Survey

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Survey

Document related concepts

no text concepts found

Transcript

Book Review IFRS 9 and CECL Credit Risk Modelling and Validation A Practical Guide with Examples Worked in R and SAS First Edition, 2019 Author - Tiziano Bellini Academic Press, Elsevier, London ISBN: 978-0-12-814940-9 Allowance for Loans and Lease Losses (ALLL), an incurred loss model based on historic loss data could not provide a robust framework to recognize credit losses in times of economic shocks (2007-08 financial crisis) for financial institutions. This prompted the Financial Accounting Standards Board (FASB) to come up with an alternative framework, Current Expected Credit Loss (CECL) model. This framework provides for accounting expected credit losses over the estimated life of loans. CECL has been adopted by US Financial Institutions from Jan. 1, 2020. A new standard, International Financial Reporting Standard 9 (IFRS 9) was issued by the International Accounting Standard Board (IASB) in 2014 to calculate expected credit loss (ECL). The implementation went live in 2018 and adopted by European Banks. IFRS 9 relies on a three-bucket classification where one-year or lifetime expected credit losses are computed, while CECL follows a lifetime perspective as a rule. Both IFRS 9 and CECL accounting standards require banks to adopt a new perspective in assessing Expected Credit Losses. In general, ECL is calculated as follows as per the new standards: ECL =∑ ( PD * LGD * EAD * DF ) Expected Credit Losses Probability of Default Loss given Default Exposure at Default Discount Factor (i) Lifetime: Present value of all cash shortfalls expected over the remaining life of instrument (i) Point-in-time PD to be calculated (i) Point-in-time LGD to be calculated (not downturn LGD) (i) Cash-flows through the lifetime of the asset (ii) Only costs directly attributable to collection of recoveries (ii) Consider all contractual terms over the lifetime (i) Discount factor calculated through current market rate or Effective Interest Rate metod (ii) 12-month – portion of lifetime ECL associated with default within 12 months (ii) PD to be extrapolated over the remaining expected lifetime of the asset (iii) Must consider reasonable and supportable forwardlooking information (iii) Must consider reasonable and supportable forward-looking information The book review is aimed to study the approach taken by the author to conform to the new standards and modelling techniques explained in the book. This book explores a wide range of models and corresponding validation procedures to calculate the ECL and is divided into six chapters. It attempts to provide a comprehensive guide to model and validate ECL under both frameworks. By including examples, problems and software solutions (both in R and SAS) it aims to attract students and academia. The author describes both the IFRS 9 and CECL standards, their mechanics to enable readers to appreciate the changes brought by the standards and ending it with the comparison. The book attempts to leverage the major similarities and tries to simplify the measuring methodology that can be applied in financial institutions, helping to meet both standards' requirements. While pointing out the similarities in the standards moving from incurred loss to expected loss and non-prescriptive nature, the author spelt out the fundamental difference. IFRS 9 requires a staging allocation process (Stages 1, 2 and 3), while CECL focuses on the lifetime loss perspective without making distinctions among credit pools. As the choice has been provided to the institutions to decide on the methodology, the book attempts to provide a comprehensive calculation approach, minimizing the efforts to calculate ECL under both standards. Probability of Default – Since both the accounting standards do not have a definition for default, the book compared the standards' default definition flexibility with that of Basel II. It explains the quantitative and qualitative indicators of the default which is the basic to prepare data for modelling. Book describes ways to build the dataset, how to include historical data, account-level panel structure, behavioral characters of assets and macro-economic variables. Though the book has cited certain behavioral variables of the asset, it is not explicit what macro-economic variables to be considered while building the data set. PD calculation has been divided into One year and Lifetime. This is to simplify the approach to calculate PD and it would not change the existing procedures adopted by financial institutions. Point-in-time PD (PIT PD) calculation focuses on the existing practice financial institutions are following and thus the forward-looking perspective is not included. The PIT PD model is based on the Generalized Linear Models (GLM), widespread usage among banks. Then the book explores further into CART, bagging, random forest and boosting to develop alternate models for Machine Learning environment. Lifetime PD calculation is contemporary in risk management and the book attempts to calculate and here it provides ways to include the forward-looking perspective to PD, a requirement under both standards. It seeks to achieve this by adding a forward-looking perspective to PIT PD and extend it to the lifetime of the assets. Four modelling approaches are studied, viz., Lifetime GLM framework, Survival Modelling, Lifetime ML Modelling, and Transition Matrix Modelling. An attempt has been made to provide relevant examples and exercises making students learn by doing. Loss Given Default – The book devotes major attention to the Workout LGD approach, which is internal to a financial institution than the other two approaches it discusses. It suggests having a distinction on the classification and calculation based on cured and written-off accounts. This modelling is at the portfolio level. The books explain in detail the LGD data concepts since the modelling depends on adequate and sufficient information in the database. These concepts are further elaborated with an example to enable the reader to understand the importance of data and its structure in LGD modelling. The book adopts a Micro-structure approach providing a comprehensive view of the postdefault recovery process. It focusses on the probability of cure and severity with GLM and classification trees methods. A multi-step procedure is adopted to capture the relationship between the probability of cure and severity with macro-economic variables to bring the forward-looking perspective to the modelling. These steps are explained with a Real Estate model, giving a complete framework for LGD modelling. The book also attempts to provide a framework in cases where data is scarce and portfolio having low default rates. Exposure at Default – The book analyses the exposure at two different scenarios (i) when full prepayments and overpayments (partial repayments) and (ii) combine them with defaults while modelling. For prepayments and overpayments, GLM is used and for prepayments, overpayments and default, Multinomial Regression is used. These two approaches apply to loan-type products, where the financial institution is not having any further commitment to lend. For uncommitted lines and revolving facilities, the book utilizes Tobit and Beta regression techniques to model the LGD. Expected Credit Loss – Though FASB does not prescribe scenario analysis (IFRS 9 does), the author suggests usage of scenario analysis would help in bringing the forward-looking perspective to the estimate. However, to address the reversion to long-term mean loss rate which is central to FASB standards, the author explores the use of Time Series analysis. The author uses the UK macroeconomic time series from the year 2000 to 2013 to describe the Vector Auto Regression and vector Error Correction techniques. A detailed analysis is attempted with a case in the book. More focus is devoted to IFRS 9 staging requirements and validation of the ECL model using simulation and other methods. The book followed a series of steps to address the changed requirements in the calculation of ECL in compliance with the standards. The discount rate to the PIT estimation of ECL is left to the financial institutions and the book do not attempt to calculate or estimate the discount rate. Each parameter PD, LGD, and EAD is explained starting with data requirement, how to collect data, the structure of the database and modelling techniques followed by validation methods. General Linear Model, traditional regression method is extensively used to estimate the risk parameters and the author build upon the concept and learning to much more innovative methods like machine learning, survival analysis and competing risk modelling. The author includes Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management in each section of the book to enable the reader to understand the codes and modelling described in the sections. Further elaborate comparison in the calculation of parameters while estimating EAD and LGD would have helped new readers and practitioner's further insight into the material. The examples and cases are limited and with more examples, first-time readers and students would have benefitted more. One major missing part is a calculation of ECL across multiple years. Though explained little and shown in few examples, a more detailed description and discussions about a PD term structure on a lifetime path, the effect of macro-economic variables and more examples of scenario based ECL calculation, would have enriched the book. The book has comprehensively covered ECL modelling with various techniques including traditional GLM and more contemporary ML methods, with hands-on training in R and SAS. This book would serve as a starting point for new practitioners and students to understand the concepts and various methodologies in modelling ECL. The models and methods discussed in the book can be used as a benchmark to implement and validate ECL estimates.