中文摘要
监管压力测试已成为设定美国最大银行资本要求的主要工具之一。美联储使用保密模型，在共享的压力情景下针对各银行特定的投资组合评估银行特定的结果。作为一项政策，所有银行使用相同的模型，尽管各机构之间存在相当大的异质性；个别银行曾主张某些模型并不适合其业务。受这一争论的启发，我们提出这样的问题：何为将各银行量身定制的模型公平聚合为一个共同模型的方式？我们认为，简单地将各银行的数据合并虽对各银行一视同仁，但存在两个缺陷：它可能扭曲合法投资组合特征的影响，并且容易受到合法信息被隐式误导用于推断银行身份的影响。为解决这些缺陷，我们比较了回归公平性的各种概念，同时兼顾预测精度与平等待遇。在线性模型的设定下，我们主张先估计再舍弃中心化的银行固定效应，认为这优于简单地忽略银行间的差异。我们还讨论了向非线性模型的扩展。
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 also discuss extensions to nonlinear models.