AERB: developing AIRB PIT–TTC PD models using external ratings

Abstract

The use of credit rating agency (‘CRA’) ratings in a credit institution’s lending practices has been directly criticised since the financial crisis, as these ratings and observed risks have diverged. Some regulators do not allow the use of external ratings as direct inputs to a credit institution’s internal PD/ratings model and hence the capital planning process as regulators prefer the use of internal assessments generally. However, regulators allow for the use of CRA’s long run average default rates (‘DR’s) to benchmark an institution’s internal PD model output. A recent study conducted by us shows however that indiscriminate use of such benchmarks can introduce important biases in credit lending. Even with these criticisms and new regulatory constraints, one can still make use of the rich CRA data available to create regulatory compliant PD models. In this paper, we propose a class of ‘Agency Replication’ style models which make use of obligor information and CRA long term DR information. Such models are extremely useful in cases where a credit institution has limited default samples where a purely internal default based model could be potentially erroneous, and where in contrast, agencies have plenty of data supporting development of robust models. In this paper we show how one can use this class of models for modelling portfolios such as Large Corporates, Banks, Insurance, etc. We discuss our experience developing approved, AIRB models augmented by external default data and hence colloquially call them Advanced External Ratings Based (‘AERB’) models. We show various simplifications of the formulation and show how they can be used in PIT-TTC based credit rating systems.