arXiv research

Study on systemic risk in European insurance sector, showing insurer connections during stress.

The MSPI predicts market stress with machine learning.

Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.

This paper uses a mean-field game to model stablecoin market dynamics and recovery.

AXI assesses bank funding costs transparently, improving loan pricing and reducing financial risk.

The paper investigates non-linear and heavy-tailed predictability in transition-energy financial markets.

Proposes a reverse stress testing framework for dynamic models.

Study reviews machine learning techniques for stress monitoring.

Method generates plausible financial stress scenarios using large deviations.

This paper examines cryptocurrency integration with traditional markets, showing how network structure and turbulence influence cross-asset spillovers.

This note improves correlation stress tests using geodesic distance.

Improved algorithm for multidimensional scaling reduces stress.

Model predicts wound and episode-level readmission risk and time to re-admit.

Study preference-based reinforcement learning in episodic kernel MDPs.

Algorithm tackles constrained reinforcement learning with concave-convex and knapsack constraints.

Develops a method for reverse stress testing in multivariate scenarios.

Algorithm improves multi-armed bandit performance by transferring reward samples.

BerlinUCB learns from episodic rewards in nonstationary contexts.

We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes, and the transition function is not known to the learner. We show $\tilde{O}(L|X|\sqrt{|A|T})$ regret bound, where $T$ is the number of episodes, $X$ is the state space, $A…

A hybrid model combines diffusion and neural operator methods for stress prediction in hyperelastic materials.

Deep learning predicts stress levels from mouse hippocampus activity.

The paper analyzes Nordic stock markets' correlation structures and regime shifts.

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…

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…

The study identifies and analyzes different market regimes in equity markets using advanced signal processing techniques.

Develops a method for stress testing correlations of financial portfolios.

Credit risk stress tests can misrepresent default probabilities due to inconsistent parameterization.

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.

Algorithm reduces episode count for CMDPs with constraints.

Paper proposes MAST to identify stress conditions in forecasting models.

Many interesting real world domains involve reinforcement learning (RL) in partially observable environments. Efficient learning in such domains is important, but existing sample complexity bounds for partially observable RL are at least exponential in the episode length. We give, to our knowledge, the first partially …

Paper defines p-biharmonic submanifolds and stress tensors in space forms.

Using Hilbert's criterion, we consider the stress-energy tensor associated to the bienergy functional. We show that it derives from a variational problem on metrics and exhibit the peculiarity of dimension four. First, we use this tensor to construct new examples of biharmonic maps, then classify maps with vanishing or…

Algorithm improves online learning in adversarial bandits.

Reverse sensitivity analysis for risk models under various stresses.

Two new methods score stress test scenarios for risk managers.

Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interaction with the environments to obtain satisfactory performance. Recently,…

This paper uses multivariate probability models to assess financial system risks.

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…

Research shows ESG signals lower exposure to market fragility during stress periods.

We introduce a new class of reinforcement learning methods referred to as {\em episodic multi-armed bandits} (eMAB). In eMAB the learner proceeds in {\em episodes}, each composed of several {\em steps}, in which it chooses an action and observes a feedback signal. Moreover, in each step, it can take a special action, c…

Paper proposes DAC-ML, a cognitive architecture that learns quickly from few episodes.

Develops a robust hedging valuation adjustment measure for dynamic hedging under liquidity-demand stress.

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…

Neural networks predict flow and elastic stresses in viscoelastic turbulence.

Paper develops a robust HVA measure for dynamic hedging under liquidity stress.