Credit risk analysis needs to comprehend two modes of analysis: longitudinal, and cross-sectional.

Longitudinal means following each individual account along its own timeline, typically expressed as “months on books”. If your x-axis is MOB, you are doing longitudinal analysis.

Cross-sectional means looking across the whole portfolio at a particular point in time. If your x-axis is Date, you are doing cross-sectional analysis.

The modelling of application scorecards is longitudinal; a set of application records is selected, and the default performance of each individual account is analysed to establish (for example) whether it defaulted before 24 MOB. True, the selection of the records may happen to have a date component, such as selecting all the accounts that opened in 2005Q1. OTOH one may select or segment on other criteria. 

The “24 MOB” above is an example of an “outcome window” AWML.

Basel calculations of risk components for each exposure (account) is a cross-sectional activity: every account that exists at 31/3/2008 must be assessed for inter alia its probability of default within the following 12 months i.e. for the time window 1/4/2008 to 31/3/2009. This applies irrespective of what MOB each account has reached, and indeed a wide distribution of MOBs will exist in a portfolio, depending on its nature.

Regular monitoring of credit risk statistics would need to reflect both modes of analysis, and typical graphical or tabular displays have both dimensions (MOB and date) in evidence somehow.

Monitoring that was only cross-sectional would tend to be too crude to be usefully interpreted: for example the simple cross-sectional fact that as of 31/3/2008 a certain portfolio has 10,000 accounts of which 100 are in default, doesn’t convey much without a detailed understanding of the composition of the portfolio and certain other issues (like collections and write-off procedures). It might be a very alarming result if this were a new product, introduced within the last year, and with a portfolio that was growing fast and still dominated by very young accounts. Conversely, it might be a good result if the portfolio were well “seasoned” and collections activities were lengthy.

Monitoring that was only longitudinal would show the overall default profile but not indicate how that profile was changing over time.

Hence good monitoring incorporates both logitudinal and cross-sectional modes, typically showing how the default MOB-profile is evolving as real time (d

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