Approaches for modelling the term-structure of default risk under IFRS 9: A tutorial using discrete-time survival analysis

TL;DR

This paper provides a comprehensive tutorial on modeling the probability of default under IFRS 9 using discrete-time survival analysis, including diagnostics and an R codebase, to improve dynamic credit loss estimation.

Contribution

It introduces a detailed, data-driven tutorial on discrete-time survival analysis for PD modeling under IFRS 9, with diagnostic tools and an R codebase, fostering standardization.

Findings

Developed a reusable set of diagnostic measures for survival models.

Provided an R-based codebase for practical implementation.

Enhanced understanding of discrete-time survival analysis in credit risk modeling.

Abstract

Under the International Financial Reporting Standards (IFRS) 9, credit losses ought to be recognised timeously and accurately. This requirement belies a certain degree of dynamicity when estimating the constituent parts of a credit loss event, most notably the probability of default (PD). It is notoriously difficult to produce such PD-estimates at every point of loan life that are adequately dynamic and accurate, especially when considering the ever-changing macroeconomic background. In rendering these lifetime PD-estimates, the choice of modelling technique plays an important role, which is why we first review a few classes of techniques, including the merits and limitations of each. Our main contribution however is the development of an in-depth and data-driven tutorial using a particular class of techniques called discrete-time survival analysis. This tutorial is accompanied by a diverse set of reusable diagnostic measures for evaluating various aspects of a survival model and the underlying data. A comprehensive R-based codebase is further contributed. We believe that our work can help cultivate common modelling practices under IFRS 9, and should be valuable to practitioners, model validators, and regulators alike.