We consider counterparty risk for Credit Default Swaps (CDS) in presence of correlation between default of the counterparty and default of the CDS reference credit. Our approach is innovative in that, besides default correlation, which was taken into account in earlier approaches, we also model...

In this paper we propose the use of an asymmetric binary link function to extend the proportional hazard model for predicting loan default. The rationale behind this approach is that the symmetry assumption, that has been widely used in the literature, could be considered as quite restrictive,...

This papers provides clear cut evidence that recessionary and financial distressed conditions, as well as banning foreclosure laws, often introduced by governments to mitigate the effects of the economic and/or financial distressed conditions on mortgage loans, have adverse effects on the loan...

This paper suggests using a multilayer artificial neural network (ANN) method, known as deep learning ANN, to predict the probability of default (PD) within the survival analysis framework. Deep learning ANN structures consider hidden interconnections among the covariates determining the PD...

Based on a large micro data set of loan accounts consisting of restructured and non-restructured loans we investigate the ability of loan-specific and macroeconomic covariates to predict the probability of default (PD). We seek to investigate whether differences in the PD between the two...