This study provides a framework based on an extension of the conditional autoregressive range (CARR) model which incorporates the impact of sudden changes in the unconditional volatility. This study proposes to use the RS estimator in the CARR model (called henceforth the CARRS model) instead of using the range. The results of the CARRS models with and without volatility breaks are compared with the results of the GARCH models with and without volatility breaks. We also compare the forecasting performance of CARRS models with the forecasting performance of EGARCH, TGARCH and FIGARCH models based on error statistics and regression approach. The findings indicate that the CARRS model with volatility breaks effectively captures the dynamics of volatility and provides better out-of-sample forecasts when compared with GARCH, EGARCH, TGARCH and FIGARCH models. We also devise a trading strategy to examine the economic significance of the proposed framework which indicates that the investor can make substantial gains (approximately 6%–10%) in return for most of cases based on volatility forecasts of CARRS model with volatility breaks. Results based on robustness check are consistent with our main findings.