Resilience to contagion in financial networks

Leo Hamed Amini, Rama Cont, Andreea Minca

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

We derive rigorous asymptotic results for the magnitude of contagion in a large counterparty network and give an analytical expression for the asymptotic fraction of defaults, in terms of network characteristics. Our results extend previous studies on contagion in random graphs to inhomogeneous-directed graphs with a given degree sequence and arbitrary distribution of weights. We introduce a criterion for the resilience of a large financial network to the insolvency of a small group of financial institutions and quantify how contagion amplifies small shocks to the network. Our results emphasize the role played by "contagious links" and show that institutions which contribute most to network instability have both large connectivity and a large fraction of contagious links. The asymptotic results show good agreement with simulations for networks with realistic sizes.

Original languageEnglish (US)
Pages (from-to)329-365
Number of pages37
JournalMathematical Finance
Volume26
Issue number2
DOIs
StatePublished - Apr 1 2016
Externally publishedYes

Fingerprint

Contagion
Resilience
resilience
Directed graphs
insolvency
Degree Sequence
Random Graphs
Directed Graph
small group
Financial networks
Shock
Connectivity
Quantify
simulation
Arbitrary
Simulation

Keywords

  • Default contagion
  • Financial stability
  • Interbank network
  • Macroprudential regulation
  • Random graphs
  • Systemic risk

ASJC Scopus subject areas

  • Applied Mathematics
  • Finance
  • Accounting
  • Economics and Econometrics
  • Social Sciences (miscellaneous)

Cite this

Resilience to contagion in financial networks. / Amini, Leo Hamed; Cont, Rama; Minca, Andreea.

In: Mathematical Finance, Vol. 26, No. 2, 01.04.2016, p. 329-365.

Research output: Contribution to journalArticle

Amini, Leo Hamed ; Cont, Rama ; Minca, Andreea. / Resilience to contagion in financial networks. In: Mathematical Finance. 2016 ; Vol. 26, No. 2. pp. 329-365.
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