  Value-at-Risk and its conditional allegory, which takes into account the
available information about the economic environment, form the centrepiece of
the Basel framework for the evaluation of market risk in the banking sector. In
this paper, a new nonparametric framework for estimating this conditional
Value-at-Risk is presented. A nonparametric approach is particularly pertinent
as the traditionally used parametric distributions have been shown to be
insufficiently robust and flexible in most of the equity-return data sets
observed in practice. The method extracts the quantile of the conditional
distribution of interest, whose estimation is based on a novel estimator of the
density of the copula describing the dynamic dependence observed in the series
of returns. Real-world back-testing analyses demonstrate the potential of the
approach, whose performance may be superior to its industry counterparts.
