Improving the CARR model using extreme range estimators

• Autores: José Luis Miralles Marcelo, José Luis Miralles Quirós, María del Mar Miralles Quirós
• Localización: Applied financial economics, ISSN 0960-3107, Vol. 23, Nº. 19-21, 2013, págs. 1635-1647
• Idioma: inglés
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• Resumen

  • 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.