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.

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Keywords Asymptotic normality, Conditional tail expectation, Extreme values
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Journal Royal Statistical Society. Journal. Series B: Statistical Methodology
Cai, J.J, Einmahl, J.H.J, de Haan, L.F.M, & Zhou, C. (2014). Estimation of the marginal expected shortfall: The mean when a related variable is extreme. Royal Statistical Society. Journal. Series B: Statistical Methodology. doi:10.1111/rssb.12069