Default cascades and systemic risk on different interbank network topologies

Research output: Contribution to journalArticle

Abstract

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’ crossexposures 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.

Original languageEnglish
Pages (from-to)1-46
Number of pages46
JournalJournal of Network Theory in Finance
Volume5
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Network Topology
Cascade
Topology
Contagion
Liquidity
Sales
Fires
Specifications
Shock
Banking
Sector
Network topology
Systemic risk
Empirical Model
Centrality
Path Length
Differentiate
Network Structure
Leverage
Specification

Keywords

  • Cascading defaults
  • Financial contagion
  • Financial stability
  • Interbank network
  • Network topology
  • Systemic risk

Cite this

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abstract = "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’ crossexposures 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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Default cascades and systemic risk on different interbank network topologies. / Scholtes, Nicolas K.

In: Journal of Network Theory in Finance, Vol. 5, No. 1, 01.01.2019, p. 1-46.

Research output: Contribution to journalArticle

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