AI 中文总结
本文针对CCAR和CECL流程中预测运行间的差异归因问题，构建合作博弈模型，对比多种归因方法的特性，为生产预测系统选择合适的归因方法提供实用框架。
AI 中文摘要
用于《全面资本分析与审查（CCAR）》和《当前预期信用损失（CECL）》流程的预测系统，整合了投资组合数据、宏观经济情景、模型规格、业务假设及管理层调整内容。当预测结果在两次运行间发生变化时，从业者需要一种归因方法，该方法需与总变化一致，且不依赖任意的输入替换序列。本文将预测缺口归因问题构建为合作博弈，并研究了多种方法：精确Shapley值、分层或嵌套Shapley值、Integrated Gradients、Gradient SHAP、Permutation SHAP及Kernel SHAP。我们比较了这些方法的分配规则、计算成本、实施要求及在生产预测系统中的局限性。该分析提供了一个实用框架，可根据输入的数量与类型、混合预测运行的可行性，以及对可解释性、可复现性和治理的需求来选择归因方法。
英文摘要
Forecasting systems used in the Comprehensive Capital Analysis and Review (CCAR) and Current Expected Credit Losses (CECL) processes combine portfolio data, macroeconomic scenarios, model specifications, business assump- tions, and management adjustments. When the forecast changes from one run to the next, practitioners need an attribu- tion that reconciles to the total change without depending on an arbitrary sequence of input replacements. This paper formulates forecast-gap attribution as a cooperative game and examines several approaches: the exact Shapley value, hierarchical or nested Shapley values, Integrated Gradients, Gradient SHAP, Permutation SHAP, and Kernel SHAP. We compare their allocation rules, computational costs, implementation requirements, and limitations in production forecasting systems. The analysis provides a practical framework for choosing an attribution method according to the number and type of inputs, the feasibility of hybrid forecast runs, and the need for interpretability, reproducibility, and governance.
