  We focus on the time-varying modeling of VaR at a given coverage $\tau$,
assessing whether the quantiles of the distribution of the returns standardized
by their conditional means and standard deviations exhibit predictable
dynamics. Models are evaluated via simulation, determining the merits of the
asymmetric Mean Absolute Deviation as a loss function to rank forecast
performances. The empirical application on the Fama-French 25 value-weighted
portfolios with a moving forecast window shows substantial improvements in
forecasting conditional quantiles by keeping the predicted quantile unchanged
unless the empirical frequency of violations falls outside a data-driven
interval around $\tau$.
