Forecasting volatility with component conditional autoregressive range model
Quarterly Journal of Securities Exchange

Quarterly Journal of Securities Exchange

Forecasting volatility with component conditional autoregressive range model

Document Type : Original Article

Authors
MohammadMahdi Bahrololoum 1
Maryam Habibi Siahpoosh 1
Mostafa Sargolzaei 2
1 Assistant Professor, Department of Finance and Banking, Faculty of Management & Accounting, Allameh Tabataba'i University, Tehran, Iran
2 Master student, Department of Finance and Banking, Faculty of Management & Accounting, Allameh Tabataba'i University, Tehran, Iran
10.22034/jse.2023.12191.2135
Abstract
The purpose of this research is to achieve a suitable model for forecasting volatility of a broad market index. In this paper the CCARR model is proposed for forecasting volatility and its estimation results are compared with the popular GARCH, CGARCH and CARR models. The model is intuitive and convenient to implement by using the maximum likelihood estimation method. GARCH and CGARCH models use price returns and CARR and CCARR models use price ranges to predict volatility. CCARR and CGARCH models assume 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. Daily data of The Tehran Stock Exchange index from 2009 to 2022 including high, low and close prices are used. The results show that range-based models such as CARR and CCARR models fit the data better than return-based models. Also two-component models fit the data better than one-component models. In general, the CCARR model generates more accurate out of sample volatility forecasts than the popular GARCH, component GARCH and CARR models.
Keywords
volatility forecasting
Price Range
Price return
long memory
Maximum likelihood
Subjects
Alizadeh, S; Brandt, M; & Diebold, F. (2002). Range-based estimation of stochastic volatility models. Journal of Finance, 57, 1047–1092
(DOI: https://doi.org/10.1111/1540-6261.00454  )
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. (DOI: https://doi.org/10.1016/0304-4076(86)90063-1 )
Chou, R. (2005). Forecasting financial volatilities with extreme values: The conditional autoregressive range (CARR) model. Journal of Money, Credit and Banking, 37, 561–582. (DOI: https://doi.org/10.1353/mcb.2005.0027 )
Degiannakis, S; & Livada, A. (2013). Realized volatility or price range: Evidence from a discrete simulation of the continuous time diffusion process. Economic Modelling, 30, 212–216. (doi: https://doi.org/10.1016/j.econmod.2012.09.027 )
Ding, Z; & Granger, C. W. J. (1996). Modeling volatility persistence of speculative returns: A new approach. Journal of Econometrics, 73, 185–215. (doi: https://doi.org/10.1016/0304-4076(95)01737-2 )
Emami Pari, H. & Abasi, E. (2016). Estimating and Predicting Tehran Stock Exchange Index Fluctuations Using Daily and Intraday Data. Master's thesis, Alzahra University. (In Persian)
Engle, R. & Lee, G (1999). A long-run and short-run component model of stock return volatility. Cointegration, Causality, and Forecasting. Oxford University Press. (URL:https://global.oup.com/academic/product/cointegration-causality-and-forecasting-9780198296836?cc=ir&lang=en&# )
Esbati, M. & Aslani, P. (2016). Tehran Stock Exchange index volatility forecasts based on open, high, low and closed price Comparison with forecasts based on close price, using GARCH models. Master's thesis, University of Raja. (In Persian)
Harris, R. D. F; Stoja, E; & Yilmaz, F. (2011). A cyclical model of exchange rate volatility. Journal of Banking & Finance, 35(11), 3055–3064. (DOI: https://doi.org/10.2139/ssrn.1604440 )
Hashemi nezhad, M, Ebrahimi, B.  & Afrasiabi, M (2016). Risk assessment and volatility modeling. Tadbir Sarmayeh. (In Persian)
Li, H. Q; & Hong, Y. M. (2011). Financial volatility forecasting with range-based autoregressive volatility model. Finance Research Letters, 8(2), 69–76.
(DOI: https://doi.org/10.1016/j.frl.2010.12.002 )
Maheu, J. (2005). Can GARCH models capture long range dependence? Studies in Nonlinear Dynamics & Econometrics, 9(4), 1–41. (DOI: https://doi.org/10.2202/1558-3708.1269 )
Mincer, J; & Zarnowitz, V. (1969). The evaluation of economic forecasts. In J. Mincer (Ed.). Economic Forecasts and Expectations (NBER) (pp. 3–46). New-York: Columbia University Press for NBER. (URL: http://www.nber.org/chapters/c1214 )
Miura, K. (2011). An Introduction to Maximum Likelihood Estimation and Information Geometry. Interdisciplinary Information Sciences. https://doi.org/10.4036/iis.2011.155
Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business, 53, 61–65. (DOI: https://doi.org/10.1086/296071 )
Raei, R. & Pouyanfar, A. (2004). Advanced investment management. The Organization for Researching and Composing University textbooks in the Humanities. (In Persian)
Shoaraee, S. & Sanaee, M. (2010). Modeling and Forecasting Tehran Stock Exchange Return using ARFIMA and FIGARCH. Journal of Financial Accounting, 6: 173-186. (In Persian) (DOR:20.1001.1.23223405.1389.2.4.10.8)
Yajima, Yoshihiro. (1985). on estimation of long-memory time series models. Australian Journal of Statistics. https://doi.org/10.1111/j.1467-842X.1985.tb00576.x
Wu, x. & Hou, X. (2019). Forecasting volatility with component conditional autoregressive range model. North American Journal of Economics and Finance. (DOI: https://doi.org/10.1016/j.najef.2019.101078)
 
Quarterly Journal of Securities Exchange
Volume 18, Issue 71
Autumn 2025
Pages 185-208