Abstract：
:In the aftermath of the financial crisis of 2007-2009, the growing body of literature on financial networks has widely documented the predictive power of topological characteristics (e.g., degree centrality measures) to explain the systemic impact or systemic exposure of financial institutions. This study shows that considering alternative topological measures based on local sub-network environment improves our ability to identify systemic institutions. To provide empirical evidence, we apply a two-step procedure. First, we recover network communities (i.e., close-peer environment) on a spillover network of financial institutions. Second, we regress alternative measures of vulnerability (i.e. firm's losses)on three levels of topological measures: the global level (i.e., firm topological characteristics computed over the whole system), local level (i.e., firm topological characteristics computed over the community to which it belongs), and aggregated level by averaging individual characteristics over the community. The sample includes 46 financial institutions (banks, broker-dealers, and insurance and real-estate companies) listed in the Standard & Poor's 500 index. Our results confirm the informational content of topological metrics based on a close-peer environment. Such information is different from that embedded in traditional system-wide topological metrics and can help predict distress of financial institutions in times of crisis.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Gandica Y,Béreau S,Gnabo JYdoi
10.1038/s41598-020-74259-7subject
Has Abstractpub_date
2020-10-19 00:00:00pages
17657issue
1issn
2045-2322pii
10.1038/s41598-020-74259-7journal_volume
10pub_type
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