提出新框架，稳定预测重大危机下的资产风险值。
Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS
- 用混合高斯过程与历史模拟法建模极端情景下的风险。
- 在三种危机情景中SVaR保持-2.10%至-2.22%稳定区间。
- 适合监管机构做资本充足压力测试，尤其关注宏观风险。
监管压力测试框架（如CCAR和ICAAP）要求在前瞻性宏观经济情景下进行稳健的受压风险价值（SVaR）估计。传统参数方法在极端冲击下常出现数值不稳定性，影响资本预测可靠性。本文扩展Vadrevu（2026）提出的混合高斯过程回归历史模拟（GPR-HS）框架，应用于前瞻性压力情景，验证了其在西亚太战争、气候风险、AI泡沫/监管三类情景下的稳定性。核心贡献是场景平均协方差稳定化（SACS）框架，通过加权聚合历史危机时期的协方差，构建稳定可解释的依赖结构。受压收益路径在252天内通过确定性漂移与随机残差生成，波动率采用带激进噪声初始化（ANI）的高斯过程回归建模。该框架在所有资产与情景中均实现一致收敛，SVaR范围为-2.1020%至-2.2231%，且保持一致性性质|SES| > |SVaR|。结果表明，GPR-HS结合SACS是适用于CCAR与ICAAP的稳定、合规的前瞻性SVaR与SES估算方法。
原文摘要 · Abstract (English)
Regulatory stress testing frameworks, including the Comprehensive Capital Analysis and Review (CCAR) and the Internal Capital Adequacy Assessment Process (ICAAP), require robust Stressed Value-at-Risk (SVaR) estimation under forward-looking macroeconomic scenarios. Traditional parametric approaches often exhibit numerical instability under extreme shocks, reducing the reliability of capital projections. This paper extends the Hybrid Gaussian Process Regression Historical Simulation (GPR-HS) framework of Vadrevu (2026) to forward-looking stress scenarios, demonstrating stability across three regimes: West Asia War, Climate Risk, and AI Bubble/Regulation. A key contribution is the Scenario-Averaged Covariance Stabilization (SACS) framework, which constructs stress covariance as a weighted aggregation of historical crisis regimes, providing stable and interpretable dependence structures. Stressed return paths are generated over a 252-day horizon using deterministic drift and stochastic residuals, while volatility is modeled via Gaussian Process Regression with Aggressive Noise Initialization (ANI). The framework exhibits consistent convergence across all assets and scenarios. SVaR ranges from -2.1020% to -2.2231%, with the coherence property |SES| > |SVaR| preserved. The results support GPR-HS with SACS as a stable and regulator-aligned approach for forward-looking SVaR and SES estimation in CCAR and ICAAP applications.
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