Internal Ratings Based (IRB) Discussion

While I recognize a lot of these points, I do think that we should not let the tail wag the dog

If the non-financial factors add predictive power, I don’t think there is any reason on a first principles basis to categorically exclude them. But of course, I do appreciate that these kind of factors can be subjective and therefore of lower quality, so we should keep an eye on that and encourage the clients to improve data quality

Also, many banks lump treatment of these kind of factors with overrides, which is almost always where the supervisory feedback is coming from. It is commonly used as a fudge factor, and that is poor practice. One can develop a disciplined, (high-quality) data based use of this type of information to avoid that pitfall

Hello

I have seen corporate model have country specific sub models, to reflect political dependencies and support or changes in legislation, e.g., the France care home scandal and changes in the legislation

In the RSU corporate model one of the submodules is a Merton model, to reflect the market movements

Given the new Basel regulation, Specialized Lending has its own exposure class and can continue to to utilize the full range of regulatory approaches; Standardized, Slotting, FIRB and AIRB. Each approach will require more data to achieve, but also lead to lower capital requirements

Standardized approach is suitable for limited exposure towards Specialized Lending

Slotting approach is suitable for institutions with limited internal data to build FIRB or AIRB PD models, but results in little risk differentiation given the stringent regulatory categories to be covered in a slotting model. The maturity risk differentiation provides capital relief for loans in the last phase until maturity

FIRB and AIRB allows the highest level of risk differentiation with PD models primarily build on internal observations (default or shadow rating models). In case of joint PD and LGD simulation models, various FSA expressed their expectation towards banks to utilize external loss and recovery information during the model build process (various form e.g. rank-ordering, calibration, MoCs). The capital benefit for AIRB towards FIRB has been reduced with the new Basel regulation given more sensitive Foundation LGD treatment, but it still provides a Risk Weight benefit of around 40 percent in higher recovery rate project finance segments (banks data quality and granularity can impact the benefit due to additional MoCs).

The guidelines for development data should be less around whether only the internal data is used vs. e.g., the full S&P ratings universe. Instead, it should be around sufficiency of internal data and representativeness of development data to application portfolio

E.g., if a bank has insufficient internal observations, then they should use a maximal universe

We also see an example where a bank lacked financials internally and had to use external financials, but the intersection of (1) being an internal client and also having both (2) external financial data and (3) external ratings resulted in too small a development dataset. This led to them using the full externally rated universe, rather than just their internal observations

Using standalone rating from ECAI as a target

Include failures in definition of default (i.e., banks that would have defaulted had they not received direct/indirect support from the government, including money or government schemes; e.g., Merrill Lynch in 2008)