Should Bank Stress Tests Be Fair?

TL;DR

This paper examines how to create fair, equitable stress test models for banks by addressing heterogeneity and fairness issues, proposing methods to improve model fairness and accuracy.

Contribution

It introduces a framework for fair aggregation of bank-specific models, highlighting the benefits of estimating and discarding bank fixed effects over ignoring differences.

Findings

Pooling data can distort legitimate portfolio features.

Estimating and discarding fixed effects improves fairness.

Impact of model choice can be material.

Abstract

Regulatory stress tests have become one of the main tools for setting capital requirements at the largest U.S. banks. The Federal Reserve uses confidential models to evaluate bank-specific outcomes for bank-specific portfolios in shared stress scenarios. As a matter of policy, the same models are used for all banks, despite considerable heterogeneity across institutions; individual banks have contended that some models are not suited to their businesses. Motivated by this debate, we ask, what is a fair aggregation of individually tailored models into a common model? We argue that simply pooling data across banks treats banks equally but is subject to two deficiencies: it may distort the impact of legitimate portfolio features, and it is vulnerable to implicit misdirection of legitimate information to infer bank identity. We compare various notions of regression fairness to address these deficiencies, considering both forecast accuracy and equal treatment. In the setting of linear models, we argue for estimating and then discarding centered bank fixed effects as preferable to simply ignoring differences across banks. We present evidence that the overall impact can be material. We also discuss extensions to nonlinear models.