transprob - Estimation of transition probabilities from credit ratings data

Syntax

[transMat, sampleTotals, idTotals] = transprob(data) [transMat, sampleTotals, idTotals] = transprob(data, Name, Value)

Description

[transMat, sampleTotals, idTotals] = transprob(data) constructs a transition matrix from historical data of credit ratings.

[transMat, sampleTotals, idTotals] = transprob(data, Name, Value) constructs a transition matrix from historical data of credit ratings with additional options specified by one or more Name, Value pair arguments.

Input Arguments

data

Using transprob to estimate transition probabilities given credit ratings historical data (i.e. credit migration data), the data input can be either of the following:

A preprocessed data structure obtained using transprobprep. This data structure contains the fields'idStart', 'numericDates', 'numericRatings', and 'ratingsLabels'.
or
An nRecords-by-3 cell array containing the historical credit ratings data of the form:
```
'00010283'  '10-Nov-1984'  'CCC'
'00010283'  '12-May-1986'  'B'
'00010283'  '29-Jun-1988'  'CCC'
'00010283'  '12-Dec-1991'  'D'
'00013326'  '09-Feb-1985'  'A'
'00013326'  '24-Feb-1994'  'AA'
'00013326'  '10-Nov-2000'  'BBB'
'00014413'  '23-Dec-1982'  'B'
```
where each row contains an ID (column 1), a date (column 2), and a credit rating (column 3). Column 3 is the rating assigned to the corresponding ID on the corresponding date. All information corresponding to the same ID must be stored in contiguous rows. Sorting this information by date is not required, but recommended for efficiency. IDs, dates and ratings are usually stored in string format, but they can also be entered in numeric format.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments, where Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

`'algorithm'`	Estimation algorithm, in string format. Valid values are `duration` or `cohort`. Default: `duration`
`'endDate'`	End date of the estimation time window, in string or numeric format. Default: Latest date in `data`
`'labels'`	Cell array of size `nRatings`-by-`1`, or `1`-by-`nRatings`, containing the credit-rating scale. It must be consistent with the ratings labels used in the third column of `data`. Default: `{'AAA','AA','A','BBB','BB','B','CCC','D'}`
`'snapsPerYear'`	Integer indicating the number of credit-rating snapshots per year to be considered for the estimation. Valid values are 1, 2, 3, 4, 6, 12. This parameter is only used with the `cohort` algorithm. Default: `1` — One snapshot per year
`'startDate'`	Start date of the estimation time window, in string or numeric format. Default: Earliest date in `data`
`'transInterval'`	Length of the transition interval, in years. Default: `1` — One year transition probabilities

Output Arguments

transMat

Matrix of transition probabilities in percent. The size of the transition matrix is nRatings-by-nRatings.

sampleTotals

Structure with fields:

totalsVec — A vector of size 1-by-nRatings.
totalsMat — A matrix of size nRatings-by-nRatings.
algorithm — A string with values 'duration' or 'cohort'.

For the 'duration' algorithm, totalsMat(i,j) contains the total transitions observed out of rating i into ratingj (all the diagonal elements are zero). The total time spent on rating i is stored in totalsVec(i). For example, if there are three rating categories, Investment Grade (IG), Speculative Grade (SG) and Default (D), and the following information:

Total time spent    IG       SG       D
in rating:       4859.09  1503.36  1162.05
 
Transitions             IG   SG    D
out of (row)       IG    0   89    7
into (column):     SG  202    0   32
                    D    0    0    0

Then

totals.totalsVec = [4859.09  1503.36  1162.05]
totals.totalsMat = [  0   89    7
                    202    0   32
                      0    0    0]
totals.algorithm = 'duration'

For the 'cohort' algorithm, totalsMat(i,j) contains the total transitions observed from rating i to rating j, and totalsVec(i) is the initial count in rating i. For example, given the following information:

Initial count       IG     SG     D
in rating:        4808   1572   1145
 
Transitions         IG     SG     D
from (row)    IG  4721     80      7
to (column):  SG   193   1347     32
               D     0      0   1145

Then

totals.totalsVec = [4808   1572   1145]
totals.totalsMat = [4721     80      7
                    193   1347     32
                      0      0   1145
totals.algorithm = 'cohort'

idTotals

Struct array of size nIDs-by-1, where nIDs is the number of distinct IDs in column 1 of data when this is a cell array or, equivalently, equal to the length of the idStart field minus 1 when data is a preprocessed data structure. For each ID in the sample, idTotals contains one structure with the following fields:

totalsVec — A sparse vector of size 1-by-nRatings.
totalsMat — A sparse matrix of size nRatings-by-nRatings.
algorithm — A string with values 'duration' or 'cohort'.

These fields contain the same information described for the output sampleTotals, but at an ID level. For example, for 'duration', idTotals(k).totalsVec contains the total time that the k-th company spent on each rating.

Definitions

Cohort Estimation

The cohort algorithm estimates the transition probabilities based on a sequence of snapshots of credit ratings at regularly spaced points in time. If the credit rating of a company changes twice between two snapshot dates, the intermediate rating is overlooked and only the initial and final ratings influence the estimates.

Duration Estimation

Unlike the cohort method, the duration algorithm estimates the transition probabilities based on the full credit ratings history, looking at the exact dates on which the credit rating migrations occur. There is no concept of snapshots in this method, and all credit rating migrations influence the estimates, even when a company's rating changes twice within a short time.

Examples

Using historical credit rating input data from Data_TransProb.mat display the first ten rows and compute the transition matrix:

load Data_TransProb
data(1:10,:)

% Estimate transition probabilities with default settings
transMat = transprob(data)

ans = 

 '00010283'    '10-Nov-1984'    'CCC'
 '00010283'    '12-May-1986'    'B'  
 '00010283'    '29-Jun-1988'    'CCC'
 '00010283'    '12-Dec-1991'    'D'  
 '00013326'    '09-Feb-1985'    'A'  
 '00013326'    '24-Feb-1994'    'AA' 
 '00013326'    '10-Nov-2000'    'BBB'
 '00014413'    '23-Dec-1982'    'B'  
 '00014413'    '20-Apr-1988'    'BB' 
 '00014413'    '16-Jan-1998'    'B'  

transMat =

   93.1170    5.8428    0.8232    0.1763    0.0376    0.0012    0.0001    0.0017
    1.6166   93.1518    4.3632    0.6602    0.1626    0.0055    0.0004    0.0396
    0.1237    2.9003   92.2197    4.0756    0.5365    0.0661    0.0028    0.0753
    0.0236    0.2312    5.0059   90.1846    3.7979    0.4733    0.0642    0.2193
    0.0216    0.1134    0.6357    5.7960   88.9866    3.4497    0.2919    0.7050
    0.0010    0.0062    0.1081    0.8697    7.3366   86.7215    2.5169    2.4399
    0.0002    0.0011    0.0120    0.2582    1.4294    4.2898   81.2927   12.7167
         0         0         0         0         0         0         0  100.0000

Using historical credit rating input data from Data_TransProb.mat, compute the transition matrix using the cohort algorithm:

load Data_TransProb

%Estimate transition probabilities with 'cohort' algorithm
transMatCoh = transprob(data,'algorithm','cohort')

transMatCoh =

   93.1345    5.9335    0.7456    0.1553    0.0311         0         0         0
    1.7359   92.9198    4.5446    0.6046    0.1560         0         0    0.0390
    0.1268    2.9716   91.9913    4.3124    0.4711    0.0544         0    0.0725
    0.0210    0.3785    5.0683   89.7792    4.0379    0.4627    0.0421    0.2103
    0.0221    0.1105    0.6851    6.2320   88.3757    3.6464    0.2873    0.6409
         0         0    0.0761    0.7230    7.9909   86.1872    2.7397    2.2831
         0         0         0    0.3094    1.8561    4.5630   80.8971   12.3743
         0         0         0         0         0         0         0  100.0000

Using historical credit rating data with ratings investment grade ('IG'), speculative grade ('SG'), and default ('D'), from Data_TransProb.mat display the first ten rows and compute the transition matrix:

load Data_TransProb
dataIGSG(1:10,:)
transMatIGSG = transprob(dataIGSG,'labels',{'IG','SG','D'})

ans = 

    '00011253'    '04-Apr-1983'    'IG'
    '00012751'    '17-Feb-1985'    'SG'
    '00012751'    '19-May-1986'    'D' 
    '00014690'    '17-Jan-1983'    'IG'
    '00012144'    '21-Nov-1984'    'IG'
    '00012144'    '25-Mar-1992'    'SG'
    '00012144'    '07-May-1994'    'IG'
    '00012144'    '23-Jan-2000'    'SG'
    '00012144'    '20-Aug-2001'    'IG'
    '00012937'    '07-Feb-1984'    'IG'


transMatIGSG =

   98.6719    1.2020    0.1261
    3.5781   93.3318    3.0901
         0         0  100.0000

Using historical credit rating data with numeric ratings for investment grade (1), speculative grade (2), and default (3), from Data_TransProb.mat display the first ten rows and compute the transition matrix:

load Data_TransProb
dataIGSGnum(1:10,:)
transMatIGSGnum = transprob(dataIGSGnum,'labels',{1,2,3})

ans = 

    '00011253'    '04-Apr-1983'    [1]
    '00012751'    '17-Feb-1985'    [2]
    '00012751'    '19-May-1986'    [3]
    '00014690'    '17-Jan-1983'    [1]
    '00012144'    '21-Nov-1984'    [1]
    '00012144'    '25-Mar-1992'    [2]
    '00012144'    '07-May-1994'    [1]
    '00012144'    '23-Jan-2000'    [2]
    '00012144'    '20-Aug-2001'    [1]
    '00012937'    '07-Feb-1984'    [1]


transMatIGSGnum =

   98.6719    1.2020    0.1261
    3.5781   93.3318    3.0901
         0         0  100.0000

Algorithms

Cohort Estimation

The algorithm first determines a sequence t₀,...,t_K of snapshot dates. The elapsed time, in years, between two consecutive snapshot dates t_k-1 and t_k is equal to 1 / ns, where ns is the number of snapshots per year. These K +1 dates determine K transition periods.

The algorithm computes , the number of transition periods in which obligor n starts at rating i. These are added up over all obligors to get N_i, the number of obligors in the sample that start a period at rating i. The number periods in which obligor n starts at rating i and ends at rating j, or migrates from i to j, denoted by , is also computed. These are also added up to get , the total number of migrations from i to j in the sample.

The estimate of the transition probability from i to j in one period, denoted by , is given by:

These probabilities are arranged in a one-period transition matrix P₀, where the i,j entry in P₀ is P_i_j.

If the number of snapshots per year ns is 4 (quarterly snapshots), the probabilities in P₀ are 3-month (or 0.25-year) transition probabilities. You may, however, be interested in 1-year or 2-year transition probabilities. The latter time interval is called the transition interval, Δt , and it is used to convert P₀ into the final transition matrix, P, according to the formula:

For example, if ns = 4 and Δt = 2, P contains the 2-year transition probabilities estimated from quarterly snapshots.

Note For the cohort algorithm, optional output arguments idTotals and sampleTotals from transprob contain the following information:

idTotals(n).totalsVec =
idTotals(n).totalsMat =
idTotals(n).algoritm = 'cohort'
sampleTotals.totalsVec =
sampleTotals.totalsMat =
sampleTotals.algoritm = 'cohort'

For efficiency, the vectors and matrices in idTotals are stored as sparse arrays.

Duration Estimation

The algorithm computes , the total time that obligor n spends in rating i within the estimation time window. These quantities are added up over all obligors to get , the total time spent in rating i, collectively, by all obligors in the sample. The algorithm also computes , the number times that obligor n migrates from rating i to rating j, with i not equal to j, within the estimation time window. And it also adds them up to get , the total number of migrations, by all obligors in the sample, from the rating i to j, with i not equal to j.

To estimate the transition probabilities, the duration algorithm first needs to compute a generator matrix . Each off-diagonal entry of this matrix is an estimate of the transition rate out of rating i into rating j, and is given by:

The diagonal entries are computed as:

With the generator matrix and the transition interval Δt (e.g., Δt = 2 corresponds to 2-year transition probabilities), the transition matrix is obtained as , where exp denotes matrix exponentiation (expm in MATLAB).

Note For the duration algorithm, optional output arguments idTotals and sampleTotals from transprob contain the following information:

idTotals(n).totalsVec =
idTotals(n).totalsMat =
idTotals(n).algoritm = 'duration'
sampleTotals.totalsVec =
sampleTotals.totalsMat =
sampleTotals.algoritm = 'duration'

For efficiency, the vectors and matrices in idTotals are stored as sparse arrays.

References

Hanson, S., T. Schuermann, "Confidence Intervals for Probabilities of Default," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2281-2301, August 2006.

Löffler, G., P. N. Posch, Credit Risk Modeling Using Excel and VBA, West Sussex, England: Wiley Finance, 2007.

Schuermann, T., "Credit Migration Matrices," in E. Melnick, B. Everitt (eds.), Encyclopedia of Quantitative Risk Analysis and Assessment, Wiley, 2008.