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 …

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 …

on insolvency risk will be defined by recalling the concepts of Cash Flow-at-Risk and Capital-at-Risk. A first check on … through a simulation model. The scenario analysis allows us to examine how financial and risk policies oriented by insolvency …This conceptual paper focuses on the relationship between insolvency, capital structure, and value creation. The aim is …

This article reviews two leading measures of financial risk and an emerging alternative. Embraced by the Basel accords …, value-at-risk and expected shortfall are the leading measures of financial risk. Expectiles offset the weaknesses of value-at-risk … (VaR) and expected shortfall. Indeed, expectiles are the only elicitable law-invariant coherent risk measures. After …

Monte Carlo (MCMC) is used for parameter estimation. We then link our proposed model to an extended version of the credit … risk model CreditRisk+. This allows exact risk aggregation via an efficient numerically stable Panjer recursion algorithm …. Furthermore, the model allows exact (without Monte Carlo simulation error) calculation of risk measures and their sensitivities …

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 …