Loss Given Default Models
Calculate the loss given default (LGD) using a Regression, Tobit, or Beta model. Calculate the estimated loss reserves using Expected Credit Loss (ECL) calculator.
Functions
Objects
Regression | Create Regression model object for loss given
default (Since R2021a) |
Tobit | Create Tobit model object for loss given default (Since R2021a) |
Beta | Create Beta model object for loss given default (Since R2022b) |
Topics
- 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
Tobitmodel 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. - Incorporate Macroeconomic Scenario Projections in Loan Portfolio ECL 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.
- Create Weighted LGD Model
This example shows how to use the
fitLGDModelfunction to create an LGD model using weighted portfolio data. - Estimate Portfolio-Level LGD with Frye-Jacobs Function
The
fryeJacobsLGDfunction allows you to predict the conditional loss given default (LGD) as a function of the conditional probability of default (PD).
Featured Examples
Model Loss Given Default
Fit different types of models to loss given default (LGD) data. This example demonstrates the following approaches:
Incorporate Macroeconomic Scenario Projections in Loan Portfolio ECL 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.
Create Weighted LGD Model
Use the fitLGDModel function to create an LGD model using weighted portfolio data. There are many reasons to create a weighted LGD model. For example, consider the scenarios where you want to weight newer data more heavily than older data, or weight certain data points more heavily because they are more relevant to the purposes of a business. In this example, you use LGD portfolio data and assign more weight to investment loans than residential loans.
