As shown below, comprehensive model management consists of three distinct components:
Basinghall offers an all-in-one model management solution
It has been created, from the ground up, to deal with model risk management and model management
For every sample, measure the realised model error and express this as the post model adjustment to align the model with the observed outcomes
Combine the observed error estimates to get cumulative error parameters, taking into account the dynamics of the model errors, e.g.
Use the latest error parameters to augment the model output with its uncertainty
The graphs clearly show the model risk quantification and the bias and noise for the BB rating grade as it was computed for successive monitoring. The model’s output is an IRB 1-year through-the-cycle PD, and hence shows only small variations (blue line).
As expected, the MRQ procedure generally moves the corrected PD (orange circles) in the direction of the observed default rate. We can see that the uncertainty is not symmetric around its mean value due to nonlinearities (in logistic regression).
As monitoring data gets collected over time it sheds further light on a model’s uncertainty and we estimate the bias and noise after each monitoring dataset becomes available. In some situations, we may want to adjust for the bias and compute a “corrected” PD.