Asymptotically efficient estimation of the conditional expected shortfall
AbstractA procedure for efficient estimation of the trimmed mean of a random variable conditional on a set of covariates is proposed. For concreteness, the focus is on a financial application where the trimmed mean of interest corresponds to the conditional expected shortfall, which is known to be a coherent risk measure. The proposed class of estimators is based on representing the estimator as an integral of the conditional quantile function. Relative to the simple analog estimator that weights all conditional quantiles equally, asymptotic efficiency gains may be attained by giving different weights to the different conditional quantiles while penalizing excessive departures from uniform weighting. The approach presented here allows for either parametric or nonparametric modeling of the conditional quantiles and the weights, but is essentially nonparametric in spirit. The asymptotic properties of the proposed class of estimators are established. Their finite sample properties are illustrated through a set of Monte Carlo experiments and an empirical application11The Stata and Matlab codes used in the simulations and in the empirical analysis are available as annexes to the electronic version of the paper..
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 4 ()
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Web page: http://www.elsevier.com/locate/csda
Expected shortfall; Quantile regression; Asymptotic efficiency;
Other versions of this item:
- Samantha Leorato & Franco Peracchi & Andrei V. Tanase, 2010. "Asymptotically Efficient Estimation of the Conditional Expected Shortfall," EIEF Working Papers Series 1013, Einaudi Institute for Economics and Finance (EIEF), revised Dec 2010.
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