On estimating the conditional expected shortfall
Copyright policy )On estimating the conditional expected shortfall
AbstractUnlike the value at risk, the expected shortfall is a coherent measure of risk. In this paper, we discuss estimation of the expected shortfall of a random variable Yt with special reference to the case when auxiliary information is available in the form of a set of predictors Xt. We consider three classes of estimators of the conditional expected shortfall of Yt given Xt: a class of fully non‐parametric estimators and two classes of analog estimators based, respectively, on the empirical conditional quantile function and the empirical conditional distribution function. We study their sampling properties by means of a set of Monte Carlo experiments and analyze their performance in an empirical application to financial data. Copyright © 2008 John Wiley & Sons, Ltd.
Applications of statistics to actuarial sciences and financial mathematics, quantile regression, Risk theory, insurance, logistic regression, Settore SECS-P/05 - ECONOMETRIA, logistic regression; quantile regression; risk measures, Monte Carlo methods, risk measures, Nonparametric estimation, value at risk
Applications of statistics to actuarial sciences and financial mathematics, quantile regression, Risk theory, insurance, logistic regression, Settore SECS-P/05 - ECONOMETRIA, logistic regression; quantile regression; risk measures, Monte Carlo methods, risk measures, Nonparametric estimation, value at risk
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