We develop the regime-switching default risk (RSDR) model as a generalization of Merton's default risk (MDR) model. The …

Credit risk is a critical issue that affects banks and companies on a global scale. Possessing the ability to … accurately predict the level of credit risk has the potential to help the lender and borrower. This is achieved by alleviating … trees, random forests, and stochastic gradient boosting to add to the current literature on credit-risk modelling. The …

measures. Our research provides useful indications for practitioners and regulators targeting more reliable risk measures in …

both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by … dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well …

selecting the risk drivers. …

Predicting the risk of corporate bankruptcy is one of the most important challenges for researchers dealing with the … issue of financial health evaluation. The risk of corporate bankruptcy is most often assessed with the use of early warning …

findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than … standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors …

The paper is an investigation on the impact of financial markets on the volatility of the green bonds credit risk … detail, we highlight that the gamma in the two Exponential models is positive: so, the "green" credit risk volatility is more … of our knowledge this is the first study that analyzes the specific credit risk component of the green bond yields: we …

The aggregation of individual risks into total risk using a weighting variable multiplied by two ratio variables … representing incidence and intensity is an important task for risk professionals. For example, expected loss (EL) of a loan is the …