Loss Given Default Models
Loss given default (LGD) estimates loss reserves using Regression and Tobit models.
|Create specified LGD model object type|
|Predict loss given default|
|Compute AUROC and ROC data|
|Plot ROC curve|
|Compute R-square, RMSE, correlation, and sample mean error of predicted and observed LGDs|
|Scatter plot of predicted and observed LGDs|
Lifetime Expected Credit Loss (ECL) Calculator
Examples and How To
- Basic Loss Given Default Model Validation
This example shows how to perform basic model validation on a loss given default (LGD) model by viewing the fitted model, estimated coefficients, and p-values.
- Compare Tobit LGD Model to Benchmark Model
This example shows how to compare a Tobit model for loss given default (LGD) against a benchmark model.
- Compare Loss Given Default Models Using Cross-Validation
This example shows how to compare loss given default (LGD) models using cross-validation.
- Expected Credit Loss Computation
This example shows how to perform expected credit loss (ECL) computations with
portfolioECLusing simulated loan data, macro scenario data, and an existing lifetime probability of default (PD) model.
- Economic Scenarios and Expected Credit Loss Calculations
This example shows how to generate macroeconomic scenarios and perform expected credit loss (ECL) calculations for a portfolio of loans.
- Modeling Probabilities of Default with Cox Proportional Hazards
This example shows how to work with consumer (retail) credit panel data to visualize observed probabilities of default (PDs) at different levels.
- Overview of Loss Given Default Models
Loss given default (LGD) is the proportion of a credit that is lost in the event of default.