The App PD thread noted that App models need not have been built on the 12-month OW which is the Basel platform.
Picking any sample of accounts and following them longitudinally from their open date, the number of defaults naturally builds up cumulatively as one progresses along the MOB axis. Thus default rate @24MOB will be a bigger number than default rate @12MOB. The graph of the cumulative emergence of defaults against MOB is a particularly useful analytical tool that visually characterises the default profile of this sample (which may be a portfolio, cohort, segment or whatever). There are subtleties AWML to do with treatment of accounts that churn.
One use of this ’emergence’ graph is to form a rough idea of the relativities between default rates at different MOB, for example cumulative defaults @24MOB would not typically be double the figure @12MOB – could be more, or less, depending on the product.
Illustrating a slightly more scientific approach: modellers may have already built a model predicting a target of “bad @24MOB” and may wish to calibrate this same model to alternatively predict “bad @12MOB”. As long as the original modelling mart is still available, it should not be too difficult to build an additional column (field) for the “bad 12MOB” flag, which can then be used as the independent variable in a regression against the original model’s score. This would provide a calibration of the model to a 12MOB basis without going to the trouble of building a whole new model for this different default target. Implicitly the hope is that the drivers (predictors) of default by 12MOB are the same as those for default by 24MOB. One can imagine objections to this assumption: it might be that certain variables are better at predicting early defaults.
But in any case, as mentioned in the earlier post, calibrating to 12MOB is still a longitudinal concept which does not closely match the Basel need to predict default in the next 12 calendar months. Hence the incorporation of Application PDs for Basel purposes needs to be more subtle AWML.
A related issue is that the predictive power of Application PDs decays AWML.