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,...

In the aftermath of the recent financial crisis there has been considerable focus on methods for predicting macroeconomic variables when their behavior is subject to abrupt changes, associated for example with crisis periods. In this paper we propose similarity based approaches as a way to...

This paper suggests a multilayer artificial neural network (ANN) method to predict the probability of default (PD) within the survival analysis framework. ANN structures consider hidden interconnections among covariates determining the PD which can lead to prediction improvements, compared to...

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...

This paper evaluates the VaR forecasting performance of the Markov regime switching (MRS) based volatility models, allowing for EGARCH effects. As is argued in the literature, this extension of the MRS model model may improve its forecasting performance due to its ability to capture leverage...