Loss Given Default Models

Estimate loss given default

Loss given default (LGD) estimates loss reserves using Regression and Tobit models.

Functions

LGD Functions

`fitLGDModel`	Create specified LGD model object type
`predict`	Predict loss given default
`modelDiscrimination`	Compute AUROC and ROC data
`modelDiscriminationPlot`	Plot ROC curve
`modelAccuracy`	Compute R-square, RMSE, correlation, and sample mean error of predicted and observed LGDs
`modelAccuracyPlot`	Scatter plot of predicted and observed LGDs

Lifetime Expected Credit Loss (ECL) Calculator

portfolioECL Compute the lifetime ECL at individual or portfolio level

Objects

`Regression`	Create`Regression`model object for loss given default
`Tobit`	Create`Tobit`model object for loss given default

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, andp-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 withportfolioECLusing 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.

Concepts

概述的损失Default Models
Loss given default (LGD) is the proportion of a credit that is lost in the event of default.

Featured Examples

Model Loss Given Default

Fit different types of models to loss given default (LGD) data. This example demonstrates the following approaches:

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Economic Scenarios and Expected Credit Loss Calculations

Generate macroeconomic scenarios and perform expected credit loss (ECL) calculations for a portfolio of loans. The workflow in this example shows important computational steps required to estimate provisions for credit losses following regulations such as IFRS 9 or CECL. In practice, the determination of loan provisions is a much more involved operational program requiring the collaboration of multiple departments in an institution. The goal of this example is to show useful computational tools that can support this process.

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