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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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3 results for “ICAAP”

SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCARq-fin.RM

SHARC explains machine learning risk models for regulatory capital, linking outputs to scenarios.

problem Inability to explain machine learning model outputs to regulatory bodies.

method SHAP-based explainability framework for Hybrid GPR-HS architecture and SVaR stress-testing.

result SHARC links SVaR outputs to scenario inputs, providing auditable traceability.

SHAP Machine Learning Regulatory Capital ICAAP CCAR

2026-07-06abs ↗pdf ↗

Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACSq-fin.RM

Paper improves SVaR estimation for stress testing under macro scenarios using a hybrid GPR-HS framework.

problem Numerical instability in traditional SVaR estimation under extreme shocks.

method Extends GPR-HS framework to forward-looking stress scenarios with SACS for stable covariance.

result Stable SVaR ranges from -2.1020% to -2.2231%, preserving coherence property.

Stress Testing SVaR Estimation Hybrid GPR-HS SACS Extreme Shocks

2026-05-26abs ↗pdf ↗

A Hybrid Gaussian Process Regression Framework for Stable Volatility-Covariance Estimation: Evidence from Global Equity Indicesq-fin.RM

Paper proposes a new GPR-HS framework for accurate VCV estimation in global equity indices.

problem Accurate forecasting of Volatility-Covariance Matrix (VCV) for regulatory processes.

method Hybrid Gaussian Process Regression-Historical Simulation (GPR-HS) framework.

result GPR-HS framework achieves regulatory compliance and outperforms static VaR benchmarks.

Gaussian Process Regression Volatility-Covariance Matrix Value-at-Risk Expected Shortfall Historical Simulation

2026-05-17abs ↗pdf ↗