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Can Markov-regime switching models improve power price forecasts? Evidence for German daily power prices

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  • Kosater, Peter
  • Mosler, Karl

Abstract

Nonlinear autoregressive Markov regime-switching models are intuitive and frequently proposed time series approaches for the modelling of electricity spot prices. In this paper such models are compared to an ordinary linear autoregressive model with regard to their forecast performance. The study is carried out using German daily spot prices from the European Energy Exchange in Leipzig. Four nonlinear models are used for the forecast study. The resultsof the study suggest that Markov regime-switching models provide better forecasts than linear models. --

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Bibliographic Info

Paper provided by University of Cologne, Department for Economic and Social Statistics in its series Discussion Papers in Statistics and Econometrics with number 1/05.

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Date of creation: 2005
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Handle: RePEc:zbw:ucdpse:105

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Keywords: Electricity spot prices; Markov regime-switching; forecasting;

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References

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  1. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
  2. Marius Ooms & M. Angeles Carnero & Siem Jan Koopman, 2004. "Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices," Econometric Society 2004 Australasian Meetings 158, Econometric Society.
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Citations

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Cited by:
  1. Christensen, T.M. & Hurn, A.S. & Lindsay, K.A., 2012. "Forecasting spikes in electricity prices," International Journal of Forecasting, Elsevier, vol. 28(2), pages 400-411.
  2. Timothy Christensen & Stan Hurn & Kenneth Lindsay, 2009. "It Never Rains but it Pours: Modeling the Persistence of Spikes in Electricity Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-48.
  3. Jónsson, Tryggvi & Pinson, Pierre & Madsen, Henrik, 2010. "On the market impact of wind energy forecasts," Energy Economics, Elsevier, vol. 32(2), pages 313-320, March.
  4. 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.
  5. Janczura, Joanna & Weron, Rafal, 2010. "An empirical comparison of alternate regime-switching models or electricity spot prices," MPRA Paper 20546, University Library of Munich, Germany.
  6. Lee, Chien-Chiang & Lee, Jun-De, 2009. "Energy prices, multiple structural breaks, and efficient market hypothesis," Applied Energy, Elsevier, vol. 86(4), pages 466-479, April.
  7. Eichler Michael & Tuerk Dennis, 2012. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Research Memorandum 036, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  8. Liebl, Dominik, 2013. "Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective," MPRA Paper 50881, University Library of Munich, Germany.
  9. Heydari, Somayeh & Siddiqui, Afzal, 2010. "Valuing a gas-fired power plant: A comparison of ordinary linear models, regime-switching approaches, and models with stochastic volatility," Energy Economics, Elsevier, vol. 32(3), pages 709-725, May.
  10. Lin, Whei-Min & Gow, Hong-Jey & Tsai, Ming-Tang, 2010. "An enhanced radial basis function network for short-term electricity price forecasting," Applied Energy, Elsevier, vol. 87(10), pages 3226-3234, October.
  11. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer, vol. 97(3), pages 239-270, July.
  12. Kosater, Peter, 2006. "On the impact of weather on German hourly power prices," Discussion Papers in Statistics and Econometrics 1/06, University of Cologne, Department for Economic and Social Statistics.
  13. Liebl, Dominik, 2010. "Modeling hourly Electricity Spot Market Prices as non stationary functional times series," MPRA Paper 25017, University Library of Munich, Germany.

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