Description Usage Arguments Details Value Author(s) Examples
Calculate Through-the-Cycle transition matrices using the cohort method transitions.
1 | cohort.TTC(transCount, initCount)
|
transCount |
transitions counts for each time-step |
initCount |
start vector counts for each time-step |
Many credit risk models require a long-run average (Through-the-Cycle) PD estimate. This has been interpreted as meaning the data from multiple years should be combined and the method capable of supporting some form of weighting of samples.
The three methods of weighting considered for data generated via the cohort method are:
Scale the number of transitions and firm counts using the a single year count to preserve dynamics, then average transitions and firms counts separately
Estimate the single-year quantities (estimate with transition matrices for each time-step), then average across years
Average annual transition matrices
The Markov property allows for direct weighting as each time-step can be regarded as distinct(independence).
SAT |
Scaled Average Transitions - compute a TTC transition matrix by first scaling and weighting the counts (start vector counts and transition counts) then calculate the transition matrices for each time-step, and finally averaging over all available time-steps. e.g., average January matrices, then February matrices or average Q1, then Q2 ...then obtain the average of the transition matrices |
SAPT |
Scaled Average Periodic Transitions - compute a TTC transition matrix by weighting the transition percentages for each time-step (calculate the transition matrices for each time-step then weigh the percentages, and finally averaging over all available time-steps. e.g., average January matrices, then February matrices or average Q1, then Q2 ...then obtain the average of the transition matrices |
USAT |
Unscaled Average Transitions - compute a TTC transition matrix by first obtaining unscaled transition matrices for each time-step then averaging over all available time-steps |
ATMP |
averageTransMatByPeriod - returns the weighted the transition percentages for each time-step (calculate the transition matrices for each time-step then weigh the percentages |
ATP |
averageTransByPeriod - returns the scaled transitions for each time-step |
ACP |
averageCountByPeriod - returns the scaled start vector counts for each time-step |
Abdoulaye (Ab) N'Diaye
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Not run:
#Set parameters
startDate <- "2000-01-01"
endDate <- "2005-01-01"
method <- "cohort"
snapshots <- 4
interval <- .25
Example<-getPIT(data,startDate, endDate,method, snapshots, interval)
lstInit <- Example$lstInitVec[lapply(Example$lstInitVec,length)>0]
lstCnt <- Example$lstCntMat[lapply(Example$lstCntMat,length)>0]
ExampleTTC <- cohort.TTC(lstCnt,lstInit)
## End(Not run)
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