Using Network Interbank Contagion in Bank Default Prediction

TL;DR

This study evaluates whether interbank contagion improves the accuracy of predicting bank defaults, finding it often outperforms traditional financial metrics in models using regression and neural networks.

Contribution

It demonstrates that interbank contagion provides significant predictive power for bank default events, surpassing established financial indicators.

Findings

Interbank contagion significantly improves default prediction accuracy.

Neural network models outperform regression models in this context.

Contagion metrics outperform traditional predictors like Tier 1 Capital Ratio.

Abstract

Interbank contagion can theoretically exacerbate losses in a financial system and lead to additional cascade defaults during downturn. In this paper we produce default analysis using both regression and neural network models to verify whether interbank contagion offers any predictive explanatory power on default events. We predict defaults of U.S. domiciled commercial banks in the first quarter of 2010 using data from the preceding four quarters. A number of established predictors (such as Tier 1 Capital Ratio and Return on Equity) are included alongside contagion to gauge if the latter adds significance. Based on this methodology, we conclude that interbank contagion is extremely explanatory in default prediction, often outperforming more established metrics, in both regression and neural network models. These findings have sizeable implications for the future use of interbank contagion as a variable of interest for stress testing, bank issued bond valuation and wider bank default prediction.