Covariate-adjusted Expected Shortfall: Some Recent Developments

November 11, 2022
12:00 pm to 1:00 pm

Event sponsored by:

Biostatistics and Bioinformatics


Ethan Fang


Xuming He, PhD


Xuming He, PhD, Professor, Department of Statistics, University of Michigan

Abstract: Expected shortfall, measuring the average outcome (e.g., portfolio loss) above a given quantile of its probability distribution, is a common financial risk measure. The same measure can be used to characterize treatment effects in the tail of an outcome distribution, with applications ranging from policy evaluation in economics and public health to biomedical investigations. Expected shortfall regression is a natural approach of modeling covariate-adjusted expected shortfalls. Because the expected shortfall cannot be written as a solution of an expected loss function at the population level, computational as well as statistical challenges around expected shortfall regression have led to stimulating research. We discuss some recent developments in this area, with a focus on a new optimization-based semiparametric approach to estimation of conditional expected shortfall that adapts well to data heterogeneity with minimal model assumptions.

Bio: As a statistician, Xuming's research is about a better understanding of statistical models and methods and a better use of statistical principles and tools to help decision making under uncertainty. His main academic activities include: scientific writing and publication, teaching statistics, mentoring doctoral students, presentations at conferences and seminars, collaboration with other researchers and professional service at all levels. (Taken from Xuming He's University of Michigan faculty page:

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