Estimation of the marginal expected shortfall: The mean when a related variable is extreme

Juan Juan Cai, John H.J. Einmahl*, Laurens de Haan, Chen Zhou

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Denote the loss return on the equity of a financial institution as X and that of the entire market as Y. For a given very small value of p>0, the marginal expected shortfall (MES) is defined as E{X|Y>QY(1-p)}, where QY(1-p) is the (1-p)th quantile of the distribution of Y. The MES is an important factor when measuring the systemic risk of financial institutions. For a wide non-parametric class of bivariate distributions, we construct an estimator of the MES and establish the asymptotic normality of the estimator when p↓0, as the sample size n→∞. Since we are in particular interested in the case p=O(1/n), we use extreme value techniques for deriving the estimator and its asymptotic behaviour. The finite sample performance of the estimator and the relevance of the limit theorem are shown in a detailed simulation study. We also apply our method to estimate the MES of three large US investment banks.

Original languageEnglish
Pages (from-to)417-442
Number of pages26
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume77
Issue number2
DOIs
Publication statusPublished - 1 Mar 2015
Externally publishedYes

Keywords

  • Asymptotic normality
  • Conditional tail expectation
  • Extreme values

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