Systemic risk assessment through high order clustering coefficient

Journal article


Cerqueti, R, Clemente, GP and Grassi, R (2020). Systemic risk assessment through high order clustering coefficient. Annals of Operations Research.
AuthorsCerqueti, R, Clemente, GP and Grassi, R
Abstract

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. 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.

This is a post-peer-review, pre-copyedit version of an article published in Annals of Operations Research. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10479-020-03525-8.

Year2020
JournalAnnals of Operations Research
PublisherSpringer Science and Business Media LLC
ISSN0254-5330
Digital Object Identifier (DOI)doi:10.1007/s10479-020-03525-8
Web address (URL)https://link.springer.com/article/10.1007%2Fs10479-020-03525-8
Publication dates
Online28 Jan 2020
Publication process dates
Accepted28 Dec 2019
Deposited11 Feb 2020
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open
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https://openresearch.lsbu.ac.uk/item/89065

Accepted author manuscript

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