用SHAP解释机器学习模型的监管资本预测，让黑箱变透明。
SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR
- 基于SHAP值分解压力情景下的风险资本构成
- 压力下均值效应比波动率影响更大，主导资本水平
- 满足FRTB、ICAAP和CCAR的可审计要求
非参数机器学习模型在监管资本估算中的应用面临核心治理挑战：无法以监管机构可审计的方式解释模型输出。这种‘黑箱’问题仍是将高斯过程回归（GPR）及其相关架构用于ICAAP和CCAR流程的主要障碍，尽管其预测性能优于传统参数方法。本文提出SHARC（SHAP for Regulatory Capital），为混合GPR-HS架构及其压力测试扩展提供可解释性框架。基于合作博弈论的Shapley Additive exPlanations（SHAP）满足局部准确性、缺失性、一致性与效率性，应用于三种宏观情景下的压力价值风险（SVaR）输出：西亚战争、气候风险、人工智能泡沫/监管负担。SHARC将SVaR分解为基线、均值驱动和波动率驱动成分，实现情景设计与资本结果间的透明关联。研究发现：第一，SHARC能一致地将非线性SVaR输出与底层情景输入关联，验证框架可靠性并提供资本驱动因素的可审计追溯；第二，在压力条件下，均值回报成分（方向性损失幅度）主导方差成分（波动率基线），对资本限额设定、头寸管理和对冲策略具有重要启示。结果表明，SHARC作为符合监管要求的可解释层，使混合GPR-HS框架完全可审计，且与FRTB、ICAAP支柱2及CCAR透明度要求一致。
原文摘要 · Abstract (English)
The adoption of non-parametric machine learning models for regulatory capital estimation introduces a fundamental governance challenge: the inability to explain model outputs in a manner auditable by supervisory bodies. This 'black box' problem remains a major barrier to the adoption of Gaussian Process Regression (GPR) and related ML architectures in ICAAP and CCAR workflows despite their predictive advantages over traditional parametric approaches. This paper addresses this barrier through SHARC (SHAP for Regulatory Capital), an explainability framework for the Hybrid GPR-HS architecture and its stress-testing extension. SHapley Additive exPlanations (SHAP), derived from cooperative game theory and satisfying the properties of Local Accuracy, Missingness, Consistency, and Efficiency, are applied to Stressed Value-at-Risk (SVaR) outputs under three macro scenarios: West Asia War, Climate Risk, and AI Bubble/Regulatory Burden. SHARC decomposes SVaR into baseline, mean-driven, and volatility-driven components, enabling transparent linkage between scenario design and capital outcomes. Two findings emerge. First, SHARC consistently links non-linear SVaR outputs to underlying scenario inputs, confirming framework fidelity and providing auditable traceability of capital drivers. Second, under stress conditions, the mean return component (directional loss magnitude) dominates the variance component (volatility baseline) in determining capital levels, with implications for capital limit-setting, position management, and hedging strategy. The results establish SHARC as a regulator-aligned explainability layer that makes the Hybrid GPR-HS framework fully auditable and consistent with FRTB, ICAAP Pillar 2, and CCAR transparency requirements.
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