modelCalibration

Compute R-square, RMSE, correlation, and sample mean error of predicted and observed LGDs

Since R2023a

Syntax

CalMeasure = modelCalibration(lgdModel,data)

[CalMeasure,CalData] = modelCalibration(___,Name,Value)

Description

CalMeasure = modelCalibration(lgdModel,data) computes the R-square, root mean square error (RMSE), correlation, and sample mean error of observed vs. predicted loss given default (LGD) data. modelCalibration supports comparison against a reference model and also supports different correlation types. By default, modelCalibration computes the metrics in the LGD scale. You can use the ModelLevel name-value pair argument to compute metrics using the underlying model's transformed scale.

[CalMeasure,CalData] = modelCalibration(___,Name,Value) specifies options using one or more name-value pair arguments in addition to the input arguments in the previous syntax.

Examples

Compute R-Square, RMSE, Correlation, and Sample Mean Error of Predicted and Observed LGDs Using Regression LGD Model

This example shows how to use fitLGDModel to fit data with a Regression model and then use modelCalibration to compute the R-Square, RMSE, correlation, and sample mean error of predicted and observed LGDs.

Load Data

Load the loss given default data.

load LGDData.mat
head(data)

      LTV        Age         Type           LGD   
    _______    _______    ___________    _________

    0.89101    0.39716    residential     0.032659
    0.70176     2.0939    residential      0.43564
    0.72078     2.7948    residential    0.0064766
    0.37013      1.237    residential     0.007947
    0.36492     2.5818    residential            0
      0.796     1.5957    residential      0.14572
    0.60203     1.1599    residential     0.025688
    0.92005    0.50253    investment      0.063182

Partition Data

Separate the data into training and test partitions.

rng('default'); % for reproducibility
NumObs = height(data);

c = cvpartition(NumObs,'HoldOut',0.4);
TrainingInd = training(c);
TestInd = test(c);

Create Regression LGD Model

Use fitLGDModel to create a Regression model using training data.

lgdModel = fitLGDModel(data(TrainingInd,:),'regression');
disp(lgdModel)    

  Regression with properties:

    ResponseTransform: "logit"
    BoundaryTolerance: 1.0000e-05
              ModelID: "Regression"
          Description: ""
      UnderlyingModel: [1×1 classreg.regr.CompactLinearModel]
        PredictorVars: ["LTV"    "Age"    "Type"]
          ResponseVar: "LGD"
           WeightsVar: ""

Display the underlying model.

lgdModel.UnderlyingModel

ans = 
Compact linear regression model:
    LGD_logit ~ 1 + LTV + Age + Type

Estimated Coefficients:
                       Estimate       SE        tStat       pValue  
                       ________    ________    _______    __________

    (Intercept)        -4.7549      0.36041    -13.193    3.0997e-38
    LTV                 2.8565      0.41777     6.8377    1.0531e-11
    Age                -1.5397     0.085716    -17.963    3.3172e-67
    Type_investment     1.4358       0.2475     5.8012     7.587e-09


Number of observations: 2093, Error degrees of freedom: 2089
Root Mean Squared Error: 4.24
R-squared: 0.206,  Adjusted R-Squared: 0.205
F-statistic vs. constant model: 181, p-value = 2.42e-104

Compute R-Square, RMSE, Correlation, and Sample Mean Error of Predicted and Observed LGDs

Use modelCalibration to compute the RSquared, RMSE, Correlation, and SampleMeanError of the predicted and observed LGDs for the test data set.

[CalMeasure,CalData] = modelCalibration(lgdModel,data(TestInd,:))

CalMeasure=1×4 table
                  RSquared     RMSE      Correlation    SampleMeanError
                  ________    _______    ___________    _______________

    Regression    0.070867    0.25988      0.26621          0.10759    

CalData=1394×4 table
    Observed     Predicted_Regression    Residuals_Regression    Weights
    _________    ____________________    ____________________    _______

    0.0064766         0.00091169               0.0055649            1   
     0.007947          0.0036758               0.0042713            1   
     0.063182            0.18774                -0.12456            1   
            0          0.0010877              -0.0010877            1   
      0.10904           0.011213                0.097823            1   
            0           0.041992               -0.041992            1   
      0.89463           0.052947                 0.84168            1   
            0         3.7188e-06             -3.7188e-06            1   
     0.072437          0.0090124                0.063425            1   
     0.036006           0.023928                0.012078            1   
            0          0.0034833              -0.0034833            1   
      0.39549          0.0065253                 0.38896            1   
     0.057675           0.071956               -0.014281            1   
     0.014439          0.0061499                0.008289            1   
            0          0.0012183              -0.0012183            1   
            0          0.0019828              -0.0019828            1   
      ⋮

Generate a scatter plot of predicted and observed LGDs using modelCalibrationPlot.

modelCalibrationPlot(lgdModel,data(TestInd,:),ModelLevel="underlying")

Compute R-Square, RMSE, Correlation, and Sample Mean Error of Predicted and Observed LGDs Using Tobit LGD Model

This example shows how to use fitLGDModel to fit data with a Tobit model and then use modelCalibration to compute R-Square, RMSE, correlation, and sample mean error of predicted and observed LGDs.

Load Data

Load the loss given default data.

load LGDData.mat
head(data)

      LTV        Age         Type           LGD   
    _______    _______    ___________    _________

    0.89101    0.39716    residential     0.032659
    0.70176     2.0939    residential      0.43564
    0.72078     2.7948    residential    0.0064766
    0.37013      1.237    residential     0.007947
    0.36492     2.5818    residential            0
      0.796     1.5957    residential      0.14572
    0.60203     1.1599    residential     0.025688
    0.92005    0.50253    investment      0.063182

Partition Data

Separate the data into training and test partitions.

rng('default'); % for reproducibility
NumObs = height(data);

c = cvpartition(NumObs,'HoldOut',0.4);
TrainingInd = training(c);
TestInd = test(c);

Create Tobit LGD Model

Use fitLGDModel to create a Tobit model using training data.

lgdModel = fitLGDModel(data(TrainingInd,:),'tobit');
disp(lgdModel)    

  Tobit with properties:

      CensoringSide: "both"
          LeftLimit: 0
         RightLimit: 1
            Weights: [0×1 double]
            ModelID: "Tobit"
        Description: ""
    UnderlyingModel: [1×1 risk.internal.credit.TobitModel]
      PredictorVars: ["LTV"    "Age"    "Type"]
        ResponseVar: "LGD"
         WeightsVar: ""

Display the underlying model.

disp(lgdModel.UnderlyingModel)

Tobit regression model:
     LGD = max(0,min(Y*,1))
     Y* ~ 1 + LTV + Age + Type

Estimated coefficients:
                       Estimate        SE         tStat       pValue  
                       _________    _________    _______    __________

    (Intercept)         0.058257     0.027279     2.1356       0.03283
    LTV                  0.20126      0.03136     6.4177    1.7064e-10
    Age                -0.095407    0.0072633    -13.135             0
    Type_investment      0.10208     0.018077     5.6471    1.8542e-08
    (Sigma)              0.29288    0.0057084     51.306             0

Number of observations: 2093
Number of left-censored observations: 547
Number of uncensored observations: 1521
Number of right-censored observations: 25
Log-likelihood: -698.383

Compute R-Square, RMSE, Correlation, and Sample Mean Error of Predicted and Observed LGDs

Use modelCalibration to compute RSquared, RMSE, Correlation, and SampleMeanError of predicted and observed LGDs for the test data set.

[CalMeasure,CalData] = modelCalibration(lgdModel,data(TestInd,:),CorrelationType="kendall")

CalMeasure=1×4 table
             RSquared     RMSE      Correlation    SampleMeanError
             ________    _______    ___________    _______________

    Tobit    0.08527     0.23712      0.29964         -0.034412   

CalData=1394×4 table
    Observed     Predicted_Tobit    Residuals_Tobit    Weights
    _________    _______________    _______________    _______

    0.0064766       0.087889           -0.081412          1   
     0.007947        0.12432            -0.11638          1   
     0.063182        0.32043            -0.25724          1   
            0       0.093354           -0.093354          1   
      0.10904        0.16718           -0.058144          1   
            0        0.22382            -0.22382          1   
      0.89463        0.23695             0.65768          1   
            0       0.010234           -0.010234          1   
     0.072437         0.1592           -0.086761          1   
     0.036006        0.19893            -0.16292          1   
            0        0.12764            -0.12764          1   
      0.39549        0.14568              0.2498          1   
     0.057675        0.26181            -0.20413          1   
     0.014439        0.14483            -0.13039          1   
            0       0.094123           -0.094123          1   
            0        0.10944            -0.10944          1   
      ⋮

Generate a scatter plot of the predicted and observed LGDs using modelCalibrationPlot.

modelCalibrationPlot(lgdModel,data(TestInd,:))

Compute R-Square, RMSE, Correlation, and Sample Mean Error of Predicted and Observed LGDs Using Beta LGD Model

This example shows how to use fitLGDModel to fit data with a Beta model and then use modelCalibration to compute R-Square, RMSE, correlation, and sample mean error of predicted and observed LGDs.

Load Data

Load the loss given default data.

load LGDData.mat
head(data)

      LTV        Age         Type           LGD   
    _______    _______    ___________    _________

    0.89101    0.39716    residential     0.032659
    0.70176     2.0939    residential      0.43564
    0.72078     2.7948    residential    0.0064766
    0.37013      1.237    residential     0.007947
    0.36492     2.5818    residential            0
      0.796     1.5957    residential      0.14572
    0.60203     1.1599    residential     0.025688
    0.92005    0.50253    investment      0.063182

Partition Data

Separate the data into training and test partitions.

rng('default'); % for reproducibility
NumObs = height(data);

c = cvpartition(NumObs,'HoldOut',0.4);
TrainingInd = training(c);
TestInd = test(c);

Create Beta LGD Model

Use fitLGDModel to create a Beta model using training data.

lgdModel = fitLGDModel(data(TrainingInd,:),'Beta');
disp(lgdModel)    

  Beta with properties:

    BoundaryTolerance: 1.0000e-05
              ModelID: "Beta"
          Description: ""
      UnderlyingModel: [1×1 risk.internal.credit.BetaModel]
        PredictorVars: ["LTV"    "Age"    "Type"]
          ResponseVar: "LGD"
           WeightsVar: ""

Display the underlying model.

disp(lgdModel.UnderlyingModel)

Beta regression model:
     logit(LGD) ~ 1_mu + LTV_mu + Age_mu + Type_mu
     log(LGD) ~ 1_phi + LTV_phi + Age_phi + Type_phi

Estimated coefficients:
                           Estimate       SE        tStat       pValue  
                           ________    ________    _______    __________

    (Intercept)_mu          -1.3772     0.13201    -10.433             0
    LTV_mu                   0.6027     0.15087     3.9948    6.6993e-05
    Age_mu                 -0.47464    0.040264    -11.788             0
    Type_investment_mu      0.45372    0.085143     5.3289    1.0941e-07
    (Intercept)_phi        -0.16336     0.12591    -1.2974       0.19462
    LTV_phi                0.055886     0.14719    0.37969       0.70421
    Age_phi                 0.22887    0.040335     5.6743     1.586e-08
    Type_investment_phi    -0.14102    0.078155    -1.8044      0.071313

Number of observations: 2093
Log-likelihood: -5291.04

Compute R-Square, RMSE, Correlation, and Sample Mean Error of Predicted and Observed LGDs

Use modelCalibration to compute RSquared, RMSE, Correlation, and SampleMeanError of predicted and observed LGDs for the test data set.

[CalMeasure,CalData] = modelCalibration(lgdModel,data(TestInd,:),CorrelationType="kendall")

CalMeasure=1×4 table
            RSquared     RMSE      Correlation    SampleMeanError
            ________    _______    ___________    _______________

    Beta    0.080804    0.24112      0.29448         -0.052396   

CalData=1394×4 table
    Observed     Predicted_Beta    Residuals_Beta    Weights
    _________    ______________    ______________    _______

    0.0064766       0.093695         -0.087218          1   
     0.007947        0.14915           -0.1412          1   
     0.063182        0.35263          -0.28945          1   
            0       0.096434         -0.096434          1   
      0.10904        0.18858         -0.079542          1   
            0         0.2595           -0.2595          1   
      0.89463        0.26767           0.62696          1   
            0       0.021315         -0.021315          1   
     0.072437        0.17736          -0.10492          1   
     0.036006        0.22556          -0.18955          1   
            0        0.13369          -0.13369          1   
      0.39549        0.16768            0.2278          1   
     0.057675        0.29159          -0.23392          1   
     0.014439         0.1617          -0.14726          1   
            0        0.10506          -0.10506          1   
            0         0.1161           -0.1161          1   
      ⋮

Generate a scatter plot of the predicted and observed LGDs using modelCalibrationPlot.

modelCalibrationPlot(lgdModel,data(TestInd,:))

Input Arguments

lgdModel — Loss given default model

Regression object | Tobit object | Beta object

Loss given default model, specified as a previously created Regression, Tobit, or Beta object using fitLGDModel.

Data Types: object

data — Data

table

Data, specified as a NumRows-by-NumCols table with predictor and response values. The variable names and data types must be consistent with the underlying model.

Data Types: table

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: [CalMeasure,CalData] = modelCalibration(lgdModel,data(TestInd,:),DataID='Testing',CorrelationType='spearman')

CorrelationType — Correlation type

"pearson" (default) | character vector with value of 'pearson', 'spearman', or 'kendall' | string with value of "pearson", "spearman", or "kendall'"

Correlation type, specified as CorrelationType and a character vector or string.

Data Types: char | string

DataID — Data set identifier

"" (default) | character vector | string

Data set identifier, specified as DataID and a character vector or string. The DataID is included in the output for reporting purposes.

Data Types: char | string

ModelLevel — Model level

'top' (default) | character vector with value 'top' or 'underlying' | string with value "top" or "underlying"

Model level, specified as ModelLevel and a character vector or string.

• 'top' — The accuracy metrics are computed in the LGD scale at the top model level.

• 'underlying' — For a Regression model only, the metrics are computed in the underlying model's transformed scale. The metrics are computed on the transformed LGD data.

Note

ModelLevel has no effect for a Tobit or Beta model because there is no response transformation.

Data Types: char | string

ReferenceLGD — LGD values predicted for data by reference model

[ ] (default) | numeric vector

LGD values predicted for data by the reference model, specified as ReferenceLGD and a NumRows-by-1 numeric vector. The modelCalibration output information is reported for both the lgdModel object and the reference model.

Data Types: double

ReferenceID — Identifier for the reference model

'Reference' (default) | character vector | string

Identifier for the reference model, specified as ReferenceID and a character vector or string. 'ReferenceID' is used in the modelCalibration output for reporting purposes.

Data Types: char | string

Output Arguments

CalMeasure — Calibration measure

table

Calibration measure, returned as a table with columns 'RSquared', 'RMSE', 'Correlation', and 'SampleMeanError'. CalMeasure has one row if only the lgdModel accuracy is measured and it has two rows if reference model information is given. The row names of CalMeasure report the model ID and data ID (if provided).

CalData — Calibration data

table

Calibration data, returned as a table with observed LGD values, predicted LGD values, and residuals (observed minus predicted). Additional columns for predicted and residual values are included for the reference model, if provided. The ModelID and ReferenceID labels are appended in the column names. The last column contains Weights.

More About

Model Calibration

Model calibration measures the accuracy of the predicted probability of LGD values using different metrics.

• R-squared — To compute the R-squared metric, modelCalibration fits a linear regression of the observed LGD values against the predicted LGD values

  $$LG{D}_{obs}=a+b\ast LG{D}_{pred}+\epsilon$$

  The R-square of this regression is reported. For more information, see Coefficient of Determination (R-Squared).

• RMSE — To compute the root mean square error (RMSE), modelCalibration uses the following formula where N is the number of observations:

  $$RMSE=\sqrt{\frac{1}{N}{\displaystyle {\sum }_{i=1}^N(LG{D}_i^{obs}-LG{D}_i^{pred}}{)}^2}$$

• Correlation — This is the correlation between the observed and predicted LGD:

  $$corr(LG{D}_{obs},LG{D}_{pred})$$

  For more information and details about the different correlation types, see corr.

• Sample mean error — This is the difference between the mean observed LGD and the mean predicted LGD or, equivalently, the mean of the residuals:

  $$SampleMeanError=\frac{1}{N}{\displaystyle {\sum }_{i=1}^N(LG{D}_i^{obs}-LG{D}_i^{pred})}$$

  If the LGD model object is created by using the WeightsVar name-value argument, the R-square, RMSE, correlation, and sample mean error of the predicted and observed LGD data are weighted quantities.

References

[1] Baesens, Bart, Daniel Roesch, and Harald Scheule. Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS. Wiley, 2016.

[2] Bellini, Tiziano. IFRS 9 and CECL Credit Risk Modelling and Validation: A Practical Guide with Examples Worked in R and SAS. San Diego, CA: Elsevier, 2019.

Version History

Introduced in R2023a

R2024a: Support for Weights column in CalData output

The CalData output supports an additional column for Weights.