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It Never Rains but it Pours: Modeling the Persistence of Spikes in Electricity Prices

  • Timothy Christensen
  • Stan Hurn
  • Kenneth Lindsay

During periods of market stress, electricity prices can rise dramatically. This paper treats these abnormal episodes or price spikes as count events and attempts to build a model of the spiking process. By contrast to the existing literature, which either ignores temporal dependence in the spiking process or attempts to model the dependence solely in terms of deterministic variables (like seasonal and day of the week effects), this paper argues that persistence in the spiking process is an important factor in building an effective model. A Poisson autoregressive framework is proposed in which price spikes occur as a result of the latent arrival and survival of system stresses. This formulation captures the salient features of the process adequately, and yields forecasts of price spikes that are superior to those obtained from na•ve models that do not account for persistence in the spiking process.

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Article provided by International Association for Energy Economics in its journal The Energy Journal.

Volume (Year): Volume 30 (2009)
Issue (Month): Number 1 ()
Pages: 25-48

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Handle: RePEc:aen:journl:2009v30-01-a02
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  1. Ralf Becker & Stan Hurn & Vlad Pavlov, 2007. "Modelling Spikes in Electricity Prices," The Economic Record, The Economic Society of Australia, vol. 83(263), pages 371-382, December.
  2. Markus Burger & Bernhard Klar & Alfred Muller & Gero Schindlmayr, 2004. "A spot market model for pricing derivatives in electricity markets," Quantitative Finance, Taylor & Francis Journals, vol. 4(1), pages 109-122.
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  5. Alvaro Cartea & Marcelo Figueroa, 2005. "Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(4), pages 313-335.
  6. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
  7. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
  8. Don Harding & Adrian Pagan, 2006. "The Econometric Analysis of Constructed Binary Time Series," Department of Economics - Working Papers Series 963, The University of Melbourne.
  9. Bystrom, Hans N. E., 2005. "Extreme value theory and extremely large electricity price changes," International Review of Economics & Finance, Elsevier, vol. 14(1), pages 41-55.
  10. Mount, Timothy D. & Ning, Yumei & Cai, Xiaobin, 2006. "Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters," Energy Economics, Elsevier, vol. 28(1), pages 62-80, January.
  11. Huisman, R. & Mahieu, R.J., 2001. "Regime Jumps in Electricity Prices," ERIM Report Series Research in Management ERS-2001-48-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.
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  17. Alvaro Escribano & Juan Ignacio Peña & Pablo Villaplana, 2002. "Modeling Electricity Prices: International Evidence," Economics Working Papers we022708, Universidad Carlos III, Departamento de Economía.
  18. Robert Breunig & Serinah Najarian & Adrian Pagan, 2003. "Specification Testing of Markov Switching Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 703-725, December.
  19. Kosater, Peter & Mosler, Karl, 2006. "Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices," Applied Energy, Elsevier, vol. 83(9), pages 943-958, September.
  20. R. K. Freeland & B. P. M. McCabe, 2004. "Analysis of low count time series data by poisson autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 701-722, 09.
  21. De Jong Cyriel, 2006. "The Nature of Power Spikes: A Regime-Switch Approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-28, September.
  22. Geman, Hélyette & Roncoroni, Andréa, 2006. "Understanding the Fine Structure of Electricity Prices," Economics Papers from University Paris Dauphine 123456789/1433, Paris Dauphine University.
  23. Bierbrauer, Michael & Menn, Christian & Rachev, Svetlozar T. & Truck, Stefan, 2007. "Spot and derivative pricing in the EEX power market," Journal of Banking & Finance, Elsevier, vol. 31(11), pages 3462-3485, November.
  24. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
  25. Taylor, James W. & Buizza, Roberto, 2003. "Using weather ensemble predictions in electricity demand forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 57-70.
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