Credit risk modeling using Bayesian networks
2010 (English)In: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, International Journal of Intelligent Systems, Vol. 25, no 4, 326-344 p.Article in journal (Refereed) Published
The main goal of this research is to demonstrate how probabilistic graphs may be used for modeling and assessment of credit concentration risk. The destructive power of credit concentrations essentially depends on the amount of correlation among borrowers. However, borrower companies correlation and concentration of credit risk exposures have been difficult for the banking industry to measure in an objective way as they are riddled with uncertainty. As a result, banks do not manage to make a quantitative link to the correlation driving risks and fail to prevent concentrations from accumulating. In this paper, we argue that Bayesian networks provide an attractive solution to these problems and we show how to apply them in representing, quantifying and managing the uncertain knowledge in concentration of credits risk exposures.We suggest the stepwise Bayesian network model building and show how to incorporate expert-based prior beliefs on the risk exposure of a group of related borrowers, and then update these beliefs through the whole model with the new information. We then explore a specific graph structure, a tree-augmented Bayesian network and show that this model provides better understanding of the risk accumulating due to business links between borrowers.We also present two strategies of model assessment that exploit the measure of mutual information and show that the constructed Bayesian network is a reliable model that can be implemented to identify and control threat from concentration of credit exposures. Finally, we demonstrate that suggested tree-augmented Bayesian network is also suitable for stress-testing analysis, in particular, it can provide the estimates of the posterior risk of losses related to the unfavorable changes in the financial conditions of a group of related borrowers.
Place, publisher, year, edition, pages
2010. Vol. 25, no 4, 326-344 p.
Concentration (process); Distributed parameter networks; Inference engines; Intelligent networks; Knowledge based systems; Risk assessment; Risk management; Speech recognition; Statistical tests; Trees (mathematics)
IdentifiersURN: urn:nbn:se:kth:diva-75450DOI: 10.1002/int.20410ISI: 000275812600002OAI: oai:DiVA.org:kth-75450DiVA: diva2:490496
QC 201202072012-02-052012-02-052012-02-07Bibliographically approved