Many years have passed since Baesens et al. published their benchmarking study of
classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S …

The level of modeling sophistication in financial services has increased considerably over
the years. Nowadays, the complexity of financial problems and the vast amount of data …

We extend the linear quantile regression with a neural network structure to enable more
flexibility in every quantile of the bank loan loss given default distribution. This allows us to …

We propose to incorporate least squares support vector machine technique into a two-stage
modelling framework to predict recovery rates of credit cards from a UK retail bank. The two …

Abstract Literature on Losses Given Default (LGD) usually focuses on mean predictions,
even though losses are extremely skewed and bimodal. This paper proposes a Quantile …

We propose estimating a model for Loss Given Default (LGD) that allows accounting for
spatial dependence between peer-to-peer (P2P) loans. We suggest the LGD two-stage …

Abstract Loss Given Default (LGD) is an essential element in effective banking supervision,
as set out in the Basel Accords. In this paper, we focus on improving LGD predictions with …

We propose a new approach for comparing Loss Given Default (LGD) models which is
based on loss functions defined in terms of regulatory capital charge. Our comparison …

Banks are obliged to provide downturn estimates for loss given defaults (LGDs) in the
internal ratings-based approach. While downturn conditions are characterized by …

We study the asymptotic behavior of the loss from defaults of a large portfolio. Inspired by the
work of Bassamboo, Juneja and Zeevi (2008), we consider a static structural model in which …