Quantifying systemic risk using Bayesian networks

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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

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Quantifying systemic risk using Bayesian networks

Creditworthiness of individual entities may offer an insight into systemic risk of financial markets

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