IDEAS home Printed from https://ideas.repec.org/a/eme/jespps/v36y2009i4p383-392.html
   My bibliography  Save this article

Forecasting in inefficient commodity markets

Author

Listed:
  • Periklis Gogas
  • Apostolos Serletis

Abstract

Purpose - This paper set out to use an autoregressive conditional heteroscedasticity (ARCH)‐type model to capture the time‐varying conditional variance of Alberta electricity prices. This is of major importance in forecasting, since ARCH‐type models allow the conditional variance to depend on elements of the information set. Design/methodology/approach - The paper uses the model to perform static and dynamic forecasts over different horizons and to compare its forecasting performance with a random walk and a moving average model. Findings - The paper provides a study of hourly electricity prices using recent advances in the financial econometrics literature. Originality/value - The contribution of the paper is its use of models of changing volatility to properly identify the type of heteroscedasticity in the data‐generation processes. This is of major importance in forecasting.

Suggested Citation

  • Periklis Gogas & Apostolos Serletis, 2009. "Forecasting in inefficient commodity markets," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 36(4), pages 383-392, September.
    • Handle: RePEc:eme:jespps:v:36:y:2009:i:4:p:383-392
    DOI: 10.1108/01443580910973592
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/01443580910973592/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/01443580910973592/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/01443580910973592?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020. "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, vol. 2(3), pages 241-258, December.
    2. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2022. "Forecasting Bitcoin Spikes: A GARCH-SVM Approach," Forecasting, MDPI, vol. 4(4), pages 1-15, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eme:jespps:v:36:y:2009:i:4:p:383-392. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emerald Support (email available below). General contact details of provider: .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.