  This paper compares the Value--at--Risk (VaR) forecasts delivered by
alternative model specifications using the Model Confidence Set (MCS) procedure
recently developed by Hansen et al. (2011). The direct VaR estimate provided by
the Conditional Autoregressive Value--at--Risk (CAViaR) models of Eengle and
Manganelli (2004) are compared to those obtained by the popular Autoregressive
Conditional Heteroskedasticity (ARCH) models of Engle (1982) and to the
recently introduced Generalised Autoregressive Score (GAS) models of Creal et
al. (2013) and Harvey (2013). The Hansen's procedure consists on a sequence of
tests which permits to construct a set of "superior" models, where the null
hypothesis of Equal Predictive Ability (EPA) is not rejected at a certain
confidence level. Our empirical results, suggest that, after the Global
Financial Crisis (GFC) of 2007-2008, highly non-linear volatility models
deliver better VaR forecasts for the European countries as opposed to other
regions. The R package MCS is introduced for performing the model comparisons
whose main features are discussed throughout the paper.
