  This paper examines the precision of estimators of Quantile-Based Risk
Measures (Value at Risk, Expected Shortfall, Spectral Risk Measures). It first
addresses the question of how to estimate the precision of these estimators,
and proposes a Monte Carlo method that is free of some of the limitations of
existing approaches. It then investigates the distribution of risk estimators,
and presents simulation results suggesting that the common practice of relying
on asymptotic normality results might be unreliable with the sample sizes
commonly available to them. Finally, it investigates the relationship between
the precision of different risk estimators and the distribution of underlying
losses (or returns), and yields a number of useful conclusions.
