On modeling credit defaults: A probabilistic Boolean network approach

作     者：Gu, Jia-Wen Siu, Tak-Kuen Zheng, Harry 

作者机构：Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong Pokfulam Road Hong Kong Department of Actuarial Studies Center for Financial Risk Macquarie University Sydney NSW Australia Department of Mathematics Imperial College London United Kingdom 

出 版 物：《Risk and Decision Analysis》 (Risk Decis. Anal.)

年 卷 期：2013年第4卷第2期

页      面：119-129页

学科分类：0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 0201[经济学-理论经济学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

主　　题：Business cycles correlated defaults entropy probabilistic Boolean networks 

摘      要：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. © 2013-IOS Press and the authors.