Numerous studies have purported to show that using online investor sentiment measures can enhance the accuracy of forecasting stock return volatility. However, many of these studies did not validate their findings with out-of-sample data, had insufficient validation samples, or failed to incorporate appropriate controls in their predictive models. None of these studies compared the predictive power of online sentiment measures with commonly used technical sentiment measures, nor did they investigate whether online sentiment measures offer any additional predictive value for volatility forecasting when controlling for technical measures. Using a large stock sample in China A-shares, we contribute to examining the additional predictive power of online sentiment measures for predicting stock volatility for different forecast horizons and different time windows, while controlling for technical measures. Methodologically, through using rolling origins and moving windows evaluation framework, we consider the benefits of using global forecasting models compared to local stock-specific models, and the robustness of our appraisal of the use of sentiment measures. We found that technical sentiment measures generally perform better than online sentiments. When controlling for technical measures, online sentiment measures have extra predictive value only when the forecasting horizon is long (e.g., one week or month), but the magnitudes of the improvement are relatively limited.