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Regulatory stress tests have become one of the main tools for setting capitalrequirements at the largest U.S. banks. The Federal Reserve uses confidentialmodels to evaluate bank-specific outcomes for bank-specific portfolios inshared stress scenarios. As a matter of policy, the same models are used forall banks, despite considerable heterogeneity across institutions; individualbanks have contended that some models are not suited to their businesses.Motivated by this debate, we ask, what is a fair aggregation of individuallytailored models into a common model? We argue that simply pooling data acrossbanks treats banks equally but is subject to two deficiencies: it may distortthe impact of legitimate portfolio features, and it is vulnerable to implicitmisdirection of legitimate information to infer bank identity. We comparevarious notions of regression fairness to address these deficiencies,considering both forecast accuracy and equal treatment. In the setting oflinear models, we argue for estimating and then discarding centered bank fixedeffects as preferable to simply ignoring differences across banks. We presentevidence that the overall impact can be material. We also discuss extensions tononlinear models.

Should Bank Stress Tests Be Fair?