transprobprep - Preprocess credit ratings data to estimate transition probabilities

Syntax

[prepData] = transprobprep(data) [prepData] = transprobprep(data, Name,Value)

Description

[prepData] = transprobprep(data) preprocesses credit ratings historical data (i.e., credit migration data) for the subsequent estimation of transition probabilities.

[prepData] = transprobprep(data, Name,Value) preprocesses credit ratings historical data (i.e., credit migration data) for the subsequent estimation of transition probabilities with additional options specified by one or more Name,Value pair arguments.

Input Arguments

data

Historical input data for credit ratings. Cell array of size nRecords-by-3 containing the credit ratings. Each row contains an ID (column 1), a date (column 2), and a credit rating (column 3). The assigned credit rating corresponds to the associated ID on the associated date. All information corresponding to the same ID must be stored in contiguous rows. Sorting this information by date is not required but is recommended. IDs, dates, and ratings are usually stored in string format, but they can also be entered in numeric format. Here is an example with all of the information in string format:

 '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'

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.

'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'}

Output Arguments

prepData

Structure with the following fields:

idStart — Array of size (nIDs+1)-by-1, where nIDs is the number of distinct IDs in column 1 of data. This array summarizes where the credit ratings information corresponding to each company starts and ends. The dates and ratings corresponding to company j in data are stored from row idStart(j) to row idStart(j+1)−1 of numericDates and numericRatings.
numericDates — Array of size nRecords-by-1, containing the dates in column 2 of data, in numeric format.
numericRatings — Array of size nRecords-by-1, containing the ratings in column 3 of data, mapped into numeric format.
ratingsLabels — Cell array of size1-by-nRatings, containing the credit rating scale.

Examples

Load input data from Data_TransProb.mat and display the first 10 rows. Here, the inputs are provided in string format.

load Data_TransProb
data(1:10,:)

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'  

% Preprocess credit ratings data
prepData = transprobprep(data)

prepData = 

           idStart: [1506x1 double]
      numericDates: [4315x1 double]
    numericRatings: [4315x1 double]
     ratingsLabels: {'AAA'  'AA'  'A'  'BBB'  'BB'  'B'  'CCC'  'D'}

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

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

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