论文标题
银行压力测试应该公平吗？
Should Bank Stress Tests Be Fair?
论文作者
论文摘要
监管压力测试已成为在美国最大银行设定资本要求的主要工具之一。美联储使用机密模型在共同的压力方案中评估针对银行特定投资组合的特定银行成果。根据政策，尽管机构之间存在相当多的异质性，但所有银行都使用相同的模型；单个银行认为，某些模型不适合其业务。我们问，在这场辩论中，单独量身定制的模型的合理聚集是什么？我们认为，简单地跨银行汇总数据同样对待银行，但会遭受两个缺陷：它可能会扭曲合法投资组合功能的影响，并且很容易被隐含的合法信息隐含误导以推断银行身份。我们比较了回归公平的各种概念，以解决这些缺陷，考虑到预测的准确性和平等待遇。在线性模型的设置中，我们主张估算，然后丢弃中心的银行固定效果，而不是简单地忽略整个银行的差异。我们提供证据表明总体影响可能是重要的。我们还讨论了非线性模型的扩展。
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.
