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Heavy tails and electricity prices: Do time series models with non-Gaussian noise forecast better than their Gaussian counterparts?

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  • Weron, Rafal
  • Misiorek, Adam

Abstract

This paper is a continuation of our earlier studies on short-term price forecasting of California electricity prices with time series models. Here we focus on whether models with heavy-tailed innovations perform better in terms of forecasting accuracy than their Gaussian counterparts. Consequently, we limit the range of analyzed models to autoregressive time series approaches that have been found to perform well for pre-crash California power market data. We expand them by allowing for heavy-tailed innovations in the form of α-stable or generalized hyperbolic noise.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 2292.

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Date of creation: Mar 2007
Date of revision: Oct 2007
Handle: RePEc:pra:mprapa:2292

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Keywords: Electricity; price forecasting; heavy tails; time series; α-stable distribution; generalized hyperbolic distribution;

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  1. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
  2. 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.
  3. 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.
  4. Rafal Weron, 2005. "Heavy tails and electricity prices," HSC Research Reports HSC/05/02, Hugo Steinhaus Center, Wroclaw University of Technology.
  5. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
  6. Peter Carr & Helyette Geman, 2002. "The Fine Structure of Asset Returns: An Empirical Investigation," The Journal of Business, University of Chicago Press, vol. 75(2), pages 305-332, April.
  7. Rafal Weron & Adam Misiorek, 2006. "Short-term electricity price forecasting with time series models: A review and evaluation," HSC Research Reports HSC/06/01, Hugo Steinhaus Center, Wroclaw University of Technology.
  8. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
  9. Weron, Rafał, 2004. "Computationally intensive Value at Risk calculations," Papers 2004,32, Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE).
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