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estimate loss given default

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

lgd functions

	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

compute the lifetime ecl at individual or portfolio level

objects

	create `regression` model object for loss given default
	create `tobit` model object for loss given default
	create `beta` model object for loss given default

topics

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.
this example shows how to compare loss given default (lgd) models using cross-validation.
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.
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.
this example shows how to work with consumer (retail) credit panel data to visualize observed probabilities of default (pds) at different levels.
loss given default (lgd) is the proportion of a credit that is lost in the event of default.

featured examples

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.