IFRS 9 Expected Credit Loss (ECL) Modelling

Overview

1 day course on IFRS 9 expected credit loss modelling, both for financial statement and capital stress testing purposes.

Learning Objectives

Learn from applied case studies how to model IFRS 9 expected credit losses (ECL) for Stage 1, 2 and 3 loans – modelling 12 month point-in-time (PIT) probability of default (PD), lifetime PD, loss given default (LGD) and exposure at default (EAD)
Learn how to calculate ECL provisions in a capital stress test
Learn sensitivity of ECL to where Significant Increase in Credit Risk (SICR) threshold is set and to migration of loans from Stage 1 to 2 in a macroeconomic downturn

Who the course is for

Credit risk management
Quants
ALM staff
Finance
Internal audit
External auditors
Bank investors – equity and credit investors

Rupesh Tailor

Rupesh Tailor is a consultant to banks in areas including Asset Liability Management and capital and liquidity management and stress testing (both ICAAP and ILAAP) with 25 years' experience, having worked for Goldman Sachs, Barclays, Bank of America Merrill Lynch, Morgan Stanley and Nordea.

Rupesh additionally consults for funds (traditional, hedge funds and distressed debt funds) on their credit investing across high yield and distressed debt markets, offering deep dive research on high yield and distressed companies.

Rupesh's clients include two Global Systemically Important Banks (GSIBs), a wide range of other banks and many of the largest hedge funds and distressed debt funds globally.

Rupesh received a MA in Economics from Cambridge University and achieved First Class Honours.

Day One

Overview

IFRS 9 Stage 1, 2 and 3 loans – classification, expected credit loss (ECL) provisioning and interest income recognition
ECL formula
ECL as probability-weighted average ECL over macroeconomic scenarios
Motivation for IFRS 9 loan loss provisioning standard vs its predecessor (IAS 39)
Cliff effect in ECL provisioning as loans migrate from Stage 1 to Stage 2 – going from 12 month ECL to lifetime ECL
Case Study 1 – Illustrative sensitivity of ECL provisioning for a range of Stage 1 loans migrating to Stage 2
Importance of Significant Increase in Credit Risk (SICR) threshold. Setting SICR threshold based on current lifetime probability of default (PD) as a multiple of lifetime PD at loan origination
Case Study 2 – Illustrative sensitivity of ECL provisioning for Stage 2 loans to changes in SICR threshold
Interaction of IFRS 9 ECL provisioning with regulatory capital (Internal Ratings Based approach, IRB) – treatment of expected provisions shortfall and surplus (IFRS 9 ECL vs 12 month through-the-cycle ECL within IRB) within regulatory capital
Case Study 3 – IFRS 9 ECL provisioning during COVID

Modelling 12 month Point-In-Time (PIT) & Lifetime PD – Method 1 – Markov Chain

Transition matrices to set out transition probabilities between different rating grades over 12 months – continuous time homogeneous method
Case Study 4 – Transition matrix PD model – illustrated with corporate loan portfolio
Case Study 5 – Logit and probit PD models – illustrated with corporate loan portfolio
Case Study 6 – Scoring PD model – illustrated with retail loan portfolio
Matrix multiplication of transition matrices combined with macroeconomic vector to arrive at lifetime PD
Case Study 7 – Using Markov Chain to estimate lifetime PD on a hitherto Stage 1 corporate loan, test for SICR threshold and estimate ECL depending on whether loan remains Stage 1 or has migrated to Stage 2

Modelling Lifetime PD – Method 2 – Parametric Survival Regression (Weibull)

Survival time of borrower or segment of borrowers linked to macroeconomic covariates according to Weibull distribution. Lifetime PD derived from survival time
Case Study 8 – Using Weibull model to estimate lifetime PD on hitherto Stage 1 retail loan portfolio, test for SICR threshold and estimate ECL depending on whether loans remain Stage 1 or have migrated to Stage 2

Modelling Lifetime PD – Other Methods