Quantifying default risk in supplier portfolios

Defaulting suppliers form a great risk for many firms. In this thesis we quantify the individual risks and losses of supplier defaults using structural credit risk models, tailored to the practitioner without subscribed database access. Supplier portfolios in the German automotive industry are used both to explore how such an implementation works, and how credit risk theory can be used to accommodate distinct characteristics of supplier default risk. Modeling elements based on the largest commercial application of structural credit risk models, Moody's KMV model, are verified and tested for practicality when applied to supply chains. We provide examples of how commonly encountered assumptions from practice can be modeled with the use of the models we propose. We then construct a portfolio model using a multi factor dependency model in which loss distributions are estimated using Monte Carlo techniques with importance sampling. Finally we illustrate some settings and examples of modeling elements unique to supply chains.