Journal of Credit Risk

This issue of The Journal of Credit Risk contains three original research papers.

In “Credit default prediction using a support vector machine and a probabilistic neural network”, the authors, Mohammad Zoynul Abedin, Chi Guotai, Sisira Colombage and Fahmida-E-Moula, address the fact that the ranking of classifiers varies for different criteria with measures under different circumstances, by proposing the simultaneous application of support vector machine and probabilistic neural network-based CDP algorithms, together with frequently used high-performance models.

“Modeling dependent risk factors with CreditRisk+” is our second paper in this issue. In this paper, an extension of the CreditRisk+  model, called the mixed vector model, is proposed by authors Xiaohang Zhang, SuBang Choe, Ji Zhu and Jill Bewick.

In the final paper “Consumer risk appetite, the credit cycle and the housing bubble” Joseph L. Breeden and José J. Canals-Cerdá explore the role of consumer risk appetite in the initiation of credit cycles and as an early trigger of the US mortgage crisis.