Deep learning predicts stress levels from mouse hippocampus activity.
problem Stress level in mice under different environments.
method Deep learning combined with neuron decoding.
result Deep learning model accurately predicts stress levels.
With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is extremely challenging. In this paper, we present a general platform for personaliz…
Study on systemic risk in European insurance sector, showing insurer connections during stress.
problem Understanding systemic risk connectedness in European insurance sector.
method Common connectedness framework applied to returns, volatility, value-at-risk, and expected shortfall.
result Insurers are a significant component of systemic risk connectedness, especially during stress episodes.
Various psychological factors affect how individuals express emotions. Yet, when we collect data intended for use in building emotion recognition systems, we often try to do so by creating paradigms that are designed just with a focus on eliciting emotional behavior. Algorithms trained with these types of data are unli…
Develops a robust hedging valuation adjustment measure for dynamic hedging under liquidity-demand stress.
problem Dynamic hedging under liquidity-demand stress
method Define robust HVA as the worst-case expected loss over a relative-entropy neighborhood of the loss distribution generated by simulated rebalancing and maturity-unwind trades.
result Distinguishes fixed-radius convention from fixed benchmark-stress convention and shows wider no-trade bands lower rebalancing costs but raise hedge-error risk.
Paper develops a robust HVA measure for dynamic hedging under liquidity stress.
problem Valuation of dynamic hedging under liquidity stress.
method Defines robust HVA as worst-case expected loss over a relative-entropy neighborhood of loss distributions for no-trade bands.
result Wider no-trade bands lower rebalancing costs but increase hedge-error risk.
News is a pertinent source of information on financial risks and stress factors, which nevertheless is challenging to harness due to the sparse and unstructured nature of natural text. We propose an approach based on distributional semantics and deep learning with neural networks to model and link text to a scarce set …
The scope of financial systemic risk research encompasses a wide range of interbank channels and effects, including asset correlation shocks, default contagion, illiquidity contagion, and asset fire sales. This paper introduces a financial network model that combines the default and liquidity stress mechanisms into a "…
We propose a random walk model of asset returns where the parameters depend on market stress. Stress is measured by, e.g., the value of an implied volatility index. We show that model parameters including standard deviations and correlations can be estimated robustly and that all distributions are approximately normal.…
If the probability of default parameters (PDs) fed as input into a credit portfolio model are estimated as through-the-cycle (TTC) PDs stressed market conditions have little impact on the results of the capital calculations conducted with the model. At first glance, this is totally different if the PDs are estimated as…
The relation between performance and stress is described by the Yerkes-Dodson Law but varies significantly between individuals. This paper describes a method for determining the individual optimal performance as a function of physiological signals. The method is based on attention and reasoning tests of increasing comp…
We consider the energy and bienergy functionals as variational problems on the set of Riemannian metrics and present a study of the biharmonic stress-energy tensor. This approach is then applied to characterise weak conformality of the Gauss map of a submanifold. Finally, working at the level of functionals, we recover…
The instability of historical risk factor correlations renders their use in estimating portfolio risk extremely questionable. In periods of market stress correlations of risk factors have a tendency to quickly go well beyond estimated values. For instance, in times of severe market stress, one would expect with certain…
Study assesses climate risks on supply chains and financial systems using detailed firm emissions data.
problem Lack of firm-level CO2 emissions data hinders assessment of transition risks from carbon pricing.
method Used detailed Hungarian firm emissions data and a simple economic ABM model to simulate carbon pricing impacts.
result 45% of companies are directly exposed to carbon pricing, leading to significant economic and financial losses.
Research shows ESG signals lower exposure to market fragility during stress periods.
problem Market fragility often occurs together, and ESG is associated with reduced exposure.
method Monthly data on S&P 500 constituents from 2014 to 2025, analyzing downside returns, volatility, illiquidity, and cofragility states.
result A one-standard-deviation increase in ESG lowers the probability of severe cofragility by 0.92 percentage points during stress periods.
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
Recently, several algorithms for strain tomography from energy-resolved neutron transmission measurements have been proposed. These methods assume that the stress-free lattice spacing d0 is a known constant limiting their application to the study of stresses generated by manufacturing and loading methods that do not…
New vine copula method forecasts portfolio risk measures robust to market downturns.
problem Inaccurate risk measure estimation for financial portfolios due to lack of cross-dependency capture.
method Combines vine copulas with ARMA-GARCH models for marginal risk estimation.
result Portfolio is robust to American market downturns but not European market.
Study examines how bank holding structures affect financial stress spread.
problem Financial stress spread in a network of bank holdings and subsidiaries.
method Investigates the spread of contagion in a multilayered banking network with different holding support rules.
result Holding structures can either amplify or mitigate financial stress, depending on network capitalization.
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.
Study examines cryptoasset service providers in Austria, revealing global integration and distinct responses to market shocks.
problem Understanding cryptoasset integration and stress behavior in national economies.
method Directly identified on-chain addresses of Austrian crypto-asset service providers, reconstructing transaction activity across multiple cryptocurrencies.
result Austrian crypto-asset service providers are globally integrated, with distinct responses to market shocks.
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
problem Limited use of range-based volatility estimators in local commodity markets.
method Adapted OHLC volatility estimators to monitor market distress across various contexts.
result OHLC-based volatility indicators detect market disruptions missed by standard momentum indicators.
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.
Extended PELCoV for bivariate Student-t copulas to monitor foreign exchange risk.
problem Monitoring financial risk under asymmetric co-movements and tail dependence.
method Extending PELCoV to Student-t copulas, tracking dynamic risk spillovers.
result Potential to detect early signs of risk underestimation during financial stress.
The quest for diversification has led to an increasing number of complex funds with a high number of strategies and non-linear payoffs. The new generation of Alternative Risk Premia (ARP) funds are an example that has been very popular in recent years. For complex funds like these, a Reverse Stress Test (RST) is regard…
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.
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.
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.
Adversarial transfer learning improves stress assessment across users.
problem Transfer learning challenges in physiological biosignals.
method Disentangled nuisance-robust representations using adversarial networks.
result Adversarial framework enhances cross-subjects stress assessment.
Study compares empirical systemic risk with balance sheet risk in interbank networks.
problem Disentangling balance sheet risk from network effects in systemic risk.
method Generalised DebtRank dynamics and maximum-entropy approach to compare observed and expected systemic risk.
result Systemic risk levels are compatible but differ significantly during turbulent times.
This note improves correlation stress tests using geodesic distance.
problem Improving financial risk management through better covariance stress tests.
method Proposes a new geometrically invariant definition of correlation stress tests.
result Demonstrates a submanifold approach to stress testing covariance matrices.
Improved algorithm for multidimensional scaling reduces stress.
problem Stress in multidimensional scaling.
method Proposed modifications of the smacof algorithm.
result Convergent majorization algorithm for Kruskal's stress formula two.
The MSPI predicts market stress with machine learning.
problem Estimating the probability of high market stress.
method L1-regularized logistic regression on stock fragility signals.
result MSPI tracks major stress episodes and improves accuracy.
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.
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.
A hybrid model combines diffusion and neural operator methods for stress prediction in hyperelastic materials.
problem Challenges in predicting stress fields in hyperelastic materials with complex microstructures.
method A hybrid surrogate framework combining a conditional denoising diffusion probabilistic model (cDDPM) and a modified DeepONet.
result The hybrid model consistently outperforms traditional methods by one to two orders of magnitude.
A new model explains relative spreads between economies using dynamic Nelson-Siegel and functional regression.
problem Analyzing and predicting relative spreads between economies in fixed income markets.
method State-space functional regression model incorporating dynamic Nelson-Siegel model and kernel PCA.
result The new model outperforms the dynamic Nelson-Siegel model in explaining relative spreads.
Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing.The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities.In this study, lon…
Environmental stresses such as drought and heat can cause substantial yield loss in agriculture. As such, hybrid crops that are tolerant to drought and heat stress would produce more consistent yields compared to the hybrids that are not tolerant to these stresses. In the 2019 Syngenta Crop Challenge, Syngenta released…
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.
The dominant automatic lexical stress detection method is to split the utterance into syllable segments using phoneme sequence and their time-aligned boundaries. Then we extract features from syllable to use classification method to classify the lexical stress. However, we can't get very accurate time boundaries of eac…
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.
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.
We derive the stress-energy tensor for polyharmonic maps between Riemannian manifolds. Moreover, we employ the stress-energy tensor to characterize polyharmonic maps where we pay special attention to triharmonic maps.
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.
DBNs improve ES and SES estimation for market risk, but tail behavior remains challenging.
problem Optimizing ES and SES estimation for market risk in banking.
method Extended DBNs for 10-day ES and SES estimation using S&P 500 index.
result DBNs perform comparably to historical simulation but struggle with tail behavior.
Complex systems can be modelled at various levels of detail. Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their predictions of the effects of interventions. We formalise this notion of consistency in the case of Structural Equation Models (SEMs) by intr…
Paper proposes MAST to identify stress conditions in forecasting models.
problem Improving reliability and transparency of univariate forecasting models under stress.
method Meta-learning and data augmentation approach to predict stress conditions.
result MAST identifies conditions leading to large errors in forecasting models.