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Network-Based Modeling and Analysis of Systemic Risk in Banking Systems


Hu, Daning; Zhao, J; Hua, Zhimin; Wong, Michael (2012). Network-Based Modeling and Analysis of Systemic Risk in Banking Systems. MIS Quarterly, 36(4):1269-1291.

Abstract

In the wake of the 2008 financial tsunami, existing methods and tools for managing financial risks have been criticizedfor weaknesses in monitoring and alleviating risks at the systemic level. A 2009 article in Nature suggested newapproaches to modeling economic meltdowns are needed to prevent future financial crises. However, existing studieshave not focused on analysis of systemic risk at the individual bank level in a banking network, which is essential formonitoring and mitigating contagious bank failures. To this end, we develop a network approach to risk management(NARM) for modeling and analyzing systemic risk in banking systems. NARM views banks as a network that is linkedthrough financial relationships. It incorporates network and financial principles into a business intelligence (BI)algorithm to analyze systemic risk attributed to each individual bank via simulations based on real-world data from theFederal Deposit Insurance Corporation. Our research demonstrates the feasibility of modeling and analyzing systemicrisk at the individual bank level in a banking network using a BI-based approach. In terms of business impacts, NARMoffers a new means for predicting contagious bank failures and determining capital injection priorities in the wake offinancial crises. Our simulation study shows that under significant market shocks, the interbank payment relationshipbecomes more influential than the correlated bank portfolio relationship in determining an individual bank’s survival. These insights should help financial regulators devise more effective policies and mechanisms to prevent the collapseof a banking system. Further, NARM and the simulation procedure driven by real-world data proposed in this study haveinstructional value to similar research areas such as bank stress testing, where time series data and business networksmay be studied.

Abstract

In the wake of the 2008 financial tsunami, existing methods and tools for managing financial risks have been criticizedfor weaknesses in monitoring and alleviating risks at the systemic level. A 2009 article in Nature suggested newapproaches to modeling economic meltdowns are needed to prevent future financial crises. However, existing studieshave not focused on analysis of systemic risk at the individual bank level in a banking network, which is essential formonitoring and mitigating contagious bank failures. To this end, we develop a network approach to risk management(NARM) for modeling and analyzing systemic risk in banking systems. NARM views banks as a network that is linkedthrough financial relationships. It incorporates network and financial principles into a business intelligence (BI)algorithm to analyze systemic risk attributed to each individual bank via simulations based on real-world data from theFederal Deposit Insurance Corporation. Our research demonstrates the feasibility of modeling and analyzing systemicrisk at the individual bank level in a banking network using a BI-based approach. In terms of business impacts, NARMoffers a new means for predicting contagious bank failures and determining capital injection priorities in the wake offinancial crises. Our simulation study shows that under significant market shocks, the interbank payment relationshipbecomes more influential than the correlated bank portfolio relationship in determining an individual bank’s survival. These insights should help financial regulators devise more effective policies and mechanisms to prevent the collapseof a banking system. Further, NARM and the simulation procedure driven by real-world data proposed in this study haveinstructional value to similar research areas such as bank stress testing, where time series data and business networksmay be studied.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:German
Date:2012
Deposited On:30 Nov 2012 13:01
Last Modified:05 Apr 2016 16:08
Publisher:M I S Research Center
ISSN:0276-7783
Other Identification Number:merlin-id:7286

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