July 20, 2015
This paper presents a model for the determination and forecast of the number of defaults andcredit changes by estimating a reduced-form ordered regression model with a large data setfrom a credit insurance portfolio. Similarly to banks with their classical credit risk managementtechniques, credit insurers measure the credit quality of buyers with rating transition matricesdepending on the economical environment. Our approach consists in modeling stochastic transitionmatrices for homogeneous groups of firms depending on macroeconomic risk factors. One ofthe main features of this business is the close monitoring of covered firms and the insurer’s abilityto cancel or reduce guarantees when the risk changes. As our primary goal is a risk managementanalysis, we try to account for this leeway and study how this helps mitigate risks in case ofshocks. This specification is particularly useful as an input for the Own Risk Solvency Assessment(ORSA) since it illustrates the kind of management actions that can be implemented byan insurer when the credit environment is stressed.