Journal of Network Theory in Finance

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Default cascades and systemic risk on different interbank network topologies

Nicolas K. Scholtes

  • We generate a large number of topologically-realistic interbank network structures using a probabilistic generation algorithm.
  • A sequential financial contagion model with cascading defaults model is run on each network (crisis simulation).
  • Modelling framework comprises random and targeted shocks of varying magnitude as well as an indirect price contagion channel.
  • Focus on network topology: Results are presented as an empirical model in which the impact of global and local network measures on contagion dynamics is measured.
     

This paper examines the relationship between the topology of interbank networks and their ability to propagate localized, idiosyncratic shocks across the banking sector via banks’ interbank claims on one another. We begin by creating a wide variety of networks and heterogeneous balance sheet structures using a generative algorithm capable of replicating key characteristics of real-world interbank networks. On each network, we run a standard financial contagion model with cascading defaults. Our modeling framework differentiates between random and targeted shocks of varying magnitude. Interbank contagion comprises a direct channel via banks’ cross-exposures and an indirect channel due to liquidity effects and external asset fire sales. Last, we develop an empirical model to test which global features of the network, aggregate banking sector balance sheet and shock properties drive contagion dynamics. Our results show a strong stabilizing role played by system leverage across all specifications. Among the global network measures, average path length, network density and assortativity are shown to drive the number of failures in a manner consistent with their definition. Similarly, the centralities of the shocked banks (across all definitions) play a significant role in the default cascade. By contrast, allowing for liquidity effects diminishes the explanatory power of the network on both global and local scales. However, taking the change in asset price (primarily driven by liquidity effects) as the dependent variable reestablishes the link between the network structure and the extent of financial contagion.
 

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