IDEAS home Printed from https://ideas.repec.org/a/taf/apfiec/v23y2013i21p1635-1647.html
   My bibliography  Save this article

Improving the CARR model using extreme range estimators

Author

Listed:
  • José Luis Miralles-Marcelo
  • José Luis Miralles-Quirós
  • María del Mar Miralles-Quirós

Abstract

The aim of this article is to analyse the forecasting ability of the conditional autoregressive range (CARR) model proposed by Chou (2005) using the S&P 500. We extend the data sample, allowing for the analysis of different stock market circumstances and propose the use of various range estimators in order to analyse their forecasting performance. Additionally, we decide to divide the full sample into four sub-samples with the aim of analysing the forecasting ability of the different range estimators in various periods. Our results show that the original CARR model can be improved depending on three factors: the trend, the level of volatility in the analysis period and the error estimator that is used to analyse the forecasting ability of each model. The Parkinson model is better for upward trends and volatilities which are higher and lower than the mean while the CARR model is better for downward trends and mean volatilities.

Suggested Citation

  • José Luis Miralles-Marcelo & José Luis Miralles-Quirós & María del Mar Miralles-Quirós, 2013. "Improving the CARR model using extreme range estimators," Applied Financial Economics, Taylor & Francis Journals, vol. 23(21), pages 1635-1647, November.
  • Handle: RePEc:taf:apfiec:v:23:y:2013:i:21:p:1635-1647
    DOI: 10.1080/09603107.2013.844325
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/09603107.2013.844325
    Download Restriction: Access to full text is restricted to subscribers.

    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. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.

    More about this item

    Statistics

    Access and download statistics

    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:taf:apfiec:v:23:y:2013:i:21:p:1635-1647. 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: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/RAFE20 .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.