Systemic risk assessment through high order clustering coefficient
In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorp...
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| Vydáno v: | Annals of operations research Ročník 299; číslo 1-2; s. 1165 - 1187 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.04.2021
Springer Springer Nature B.V |
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| ISSN: | 0254-5330, 1572-9338 |
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| Abstract | In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order
l
of a node
i
introduced in Cerqueti et al. (2018). Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks. |
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| AbstractList | In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order l of a node i introduced in Cerqueti et al. (2018). Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks. In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order l of a node i introduced in Cerqueti et al. (2018). Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks. |
| Audience | Academic |
| Author | Cerqueti, Roy Clemente, Gian Paolo Grassi, Rosanna |
| Author_xml | – sequence: 1 givenname: Roy surname: Cerqueti fullname: Cerqueti, Roy organization: Department of Economic and Social Sciences, Sapienza University of Rome, School of Business, London South Bank University – sequence: 2 givenname: Gian Paolo surname: Clemente fullname: Clemente, Gian Paolo organization: Department of Mathematics for Economics, Financial and Actuarial Sciences, Universitá Cattolica del Sacro Cuore – sequence: 3 givenname: Rosanna orcidid: 0000-0001-5292-1723 surname: Grassi fullname: Grassi, Rosanna email: rosanna.grassi@unimib.it organization: Department of Statistics and Quantitative Methods, University of Milano - Bicocca |
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| Cites_doi | 10.1038/nature09659 10.1103/PhysRevE.71.065103 10.1016/j.frl.2018.03.002 10.1016/j.ejor.2017.11.056 10.1016/j.jebo.2012.05.016 10.2307/2077918 10.1016/j.physa.2013.09.010 10.1016/j.physrep.2009.11.002 10.1016/j.chaos.2017.12.007 10.1016/j.jfs.2013.03.001 10.1007/s11403-016-0182-z 10.1017/CBO9780511815478 10.1038/30918 10.1073/pnas.122653799 10.1080/07350015.2015.1017643 10.1073/pnas.0400087101 10.1016/j.jedc.2018.07.001 10.1016/j.jfineco.2011.12.010 10.1103/PhysRevE.76.026107 10.1016/j.jedc.2012.04.001 10.1016/S0378-8733(99)00019-2 10.1007/s42001-017-0008-3 10.1016/j.jfs.2018.03.010 10.1007/s10479-015-1857-x 10.1007/s10479-016-2362-6 10.1016/j.ejor.2018.03.041 10.1007/s10479-017-2401-y 10.1016/j.jbankfin.2017.05.010 10.2139/ssrn.1786571 10.2139/ssrn.258430 |
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| DOI | 10.1007/s10479-020-03525-8 |
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| Keywords | Cross-border banking G20 C02 Network analysis Systemic risk Clustering coefficient Community structures G28 |
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| SubjectTerms | Banking Business and Management Clustering Combinatorics Evaluation Market risk Methods Operations research Operations Research/Decision Theory Risk assessment S.I.: Recent Developments in Financial Modeling and Risk Management Theory of Computation |
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| Title | Systemic risk assessment through high order clustering coefficient |
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