Estimating the impact of supply chain network contagion on financial stability

ArXiv ID: 2305.04865 “View on arXiv”

Authors: Unknown

Abstract

Realistic credit risk assessment, the estimation of losses from counterparty’s failure, is central for the financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters demonstrated that supply chain shocks do contribute to large financial losses. Based on a unique nation-wide micro-dataset, containing practically all supply chain relations of all Hungarian firms, together with their bank loans, we estimate how firm-failures affect the supply chain network, leading to potentially additional firm defaults and additional financial losses. Within a multi-layer network framework we define a financial systemic risk index (FSRI) for every firm, quantifying these expected financial losses caused by its own- and all the secondary defaulting loans caused by supply chain network (SCN) shock propagation. We find a small fraction of firms carrying substantial financial systemic risk, affecting up to 16% of the banking system’s overall equity. These losses are predominantly caused by SCN contagion. For every bank we calculate the expected loss (EL), value at risk (VaR) and expected shortfall (ES), with and without accounting for SCN contagion. We find that SCN contagion amplifies the EL, VaR, and ES by a factor of 4.3, 4.5, and 3.2, respectively. These findings indicate that for a more complete picture of financial stability and realistic credit risk assessment, SCN contagion needs to be considered. This newly quantified contagion channel is of potential relevance for regulators’ future systemic risk assessments.

Keywords: Credit Risk, Supply Chain Network, Systemic Risk, Network Contagion, Risk Management

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced multi-layer network theory and complex systems modeling (high math), while being heavily data-driven using a unique nationwide micro-dataset with concrete financial metrics like VaR and ES, and practical implications for regulatory stress testing.
  flowchart TD
    A["Research Goal: Impact of SCN Contagion<br>on Financial Stability"] --> B["Data Inputs<br>Nation-wide micro-dataset"]
    B --> C["Methodology: Multi-layer Network Model"]
    C --> D["Computations: Calculate FSRI<br>for each firm"]
    D --> E["Key Finding 1:<br>Small fraction of firms carry<br>substantial systemic risk<br>affecting up to 16% of bank equity"]
    C --> F["Computations: EL, VaR, ES<br>with & without SCN contagion"]
    F --> G["Key Finding 2:<br>SCN contagion amplifies<br>EL: 4.3x, VaR: 4.5x, ES: 3.2x"]