The Value-at-Risk (VaR) forecasting ability of Filtered Historical Simulation (FHS) is assessed using both simulated and empirical data. Three data generating processes are used to simulate several return samples. A backtesting exercise is implemented to assess the performance of FHS based on a normal-GARCH model. In addition, the performance of a GARCH model with t-Student and Skewed-t distributional assumptions for the residuals is also investigated. The simulation results are clearly in favour of the accuracy of FHS based on a normal-GARCH. Data on six well known active stock indices is used to produce empirical results. To evaluate FHS, four competing GARCH-type specifications, combined with three different innovation assumptions (normal, t-Student and Skewed-t), are used to capture time series dynamics. Though all the models demonstrate a good performance, the overall coverage results are in favour of the normal-GARCH. The use of GARCH models produces less favorable results for FHS with respect to the independence of the VaR violations. The choice of an asymmetric GARCH structure to model the volatility dynamics of the empirical data results in a substantial improvement with respect to this issue. Furthermore, our results support the argument that distributionally nonparametric models do not depend on the distribution assumed in the filtering stage.