Nonparametric Estimation of Conditional Expected Shortfall
Copyright policy )Nonparametric Estimation of Conditional Expected Shortfall
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 satisfying strong mixing conditions. An empirical illustration is given for several stock index returns, namely CAC40, DAX30, S&P500, DJ1, and Nikkei225.
- University of Amsterdam Netherlands
- University of Melbourne Australia
- University of Geneva Switzerland
Risk Management, séries temporelles, distribution des pertes à haute sévérité, Time Series, pertes espérées conditionnelles, VaR conditionnel, 650, Kernel, Conditional VaR, Loss Severity Distribution, Conditional Expected Shortfall, Modèle non-paramétrique, noyau des estimateurs, Nonparametric, gestion des risques, ddc: ddc:650
Risk Management, séries temporelles, distribution des pertes à haute sévérité, Time Series, pertes espérées conditionnelles, VaR conditionnel, 650, Kernel, Conditional VaR, Loss Severity Distribution, Conditional Expected Shortfall, Modèle non-paramétrique, noyau des estimateurs, Nonparametric, gestion des risques, ddc: ddc:650
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