Lifetime Models for Probability of Default

Estimate loss reserves based on lifetime analysis

Lifetime models for probability of default (PD) estimate loss reserves based on a "lifetime" analysis conditional on macroeconomic scenarios.

Functions

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PD Functions

`fitLifetimePDModel`	Create specified lifetime PD model object type
`predict`	Compute conditional PD
`predictLifetime`	Compute cumulative lifetime PD, marginal PD, and survival probability
`modelDiscrimination`	Compute AUROC and ROC data
`modelDiscriminationPlot`	Plot ROC curve
`modelAccuracy`	Compute RMSE of predicted and observed PDs on grouped data
`modelAccuracyPlot`	Plot observed default rates compared to predicted PDs on grouped data

Lifetime Expected Credit Loss (ECL) Calculator

portfolioECL Compute the lifetime ECL at individual or portfolio level

Objects

`Logistic`	Create `Logistic` model object for lifetime probability of default
`Probit`	Create `Probit` model object for lifetime probability of default
`Cox`	Create `Cox` model object for lifetime probability of default

Examples and How To

Basic Lifetime PD Model Validation
This example shows how to perform basic model validation on a lifetime probability of default (PD) model by viewing the fitted model, estimated coefficients, and p-values.
Compare Logistic Model for Lifetime PD to Champion Model
This example shows how to compare a new Logistic model for lifetime PD against a "champion" model.
Compare Lifetime PD Models Using Cross-Validation
This example shows how to compare three lifetime PD models using cross-validation.
Expected Credit Loss Computation
This example shows how to perform expected credit loss (ECL) computations with portfolioECL using simulated loan data, macro scenario data, and an existing lifetime probability of default (PD) model.
Compare Model Discrimination and Accuracy to Validate of Probability of Default
This example shows some differences between discrimination and accuracy metrics for the validation of probability of default (PD) models.
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.
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.

Concepts

Overview of Lifetime Probability of Default Models
Estimate loss reserves based on a lifetime analysis conditional on macroeconomic scenarios.

Featured Examples

Stress Testing of Consumer Credit Default Probabilities Using Panel Data

Use consumer credit panel data to visualize default rates, fit a model to predict probabilities of default, and perform a stress testing analysis.

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Modeling Probabilities of Default with Cox Proportional Hazards

Work with consumer (retail) credit panel data to visualize observed probabilities of default (PDs) at different levels. It also shows how to fit a Cox proportional hazards (PH) model, also known as Cox regression, to predict PDs. In addition, it shows how to perform a stress-testing analysis, how to model lifetime PDs, and how to calculate the lifetime expected credit loss (ECL) value using portfolioECL.

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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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