[R-SIG-Finance] transition matrices and "robustness"

[Sean O'Riordain]

Good evening,

I'm quite new to the risk modeling arena and recently I starting to
look at some loans and their state transitions. For simplicity I will
describe a cut-down version - NORM=normal, OD means between 1 and 89
days after missing a payment and NPL means that there is a payment
which is more than 89 days overdue.  My base data is the daily state
transition list and the end of month state of the loan book.  I
calculate the 3x3 probability transition matrix using a generator
matrix method - loosely based on [1].

For transition  / generator matrices, has anybody any suggestions for
how I might look at the robustness / sensitivity of the calculations?
My own thought was to calculate the matrix say N times leaving out a
sample of 1/N of the input loans, and then look at the summary() type
stats for each point in the matrix - thoughts? or is this just daft
talk?  In a single variable I can plot the output density and look at
the summary() data - but I have no clue really how to do this for a
transition matrix.

Then there is the question of time varying transition matrices and how
to understand them and even <gasp> estimate where the matrix is headed
over the next three months?

Has anybody any suggestions as to where I might look for other ideas
on where to go with this or has anybody done this type of loan book
credit risk modeling?

Many thanks in advance!

Best regards,
Sean O'Riordain
Dublin, Ireland.
seanpor at acm.org


[1] Lando, D., and T. Skodeberg (2002). "Analyzing ratings transitions
and rating drift with continuous observations." Journal of Banking and
Finance 26: 423-444.