On the Computational Complexity of Measuring Global Stability of Banking Networks

TL;DR

This paper investigates the computational complexity of measuring the stability of banking networks against shocks, aiming to understand how network structure influences systemic risk and stability assessment.

Contribution

It formalizes models of homogeneous and heterogeneous banking networks, defines stability measures, and analyzes the computational complexity of evaluating these measures across different network topologies.

Findings

Complexity results vary with network topology and parameters.

Certain network structures are more prone to instability.

Insights into properties affecting systemic stability.

Abstract

Threats on the stability of a financial system may severely affect the functioning of the entire economy, and thus considerable emphasis is placed on the analyzing the cause and effect of such threats. The financial crisis in the current and past decade has shown that one important cause of instability in global markets is the so-called financial contagion, namely the spreading of instabilities or failures of individual components of the network to other, perhaps healthier, components. This leads to a natural question of whether the regulatory authorities could have predicted and perhaps mitigated the current economic crisis by effective computations of some stability measure of the banking networks. Motivated by such observations, we consider the problem of defining and evaluating stabilities of both homogeneous and heterogeneous banking networks against propagation of synchronous idiosyncratic shocks given to a subset of banks. We formalize the homogeneous banking network model of Nier et al. and its corresponding heterogeneous version, formalize the synchronous shock propagation procedures, define two appropriate stability measures and investigate the computational complexities of evaluating these measures for various network topologies and parameters of interest. Our results and proofs also shed some light on the properties of topologies and parameters of the network that may lead to higher or lower stabilities.