Affiliations: Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong, China | Department of Actuarial Studies, and Center for Financial Risk, Faculty of Business and Economics, Macquarie University, Sydney, NSW, Australia | Department of Mathematics, Imperial College, London, UK
Note: [] Corresponding author: Jia-Wen Gu, Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, China. E-mail: [email protected]
Note: [] Research supported in part by RGC Grants 7017/07P, HKU CRCG Grants and HKU Strategic Research Theme Fund on Computational Physics and Numerical Methods.
Abstract: One of the central issues in credit risk measurement and management is modeling and predicting correlated defaults. In this paper 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 the Probabilistic Boolean Network (PBN). 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 based on an entropy approach is used to estimate the model parameters. Using real default data, we build a PBN for explaining the default structure and making reasonably good predictions of joint defaults in different sectors.
Keywords: Business cycles, entropy, correlated defaults, probabilistic Boolean networks
