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.
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.
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.
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.
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.
Adaptive Stress Testing detects financial fraud by simulating potential failures.
problem Detecting and mitigating vulnerabilities in financial systems.
method Developed a simplified model using historical data and reinforcement learning.
result Identified the most likely path to system failure and improved fraud detection.
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.
This paper uses multivariate probability models to assess financial system risks.
problem Assessing systemic risk in financial systems.
method Computes multivariate conditional probability distributions for elliptical distributions, focusing on Student-t and Normal models.
result Proposes measures of stress impact and systemic risk.
A quick review of European financial stability institutions and the role of stress tests in the current juridical system.
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.
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.
Unified model for network risks, including bilateral and central clearing, with practical applications.
problem Managing risks in financial networks with multiple trading types.
method Developed a one-period XVA model with explicit formulas for various quantities.
result Illustrated practical uses for stress testing and portfolio optimization.
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.
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.
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.
We construct a continuous time model for price-mediated contagion precipitated by a common exogenous stress to the banking book of all firms in the financial system. In this setting, firms are constrained so as to satisfy a risk-weight based capital ratio requirement. We use this model to find analytical bounds on the …
I studied the convergence of regional house prices to national prices in USA by analyzing time-series of house price indices of 9 Census Divisions. I found the evidence of the convergence in some parts of the country using asymmetric unit root tests. The fact that the evidence of the convergence is not present in large…
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…
Model shows how financial contagion spreads through complex interdependencies.
problem Understanding how banks fail in an interconnected financial system.
method Unified model combining direct and indirect dependencies; three reconstruction methods.
result Hierarchical cascades reveal dominant banks in failures.
This paper proposes non-stationary factor models for financial stress in the UK.
problem Managing financial vulnerabilities in the UK's complex financial system.
method Creation of non-stationary factor models to capture financial stress.
result Non-stationary factor models can better capture financial stress, especially tail events.
Model predicts asset prices from initial shocks using neural networks.
problem Missing data on actual asset liquidations limits model calibration.
method Dual neural network structure, first stage maps shocks to liquidations, second stage uses liquidations to predict prices.
result Model accurately predicts equilibrium prices from initial shocks without liquidation data.
The 1/3 Financial Rule helps prevent household bankruptcy through balanced spending, savings, and debt repayment.
problem Reducing household bankruptcy risk through effective financial planning.
method Mathematical modeling, game theory, behavioral finance, and technological analysis.
result The 1/3 Financial Rule emerges as a robust solution for supporting household financial stability.
Financial Wind Tunnel generates versatile market data for model testing.
problem Inconsistent market dynamics across different scales and sources.
method Retrieval-augmented diffusion-based simulator integrating macro and micro patterns.
result Enhanced performance and adaptability of downstream models in complex markets.
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.
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 examine three methods of constructing correlated Student-t random variables. Our motivation arises from simulations that utilise heavy-tailed distributions for the purposes of stress testing and economic capital calculations for financial institutions. We make several observations regarding the suitability of the …
Narrative disclosures in 10-K filings improve bankruptcy prediction beyond accounting ratios.
problem Traditional bankruptcy prediction models rely on accounting ratios, which may not capture early warning signals.
method Developed a PB Stress Score based on distress-specific language in 10-K narratives, evaluated against accounting and dictionary benchmarks.
result Adding the PB Stress Score increases AUC from 0.8323 to 0.9019 and improves top-decile bankruptcy capture from 44.12% to 64.71%.
Analyzes how uncertainty in financial networks affects stability.
problem Understanding how uncertainty in financial networks impacts stability.
method Introduced a minimal stochastic dynamical model of the interbank network with linear interactions. Derived the interaction correction to the stress expectation and studied it on the short-medium timescale.
result Interactions increase the stress expectation on average, highlighting the importance of disclosure.
Enhanced stock market strategy using stress index and financial news sentiment analysis.
problem Improving risk assessment and prediction in equity markets.
method Combines financial stress indicator with sentiment analysis of financial news.
result Improved performance with higher Sharpe ratio and reduced drawdowns.
This work proposes an augmented variant of DebtRank with uncertainty intervals as a method to investigate and assess systemic risk in financial networks, in a context of incomplete data. The algorithm is tested against a default contagion algorithm on three ensembles of networks with increasing density, estimated from …
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…
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.
In a financial market, for agents with long investment horizons or at times of severe market stress, it is often changes in the asset price that act as the trigger for transactions or shifts in investment position. This suggests the use of price thresholds to simulate agent behavior over much longer timescales than are…
Paper introduces lexical ratio to measure portfolio diversification.
problem Traditional diversification metrics overlook non-numerical relationships.
method Uses textual data to capture diversification dimensions through entropy-based insights.
result Lexical ratio (LR) outperforms traditional metrics in optimizing portfolio returns.
Paper uses interbank contagion to predict U.S. bank defaults, finding it highly explanatory.
problem Predicting U.S. bank defaults using interbank contagion.
method Regression and neural network models were used to analyze U.S. commercial bank data.
result Interbank contagion is highly explanatory in default prediction, often outperforming established metrics.
We propose a novel approach for sampling realistic financial correlation matrices. This approach is based on generative adversarial networks. Experiments demonstrate that generative adversarial networks are able to recover most of the known stylized facts about empirical correlation matrices estimated on asset returns.…
New method uses nested optimal transport for financial time series evaluation.
problem Lack of consensus metric for evaluating generative models in finance.
method Nested optimal transport distance for time-causal tasks, with a parallelizable algorithm.
result Substantial speedups and robustness to financial tasks.
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.
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.
Model assesses how supply chain disruptions affect financial stability.
problem Systemic risk in production networks and its financial implications.
method Data-driven econo-financial stress-testing framework combining supply chain and interbank networks.
result Increase of up to 28% in financial systemic risk due to production network contagion.
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
Study evaluates financial anomaly detection methods on Canadian stock market.
problem Detecting financial anomalies in the Canadian stock market.
method Topological data analysis (TDA), principal component analysis (PCA), and neural network-based approaches.
result Neural network-based methods achieve the strongest performance in detecting financial anomalies.
PortBench benchmarks LLMs for PM, revealing their weaknesses in diversification and robustness.
problem Lack of benchmarks for LLM-driven portfolio management, especially in diversification and robustness.
method Developed a comprehensive benchmark with a static QA dataset and a dynamic allocation pipeline, introducing metrics to evaluate correlation and robustness.
result 90% of LLMs fail to outperform a basic equal-weight allocation, highlighting their limitations in diversification and robustness.
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.
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.
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 second-order Esscher transform for Lévy models in financial markets.
problem Risk management and quantification in markets with jumps and Lévy dynamics.
method Derives densities, equivalent measures, and pricing formulas for European call options.
result Option prices are bounded and monotonic with the second-order Esscher parameter.
ValueBlindBench tests LLM-generated investment rationales for validity before returns are known.
problem Delayed-ground-truth evaluation of LLM-generated investment rationales.
method Agreement-gated stress testing protocol to validate LLM-judged rationales.
result ValueBlindBench prevents overclaims and identifies flawed financial constructs.