2025 Noether Distinguished Scholar Award - Taming the Tail with Expected Shortfall Regression

Music City Center 

Expected shortfall, which measures the average outcome (e.g., portfolio loss) beyond a specified quantile of its probability distribution, is a widely used financial risk measure. This metric can also be employed 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 offers a natural approach for modeling covariate-adjusted expected shortfalls, yet it presents several challenges in estimation and prediction. In this presentation, I will begin with an accessible introduction to expected shortfall regression and share my personal journey in this riveting area of research, which has captivated students and scholars in statistics, econometrics, finance, and operations research. I will then introduce a novel optimization-based approach to linear expected shortfall regression, demonstrating a compelling example of interpretable machine learning in action. Finally, I will conclude with an outline of future work needed to advance the theory and practice of expected shortfall regression in the era of big data.