A multilevel analysis to systemic exposure: insights from local and system-wide information

TL;DR

This paper demonstrates that local network measures, based on community structures within financial networks, enhance the prediction of systemic risk of financial institutions beyond traditional global metrics.

Contribution

It introduces a multilevel analysis framework incorporating local community-based topological measures to better identify systemic institutions.

Findings

Local topological measures improve systemic risk prediction.

Community-based metrics provide unique information not captured by global measures.

Systemic vulnerability can be better assessed using multilevel network analysis.

Abstract

In the aftermath of the financial crisis, the growing literature on financial networks has widely documented the predictive power of topological characteristics (e.g. degree centrality measures) to explain the systemic impact or systemic vulnerability of financial institutions. In this work, we show that considering alternative topological measures based on local sub-network environment improves our ability to identify systemic institutions. To provide empirical evidence, we apply a two-step procedure. First, we recover network communities (i.e. close-peer environment) on a spillover network of financial institutions. Second, we regress alternative measures of vulnerability on three levels of topological measures: the global level (i.e. firm topological characteristics computed over the whole system), local level (i.e. firm topological characteristics computed over the community) and aggregated level by averaging individual characteristics over the community. The sample includes $46$ financial institutions (banks, broker-dealers, insurance and real-estate companies) listed in the Standard \& Poor's 500 index. Our results confirm the informational content of topological metrics based on close-peer environment. Such information is different from the one embeds in traditional system wide topological metrics and is proved to be predictor of distress for financial institutions in time of crisis.