Nonparametric versus Parametric Expected Shortfall

58 Pages Posted: 15 Mar 2016 Last revised: 8 May 2019

See all articles by Doug Martin

Doug Martin

University of Washington

Shengyu Zhang

HomeStreet Bank

Date Written: November 15, 2018

Abstract

We use influence functions as a basic tool to study unconditional non-parametric and parametric expected shortfall (ES) estimators with regard to returns data influence, standard errors and coherence. Non-parametric ES estimators have a monotonically decreasing influence function of returns. ES maximum-likelihood estimator (MLE) influence functions are non-monotonic and approximately symmetric, resulting in large positive returns contributing to risk. However, ES MLE’s have the lowest possible asymptotic variance among consistent ES estimators. Influence functions are used to derive large sample standard error formulas for both types of ES estimators for normal and t-distributions and evaluate non-parametric ES estimator inefficiency. Monte Carlo results determine finite-sample sizes for which the standard errors of which both types of ES estimators are sufficiently accurate to be used in practice. Non-monotonicity of ES MLE’s led us to study modification of normal distribution MLE’s in which standard deviation is replaced by semi-standard deviation (SSD). Influence function theory is used to establish a condition under which an SSD based ES risk estimator has a monotonic influence functions and the underlying risk measures is coherent. It is also shown that the SSD based estimator’s asymptotic standard error is only slightly larger than that of the standard deviation-based estimator.

Keywords: Risk, expected shortfall (ES), maximum-likelihood estimator (MLE), influence functions, estimator variance, estimator standard error.

JEL Classification: C13, C14, G10

Suggested Citation

Martin, R. Douglas and Zhang, Shengyu, Nonparametric versus Parametric Expected Shortfall (November 15, 2018). Available at SSRN: https://ssrn.com/abstract=2747179 or http://dx.doi.org/10.2139/ssrn.2747179

R. Douglas Martin (Contact Author)

University of Washington ( email )

Applied Mathematics & Statistics
Dept. of Statistics
Seattle, WA 98195
United States

Shengyu Zhang

HomeStreet Bank ( email )

601 Union Street
Ste. 2000
Seattle, WA 98101
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
238
Abstract Views
1,087
rank
129,454
PlumX Metrics