  We propose nonparametric estimators for conditional value-at-risk (CVaR) and
conditional expected shortfall (CES) associated with conditional distributions
of a series of returns on a financial asset. The return series and the
conditioning covariates, which may include lagged returns and other exogenous
variables, are assumed to be strong mixing and follow a nonparametric
conditional location-scale model. First stage nonparametric estimators for
location and scale are combined with a generalized Pareto approximation for
distribution tails proposed by Pickands (1975) to give final estimators for
CVaR and CES. We provide consistency and asymptotic normality of the proposed
estimators under suitable normalization. We also present the results of a Monte
Carlo study that sheds light on their finite sample performance. Empirical
viability of the model and estimators is investigated through a backtesting
exercise using returns on future contracts for five agricultural commodities.
