Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS

• URL: http://arxiv.org/abs/2606.07575v1
• Date: Tue, 26 May 2026 18:02:45 GMT
• Title: Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS
• Abstract summary: This paper extends the Hybrid Gaussian Process Regression Historical Simulation (GPR-HS) framework to forward-looking stress scenarios.<n>A key contribution is the Scenario-Averaged Covariance Stabilization (SACS) framework, which constructs stress covariance as a weighted aggregation of historical crisis regimes.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Susceptible Reservoir Architectures for Regime-Conditional Volatility Forecasting [0.0]
  We introduce Susceptible Architectures (SUSA), a reservoir-design principle for volatility forecasting, and its two concrete implementations.<n>We evaluate models on 16 U.S. equity and exchange-traded-fund series using three disjoint chronological training, validation, and test folds.<n>The proposed models perform competitively with GARCH, achieving statistically significant QLIKE improvements for specific assets.
  arXiv  Detail & Related papers  (2026-07-24T17:03:08Z)
• SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR [0.0]
  This paper describes an explainability framework for the Hybrid GPR-HS architecture and its stress-testing extension.<n>It decomposes Stressed Value-at-Risk into baseline, mean-driven, and volatility-driven components, enabling transparent linkage between scenario design and capital outcomes.<n>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.
  arXiv  Detail & Related papers  (2026-07-06T16:45:11Z)
• A Hybrid Gaussian Process Regression Framework for Stable Volatility-Covariance Estimation: Evidence from Global Equity Indices [0.0]
  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.<n>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.
  arXiv  Detail & Related papers  (2026-05-17T05:52:54Z)
• Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration [72.0672328514289]
  Multimodal learning often grapples with the challenge of low-quality data.<n>We propose a unified framework, termed Conformal Predictive Self-Calibration.<n>Our framework consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-05T14:48:52Z)
• SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment [52.87618765740484]
  This paper defines the Self-Evolving Evolutionary Agent and introduces SEA-Eval -- the first benchmark designed specifically for evaluating SEAs.<n> SEA-Eval establishes $SR$ and $T$ as primary metrics and enables through sequential task stream design the independent of evolutionary gain, evolutionary stability, and implicit alignment convergence.
  arXiv  Detail & Related papers  (2026-04-10T05:49:50Z)
• RDEx-SOP: Exploitation-Biased Reconstructed Differential Evolution for Fixed-Budget Bound-Constrained Single-Objective Optimization [13.368759036974224]
  This report documents RDEx-SOP, an exploitation-biased success-history differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition.<n>We evaluate RDEx-SOP on the official CEC 2025 SOP benchmark with the U-score framework (Speed and Accuracy categories)
  arXiv  Detail & Related papers  (2026-03-28T02:18:38Z)
• RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach [19.34848029403215]
  Bus holding control is challenging due to traffic and passenger demand.<n>Standard actor-critic algorithms suffer from Q-value instability in volatile environments.<n>We propose a robust ensemble soft actor-critic framework to disentangle uncertainties.
  arXiv  Detail & Related papers  (2026-03-19T01:37:45Z)
• $V_{0.5}$: Generalist Value Model as a Prior for Sparse RL Rollouts [81.48669089692189]
  Generalist Value Models (such as $V_0.5$) achieve pre-trained value estimation by explicitly encoding model capabilities in-context.<n>We propose $V_0.5$, which adaptively fuses the baseline predicted by such value model with the empirical mean derived from sparse rollouts.<n>$V_0.5$ significantly outperforms GRPO and DAPO, achieving faster convergence and over some 10% performance improvement.
  arXiv  Detail & Related papers  (2026-03-11T14:57:41Z)
• Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation [54.29523408543184]
  Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment.<n>Existing approaches either focus on improving average simulation fidelity or adopt conservative regularization.<n>We propose Sim2Act, a robust simulation-to-decision framework that addresses both simulator and policy robustness.
  arXiv  Detail & Related papers  (2026-03-10T00:51:47Z)
• On the Plasticity and Stability for Post-Training Large Language Models [54.757672540381236]
  We identify a root cause as the conflict between plasticity and stability gradients.<n>We propose Probabilistic Conflict Resolution (PCR), a framework that models gradients as random variables.<n>PCR significantly smooths the training trajectory and achieves superior performance in various reasoning tasks.
  arXiv  Detail & Related papers  (2026-02-06T07:31:26Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes [0.0]
  This paper proposes a reinforcement learning (RL) framework for insurance reserving that integrates tail-risk sensitivity, macroeconomic regime modeling, and regulatory compliance.<n>The framework also accommodates fixed-shock stress testing and regime-stratified analysis, providing a principled and principled approach to reserving under uncertainty.
  arXiv  Detail & Related papers  (2025-04-13T01:43:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.