Bellini - How to Model and Validate Expected Credit Losses for IFRS9 and CECL : A Practical Guide with Examples Worked in Excel, R, and SAS.

First edition

Tiziano Bellini

1. Tables in Chapter 1
2. Tables in Chapter 2
3. Tables in Chapter 3
4. Tables in Chapter 4
5. Tables in Chapter 5
6. Tables in Chapter 6

1. Figures in Chapter 1
2. Figures in Chapter 2
3. Figures in Chapter 3
4. Figures in Chapter 4
5. Figures in Chapter 5
6. Figures in Chapter 6

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Tiziano Bellini received his PhD degree in statistics from the University of Milan after being a visiting PhD student at the London School of Economics and Political Science. He is a Qualified Chartered Accountant and Registered Auditor. He gained wide risk management experience across Europe (including in London) and in New York. He is currently Director at BlackRock Financial Market Advisory (FMA) in London. Previously he worked at Barclays Investment Bank, EY Financial Advisory Services in London, HSBC's headquarters, Prometeia in Bologna, and other leading Italian companies. He is a guest lecturer at Imperial College in London, and at the London School of Economics and Political Science. Formerly, he served as a lecturer at the University of Bologna and the University of Parma. Tiziano is the author of Stress Testing and Risk Integration in Banks, A Statistical Framework and Practical Software Guide (in Matlab and R) edited by Academic Press. He has published in the European Journal of Operational Research, Computational Statistics and Data Analysis, and other top-reviewed journals. He has given numerous training courses, seminars, and conference presentations on statistics, risk management, and quantitative methods in Europe, Asia, and Africa.

Tiziano Bellini

A series of concerns have been expressed since the adoption of the incurred losses paradigm by both the International Accounting Standard Board (IASB) and the Financial Accounting Standard Board (FASB). The recent (20072009) financial crisis uncovered this issue by inducing a review of accounting standards which culminated with the International Financial Reporting Standard number 9 (IFRS 9) and Current Expected Credit Loss (CECL).

This book provides a comprehensive guide on credit risk modelling and validation for IFRS 9 and CECL expected credit loss (ECL) estimates. It is aimed at graduate, master students and practitioners. As a distinctive practical imprint, software examples in R and SAS accompany the reader through the journey. The choice of these tools is driven by their wide use both in banks and academia.

Despite the non-prescriptive nature of accounting standards, common practice suggests to rely on the so-called probability of default (PD), loss given default (LGD), and exposure at default (EAD) framework. Other non-complex methods based on loss-rate, vintage, cash flows are considered as a corollary. In practice, banks estimate their ECLs as the present value of the above three parameters' product over a one-year or lifetime horizon. Based on this, a distinction arises between CECL and IFRS 9. If the former follows a lifetime perspective for all credits, the latter classifies accounts in three main buckets: stage 1 (one-year ECL), stage 2 (lifetime ECL), stage 3 (impaired credits). The key innovation introduced by the new accounting standards subsumes a shift towards a forward-looking and lifetime perspective. Therefore a link between macroeconomic variables (MVs), behavioural variables (BVs), and the above three parameters is crucial for our dissertation. Such a framework is also a natural candidate for stress testing projections.

From an organizational standpoint, Chapter serves the purpose to introduce IFRS 9 and CECL. It points out their similarities and differences. Then the focus is on the link connecting expected credit loss estimates and capital requirements. A book overview is provided as a guide for the reader willing to grasp a high-level picture of the entire ECL modelling and validation journey.

Chapter focuses on one-year PD modelling. Two main reasons suggest our treating one-year and lifetime separately. Firstly, banks have been developing one-year PD models over the last two decades for Basel II regulatory requirements. Secondly, a building-block-structure split in one-year and lifetime PD facilitates the learning process. As a starting point, this chapter focuses on how default events are defined for accounting purposes and how to build a consistent PD database. Moving towards modelling features, firstly, generalized linear models (GLMs) are explored as a paradigm for one-year PD estimates. Secondly, machine learning (ML) algorithms are studied. Classification and regression trees (CARTs), bagging, random forest, and boosting are investigated both to challenge existing models, and explore new PD modelling solutions. In line with the most recent literature, the choice of these approaches is driven both by their effectiveness and easy implementation. If a wide data availability encourages the use of data driven methods, low default portfolios and data scarcity are other challenges one may need to face. Bespoke methods are scrutinized to address issues related to limited number of defaults, and ad hoc procedures are explored to deal with lack of deep historical data.