This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Marius Ooms
M. Angeles Carnero
Siem Jan Koopman

Additional information is available for the following registered author(s):

Abstract

In this paper we consider different periodic extensions of regression models with autoregressive fractionally integrated moving average disturbances for the analysis of daily spot prices of electricity. We show that day-of-the-week periodicity and long memory are important determinants for the dynamic modelling of the conditional mean of electricity spot prices. Once an effective description of the conditional mean of spot prices is empirically identified, focus can be directed towards volatility features of the time series. For the older electricity market of Nord Pool in Norway, it is found that a long memory model with periodic coefficients is required to model daily spot prices effectively. Further, strong evidence of conditional heteroskedasticity is found in the mean corrected Nord Pool series. For daily prices at three emerging electricity markets that we consider (APX in The Netherlands, EEX in Germany and Powernext in France) periodicity in the autoregressive coefficients is also stablished, but evidence of long memory is not found and existence of dynamic behaviour in the variance of the spot prices is less pronounced. The novel findings in this paper can have important consequences for the modelling and forecasting of mean and variance functions of spot prices for electricity and associated contingent assets

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. 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://www.tinbergen.nl/discussionpapers/03071.pdf
File Format: application/pdf
File Function: main text
Download Restriction: no

Publisher Info
Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 158.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 11 Aug 2004
Date of revision:
Handle: RePEc:ecm:ausm04:158

Contact details of provider:
Phone: 1 212 998 3820
Fax: 1 212 995 4487
Email:
Web page: http://www.econometricsociety.org/pastmeetings.asp
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords: GARCH; Long Memory;

Other versions of this item:

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C5 - Mathematical and Quantitative Methods - - Econometric Modeling

This paper has been announced in the following NEP Reports:

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.:
  1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188. [Downloadable!] (restricted)
  2. Byström, Hans, 2001. "Extreme Value Theory and Extremely Large Electricity Price Changes," Working Papers 2001:19, Lund University, Department of Economics.
  3. Doornik, Jurgen A. & Ooms, Marius, 2003. "Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 333-348, March. [Downloadable!] (restricted)
    Other versions:
  4. Jong, C.M. de & Huisman, R., 2002. "Option Formulas for Mean-Reverting Power Prices with Spikes," Research Paper ERS-2002-96-F&A Revision_, 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 Uni. [Downloadable!]
  5. Ooms, Marius & Franses, Philip Hans, 1997. "On Periodic Correlations between Estimated Seasonal and Nonseasonal Components in German and U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 470-81, October.
  6. Oberhofer, W & Kmenta, J, 1974. "A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models," Econometrica, Econometric Society, vol. 42(3), pages 579-90, May. [Downloadable!] (restricted)
  7. Robinson, Peter M. & Yajima, Yoshihiro, 2002. "Determination of cointegrating rank in fractional systems," Journal of Econometrics, Elsevier, vol. 106(2), pages 217-241, February. [Downloadable!] (restricted)
    Other versions:
  8. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September. [Downloadable!] (restricted)
  9. Ooms, M. & Franses, Ph.H.B.F., 1998. "A seasonal periodic long memory model for monthly river flows," Econometric Institute Report EI 9842 Revision_Date: 20, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
  10. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
  11. Wilkinson, Louise & Winsen, Joseph, 2002. "What We Can Learn from a Statistical Analysis of Electricity Prices in New South Wales," The Electricity Journal, Elsevier, vol. 15(3), pages 60-69, April. [Downloadable!] (restricted)
  12. Tim Bollerslev & Robert F. Engle & Daniel B. Nelson, 1993. "ARCH Models," University of California at San Diego, Economics Working Paper Series 93-49, Department of Economics, UC San Diego. [Downloadable!]
    Other versions:
    • Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038 Elsevier. [Downloadable!] (restricted)
  13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
  14. Álvaro Escribano & Juan Ignacio Peña & Pablo Villaplana, 2002. "Modeling Electricity Prices: International Evidence," Economics Working Papers we022708, Universidad Carlos III, Departamento de Economía. [Downloadable!]
  15. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July. [Downloadable!] (restricted)
Full references

Cited by:
(explanations, 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.)

  1. Sandro Sapio, 2004. "Market Design, Bidding Rules, and Long Memory in Electricity Prices," LEM Papers Series 2004/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
  2. Hipòlit Torró, 2007. "Forecasting Weekly Electricity Prices at Nord Pool," Working Papers 2007.88, Fondazione Eni Enrico Mattei. [Downloadable!]
  3. Niels Haldrup & Morten O. Nielsen, 2004. "A Regime Switching Long Memory Model for Electricity Prices," Economics Working Papers 2004-2, School of Economics and Management, University of Aarhus. [Downloadable!]
    Other versions:
  4. Haldrup; Niels & Morten Oerregaard Nielsen, 2005. "Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices," Economics Working Papers 2005-18, School of Economics and Management, University of Aarhus. [Downloadable!]
    Other versions:
Statistics
Access and download statistics

Did you know? RePEc stands for Research Papers in Economics.

This page was last updated on 2009-12-2.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.