Financial Contagion

Community-level Contagion among Diverse Financial Assets ArXiv ID: 2509.15232 “View on arXiv” Authors: An Pham Ngoc Nguyen, Marija Bezbradica, Martin Crane Abstract As global financial markets become increasingly interconnected, financial contagion has developed into a major influencer of asset price dynamics. Motivated by this context, our study explores financial contagion both within and between asset communities. We contribute to the literature by examining the contagion phenomenon at the community level rather than among individual assets. Our experiments rely on high-frequency data comprising cryptocurrencies, stocks and US ETFs over the 4-year period from April 2019 to May 2023. Using the Louvain community detection algorithm, Vector Autoregression contagion detection model and Tracy-Widom random matrix theory for noise removal from financial assets, we present three main findings. Firstly, while the magnitude of contagion remains relatively stable over time, contagion density (the percentage of asset pairs exhibiting contagion within a financial system) increases. This suggests that market uncertainty is better characterized by the transmission of shocks more broadly than by the strength of any single spillover. Secondly, there is no significant difference between intra- and inter-community contagion, indicating that contagion is a system-wide phenomenon rather than being confined to specific asset groups. Lastly, certain communities themselves, especially those dominated by Information Technology assets, tend to act as major contagion transmitters in the financial network over the examined period, spreading shocks with high densities to many other communities. Our findings suggest that traditional risk management strategies such as portfolio diversification through investing in low-correlated assets or different types of investment vehicle might be insufficient due to widespread contagion. ...

Systemic Risk in the European Insurance Sector ArXiv ID: 2505.02635 “View on arXiv” Authors: Giovanni Bonaccolto, Nicola Borri, Andrea Consiglio, Giorgio Di Giorgio Abstract This paper investigates the dynamic interdependencies between the European insurance sector and key financial markets-equity, bond, and banking-by extending the Generalized Forecast Error Variance Decomposition framework to a broad set of performance and risk indicators. Our empirical analysis, based on a comprehensive dataset spanning January 2000 to October 2024, shows that the insurance market is not a passive receiver of external shocks but an active contributor in the propagation of systemic risk, particularly during periods of financial stress such as the subprime crisis, the European sovereign debt crisis, and the COVID-19 pandemic. Significant heterogeneity is observed across subsectors, with diversified multiline insurers and reinsurance playing key roles in shock transmission. Moreover, our granular company-level analysis reveals clusters of systemically central insurance companies, underscoring the presence of a core group that consistently exhibits high interconnectivity and influence in risk propagation. ...

Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk ArXiv ID: 2402.06032 “View on arXiv” Authors: Unknown Abstract Accurately defining, measuring and mitigating risk is a cornerstone of financial risk management, especially in the presence of financial contagion. Traditional correlation-based risk assessment methods often struggle under volatile market conditions, particularly in the face of external shocks, highlighting the need for a more robust and invariant predictive approach. This paper introduces the Causal Network Contagion Value at Risk (Causal-NECO VaR), a novel methodology that significantly advances causal inference in financial risk analysis. Embracing a causal network framework, this method adeptly captures and analyses volatility and spillover effects, effectively setting it apart from conventional contagion-based VaR models. Causal-NECO VaR’s key innovation lies in its ability to derive directional influences among assets from observational data, thereby offering robust risk predictions that remain invariant to market shocks and systemic changes. A comprehensive simulation study and the application to the Forex market show the robustness of the method. Causal-NECO VaR not only demonstrates predictive accuracy, but also maintains its reliability in unstable financial environments, offering clearer risk assessments even amidst unforeseen market disturbances. This research makes a significant contribution to the field of risk management and financial stability, presenting a causal approach to the computation of VaR. It emphasises the model’s superior resilience and invariant predictive power, essential for navigating the complexities of today’s ever-evolving financial markets. ...

Structured factor copulas for modeling the systemic risk of European and United States banks ArXiv ID: 2401.03443 “View on arXiv” Authors: Unknown Abstract In this paper, we employ Credit Default Swaps (CDS) to model the joint and conditional distress probabilities of banks in Europe and the U.S. using factor copulas. We propose multi-factor, structured factor, and factor-vine models where the banks in the sample are clustered according to their geographic location. We find that within each region, the co-dependence between banks is best described using both, systematic and idiosyncratic, financial contagion channels. However, if we consider the banking system as a whole, then the systematic contagion channel prevails, meaning that the distress probabilities are driven by a latent global factor and region-specific factors. In all cases, the co-dependence structure of bank CDS spreads is highly correlated in the tail. The out-of-sample forecasts of several measures of systematic risk allow us to identify the periods of distress in the banking sector over the recent years including the COVID-19 pandemic, the interest rate hikes in 2022, and the banking crisis in 2023. ...

Modeling Inverse Demand Function with Explainable Dual Neural Networks ArXiv ID: 2307.14322 “View on arXiv” Authors: Unknown Abstract Financial contagion has been widely recognized as a fundamental risk to the financial system. Particularly potent is price-mediated contagion, wherein forced liquidations by firms depress asset prices and propagate financial stress, enabling crises to proliferate across a broad spectrum of seemingly unrelated entities. Price impacts are currently modeled via exogenous inverse demand functions. However, in real-world scenarios, only the initial shocks and the final equilibrium asset prices are typically observable, leaving actual asset liquidations largely obscured. This missing data presents significant limitations to calibrating the existing models. To address these challenges, we introduce a novel dual neural network structure that operates in two sequential stages: the first neural network maps initial shocks to predicted asset liquidations, and the second network utilizes these liquidations to derive resultant equilibrium prices. This data-driven approach can capture both linear and non-linear forms without pre-specifying an analytical structure; furthermore, it functions effectively even in the absence of observable liquidation data. Experiments with simulated datasets demonstrate that our model can accurately predict equilibrium asset prices based solely on initial shocks, while revealing a strong alignment between predicted and true liquidations. Our explainable framework contributes to the understanding and modeling of price-mediated contagion and provides valuable insights for financial authorities to construct effective stress tests and regulatory policies. ...

Investment Opportunities and Strategies in an Era of Coronavirus Pandemic ArXiv ID: ssrn-3567445 “View on arXiv” Authors: Unknown Abstract The COVID-19 continues to hit the world economy as well as the financial markets. As a result of the coronavirus spread across all continents, the majority of t Keywords: COVID-19 Impact, Market Volatility, Systemic Risk, Economic Shock, Financial Contagion, Global Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper relies on qualitative analysis and sector descriptions without advanced mathematical models, and the empirical component is limited to basic stock price observations and news citations rather than rigorous backtesting or data analysis. flowchart TD A["Research Goal:<br>Assess COVID-19 impact on markets and identify investment strategies"] --> B{"Key Methodology"}; B --> C["Data: Global Equities, Volatility Indices, Economic Indicators"]; B --> D["Analysis: Systemic Risk &<br>Financial Contagion Modeling"]; C --> E["Computational Process:<br>Shock Simulation & Volatility Correlation"]; D --> E; E --> F["Key Findings & Outcomes"]; F --> G["Identified High-Risk Sectors"]; F --> H["Revealed Opportunities in Resilient Assets"]; F --> I["Strategic Recommendations for Mitigating Economic Shock"];