This paper deals with stress tests for credit risk and shows how exploiting the discretion when setting up and implementing a model can drive the results of a quantitative stress test for default probabilities. For this purpose, we employ several variations of a CreditPortfolioView-style model...

This study investigates the link between capital market discipline and bank-level credit risk with a special emphasis on the role of bank ownership structure. Focusing on a large emerging market, Turkey, characterized by prominent state bank presence, our baseline regression results indicate...

This study investigates the link between capital market discipline and bank-level credit risk with a special emphasis on the role of bank ownership structure. Focusing on a large emerging market, Türkiye, characterized by a prominent state bank presence, our baseline regression results indicate...

This paper proposes a model to conduct macro stress test of credit risk for the banking sector based on scenario analysis. We employ an original bank-level data set that splits bank credit portfolios in 21 granular categories, covering household and corporate loans. The results corroborate the...

On 3 December EY hosted a SUERF conference on banking reform with Sir Howard Davies, the Chairman of RBS, and Dame Colette Bowe, the Chairman of the Banking Standards Board, as the two keynote speakers. Professor David Miles (Imperial College) gave the SUERF 2015 Annual Lecture on Capital and...

The article addresses the issue of stress testing based on the probability of bankruptcy and a rating migration matrix. The analysis is conducted on a sample of listed companies in Poland in the years 1998-2016, and the forecasts are made for the years 2016-2018. Particular attention is paid to...

The purpose of this study is to assess model risk with respect to parameter estimation for a simple binary logistic regression model applied as a predictive model. The assessment is done by comparing the effectiveness of eleven different parameter estimation methods. The results from the...

The credit risk assessment process is necessary for maintaining financial stability, cost and time efficiency, model performance accuracy, comparability analysis and future business implications in the commercial banking sector. By accurately predicting credit risk, highly regulated banks can...

From an Internal Audit perspective, the integration of Artificial Intelligence (AI) into credit risk modelling through Machine Learning (ML) algorithms presents significant challenges due to the complexity and multidimensional nature of these models. While AI enhances predictive performance and...