This paper explores the role of heterogeneity and leverage effect on the predictability of the AddRS volatility estimator (Kumar & Maheswaran, 2014a) using daily, weekly and monthly volatility components. Similar to the model setting of heterogeneous autoregressive (HAR) model (Corsi, 2009), we introduce the frameworks (HAR – AddRS and HAR – AddRS – L) to incorporate the impact of heterogeneity and leverage effect in modeling the AddRS volatility estimator. We find that the heterogeneity and leverage effect significantly impact the volatility prediction and when taken together produce a better in-sample fit. To evaluate the out-of-sample performance of our new volatility models, we compare the forecasting performance of our models with that of other traditional benchmark models forecasts using the error statistic approach and Hansen (2005) superior predictive ability (SPA) test. The results show that the HAR – AddRS and HAR – AddRS – L models provide more accurate volatility forecasts for the out-of-sample. We also undertake the economic significance analysis to highlight that a substantial economic gain is achieved when the volatility forecasts based on the HAR – AddRS – L model are used to implement various trading strategies, however, the same is not true when the volatility forecasts are based on the traditional returns-based conditional volatility models.