Cascading Failures in Bi-partite Graphs: Model for Systemic Risk Propagation

TL;DR

This paper introduces a bi-partite network model to simulate systemic risk in financial systems, effectively predicting bank failures and highlighting the impact of commercial real estate assets during crises.

Contribution

The paper presents a novel bi-partite network model for systemic risk analysis, validated with real data, and identifies key asset types contributing to bank failures.

Findings

Model predicts a significant portion of actual bank failures.

Commercial real estate assets are major failure drivers.

Model useful for systemic risk stress testing.

Abstract

As economic entities become increasingly interconnected, a shock in a financial network can provoke significant cascading failures throughout the system. To study the systemic risk of financial systems, we create a bi-partite banking network model composed of banks and bank assets and propose a cascading failure model to describe the risk propagation process during crises. We empirically test the model with 2007 US commercial banks balance sheet data and compare the model prediction of the failed banks with the real failed banks after 2007. We find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation. The results suggest that this model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the failure of over 350 US commercial banks during 2008-2011.