Maybe time to bring up a subject that contains more difficulties than one would expect: re-aging. When an account has gone into default – at some point in time – how long can it be before the account can again be considered ‘good’, and under what circumstances.

Re-aging needs to be part and parcel of the default definition. The default definitions in typical context are really point-in-time default definitions, easy to relate to if one imagines an account running along longitudinally in a good status, and then at some first point in time triggering the default definition, whatever that is (something like 90DPD on an amount of at least $100).

But the difficulties are, what happens next? Suppose the customer makes some partial or full payment, such that in the next grain of time (e.g. the next month) their point-in-time status is not in default. Perhaps they are fully current (=zero DPD), or perhaps their partial payment has pulled them back to a 30DPD or 60DPD status. How does this affect modelling and other activities?

It does not affect application PD modelling, which is longitudinal from the start of the account (MOB=0), and the modelling target is “went bad ever within a certain OW”; as soon as any account first triggers default, it has established its target status as “went bad” and what happens beyond doesn’t matter for the PD model.

It’s a more complicated story for the LGD model AWML.

The first step is to recognise that besides the point-in-time aspect of default, there is also an episodic aspect, which is the interval of time until the account can be considered good again. Why is this episodic definition needed? Can’t we manage just with applying the point-in-time definition at each successive point in time? The problem is that, depending on the granularity of time (e.g. monthly), it would then be possible to have many separate bad episodes for an account within a fairly short time window such as a 12-month window. An account’s status might go something like GGBGGBBGGGB. This patchy pattern then causes headaches for any cross-sectional analyses, and particularly for the basis of the LGD modelling.

The common-sense feeling is that the above pattern represents one extended bad episode, not three separate bad points (months) separated by good points. In banking language there needs to be a re-aging rule that says the account can’t be considered G immediately that the point-in-time default conditions don’t hold. Instead, there is a new status which is “not in default but still in a re-aging period”.

My preferred terminology is to call this situation “not bad but still in a bad episode”: and to use “E” as the code for any such time grains. Thus the above pattern would be GGBEEBBEEEB (if there is a re-aging rule that says an account must be good for several successive months before it can be fully G again.