Systemic risk assessment through high order clustering coefficient
Cerqueti, R, Clemente, GP and Grassi, R (2020). Systemic risk assessment through high order clustering coefficient. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03525-8
|Authors||Cerqueti, R, Clemente, GP and Grassi, R|
© 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.
|Journal||Annals of Operations Research|
|Digital Object Identifier (DOI)||https://doi.org/10.1007/s10479-020-03525-8|
|Web address (URL)||https://link.springer.com/article/10.1007%2Fs10479-020-03525-8|
|Online||28 Jan 2020|
|Publication process dates|
|Accepted||28 Dec 2019|
|Deposited||11 Feb 2020|
|Accepted author manuscript|
File Access Level
0views this month
0downloads this month