Modeling Credit Defaults By Probabilistic Boolean Networks

Abstract

One of the central issues in credit risk measurement and management is modeling and predicting correlated defaults. In this talk we introduce a novel model to investigate the relationship between correlated defaults of different industrial sectors and business cycles as well as the impacts of business cycles on modeling and predicting correlated defaults using Probabilistic Boolean Networks (PBNs). The key idea of the PBN is to decompose a transition probability matrix describing correlated defaults of different sectors into several BN matrices which contain information about business cycles. An efficient estimation method is proposed to estimate the model parameters. Using real default data, we build a PBN for explaining the default structure and make reasonably good prediction of joint defaults in different sectors.