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Riskrank to Predict Systemic Banking Crises With Common Exposures

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Title:Riskrank to Predict Systemic Banking Crises With Common Exposures
Authors:Sarlin, Peter
Giudici, Paolo
Spelta, Alessandro
Björk, Kaj-Mikael
Keywords:Machine Learning and Network Analytics in Finance
early-warning models, macroprudential oversight, financial networks, RiskRank, systemic risk
Date Issued:03 Jan 2018
Abstract:Systemic risk has remained at the nexus of macro-financial research and policymaking in most parts of the world. Much of the attention has focused on understanding implication of the interconnectedness of financial markets. Instead of focusing only on networks, we use and test the utility of network structures in a novel way. We use RiskRank as a framework to test the use of networks of financial systems, and particularly focus on testing the utility of the network dimension of common exposures (funding composition and portfolio overlap). RiskRank provides an ideal playground for testing the extent to which direct and common exposures perform in capturing transmission of financial crises. The results in this paper highlight the importance of common exposures. We show that funding and portfolio composition overlap are significant channels of contagion and should be accounted for when measuring systemic risk.
Pages/Duration:9 pages
URI:http://hdl.handle.net/10125/50055
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.168
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Network Analytics in Finance


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