Network-based computational techniques to determine the risk drivers of bank failures during a systemic banking crisis

Andreas Krause, Simone Giansante

OAI: oai:purehost.bath.ac.uk:openaire_cris_publications/ce05fa66-dcaf-48b0-b5d3-520598946c52 • DOI: https://doi.org/10.1109/TETCI.2018.2805319

This paper employs a computational model of solvency and liquidity contagion assessing the vulnerability of banks to systemic risk. We find that the main risk drivers relate to the financial connections a bank has and the market concentration, apart from the size of the bank triggering the contagion, while balance sheets play only a minor role. We also find that market concentration might facilitate banks to withstand liquidity shocks better while exposing them to larger solvency chocks. Our results are validated through an out-of-sample forecasting that shows that both type I and type II prediction errors are reduced if we include network characteristics in our prediction model.

Solvency liquidity Interbank loans Network topology Banking crises systemic risk systemic crisis bank failure