This paper aims to investigate the usefulness of exogenous predictors to forecast crude oil volatility. We use the recent expansion of the general-to-specific (GETS) procedure to conditionally heteroskedastic models to estimate a parsimonious predictive model of crude oil volatility from a large set of predictors. Our results show that the GETS algorithm achieves good predictive accuracy compared to its competitors at the 1-day horizon. However, this accuracy deteriorates for more distant forecast horizons. We argue that it may be due to the fact that the GETS procedure is based on tests that are key in assessing explanatory power as opposed to reducing expected prediction error. Among its competitors, DMA achieves good predictive power in almost all situations. Still, our analysis provides interesting insights on the variables best suited to forecast crude oil volatility. In particular, forecasters might benefit from better exploiting the predictive content of exchange rates.