Drawing together several themes, today’s post recommends how to assemble modelling marts that will be representative for use in Basel context.

Basel context is a cross-sectional context: at some point in time, such as the most recent calendar month end, the bank must assess the risk components (PD, EAD, LGD and hence [or otherwise?] the expected loss EL) for the time exposure of the next 12 months. As the point in time is fixed and the coverage is all at-risk exposures, accounts will be encountered in all stages of credit status (and any MOB): G, I, point-in-time bad B, episodic bad E, plus whatever collections and recoveries statuses may obtain.

PD models for this context would primarily be behavioural models, built to predict a 12-month OW. (BTW earlier posts discuss the transitional use of application PDs for this purpose.)  EAD and LGD models are needed. Several modelling marts are therefore needed. How many, and how assembled in order to be representative when put to work together in Basel duty?

My suggestions below are open to discussion & debate – tell us if you have alternative views or practices.

  1. The underlying sampling frame is to pick a point in time and observe all accounts at that point in time. Because of the need for 12 month OW, this point in time will be at least 12 months before the data horizon (current time)
  2. This sample frame can be overlaid to increase the modelling mart: e.g. take several points in time, a month or a quarter apart. Naturally, the additional information is correlated but that presents no great problem as long as one doesn’t treat it as independent. A limitation is that as ones reaches further back into history, the models become less relevant to the future. 
  3. Plan to segment the fairly extensively; a cross-section will include many diverse animals better handled in their own (albeit small) cages than handled with one cover-all model. “Segmentation” is a popular word but you could also call this “decision-tree”, CART, etc.
  4. Each segment = separate mart = completely separate model 
  5. Segment PD behavioural: at minimum need to segment E from G. Recall that E is an account that is not point-in-time bad but is episodic bad i.e. has not yet re-aged. Further subsegmentation is likely to be sensible, into say the various levels of I (Indeterminate). Naturally, no PD model is required for status B or C,R, etc.
  6. Target variable PD: whether the start of a new bad episode is encountered during the following 12 months. A definitional issue AWML arises as to how to handle segment E.
  7. Segmenting LGD: may leave this for another day  …