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 info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to copy this content. Please contact info@risk.net to find out more.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here