bierbrauer & Stefan Trueck & Rafal Weron, 2005. "Modeling electricity prices with regime switching models," Econometrics 0502005, EconWPA. [Downloadable!]
  2. Cyriel De Jong, 2006. "The Nature of Power Spikes: A Regime-Switch Approach," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 10(3), pages 1361-1361. [Downloadable!] (restricted)
  3. Adam Misiorek & Stefan Trueck & Rafal Weron, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 10(3), pages 1362-1362. [Downloadable!] (restricted)
  4. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2003. "Leading Indicators for Euro Area Inflation and GDP Growth," CEPR Discussion Papers 3893, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  5. James H. Stock & Mark W. Watson, 1992. "A Procedure for Predicting Recessions With Leading Indicators: Econometric Issues and Recent Experience," NBER Working Papers 4014, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  6. Alvaro Cartea & Marcelo Gustavo Figueroa, 2005. "Pricing in Electricity Markets: a Mean Reverting Jump Diffusion Model with Seasonality," Birkbeck Working Papers in Economics and Finance 0507, Birkbeck, School of Economics, Mathematics & Statistics. [Downloadable!]
    Other versions:
  7. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April. [Downloadable!] (restricted)
  8. Guthrie, Graeme & Videbeck, Steen, 2007. "Electricity spot price dynamics: Beyond financial models," Energy Policy, Elsevier, vol. 35(11), pages 5614-5621, November. [Downloadable!] (restricted)
  9. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December. [Downloadable!] (restricted)
  10. Michael ARTIS & Anindya BANERJEE & Massimiliano MARCELLINO, 2001. "Factor Forecasts for the UK," Economics Working Papers ECO2001/15, European University Institute. [Downloadable!]
    Other versions:
  11. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July. [Downloadable!] (restricted)
  12. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages C62-85, May. [Downloadable!] (restricted)
  13. Apostolos Serletis & Akbar Shahmoradi, 2006. "Measuring and Testing Natural Gas and Electricity Markets Volatility: Evidence from Alberta's Deregulated Markets," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 10(3), pages 1341-1341. [Downloadable!] (restricted)
  14. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October. [Downloadable!] (restricted)
    Other versions:
  15. Francis X. Diebold & Monika Piazzesi & Glenn D. Rudebusch, 2005. "Modeling Bond Yields in Finance and Macroeconomics," PIER Working Paper Archive 05-008, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania. [Downloadable!]
    Other versions:
  16. Giulio Bottazzi & Sandro Sapio & Angelo Secchi, 2004. "Some Statistical Investigations on the Nature and Dynamics of Electricity Prices," LEM Papers Series 2004/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
  17. Andrew C. Worthington & Adam Kay-Spratley & Helen Higgs, 2002. "Transmission of prices and price volatility in Australian electricity spot markets: A multivariate GARCH analysis," School of Economics and Finance Discussion Papers and Working Papers Series 114, School of Economics and Finance, Queensland University of Technology. [Downloadable!]
  18. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September. [Downloadable!] (restricted)
    Other versions:
  19. Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376. [Downloadable!] (restricted)
    Other versions:
  20. Marcellino, Massimiliano, 2006. "Leading Indicators," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
  21. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November. [Downloadable!] (restricted)
    Other versions:
  22. Ben Bernanke & Jean Boivin & Piotr S. Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, MIT Press, vol. 120(1), pages 387-422, January.
    Other versions:
  23. Altissimo, Filippo & Bassanetti, Antonio & Cristadoro, Riccardo & Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia & Veronese, Giovanni, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  24. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, . "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
    Other versions:
  25. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc. [Downloadable!]
    Other versions:
  26. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  27. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September. [Downloadable!] (restricted)
    Other versions:
    • Huisman, R. & Mahieu, R.J., 2001. "Regime Jumps in Electricity Prices," Research Paper ERS-2001-48-F&A Revision_, 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 Uni. [Downloadable!]
  28. Rafal Weron & Ingve Simonsen & Piotr Wilman, 2003. "Modeling highly volatile and seasonal markets: evidence from the Nord Pool electricity market," Econometrics 0303007, EconWPA. [Downloadable!]
  29. Niels Haldrup & Morten Nielsen, 2006. "Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 10(3), pages 1367-1367. [Downloadable!] (restricted)
    Other versions:
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