Contagion on Financial Networks: An Introduction

ArXiv ID: 2402.08071 “View on arXiv”

Authors: Unknown

Abstract

This mini-project models propagation of shocks, in time point, through links in connected banks. In particular, financial network of 100 banks out of which 15 are shocked to default (that is, 85.00% of the banks are solvent) is modelled using Erdos and Renyi network – directed, weighted and randomly generated network. Shocking some banks in a financial network implies removing their assets and redistributing their liabilities to other connected ones in the network. The banks are nodes and two ranges of probability values determine tendency of having a link between a pair of banks. Our major finding shows that the ranges of probability values and banks’ percentage solvency have positive correlation.

Keywords: Financial Networks, Shock Propagation, Erdos-Renyi Network, Systemic Risk, Bank Solvency, Fixed Income/Banking

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper introduces stochastic differential equations, graph theory, and network science models like Erdős–Rényi with probabilistic formulas, indicating advanced math complexity. However, it lacks empirical data, backtests, or implementation details, relying instead on a conceptual mini-project model without real-world validation.

  flowchart TD
    A["Research Goal: How does shock propagate in a financial network<br>and what factors affect bank solvency?"] --> B["Model Setup"]
    B --> C["Define Network: 100 Banks in Erdos-Renyi Network<br>Directed & Weighted Links"]
    C --> D["Input Data: 15 Banks Shocked to Default<br>Redistribute Liabilities to Connected Banks"]
    D --> E["Compute Processes: Simulate Shock Propagation<br>via Cascading Defaults"]
    E --> F["Key Findings: Positive Correlation Found<br>Higher Link Probability = Higher Bank Solvency"]