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Improving the CARR model using extreme range estimators


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


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

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    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.

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