Advanced Search
MyIDEAS: Login to save this paper or follow this series

Forecasting electricity spot market prices with a k-factor GIGARCH process

Contents:

Author Info

  • Abdou Kâ Diongue

    ()
    (UFR SAT - Université Gaston Berger de Saint-Louis Sénégal - Université Gaston Berger de Saint-Louis)

  • Dominique Guegan

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne)

  • Bertrand Vignal

    ()
    (EDF - EDF - Recherche et Développement)

Abstract

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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://halshs.archives-ouvertes.fr/docs/00/44/16/26/PDF/B07058-2.pdf
Download Restriction: no

Bibliographic Info

Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00188264.

as in new window
Length:
Date of creation: Nov 2007
Date of revision:
Handle: RePEc:hal:cesptp:halshs-00188264

Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00188264
Contact details of provider:
Web page: http://hal.archives-ouvertes.fr/

Related research

Keywords: Conditional mean ; conditional variance ; forecast ; electricity prices ; GIGARCH process;

Other versions of this item:

Find related papers by JEL classification:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  2. Ferrara, Laurent & Guegan, Dominique, 2001. "Forecasting with k-Factor Gegenbauer Processes: Theory and Applications," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 20(8), pages 581-601, December.
  3. Dominique Guegan, 2004. "How Can We Define the Long Memory Concept? An Econometric Survey," Econometric Society 2004 Australasian Meetings, Econometric Society 361, Econometric Society.
  4. Dominique Guegan, 2005. "How can we Define the Concept of Long Memory? An Econometric Survey," Econometric Reviews, Taylor & Francis Journals, Taylor & Francis Journals, vol. 24(2), pages 113-149.
  5. 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.
  6. 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) halshs-00259225, HAL.
  7. Siem Jan Koopman & Marius Ooms & M. Angeles Carnero, 2005. "Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 05-091/4, Tinbergen Institute.
  8. 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, De Gruyter, vol. 10(3), pages 1-36, September.
  9. 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.
  10. 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, Elsevier, vol. 21(3), pages 435-462.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Angelica Gianfreda & Luigi Grossi, 2011. "Forecasting Italian Electricity Zonal Prices with Exogenous Variables," Working Papers, University of Verona, Department of Economics 01/2011, University of Verona, Department of Economics.
  2. Erdogdu, Erkan, 2010. "A paper on the unsettled question of Turkish electricity market: Balancing and settlement system (Part I)," MPRA Paper 19090, University Library of Munich, Germany.
  3. Rahimiyan, Morteza & Morales, Juan M. & Conejo, Antonio J., 2011. "Evaluating alternative offering strategies for wind producers in a pool," Applied Energy, Elsevier, Elsevier, vol. 88(12), pages 4918-4926.
  4. 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, Elsevier, vol. 87(10), pages 3226-3234, October.
  5. repec:hal:journl:halshs-00185370 is not listed on IDEAS
  6. repec:hal:journl:halshs-00375531 is not listed on IDEAS
  7. Liu, Heping & Shi, Jing, 2013. "Applying ARMA–GARCH approaches to forecasting short-term electricity prices," Energy Economics, Elsevier, Elsevier, vol. 37(C), pages 152-166.
  8. Foued Saâdaoui, 2013. "The Price and Trading Volume Dynamics Relationship in the EEX Power Market: A Wavelet Modeling," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 42(1), pages 47-69, June.
  9. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, Elsevier, vol. 87(11), pages 3606-3610, November.
  10. Tryggvi Jónsson & Pierre Pinson & Henrik Madsen & Henrik Aalborg Nielsen, 2014. "Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression," Energies, MDPI, Open Access Journal, vol. 7(9), pages 5523-5547, August.
  11. Amjady, Nima & Keynia, Farshid, 2010. "A new spinning reserve requirement forecast method for deregulated electricity markets," Applied Energy, Elsevier, Elsevier, vol. 87(6), pages 1870-1879, June.
  12. repec:hal:journl:halshs-00259193 is not listed on IDEAS

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:hal:cesptp:halshs-00188264. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.