Method generates plausible financial stress scenarios using large deviations.
problem Misleading risk management by overlooking or overemphasizing implausible scenarios.
method Exploits large-deviations principle to concentrate risk factors near most likely stress configurations.
result Can generate informative stress scenarios even with limited historical data.
Develops a method for reverse stress testing in multivariate scenarios.
problem Reconstructing a multivariate stress scenario from a single exogenous shock.
method Maximizing conditional density under three distributional assumptions.
result Simulated scenarios are economically coherent and reproduce risk-reward asymmetry.
Two new methods score stress test scenarios for risk managers.
problem Comparing and evaluating stress test scenarios for risk managers.
method Inspired by Archer-Mouy-Selmi, two methodologies for scoring stress test scenarios.
result New methods can compare and evaluate stress test scenarios.
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.
Develops a method for stress testing correlations of financial portfolios.
problem Stress testing correlations in financial asset portfolios.
method Parametric representation of correlations, Bayesian variable selection, joint distribution of stress scenarios.
result Inference of worst-case correlation scenarios using stress tests.
LLM generates coherent macroeconomic stress scenarios for portfolio risk assessment.
problem Macro-financial stress testing and portfolio risk assessment using traditional methods.
method Hybrid prompt-RAG pipeline combining structured prompting and retrieval of country fundamentals and news.
result LLM-generated scenarios yield stable tail-risk amplification with limited sensitivity to retrieval choices.
Paper models cloud outages for cyber insurance stress-testing.
problem Cyber insurance portfolios' vulnerability to simultaneous cloud outages.
method Modeling and calibrating cloud-outage scenarios, measuring diversification.
result Cloud-outage diversification can protect against accumulation risk.
DARL uses DDPMs to generate synthetic market crash scenarios for robust portfolio optimization.
problem Challenges in capturing complex market dynamics and aligning with diverse investor preferences.
method Synergistic integration of DDPMs and DRL for portfolio management.
result DARL outperforms traditional methods in delivering superior risk-adjusted returns and resilience against crises.
Proposes a method to incorporate current market conditions in VaR and stress testing.
problem Inaccurate VaR and stress testing under changing market conditions.
method Clusters market conditions using Variational Inference (VI) and historical data weighting.
result Proposed approach provides more accurate insights into portfolio risk under near-term market changes.
In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives portfolio, the so-called `London Whale', partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for…
Machine learning improves financial stress testing in Indian markets.
problem Conventional stress testing limitations in Indian financial markets.
method Dimensionality reduction, latent factor modeling, Variational Autoencoders, Monte Carlo simulation.
result Improved flexibility, robustness, and realism in financial stress testing.
Geospatial framework assesses climate risks for California's banking and exposed sectors.
problem Evaluating climate risks on banking and exposed sectors in California.
method Integrates hazard mapping, exposure analysis, and scenario-based financial risk assessment.
result Framework supports portfolio monitoring and institutional readiness under new standards.
Proposes a reverse stress testing framework for dynamic models.
problem Finding plausible models under adverse stresses.
method Compound Poisson process, Kullback-Leibler divergence, optimization problem.
result Intensity and severity of process depend on time and state.
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.
New risk factors improve stress testing accuracy.
problem Improving stress testing accuracy with new risk factors.
method Adapted PCA and autoencoders for dimension reduction and interpretation.
result Aggregated risk factors enhance stress testing outcomes.
Credit risk stress tests can misrepresent default probabilities due to inconsistent parameterization.
problem Misleading default probability projections in credit risk stress tests.
method Analysis of credit risk stress testing models and their parameterization.
result Current portfolios tend to align with through-the-cycle portfolios, leading to spurious default rate projections.
Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time requirements are high. Consequently, simulation-driven methods such as Adaptive Stress Testing (AST) have b…
Sparse regression models CMs from oscillatory shear data efficiently.
problem Discovering parsimonious constitutive models from oscillatory shear experiments.
method Sparse regression with tensor basis functions, l1 regularization, and greedy two-stage algorithm.
result Inferred CMs extrapolate well beyond training data and flow conditions.
The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by means of Monte Carlo simulation, it becomes imp…
New deep learning method improves financial stress testing accuracy.
problem Traditional stress testing methods are criticized for unrealistic assumptions and estimation errors.
method Proposes a novel Deep Learning approach for Dynamic Balance Sheet Stress Testing.
result Empirical results show significant improvement in accuracy over traditional methods.
DGNN predicts financial margin calls under stress tests.
problem Forecasting margin calls in dynamic financial networks.
method Dynamic Graph Neural Network (DGNN) architecture.
result DGNN produces accurate forecasts up to 21 days.
We reverse engineer dynamics of financial contagion to find the scenario of smallest exogenous shock that, should it occur, would lead to a given final systemic loss. This reverse stress test can be used to identify the potential triggers of systemic events, and it removes the arbitrariness in the selection of shock sc…
A new approach for green investing in Indian markets considers environmental factors.
problem Identifying and managing climate risk in sustainable investing.
method Combining ESG ratings with modern portfolio theory and scenario analysis.
result The green portfolio performs better than market returns, highlighting the importance of climate risk.
An integrated and extendable approach for stress-testing loan portfolios
problem Stress-testing loan portfolios
method Simulate completed portfolios, generate uncertain cash flow history, compute credit risk metrics
result Enhanced stress-testing practices within any bank
We propose a Monte Carlo simulation method to generate stress tests by VaR scenarios under Solvency II for dependent risks on the basis of observed data. This is of particular interest for the construction of Internal Models and requirements on evaluation processes formulated in the Commission Delegated Regulation. The…
Stablecoin system improves resilience to extreme market events.
problem Vulnerability of stablecoins to extreme volatility and adversarial attacks.
method MVF-Composer uses multi-agent simulations to stress-test and down-weight manipulative signals.
result Reduces peak peg deviation by 57% and mean recovery time by 3.1x under adversarial conditions.
A method using optimal transport removes arbitrage in option prices for stress-testing.
problem Removing arbitrage opportunities in option prices for regulatory stress-tests.
method Optimal transport approach to project signed marginal measures onto martingale measures.
result Strong duality formula and convergence results for the regularized problem.
This research proposes methods to model and assess liability liquidity risk in asset management.
problem Lack of standardized models for liability liquidity risk in asset management.
method Statistical models, zero-inflated models, aggregate and individual-based approaches, and factor models.
result Developed mathematical and statistical approaches to estimate and assess redemption shocks.
Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce. Here we consider one such real world example viz., accurate identific…
RL-CVaR model improves insurance reserving under economic stress.
problem Managing insurance reserve setting under claim development uncertainty and macroeconomic stress.
method Reinforcement Learning (PPO) with CVaR constraints, trained under regime-aware curriculum.
result RL-CVaR policy reduces solvency violations and tail-risk compared to classical methods.
The paper discusses fairness in bank stress tests, comparing various methods to address institutional differences.
problem Fair aggregation of bank-specific stress test models into a common model.
method Comparing various notions of regression fairness, including estimating and discarding centered bank fixed effects.
result The method of estimating and discarding centered bank fixed effects is preferable for linear models, improving forecast accuracy and equal treatment.
New RBM model outperforms copula models in credit risk management.
problem Approximating credit portfolio losses accurately and efficiently.
method Restricted Boltzmann Machines for universal approximation of loss distributions.
result RBM model outperforms parametric copula models in various credit risk tasks.
This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the probl…
Developing an AI economist agent using RAG, knowledge graphs, and LLMs for economic scenario analysis.
problem Economic scenario analysis using large language models and knowledge graphs.
method Proposing an RAG-based AI economist framework that utilizes knowledge graphs and LLMs.
result Improves economic coherence and traceability in generated reports.
Framework for transitioning financial models from risk-neutral to real-world measure.
problem Transitioning financial models from risk-neutral to real-world measure to better reflect market dynamics and investor preferences.
method Leveraging probability theory, specifically Girsanov's theorem, to incorporate real-world dynamics into financial models.
result Validation of the robustness and practical relevance of the methodology through case studies involving financial forecasts and stress tests.
New method for risk allocation under multimodality of loss distribution.
problem Risk assessment under multimodal conditional loss distribution.
method Maximum Likelihood Allocation (MLA) and multimodality adjustment.
result Multimodality adjustment improves soundness of risk allocations.
During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure. Adaptive Stress Testing (AST) provides a way to search for the most-likely failure scenario as a Markov decision process (MDP). Our previous work used a deep rei…
We develop a novel stress-test framework to monitor systemic risk in financial systems. The modular structure of the framework allows to accommodate for a variety of shock scenarios, methods to estimate interbank exposures and mechanisms of distress propagation. The main features are as follows. First, the framework al…
New method finds failures in high-fidelity simulators with fewer steps.
problem Finding failures in high-fidelity simulators is expensive and impractical.
method Adaptive stress testing with backward algorithm adaptation from low-fidelity to high-fidelity.
result Significantly fewer high-fidelity simulation steps needed to find failures.
We study a simple, solvable model that allows us to investigate effects of credit contagion on the default probability of individual firms, in both portfolios of firms and on an economy wide scale. While the effect of interactions may be small in typical (most probable) scenarios they are magnified, due to feedback, by…
New method selects critical DER scenarios for distribution grid investment planning.
problem Determining critical DER adoption scenarios for risk assessment in distribution grids.
method Bayesian Optimization framework using Gaussian Process surrogates and Pareto-critical acquisition function.
result Statistical guarantee and significant speed-up over exhaustive search in selecting critical DER scenarios.
Paper introduces a new method for calibrating ESGs to both historical and forward-looking data.
problem Lack of a generally accepted methodology for calibrating ESGs to forward-looking information.
method Conditional Scenario Simulator framework for consistent calibration of economic and financial variables.
result Framework can embed various financial and macroeconomic models and demonstrate practical examples in frequentist and Bayesian settings.
New method models covariates and responses without parametric assumptions using manifold learning.
problem Losing explanatory power for responses in standard factor models applied to covariates alone.
method Anisotropic diffusion maps for learning low-dimensional embeddings.
result Kalman filtering in diffusion-map coordinates improves joint covariate-response prediction.
Study develops hybrid model to mitigate stablecoin liquidity risk.
problem Increasing integration of stablecoins introduces liquidity risk during market stress.
method Hybrid monetary architecture with 100% reserve backing and liquidity facilities.
result Demonstrates significant reduction in peg deviations and stress persistence.
A new method improves feature importance and model stress-testing reliability.
problem Estimating feature contributions in machine learning models for trust and transparency.
method Replacing multiple random permutations with a single, deterministic, and optimal permutation.
result Improved bias-variance tradeoffs and accuracy in challenging scenarios.
New benchmarks focus on LLM risk in finance, not just accuracy.
problem Standard benchmarks ignore LLM financial risks, leading to unsafe deployment.
method Three-level agenda: model, workflow, and system stress-testing.
result Hidden weaknesses in LLMs are missed by traditional benchmarks.
Study reviews machine learning techniques for stress monitoring.
problem Improving accuracy of stress monitoring devices.
method Reviewed machine learning techniques for various stress indicators.
result Choosing the right classifier depends on multiple factors.
GE finds failures in autonomous systems without domain heuristics.
problem Finding failures in autonomous systems without domain-specific heuristics.
method Adaptive stress testing using go-explore (GE) algorithm.
result GE finds failures in scenarios other RL techniques cannot solve.