Contagion risk in the interbank market: a probabilistic approach to cope with incomplete structural information
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http://dx.doi.org/10.1080/14697688.2016.1178855 |
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Title
Contagion risk in the interbank market: a probabilistic approach to cope with incomplete structural informationDate
2016Publisher
Taylor & FrancisISSN
1469-7688; 1469-7696Bibliographic citation
Mattia Montagna & Thomas Lux (2017) Contagion risk in the interbank market: a probabilistic approach to cope with incomplete structural information, Quantitative Finance, 17:1, 101-120Type
info:eu-repo/semantics/articlePublisher version
http://www.tandfonline.com/doi/full/10.1080/14697688.2016.1178855Subject
Abstract
One lesson of the financial crisis erupting in 2008 has been that domino effects constitute a serious
threat to the stability of the financial sector, i.e. the failure of one node in the interbank network
might ... [+]
One lesson of the financial crisis erupting in 2008 has been that domino effects constitute a serious
threat to the stability of the financial sector, i.e. the failure of one node in the interbank network
might entail the danger of contagion to large parts of the entire system. How important this effect
is, depends on the exact topology of the network on which the supervisory authorities have typically
very incomplete knowledge. In order to explore the extent of contagion effects and to analyse the
effectiveness of macroprudential measures to contain such effects, a reconstruction of the quantitative
features of the empirical network would be needed. We propose a probabilistic approach to such a
reconstruction: we propose to combine some important known quantities (like the size of the banks)
with a realistic stochastic representation of the remaining structural elements. Our approach allows
us to evaluate relevant measures for the contagion risk after default of one unit (i.e. the number of
expected subsequent defaults, or their probabilities). For some quantities we are able to derive closed
form solutions, others can be obtained via computational mean-field approximations. [-]
Is part of
Quantitative Finance, 2016, vol. 17, núm 1Rights
© 2016 Informa UK Limited, trading as Taylor & Francis Group
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