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IFRS 9 and CECL Credit Risk Modelling and Validation,

Edition 1 A Practical Guide with Examples Worked in R and SAS

Editors: By Tiziano Bellini

Publication Date: 31 Jan 2019

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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.

Key Features

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

About the author

By Tiziano Bellini, BlackRock Financial Market Advisory, London, UK

1. Introduction to Expected Credit Loss Modelling and Validation1.1 Introduction 1.2 IFRS 9 1.21 Staging Allocation 1.22 ECL Ingredients1.23 Scenario Analysis and ECL1.3 CECL 1.31 Loss-Rate Methods 1.32 Vintage Methods1.33 Discounted Cash Flow Methods1.34 Probability of Default Method (PD, LGD, EAD)1.35 IFRS 9 vs CECL 1.4 ECL and Capital Requirements1.41 Internal Rating-Based Credit Risk-Weighted Assets1.42 How ECL Affects Regulatory Capital and Ratios1.5 Book Structure at a Glance1.6 Summary

2. One-Year PDs2.1 Introduction 2.2 Default Definition and Data Preparation 2.21 Default Definition 2.22 Data Preparation 2.3 Generalized Linear Models (GLMs) 2.31 GLM (Scorecard) Development2.32 GLM Calibration2.33 GLM Validation2.4 Machine Learning (ML) Modelling2.41 Classification and Regression Trees (CART)2.42 Bagging, Random Forest, and Boosting2.43 ML Model Calibration2.44 ML Model Validation2.5 Low Default Portfolio, Market-Based, and Scarce Data Modelling2.51 Low Default Portfolio Modelling2.52 Market Based Modelling2.53 Scarce Data Modelling2.54 Hints on Low Default Portfolio, Market-Based, and Scarce Data Model Validation 2.6 SAS Laboratory 2.7 Summary 2.8 Appendix A From Linear Regression to GLMs2.9 Appendix B Discriminatory Power Assessment

3. Lifetime PDs 13.1 Introduction3.2 Data Preparation 3.21 Default Flag Creation 3.22 Account-Level (Panel) Database Structure3.3 Lifetime GLM Framework3.31 Portfolio-level GLM Analysis3.32 Account-Level GLM Analysis3.33 Lifetime GLM Validation3.4 Survival Modelling 3.41 Kaplan Meier (KM) Survival Analysis3.42 Cox Proportional Hazard (CPH) Survival Analysis3.43 Accelerated Failure Time (AFT) Survival Analysis3.44 Survival Model Validation3.5 Lifetime Machine Learning (ML) Modelling3.51 Bagging, Random Forest, and Boosting Lifetime PD3.52 Random Survival Forest Lifetime PD3.53 Lifetime ML Validation3.6 Transition Matrix Modelling3.61 Na_ve Markov Chain Modelling3.62 Merton-Like Transition Modelling3.63 Multi State Modelling3.64 Transition Matrix Model Validation3.7 SAS Laboratory 3.8 Summary

4. LGD Modelling4.1 Introduction4.2 LGD Data Preparation4.21 LGD Data Conceptual Characteristics 4.22 LGD Database Elements4.3 LGD Micro-Structure Approach4.31 Probability of Cure 4.32 Severity4.33 Defaulted Asset LGD4.34 Forward-Looking Micro-Structure LGD Modelling4.35 Micro-Structure Real Estate LGD Modelling4.36 Micro-Structure LGD Validation4.4 LGD Regression Methods441 Tobit Regression4.42 Beta Regression4.43 Mixture Models and forward-looking Regression4.44 Regression LGD Validation4.5 LGD Machine Learning (ML) Modelling4.51 Regression Tree LGD4.52 Bagging, Random Forest, and Boosting LGD4.53 Forward-Looking Machine Learning LGD4.54 Machine Learning LGD Validation4.6 Hints on LGD Survival Analysis4.7 Scarce Data and Low Default Portfolio LGD Modelling4.71 Expert Judgement LGD Process4.72 Low Default Portfolio LGD4.73 Hints on How to Validate Scarce Data and Low Default Portfolio LGDs4.8 SAS Laboratory4.9 Summary

5. Prepayments, Competing Risks and EAD Modelling5.1 Introduction5.2 Data Preparation5.21 How to Organize Data5.3 Full Prepayment Modelling5.31 Full Prepayment via GLMs5.32 Machine Learning (ML) Full Prepayment Modelling5.33 Hints on Survival Analysis5.34 Full Prepayment Model Validation5.4 Competing Risk Modelling5.41 Multinomial Regression Competing Risks Modelling5.42 Full Evaluation Procedure5.43 Competing Risk Model Validation5.5 EAD Modelling5.51 A Competing-Risk-Like EAD Framework5.52 Hints on EAD Estimation via Machine Learning (ML)5.53 EAD Model Validation5.6 SAS Laboratory 5.7 Summary

6. Scenario Analysis and Expected Credit Losses6.1 Introduction6.2 Scenario Analysis6.21 Vector Auto-Regression (VAR) and Vector Error-Correction (VEC) Modelling6.22 VAR and VEC Forecast6.23 Hints on GVAR Modelling6.3 ECL Computation in Practice 6.31 Scenario Design and Satellite Models6.32 Lifetime ECL6.33 IFRS 9 Staging Allocation6.4 ECL Validation6.41 Historical and Forward-Looking Validation6.42 Credit Portfolio Modelling and ECL Estimation6.5 SAS Laboratory6.6 Summary

Title Reviews

"IFRS 9 and CECL Credit Risk Modelling and Validation: A Practical Guide with Examples Worked in R and SAS by Tiziano Bellini is a precious resource for industry practitioners, researchers and students in the field of credit risk modeling and validation. The author does a great job in covering the various topics in a scientifically sound and comprehensive way without losing practitioner focus. The SAS and R case studies further contribute to its value and make it indispensable for anyone working in credit risk!"—Bart Baesens, KU Leuven and the University of Southampton

"It is commendable that practitioners like Dr Tiziano Bellini find the time to write volumes on the important industry developments in risk management. This timely volume provides a guide to credit risk modelling and validation in the context of IFRS 9 and CECL expected credit loss estimates. The book is thus developed in the context of the familiar PD, LGD and EAD framework. Recent challenging developments are discussed, for example the treatment of lifetime losses is very timely. The last part of the book, where multivariate time series models are brought into play, can also give ideas to researchers who may wish to make their work more relevant for the industry. More generally, this volume provides an unparalleled guide for graduate and MSc students. Examples in R and SAS make the book a must-have for risk management practitioners."—Damiano Brigo, Imperial College London

ISBN: 9780128149409

Page Count: 316

Retail Price (USD) :

Christoffersen, Elements of Financial Risk Management, 2e, 9780123744487, 2011, 344pp, $69.95
Gilli, Numerical Methods and Optimization in Finance, 9780123756626, 2011, 600pp, $105.00
Stowell, Investment Banks, Hedge Funds, and Private Equity, 2e, 2012, 9780124158207, 672pp, $79.95

Upper-division undergraduates, graduate students, and professionals working in economic modelling and statistics.

IFRS 9 and CECL Credit Risk Modelling and Validation,

Editors: By Tiziano Bellini

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About the author

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