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 …

Purpose The purpose of this study is to evaluate the global developments in the area of
fintech solutions by analyzing Islamic and Conventional banks core accounting and market …

This study is conducted to investigate the prediction of corporate financial distress based on
the Merton (1974) market-based Distance to Default (DD) model over the period from 1997 …

It is commonly observed that high grade loans with better ratings are often associated with
low recoveries if they default (ie with relatively high loss-given-default (LGD)). To address …

This paper investigates the variation in nonperforming loans over the economic cycle and
the effect of past returns based on a nonparametric quantile analysis of the largest Islamic …

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 …

The recovery rate on defaulted corporate bonds has a time-varying distribution, a topic that
has received limited attention in the literature. We apply machine learning approaches for …