We consider a nonparametric method to estimate conditional expected shortfalls, i.e. conditional expected losses knowing that losses are larger than a given loss quantile. We derive the asymptotic properties of kernel estimators of conditional expected shortfalls in the context of a stationary process satisfactory strong mixing conditions. An empirical illustration is given for several stock index returns, namely CAC40, DAX30, SNP500, DJI, and Nikkei225.

Keywords : Nonparametric, Kernel, Time Series, Conditional VaR, Conditional Expected Shortfall, Risk Management, Loss Severity Distribution.

JEL : C14, D81, G10, G21, G22, G28.