predict
Description
computes the loss given default (LGD). LGD
= predict(lgdModel
,data
)
When using a Regression
model, the
predict
function operates on the underlying compact
statistical model and then transforms the predicted values back to the LGD
scale.
When using a Tobit
model, the
predict
function operates on the underlying
Tobit
regression model and returns the unconditional expected
value of the response, given the predictor values.
Examples
Use Regression LGD Model to Predict LGD
This example shows how to use fitLGDModel
to fit data with a Regression
model and then predict the loss given default (LGD) values.
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: [1x1 classreg.regr.CompactLinearModel] PredictorVars: ["LTV" "Age" "Type"] ResponseVar: "LGD" WeightsVar: ""
Display the underlying model.
disp(lgdModel.UnderlyingModel)
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
Predict LGD on Test Data
Use predict
to predict the LGD for the test data set.
predictedLGD = predict(lgdModel,data(TestInd,:))
predictedLGD = 1394×1
0.0009
0.0037
0.1877
0.0011
0.0112
0.0420
0.0529
0.0000
0.0090
0.0239
⋮
You can analyze and validate these predictions using modelDiscrimination
and modelCalibration
.
Use Tobit LGD Model to Predict LGD
This example shows how to use fitLGDModel
to fit data with a Tobit
model and then predict the loss given default (LGD) values.
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: [0x1 double] ModelID: "Tobit" Description: "" UnderlyingModel: [1x1 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 0.027277 2.1357 0.032819 LTV 0.20126 0.031352 6.4193 1.6887e-10 Age -0.095407 0.0072648 -13.133 0 Type_investment 0.10208 0.018077 5.6471 1.8544e-08 (Sigma) 0.29288 0.0057081 51.309 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
Predict LGD on Test Data
Use predict
to predict the LGD for the test data set.
predictedLGD = predict(lgdModel,data(TestInd,:))
predictedLGD = 1394×1
0.0879
0.1243
0.3204
0.0934
0.1672
0.2238
0.2370
0.0102
0.1592
0.1989
⋮
You can analyze and validate these predictions using modelDiscrimination
and modelCalibration
.
Use Beta LGD Model to Predict LGD
This example shows how to use fitLGDModel
to fit data with a Beta
model and then predict the loss given default (LGD) values.
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: [1x1 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
Predict LGD on Test Data
Use predict
to predict the LGD for the test data set.
predictedLGD = predict(lgdModel,data(TestInd,:))
predictedLGD = 1394×1
0.0937
0.1492
0.3526
0.0964
0.1886
0.2595
0.2677
0.0213
0.1774
0.2256
⋮
You can analyze and validate these predictions using modelDiscrimination
and modelCalibration
.
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
Output Arguments
LGD
— Loss given default values
vector
Loss given default values, returned as a
NumRows
-by-1
numeric vector.
More About
Prediction with LGD Models
Use a Regression, Tobit, or Beta model to predict LGD.
Regression
LGD models
first predict on the transformed space using the underlying linear regression model,
and then apply the inverse transformation to return predictions on the LGD scale.
For more information on the supported transformations and their inverses, see Loss Given Default Regression Models.
Tobit
LGD models return the
unconditional expected value of the response, given the predictor values. For more
information, see Loss Given Default Tobit Models.
Beta
LGD models return the
mean of the beta distribution, given the predictor values. For more information, see
Beta Regression Models.
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 R2021aR2022b: Support for Beta
model
The lgdModel
input supports an option for a
Beta
model object that you can create using fitLGDModel
.
See Also
Tobit
| Regression
| Beta
| modelCalibration
| modelCalibrationPlot
| modelDiscriminationPlot
| modelDiscrimination
| fitLGDModel
MATLAB 명령
다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.
명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)