Studies of volatility modelling and estimation usually rely on the returns information provided by the closing prices,whereas very few studies employ price ranges,which incorporate more information on intraday price movements, to model volatility. The paper extends the classical conditional autoregressive range( CARR) model and proposes a two-factor stochastic conditional range( 2 FSCR) model with Gamma distribution for price ranges. The proposed model mimics the structure of the stochastic volatility( SV) model and can capture the long-range dependence( long memory property) of volatility. The maximum likelihood estimation method based on the continuous particle filters is employed to estimate the parameters of the 2 FSCR model.Monte Carlo simulations show that the method performs well. The 2 FSCR model is tested using data on Shanghai Stock Exchange Composite Index( SSE),Shenzhen Stock Exchange Component Index( SZSE),Hong Kong Hang Seng Index( HSI) and United States Standard & Poor's 500 Index( SPX). The results show that the 2 FSCR model fits the data better than both the CARR model and the single-factor SCR model. Model diagnostics suggest that the 2 FSCR model can describe the extreme tails of the price range distribution better than the CARR and SCR models,and can capture the dynamics of the volatility( time-varying volatility,volatility clustering,and long memory property of the volatility). Using the price range and realized volatility as the benchmarks,out-of-sample predictive ability of different models,namely,the CARR,the SCR and the2 FSCR,is compared based on the rolling window scheme. The results show that the 2 FSCR model does have superior predictive accuracy compared with the CARR model and the SCR model.