IIFRS 9 and CECL credit risk modelling and validation : a practical guide with examples worked in R and SAS

Front Cover -- IFRS 9 and CECL Credit Risk Modelling and Validation -- Copyright -- Contents -- Tiziano Bellini's Biography -- Preface -- Acknowledgements -- 1 Introduction to Expected Credit Loss Modelling and Validation -- Key Abbreviations and Symbols -- 1.1 Introduction -- 1.2 IFRS 9 -- 1.2.1 Staging Allocation -- 1.2.2 ECL Ingredients -- 1.2.3 Scenario Analysis and ECL -- 1.3 CECL -- 1.3.1 Loss-Rate Methods -- 1.3.2 Vintage Methods -- 1.3.3 Discounted Cash Flow Methods -- 1.3.4 Probability of Default Methods -- 1.3.5 IFRS 9 vs. CECL -- 1.4 ECL and Capital Requirements -- 1.4.1 Internal Rating-Based Credit Risk-Weighted Assets -- 1.4.2 How ECL Affects Regulatory Capital and Ratios -- 1.5 Book Structure at a Glance -- 1.6 Summary -- References -- 2 One-Year PD -- Key Abbreviations and Symbols -- 2.1 Introduction -- 2.2 Default De nition and Data Preparation -- 2.2.1 Default De nition -- 2.2.2 Data Preparation -- 2.3 Generalised Linear Models (GLMs) -- 2.3.1 GLM (Scorecard) Development -- 2.3.2 GLM Calibration -- 2.3.3 GLM Validation -- 2.4 Machine Learning (ML) Modelling -- 2.4.1 Classi cation and Regression Trees (CART) -- 2.4.2 Bagging, Random Forest, and Boosting -- 2.4.3 ML Model Calibration -- 2.4.4 ML Model Validation -- 2.5 Low Default Portfolio, Market-Based, and Scarce Data Modelling -- 2.5.1 Low Default Portfolio Modelling -- 2.5.2 Market-Based Modelling -- 2.5.3 Scarce Data Modelling -- 2.5.4 Hints on Low Default Portfolio, Market-Based, and Scarce Data Model Validation -- 2.6 SAS Laboratory -- 2.7 Summary -- Suggestions for Further Reading -- 2.8 Appendix A. From Linear Regression to GLM -- 2.9 Appendix B. Discriminatory Power Assessment -- Exercises -- References -- 3 Lifetime PD -- Key Abbreviations and Symbols -- 3.1 Introduction -- 3.2 Data Preparation -- 3.2.1 Default Flag Creation.