Forecasting electricity spot market prices with a k-factor GIGARCH process
In this article, we investigate conditional mean and variance forecasts using a dynamic model following a k-factor GIGARCH process. We are particularly interested in calculating the conditional variance of the prediction error. We apply this method to electricity prices and test spot prices forecasts until one month ahead forecast. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria.
|Date of creation:||Nov 2007|
|Date of revision:||Nov 2009|
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- Dominique Guegan, 2005.
"How can we Define the Concept of Long Memory? An Econometric Survey,"
Taylor & Francis Journals, vol. 24(2), pages 113-149.
- Guégan D., 2004. "How Can We Define The Concept of Long Memory? An Econometric Survey," School of Economics and Finance Discussion Papers and Working Papers Series 178, School of Economics and Finance, Queensland University of Technology.
- Dominique Guegan, 2005. "How can we define the concept of long memory ? An econometric survey," Post-Print halshs-00179343, HAL.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 134-44, January.
- Dominique Guegan, 2004. "How Can We Define the Long Memory Concept? An Econometric Survey," Econometric Society 2004 Australasian Meetings 361, Econometric Society.
- Ferrara, Laurent & Guegan, Dominique, 2001. "Forecasting with k-Factor Gegenbauer Processes: Theory and Applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(8), pages 581-601, December.
- 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.
- Dominique Guegan, 2003. "A prospective study of the k-factor Gegenbauer processes with heteroscedastic errors and an application to inflation rates," Post-Print halshs-00201314, HAL.
- Dominique Guegan & Abdou Kâ Diongue & Bertrand Vignal, 2004. "A k- factor GIGARCH process : estimation and application to electricity market spot prices," Post-Print halshs-00188533, HAL.
- 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.
- Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
- Abdou Kâ Diongue & Dominique Guegan, 2008.
"The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics,"
Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers)
- Abdou Kâ Diongue & Dominique Guegan, 2008. "The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics," Documents de travail du Centre d'Economie de la Sorbonne b08013, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
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