Forecasting volatility with component conditional autoregressive range model

Xinyu Wu and Xinmeng Hou

The North American Journal of Economics and Finance, 2020, vol. 51, issue C

Abstract: In this paper, we propose a component conditional autoregressive range (CCARR) model for forecasting volatility. The proposed CCARR model assumes that the price range comprises both a long-run (trend) component and a short-run (transitory) component, which has the capacity to capture the long memory property of volatility. The model is intuitive and convenient to implement by using the maximum likelihood estimation method. Empirical analysis using six stock market indices highlights the value of incorporating a second component into range (volatility) modelling and forecasting. In particular, we find that the proposed CCARR model fits the data better than the CARR model, and that it generates more accurate out-of-sample volatility forecasts and contains more information content about the true volatility than the popular GARCH, component GARCH and CARR models.

Keywords: Price range; CARR; CCARR; GARCH; CGARCH; Volatility forecasting (search for similar items in EconPapers)
JEL-codes: C32 C5 G17 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:51:y:2020:i:c:s106294081930083x

DOI: 10.1016/j.najef.2019.101078

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