New Framework for Stress Testing Financial Risk Under Extreme Scenarios
A new preprint introduces the GPR-HS framework with Scenario-Averaged Covariance Stabilization (SACS) to improve Stressed Value-at-Risk estimation under forward-looking macroeconomic shocks. The work extends an earlier hybrid Gaussian Process Regression Historical Simulation approach to three stress regimes—West Asia War, Climate Risk, and AI Bubble/Regulation—over a 252-day horizon. The method aims to address numerical instability in traditional parametric models used in regulatory capital frameworks such as CCAR and ICAAP.
The paper, submitted to arXiv by Ujjwala Vadrevu, extends a previously proposed Hybrid Gaussian Process Regression Historical Simulation (GPR-HS) framework to forward-looking stress testing scenarios relevant to bank capital regulation. The central innovation is the Scenario-Averaged Covariance Stabilization (SACS) mechanism, which constructs stress covariance matrices as weighted aggregations of historical crisis regimes, yielding more stable and interpretable dependence structures than conventional parametric methods. Stressed return paths are simulated over a 252-day horizon using deterministic drift combined with stochastic residuals, while volatility is modeled through Gaussian Process Regression with Aggressive Noise Initialization (ANI). Across all three macro scenarios and all assets tested, the framework demonstrates consistent convergence, with SVaR estimates ranging from -2.1020% to -2.2231% and the coherence property |SES| > |SVaR| preserved throughout. The authors argue these properties make GPR-HS with SACS a regulator-aligned candidate for CCAR and ICAAP stress testing applications. The work is a 15-page preprint and has not yet undergone formal peer review.
What's missing
As a preprint, the paper has not been peer-reviewed, and independent empirical validation on out-of-sample or real regulatory datasets is not reported. The choice of scenario weights in the SACS aggregation and the sensitivity of results to those weights are not fully characterized. The paper does not compare GPR-HS/SACS performance against existing industry-standard or regulatory benchmark models on common datasets, limiting assessment of practical improvement. The referenced prior work (Vadrevu 2026) is itself a preprint, meaning the methodological foundation also lacks peer-reviewed validation.
What different sources said
- arXiv cs.LGCenter
Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS
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