A data-driven econo-financial stress-testing framework to estimate the effect of supply chain networks on financial systemic risk

ArXiv ID: 2502.17044 “View on arXiv”

Authors: Unknown

Abstract

Supply chain disruptions constitute an often underestimated risk for financial stability. As in financial networks, systemic risks in production networks arises when the local failure of one firm impacts the production of others and might trigger cascading disruptions that affect significant parts of the economy. Here, we study how systemic risk in production networks translates into financial systemic risk through a mechanism where supply chain contagion leads to correlated bank-firm loan defaults. We propose a financial stress-testing framework for micro- and macro-prudential applications that features a national firm level supply chain network in combination with interbank network layers. The model is calibrated by using a unique data set including about 1 million firm-level supply links, practically all bank-firm loans, and all interbank loans in a small European economy. As a showcase we implement a real COVID-19 shock scenario on the firm level. This model allows us to study how the disruption dynamics in the real economy can lead to interbank solvency contagion dynamics. We estimate to what extent this amplifies financial systemic risk. We discuss the relative importance of these contagion channels and find an increase of interbank contagion by 70% when production network contagion is present. We then examine the financial systemic risk firms bring to banks and find an increase of up to 28% in the presence of the interbank contagion channel. This framework is the first financial systemic risk model to take agent-level dynamics of the production network and shocks of the real economy into account which opens a path for directly, and event-driven understanding of the dynamical interaction between the real economy and financial systems.

Keywords: Supply chain contagion, Systemic risk, Financial stress testing, Interbank networks, Macroprudential regulation, Macro / Interbank

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper employs established network propagation models (like DebtRank concepts) with moderate mathematical formulation, but its primary strength lies in extensive real-world data application, including 1M+ firm links and full interbank data for stress-testing with a specific shock scenario (COVID-19).

  flowchart TD
    A["Research Goal<br>Estimate how supply chain disruptions<br>translate into financial systemic risk"] --> B{"Methodology"};
    B --> C["Data Inputs<br>• National Supply Chain Network (1M links)<br>• Bank-Firm Loans<br>• Interbank Network"];
    C --> D["Computational Model<br>Financial Stress-Testing Framework"];
    D --> E{"Scenario Analysis"};
    E --> F["Real Economy Shock<br>COVID-19 Supply Disruption"];
    D --> G["Simulation<br>Cascading Defaults &<br>Solvency Contagion"];
    F --> G;
    G --> H["Key Findings & Outcomes<br>• 70% increase in interbank contagion<br>• 28% increase in financial systemic risk<br>• Framework enables event-driven<br>analysis of real economy-finance interaction"];