Traditionally speaking, IRB models focus on obligor and transaction characteristics, since these models are TTC and average over the business cycle (exceptions are Best Estimate Expected Loss models). In addition to this, the IFRS 9 credit risk models could be made sensitive to macroeconomic factors, since they are required to be PiT. Consequently, a review of the variables selected for IFRS 9 models should answer two concise questions:
- To what extent are risk drivers related to obligor and transaction characteristics in line with expectations? Are they similar to the ones in IRB models, if present?
- Are these risk drivers related to the selected macroeconomic factors, reasonable, and do they maintain significance in the presence of newly acquired data?
The latter point especially deserves special attention. In our experience, financial institutions have felt compelled and even obliged to include macroeconomic factors in their IFRS 9 models, since these factors embody their PiT nature. In practice, however, it is possible that no robust relationship between the business cycle, on the one hand, and default and loss rates, on the other hand, exists. For instance, in the case of LGD models, financial institutions often postpone selling collateral until the economic conditions have improved, which often causes economic losses occurring during the depth of a crisis to be relatively mild. Even if a correlation between business cycle and default or loss rate exists, it is difficult to quantify this reliably, given the relatively short time series (often spanning only one full business cycle) that are available for modelling. In this light, it is important to challenge the rigorousness of the statistical procedures with which this relationship is tested. Moreover, in the presence of new data, it is important to monitor the efficiency of macroeconomic factors as meaningful risk drivers.