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 …

Accurately forecasting loss given default (LGD) poses challenges, due to its highly skewed
distributions and complex nonlinear dependencies with predictors. To this end, we propose …

Machine learning has increasingly found its way into the credit risk literature. The
approaches mainly focus on better forecasting credit risk parameters and are often shown to …

I investigate the exposure of sectoral equity portfolios to climate transition risks by
augmenting a three-factor asset pricing model with a green-minus-brown (GMB) factor as a …

In recent years, significant achievements have been made in time series forecasting using
deep learning methods, particularly the Long Short-Term Memory Network (LSTM) …

We propose a new family of distributions, so-called the unit ratio-extended Weibull family
(UREW). It is derived from ratio transformation in an extended Weibull random variable. The …

The loss given default (LGD) is an important credit risk parameter in the regulatory system
for financial institutions. Due to the complex structure of the LGD distribution, we propose a …

In the peer-to-peer (P2P) lending market, borrowers apply for a loan through a virtual
platform and get money from investors if they meet certain criteria. Meanwhile, investors lend …

Se analiza de manera empírica la base de datos de la Fintech estadounidense
LendingClub, la empresa precursora del mercado de préstamos digitales persona a …

This thesis presents three empirical studies about the peer-to-peer (P2P) lending market.
The first study examines whether the announcement of a government support policy could …