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arXiv research

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.

168,695 papers · 148 categories

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53105158210 · Jun 202019922001200920172026
48 results for Financial Crisis Detection

Study detects anomalies in financial markets using GNN and nonextensive entropy.

problem Detecting anomalies in global financial markets with many correlated assets.
method Used Graph Neural Networks (GNN) with nonextensive entropy to measure uncertainty.
result Anomalies are statistically different for nonextensive entropy parameters before, during, and after a crisis.

Paper presents a DRL framework for detecting and anticipating financial crises.

problem Detecting and adapting to financial crises using deep reinforcement learning.
method Two sub-networks, one for past performances and standard deviations, the other for contextual features. Adversarial training for robustness.
result Framework substantially outperforms traditional methods in detecting and anticipating crises.

Study reveals structural differences in financial networks near and far from crises using balance theory.

problem Understanding the complex behavior of stocks and their collective behavior in financial crises.
method Investigates financial networks by triplet interaction in the framework of balance theory, focusing on higher-order interactions.
result Formation of an ordered structure in crisis networks makes them resistant to disorder, with a critical temperature measuring crisis strength.

Following the thermodynamic formulation of multifractal measure that was shown to be capable of detecting large fluctuations at an early stage, here we propose a new index which permits us to distinguish events like financial crisis in real time . We calculate the partition function from where we obtain thermodynamic q…

2012-04-14abs ↗pdf ↗

SRR detects early signs of financial crises using multi-layer graphs.

problem Predicting systemic financial transitions from evolving market interactions.
method Systemic Risk Radar (SRR) models financial markets as multi-layer graphs.
result Graph-derived features provide useful early-warning signals compared to feature-based models.

We investigate the tendency for financial instruments to form clusters when there are multiple factors influencing the correlation structure. Specifically, we consider a stock portfolio which contains companies from different industrial sectors, located in several different countries. Both sector membership and geograp…

2015-05-07abs ↗pdf ↗

We detect and quantify asymmetries in volatility spillovers using the realized semivariances of petroleum commodities: crude oil, gasoline, and heating oil. During the 1987--2014 period we document increasing spillovers from volatility among petroleum commodities that substantially change after the 2008 financial crisi…

2014-05-10abs ↗pdf ↗

We develop a topology data analysis-based method to detect early signs for critical transitions in financial data. From the time-series of multiple stock prices, we build time-dependent correlation networks, which exhibit topological structures. We compute the persistent homology associated to these structures in order…

2017-01-21abs ↗pdf ↗

The study examines cross-border lending behavior from G7 countries, showing changes in driving factors after the 2008 financial crisis.

problem Understanding the factors affecting cross-border lending behavior among G7 countries.
method Employed a gravity model to analyze bilateral and global factors influencing cross-border lending.
result Driving factors for cross-border lending have changed since the 2008 financial crisis, with continent variable becoming more significant.

Since 2007, several contributions have tried to identify early-warning signals of the financial crisis. However, the vast majority of analyses has focused on financial systems and little theoretical work has been done on the economic counterpart. In the present paper we fill this gap and employ the theoretical tools of…

2015-07-31abs ↗pdf ↗

The paper uses machine learning to predict the impact of the Ukraine crisis on financial markets.

problem Quantifying the impact of the Ukraine crisis on financial markets.
method Selected economic indexes, created datasets, and used machine learning (Linear Regression) for forecasting.
result The model accurately predicted the effects of the Ukraine crisis on financial markets.

Decomposes financial networks to reveal cause-effect hierarchies during crises.

problem Complex financial networks are hard to interpret due to Granger causality.
method Helmholtz-Hodge-Kodaira decomposition to separate networks into rotational and gradient components.
result Precious metals and pharmaceutical products are identified as causal drivers during crises.

Using the eigenvalues and eigenvectors of correlations matrices of some of the main financial market indices in the world, we show that high volatility of markets is directly linked with strong correlations between them. This means that markets tend to behave as one during great crashes. In order to do so, we investiga…

2011-02-07abs ↗pdf ↗

The 2008 financial crisis revealed banking consolidation paradoxically increased systemic fragility and global financial contagion with negligible spatial decay.

problem Fundamental vulnerabilities in interconnected banking systems during the 2008 financial crisis were inadequately addressed by existing frameworks.
method Developed a unified spatial-network framework using spectral analysis of network Laplacian operators combined with spatial difference-in-differences identification.
result Banking consolidation paradoxically increased systemic fragility and global financial contagion with negligible spatial decay.

Since beginning of the 2008 financial crisis almost half a trillion euros have been spent to financially assist EU member states in taxpayer-funded bail-outs. These crisis resolutions are often accompanied by austerity programs causing political and social friction on both domestic and international levels. The questio…

2014-03-06abs ↗pdf ↗

Geometric framework for portfolio analysis detects financial crises and evaluates performance.

problem Detecting financial crises and evaluating portfolio performance in volatile markets.
method Geometric framework, copula models, statistical computing.
result Automated crisis detection and new portfolio score for performance evaluation.

The study finds significant financial sector volatility and tail risk spillovers to real economy sectors.

problem Volatility and tail risk spillovers from financial to real economy sectors.
method New measure of tail risk spillover, empirical analysis of U.S. economy 2001-2011.
result Significant volatility and tail risk spillovers from financial to real economy sectors, especially during crises.

This summarizes the study of the financial and economic crisis in Europe. The starting questions were: 1) Why do we have a crisis? Unde venis? 2) What will be the outcome? Quo vadis? Here is the reasoning which touches many areas, ranging from financial to politics and from psychology and economy.

2013-05-23abs ↗pdf ↗

The paper addresses XVA valuation under market crises using a renewal process.

problem XVA valuation without considering market crises and illiquidity.
method Using an alternating renewal process, the paper develops a framework to price XVA under a state-dependent financial regime.
result The XVA price is characterized as a solution to a backward stochastic differential equation (BSDE).

The study assesses how financial markets' efficiency changed during the COVID-19 crisis.

problem The impact of COVID-19 on financial market efficiency.
method Dynamic estimation method for Hurst exponent and memory parameter using alpha-stable distribution and dependence structure.
result Financial markets' efficiency varied during the COVID-19 crisis, with some indices showing less impact than others.

The cohomology theory for financial market can allow us to deform Kolmogorov space of time series data over time period with the explicit definition of eight market states in grand unified theory. The anti-de Sitter space induced from a coupling behavior field among traders in case of a financial market crash acts like…

2016-06-09abs ↗pdf ↗

The paper applies information theory to financial markets, improving risk management and asset allocation.

problem Improving risk management and asset allocation in financial markets.
method Information-theoretic measures (entropy, mutual information, etc.) applied to financial time series.
result Normalized mutual information (NMI) is a powerful measure of temporal dependence in financial markets.

This research improves value-at-risk estimation during financial crises using non-extensive statistical methods.

problem Underestimation of value-at-risk during financial crises.
method Non-extensive value-at-risk model based on Tsallis entropy and q-Gaussian probability density function.
result The q-Gaussian model provides better value-at-risk estimation during financial crises.