Crude oil prices are particularly volatile. Managing such price risks is vital for participants in financial markets, in particular the oil market. In the perspective of a long position, we conduct an in-depth study of popular existing statistical approaches as well as a recently developed method to estimate Value at Risk of the next day's oil price — a measurement of potential extreme price risks. We then validate the estimations via tests of accuracy, independence, and a combination of both criteria. The approaches that capture heteroscedasticity in the data, namely conditional Extreme Value Theory and Filtered Historical Simulation, perform considerably better than the pure bootstrapping method — Historical Simulation — and the (sub)asymptotic-target approach — Average Conditional Exceedance Rate.