Heterogeneity, correlations and financial contagion

TL;DR

This paper analyzes how features like heterogeneity, correlations, and network structure influence financial contagion, revealing that network topology significantly affects systemic stability and the effectiveness of targeted policies.

Contribution

It empirically examines the impact of degree heterogeneity, size distribution, and correlations on contagion, providing insights into network resilience and policy implications.

Findings

Heterogeneous degree distributions increase resilience to random bank failures.

Networks with power law size distributions are more prone to contagion.

Disassortative mixing enhances overall system stability.

Abstract

We consider a model of contagion in financial networks recently introduced in the literature, and we characterize the effect of a few features empirically observed in real networks on the stability of the system. Notably, we consider the effect of heterogeneous degree distributions, heterogeneous balance sheet size and degree correlations between banks. We study the probability of contagion conditional on the failure of a random bank, the most connected bank and the biggest bank, and we consider the effect of targeted policies aimed at increasing the capital requirements of a few banks with high connectivity or big balance sheets. Networks with heterogeneous degree distributions are shown to be more resilient to contagion triggered by the failure of a random bank, but more fragile with respect to contagion triggered by the failure of highly connected nodes. A power law distribution of balance sheet size is shown to induce an inefficient diversification that makes the system more prone to contagion events. A targeted policy aimed at reinforcing the stability of the biggest banks is shown to improve the stability of the system in the regime of high average degree. Finally, disassortative mixing, such as that observed in real banking networks, is shown to enhance the stability of the system.