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.
This work extends elasticity theory to curved spaces, solving stress potentials.
Adaptive Stress Testing detects financial fraud by simulating potential failures.
Deep learning predicts stress levels from mouse hippocampus activity.
Machine learning improves financial stress testing in Indian markets.
This paper uses multivariate probability models to assess financial system risks.
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…
Proposes a method to incorporate current market conditions in VaR and stress testing.
New deep learning method improves financial stress testing accuracy.
In this work, we develop Gaussian process regression (GPR) models of hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this a…
Autoencoders identify brain networks linked to stress and genotype.
This work is based on the talk delivered at Poisson 2008. We review the recent advances in Generalized Kahler geometry while stressing the use of Poisson and symplectic geometry. The derivation of the generalized Kahler potential is sketched and the relevant global issues are discussed.
Paper uses MLMC for SCR calculation and stress tests, showing computational efficiency.
Proposes a reverse stress testing framework for dynamic models.
Study reviews machine learning techniques for stress monitoring.
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…
Method generates plausible financial stress scenarios using large deviations.
The 1/3 Financial Rule helps prevent household bankruptcy through balanced spending, savings, and debt repayment.
Bayesian neural networks predict stress fields and uncertainty in materials.
This note improves correlation stress tests using geodesic distance.
The main purpose of this paper is to revisit the well known potentials, called stress functions, needed in order to study the parametrizations of the stress equations, respectively provided by G.B. Airy (1863) for 2-dimensional elasticity, then by E. Beltrami (1892), J.C. Maxwell (1870) and G. Morera (1892) for 3-dimen…
Improved algorithm for multidimensional scaling reduces stress.
The MSPI predicts market stress with machine learning.
Following the financial crisis of the late 2000s, policy makers have shown considerable interest in monitoring financial stability. Several central banks now publish indices of financial stress, which are essentially based upon market related data. In this paper, we examine the potential for improving the indices by de…
Develops a method for reverse stress testing in multivariate scenarios.
The elastic energy functional of a thin elastic rod or sheet is generalized to the case of an M-dimensional manifold in N-dimensional space. We derive potentials for the stress field and curvatures and find the generalized von Karman equations for a manifold in elastic equilibrium. We perform a scaling analysis of an M…
A hybrid model combines diffusion and neural operator methods for stress prediction in hyperelastic materials.
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…
Develops a method for stress testing correlations of financial portfolios.
Credit risk stress tests can misrepresent default probabilities due to inconsistent parameterization.
In the last years, increasing efforts have been put into the development of effective stress tests to quantify the resilience of financial institutions. Here we propose a stress test methodology for central counterparties based on a network characterization of clearing members, whose links correspond to direct credits …
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.
The occurrence of aftershocks following a major financial crash manifests the critical dynamical response of financial markets. Aftershocks put additional stress on markets, with conceivable dramatic consequences. Such a phenomenon has been shown to be common to most financial assets, both at high and low frequency. It…
Paper proposes MAST to identify stress conditions in forecasting models.
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…
Extended PELCoV for bivariate Student-t copulas to monitor foreign exchange risk.
Reverse sensitivity analysis for risk models under various stresses.
Two new methods score stress test scenarios for risk managers.
Simulating dynamic rupture propagation is challenging due to the uncertainties involved in the underlying physics of fault slip, stress conditions, and frictional properties of the fault. A trial and error approach is often used to determine the unknown parameters describing rupture, but running many simulations usuall…
We study how the phenomenon of contagion can take place in the network of the world's stock exchanges due to the behavioral trait "blindeness to small changes". On large scale individual, the delay in the collective response may significantly change the dynamics of the overall system. We explicitely insert a term descr…
Sparse regression models CMs from oscillatory shear data efficiently.
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.
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.