Computation of systemic risk measures: a mixed-integer programming approach

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buir.contributor.authorArarat, Çaǧın
buir.contributor.orcidArarat, Çaǧın|0000-0002-6985-7665
dc.citation.epage2145en_US
dc.citation.issueNumber6
dc.citation.spage2130
dc.citation.volumeNumber71
dc.contributor.authorArarat, Çaǧın
dc.contributor.authorMeimanjan, N.
dc.date.accessioned2024-03-21T18:26:25Z
dc.date.available2024-03-21T18:26:25Z
dc.date.issued2023-09-22
dc.departmentDepartment of Industrial Engineering
dc.description.abstractSystemic risk is concerned with the instability of a financial system whose members are interdependent in the sense that the failure of a few institutions may trigger a chain of defaults throughout the system. Recently, several systemic risk measures have been proposed in the literature that are used to determine capital requirements for the members subject to joint risk considerations. We address the problem of computing systemic risk measures for systems with sophisticated clearing mechanisms. In particular, we consider an extension of the Rogers-Veraart network model where the operating cash flows are unrestricted in sign. We propose a mixed-integer programming problem that can be used to compute clearing vectors in this model. Because of the binary variables in this problem, the corresponding (set-valued) systemic risk measure fails to have convex values in general. We associate nonconvex vector optimization problems with the systemic risk measure and provide theoretical results related to the weighted-sum and Pascoletti-Serafini scalarizations of this problem. Finally, we test the proposed formulations on computational examples and perform sensitivity analyses with respect to some model-specific and structural parameters. Copyright: © 2023 INFORMS.
dc.identifier.doi10.1287/opre.2021.0040
dc.identifier.urihttps://hdl.handle.net/11693/115061
dc.language.isoen_US
dc.publisherINFORMS Inst.for Operations Res.and the Management Sciences
dc.relation.isversionofhttps://dx.doi.org/10.1287/opre.2021.0040
dc.rightsCC BY 4.0 DEED (Attribution 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.source.titleOperations Research
dc.subjectSystemic risk measure
dc.subjectSet-valued risk measure
dc.subjectEisenberg-Noe model
dc.subjectRogers-Veraart model
dc.subjectMixed-integer programming
dc.subjectVector optimization
dc.titleComputation of systemic risk measures: a mixed-integer programming approach
dc.typeArticle

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