A defined-benefit pension is only as safe as its sponsor, yet sponsor distress is under-instrumented relative to plan-side modelling. We present a transparent, reproducible early-warning pipeline built entirely on public data. A Sponsor Vulnerability Index (SVI), a gradient-boosted classifier over point-in-time SEC EDGAR balance-sheet fundamentals and two macro series, predicts a filings-based distress state one to two quarters ahead. On 36 U.S. sponsors in five sector groups (1,418 firm-quarters, 2015Q1-2025Q3) it reaches an out-of-fold AUC-ROC of 0.887 and PR-AUC of 0.711 (base rate 11.9%) under leave-one-firm-out cross-validation. A Monte Carlo VaR with a stylized transition-stress overlay shows that climate-type stress is negligible for a diversified plan but adds 20-43% to one-year 95% VaR when 22.5% of assets are held in sponsor securities. A dynamic self-investment cap maps the SVI to a limit that falls from 8% to below 1% in acute distress and recovers with the sponsor, which we illustrate on PG&E's 2019 bankruptcy and Boeing's persistent fragility. We report the method's failure modes alongside its results (book-equity labels that also fire on non-credit events such as spin-offs, positives concentrated in one firm, and stylized stress parameters) and release code, data and results under a concept DOI.
Figure 1: Left: out-of-fold ROC curve (leave-one-firm-out, AUC =0.887 ). Right: top features by XGBoost gain.
Figure 2: SVI (full-sample refit) for a subset of sponsors, 2015–2025. Distress episodes at PG&E (2018–20), Delta and Occidental (2020–21) and General Electric (2018–19) cross the alert line; Boeing stays above it almost throughout. GE’s second excursion (2023–25) coincides with the spin-offs of GE HealthCare and GE Vernova, a book-equity reduction that is not credit distress. IBM and Walmart stay below the line in this fit.
Sponsor
Positive qtrs
Peak (refit)
Quarter
Peak (out-of-fold)
Quarter
PG&E
8
0.99
2019 Q4
0.99
2020 Q2
Delta
8
0.99
2020 Q4
0.99
2020 Q4
Boeing
42
0.99
2020 Q4
0.96
2020 Q4
General Electric
11
0.97
2018 Q3
0.98
2018 Q3
Coca-Cola
2
0.76
2017 Q4
0.79
2017 Q4
IBM
0
0.62
2015 Q1
0.73
2018 Q4
Table 2: Peak SVI per sponsor: full-sample refit vs. out-of-fold (held-out) scores. Threshold 0.72 .
Sponsor sector, self-investment w
Baseline
Stressed
Add-on
Energy, 22.5%
17.8
25.6
+43%
Energy, 10%
13.5
16.7
+23%
Industrials, 22.5%
15.4
19.1
+23%
Travel, 22.5%
20.0
24.5
+22%
Utilities, 22.5%
12.4
14.8
+20%
Any sector, 0%
11.6–11.7
11.6–11.7
≈0
Table 3: One-year 95% VaR (% of portfolio): baseline vs. stylized transition stress.
Figure 3: Baseline vs. stressed one-year 95% VaR by sponsor sector and self-investment weight.
Sponsor
Peak quarter
Peak SVI
Cap at peak
SVI 2025 Q3
Cap 2025 Q3
PG&E
2019 Q4
0.99
0.9%
0.02
7.9%
Delta
2020 Q4
0.99
0.9%
0.03
7.8%
Boeing
2020 Q4
0.99
0.9%
0.97
1.0%
General Electric
2018 Q3
0.97
1.0%
0.33
5.6%
Exxon Mobil
2020 Q4
0.46
4.7%
0.06
7.5%
Walmart
2020 Q1
0.32
5.7%
0.04
7.7%
Table 4: Dynamic cap (base 10%, mESG=0.8 ) at peak SVI vs. latest quarter (2025 Q3).
Figure 4: Dynamic-cap trajectories (solid) and SVI × 10 (dotted) for PG&E and Boeing, against the static 10% limit and the 8% ceiling implied by mESG=0.8 .
The Spatial-Temporal Graph Attention Network (ST-GAT) framework was created to serve as an explainable GNN-based solution for detecting bank distress early warning signs and for conducting macro-prudential surveillance of the interbank system in the United States. The ST-GAT framework models 8,103 FDIC insured institutions across 58 quarterly snapshots (2010Q1-2024Q2). Bilateral exposures were reconstructed from publicly available FDIC Call Reports using maximum entropy estimation to produce a dynamic directed weighted graph. The framework achieves the highest AUPRC among all GNN architectures (0.939 +/- 0.010), trailing only XGBoost (0.944). Ablation analysis confirms the BiLSTM temporal component contributes +0.020 AUPRC; temporal attention weights exhibit a monotonically decreasing pattern consistent with long-run structural vulnerability weighting. Permutation importance identifies ROA (0.309) and NPL Ratio (0.252) as dominant predictors, consistent with post-mortem analyses of the 2023 regional banking crisis. All data are publicly available FDIC Call Reports and FRED series; all code and results are released.
Mohammad Nasir Uddin
Data Analytics and Applied AI Researcher, Westcliff University, Irvine, CA, USA
Accurate forecasting of the Volatility-Covariance Matrix (VCV) is central to regulatory capital adequacy processes such as the Internal Capital Adequacy Assessment Process (ICAAP) and the Comprehensive Capital Analysis and Review (CCAR). Traditional econometric models, including GARCH-family and Exponentially Weighted Moving Average (EWMA) approaches, suffer from parametric rigidity, distributional assumptions, and numerical instability under stress, leading to systematic underestimation of tail risk. This paper proposes and validates a novel Hybrid Gaussian Process Regression-Historical Simulation (GPR-HS) framework for estimating Value-at-Risk (VaR) and Expected Shortfall (ES) across a diversified portfolio of seven major global equity indices. The framework decouples the VCV estimation problem: individual asset volatilities are modelled dynamically using Univariate GPR with a Matern 5/2 kernel, while inter-asset correlations are estimated via stable historical covariance. A key methodological contribution is the Aggressive Noise Initialization (ANI) strategy, which sets the initial White Noise kernel variance equal to the empirical variance of the training returns, ensuring Gram matrix positive-definiteness, regularization, and conservative, regulatory-compliant forecasts. Evaluated using an expanding window forward-chaining cross-validation scheme over June 2020 -June 2025, the GPR-HS framework achieves regulatory compliance in the majority of test splits; including a 100% ES pass rate at the portfolio level, while outperforming the static Historical VaR benchmark in 71.4% of univariate cases by Quadratic Loss and 100% of cases by violation count.
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.