WoE Transformation for Loss Given Default Models

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In the intro section of my MOB package (https://github.com/statcompute/MonotonicBinning#introduction), reasons and benefits of using WoE transformations in the context of logistic regressions with binary outcomes had been discussed. What’s more, the similar idea can be easily generalized to other statistical models in the credit risk area, such as LGD (Loss Given Default) models with fractional outcomes.

Measuring the ratio between net and gross charge-offs, LGD can take any value within the unity interval of [0, 1] with no unanimous consensus on the distributional assumption either academically or empirically. In the banking industry, a popular approach to model LGD is the use of Quasi-Binomial models that makes no assumption of any statistical distribution but merely specifies the conditional mean by a Logit link function. With the specification of Logit link, the idea of WoE transformations can be ported directly from logistic regressions to Quasi-Binomial models.

The example below shows how to perform WoE transformations through monotonic binning based upon the fractional outcome, e.g. LGD, by using the function qtl_lgd() (https://github.com/statcompute/MonotonicBinning/blob/master/code/qtl_lgd.R).

As demonstrated in the outcome table, the average LGD increases along with the LTV (Loan-to-Value) and the WoE transformation of LTV is strictly linear with respect to the Logit of average LGD.

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