Modeling the Probability of Default Term Structure Using Different Methodologies Under IFRS 9

Abstract

To mitigate credit risk, banks are required to set aside a specific amount as a safety net to absorb the expected loss on a banks’ loan portfolio called loan loss provisions (LLPs) or provisions for bad debts. All banks worldwide had to adopt International Financial Reporting Standard 9 (IFRS 9) as the financial reporting standard. Unlike its predecessor (i.e., International Accounting Standard 39, IAS 39), IFRS 9 accelerates the recognition of losses by requiring provisions to cover both already-incurred losses and some losses expected in the future by calculating the expected credit loss (ECL). To evaluate if the obligor’s credit quality has deteriorated, the IFRS 9 standard requires banks to compare the obligor’s probability of default (PD) at the inception phase of the loan and at the reporting date. Thus, three methodologies are explored in this study (i.e., Cox proportional hazard (PH), Extended Cox PH, and Random Boosting Forest (RBF)) for computation of the PD term structures using Kaplan–Meier as the benchmark model under IFRS 9. The purpose of this research is to illustrate the application of three methodologies on the publicly available mortgage loan portfolio from Freddie Mac using different measures of goodness-of-fit and the predictive accuracy measure, i.e., the Concordance index (C-index). The comparison analysis reveals that the extended Cox PH and RBF models provide better predictive accuracy (higher C-index) but at the cost of increased complexity and potential overfitting (higher information criteria). However, Cox PH has shown the most efficient fit, and offers a stable and understandable hazard trajectory. Finally, for reproducibility, the SAS and R codes are included to illustrate how each of the results (in form of a table or figure) were obtained.