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On estimating the conditional expected shortfall
Author(s) -
Peracchi Franco,
Tanase Andrei V.
Publication year - 2008
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.729
Subject(s) - expected shortfall , estimator , quantile , conditional probability distribution , econometrics , value at risk , quantile function , conditional expectation , importance sampling , random variable , monte carlo method , mathematics , statistics , economics , finance , moment generating function , risk management
Unlike 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 Y t with special reference to the case when auxiliary information is available in the form of a set of predictors X t . We consider three classes of estimators of the conditional expected shortfall of Y t given X t : 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.