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.
Krause A., Giansante S. (2018). Network-Based Computational Techniques to Determine the Risk Drivers of Bank Failures during a Systemic Banking Crisis. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2(3), 174-184 [10.1109/TETCI.2018.2805319].
Network-Based Computational Techniques to Determine the Risk Drivers of Bank Failures during a Systemic Banking Crisis
Giansante S.
2018-01-01
Abstract
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.File | Dimensione | Formato | |
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