Continuing the re-aging theme: a clear episodic definition of default is important as the basis for LGD modelling.
Whether one thinks of this issue in terms of the re-aging rule, or in terms of default episodes, is two sides of the same coin: re-aging is the rule that determines when the episode ends, and the default episode is the period of time from the initial triggering of the (point-in-time) default definition until that end point. I find it easier to talk in terms of the default episodes (a.k.a. “bad episodes”) because those are the indivisible modelling units.
One has to be able to clearly identify, enumerate and isolate the separate default episodes. If your default definition doesn’t produce this level of clarity, there will be some ugly problems in the LGD modelling phase.
The ideal is a fairly heavily “congealed” approach, that tends to produce few, long, well separated episodes rather than many, potentially short and frequent ones. The motivation is that each episode becomes a modelling unit for LGD. Common sense and business knowledge would suggest that the modelling of LGD issues would be more coherent with a more congealed approach – otherwise one might end up with a larger mart of bad episodes, many of them short and ending in no loss, and many of them correlated and to some extent duplicating each other.
Also the re-aging rule should be invariant to time granularity – it wouldn’t accord with intuition if a change from monthly to weekly data (for example) could substantially change the number and extent of the default episodes. Hence a rule referring to a re-aging period in absolute time units (e.g. X months) is sensible.
These issues were identified and addressed in an APRA note some years