Quantifying systemic risk using Bayesian networks
Creditworthiness of individual entities may offer an insight into systemic risk of financial markets
CLICK HERE TO VIEW THE PDF
Sumit Sourabh, Markus Hofer and Drona Kandhai develop a novel framework using Bayesian networks to capture distress dependence in the context of counterparty credit risk. Then, they apply this methodology to a wrong-way risk model and stress-scenario testing. Their results show that stress propagation in an interconnected financial system can have a significant impact on counterparty credit exposures
Since the global financial crisis of 2007–8, modelling counterparty
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact [email protected] or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact [email protected] to find out more.
You are currently unable to copy this content. Please contact [email protected] to find out more.
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email [email protected]
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email [email protected]
More on Banking
7 days in 60 seconds
RBS, dividend curves and system failures
The week on Risk.net, March 28-April 3, 2020
Receive this by email