Systemic risk assessment through high order clustering coefficient

TL;DR

This paper introduces a new systemic risk measure for financial networks based on high-order clustering coefficients, demonstrating its effectiveness through empirical analysis of the global banking network over recent years.

Contribution

It develops a novel systemic risk measure utilizing high-order clustering coefficients, extending previous concepts to better capture network structure impacts.

Findings

The measure effectively tracks systemic risk changes over time.

Empirical results highlight increased risk during financial crises.

Regulatory impacts are reflected in network risk dynamics.

Abstract

In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order $l$ of a node $i$ introduced in Cerqueti et al. (2018). Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks.