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Modeling LGD with Proportional Odds Model

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The LGD model is an important component in the expected loss calculation. In https://statcompute.wordpress.com/2015/11/01/quasi-binomial-model-in-sas, I discussed how to model LGD with the quasi-binomial regression that is simple and makes no distributional assumption. In the real-world LGD data, we usually would observe 3 ordered categories of values, including 0, 1, and in-betweens. In cases with a nontrivial number of 0 and 1 values, the ordered logit model, which is also known as Proportional Odds model, can be applicable. In the demonstration below, I will show how we can potentially use the proportional odds model in the LGD model development. First of all, we need to categorize all numeric LGD values into three ordinal categories. As shown below, there are more than 30% of 0 and 1 values. df <- read.csv(“lgd.csv”) df$lgd <- round(1 – df$Recovery_rate, 4) df$lgd_cat <- cut(df$lgd, breaks =…
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