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

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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.
Original languageEnglish
Pages (from-to)174-184
Number of pages11
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Volume2
Issue number3
Early online date23 May 2018
DOIs
Publication statusPublished - 30 Jun 2018

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Computational techniques
Banking crisis
Bank failure
Market concentration
Solvency
Contagion
Systemic risk
Prediction model
Prediction error
Liquidity shocks
Balance sheet
Out-of-sample forecasting
Liquidity
Computational model
Vulnerability

Keywords

  • Solvency
  • liquidity
  • interbank loans
  • network topology
  • banking crises
  • systemic risk
  • systemic crisis
  • bank failure

Cite this

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title = "Network-based computational techniques to determine the risk drivers of bank failures during a systemic banking crisis",
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.",
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