RTransProb (version 0.3.3)

cohort.TTC: Cohort - Data Weighting and "TTC" Calculation

Description

Calculate Through-the-Cycle transition matrices using the cohort method transitions.

Usage

cohort.TTC(transCount, initCount)

Arguments

transCount

transitions counts for each time-step

initCount

start vector counts for each time-step

Value

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

Details

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:

1. Scale the number of transitions and firm counts using the a single year count to preserve dynamics, then average transitions and firms counts separately

2. Estimate the single-year quantities (estimate with transition matrices for each time-step), then average across years

3. Average annual transition matrices

The Markov property allows for direct weighting as each time-step can be regarded as distinct(independence).

Examples

# NOT RUN {
# }
# 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)
 
# }