Adapting the Default Weighted Survival Analysis Modelling Approach to Model IFRS 9 LGD

Morne Joubert,Helgard Raubenheimer,Willem D Schutte,Tanja Verster

doi:10.3390/risks9060103

Abstract

Survival analysis is one of the techniques that could be used to predict loss given default (LGD) for regulatory capital (Basel) purposes. When using survival analysis to model LGD, a proposed methodology is the default weighted survival analysis (DWSA) method. This paper is aimed at adapting the DWSA method (used to model Basel LGD) to estimate the LGD for International Financial Reporting Standard (IFRS) 9 impairment requirements. The DWSA methodology allows for over recoveries, default weighting and negative cashflows. For IFRS 9, this methodology should be adapted, as the estimated LGD is a function of in the expected credit losses (ECL). Our proposed IFRS 9 LGD methodology makes use of survival analysis to estimate the LGD. The Cox proportional hazards model allows for a baseline survival curve to be adjusted to produce survival curves for different segments of the portfolio. The forward-looking LGD values are adjusted for different macro-economic scenarios and the ECL is calculated for each scenario. These ECL values are probability weighted to produce a final ECL estimate. We illustrate our proposed IFRS 9 LGD methodology and ECL estimation on a dataset from a retail portfolio of a South African bank.

Highlights

Loss given default (LGD) is the percentage loss incurred by a bank when a customer is unable to pay back a loan, and it is commonly acknowledged that loss given default (LGD) is the proportion of the exposure at default (EAD) that remains unpaid in this case
The International Financial Reporting Standard (IFRS) 9 LGD is calculated by applying the methodology described in Section 2 to a retail credit portfolio of one of South Africa’s major banks
A low number of accounts were observed where months on book is more than 180, causing the LGD values to be volatile past this point

Summary

Introduction

Loss given default (LGD) is the percentage loss incurred by a bank (economic loss) when a customer is unable to pay back a loan (customer defaults), and it is commonly acknowledged that LGD is the proportion of the exposure at default (EAD) that remains unpaid in this case. This research aims to adapt the DWSA methodology in such a way that it satisfies the requirements of IFRS 9, as our research points to limited resources on methodologies to model LGD for IFRS 9 purposes. In this regard, under IFRS 9, Breed et al (2019) proposed the use of a weighted logistic regression model for LGD, while Krüger et al (2018) uses a variation of copulas to predict term structures of LGDs and ECLs. Chawla et al (2016) proposed the use of parametric distributions to model the LGD probability distribution function, while Schutte et al (2020) used marginal recovery rates that were estimated using run-off triangles.

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