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