IFRS 9 and CECL Credit Risk Modelling and Validation
A Practical Guide with Examples Worked in R and SAS
Business & Finance, Economics, Econometrics, Management & Leadership, Planning & Forecasting
| Author: | Tiziano Bellini | ISBN: | 9780128149416 |
| Publisher: | Elsevier Science | Publication: | January 15, 2019 |
| Imprint: | Academic Press | Language: | English |
| Author: | Tiziano Bellini |
| ISBN: | 9780128149416 |
| Publisher: | Elsevier Science |
| Publication: | January 15, 2019 |
| Imprint: | Academic Press |
| Language: | English |
IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management.
- Offers a broad survey that explains which models work best for mortgage, small business, cards, commercial real estate, commercial loans and other credit products
- Concentrates on specific aspects of the modelling process by focusing on lifetime estimates
- Provides an hands-on approach to enable readers to perform model development, validation and audit of credit risk models
IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management.
- Offers a broad survey that explains which models work best for mortgage, small business, cards, commercial real estate, commercial loans and other credit products
- Concentrates on specific aspects of the modelling process by focusing on lifetime estimates
- Provides an hands-on approach to enable readers to perform model development, validation and audit of credit risk models
