Choosing a Corporate Default Model and Validating Credit Ratings

Summary

The document considers building an internal rating model that maps corporate default probabilities to rating categories. The proposed approach is logistic regression using financial ratios as fixed effects, with sector and year as random effects to represent shared default patterns. The question describes a panel of firm observations over several years, with default recorded as a binary outcome, and asks whether a generalized linear mixed model is suitable.

The response points to structural credit models in the Merton family as a possible direction and cautions that regression alone may not be adequate for a consequential rating system. It emphasizes that model choice depends on the intended use, such as lending, firm risk management, or trading, and that regulatory expectations and internal model validation matter. The exchange does not compare model performance, specify calibration or validation procedures, or establish that structural models are preferable in every setting. Its contribution is chiefly to frame model selection as an application and governance problem, not to provide an implementation recipe.

Key ideas

• A logistic mixed model could use firm ratios as predictors and sector or year effects to represent grouped variation.
• The proposed outcome is default status, with predicted probabilities mapped into rating bands.
• Structural credit models such as Merton models are raised as alternatives to regression-based approaches.
• Model choice depends on the intended business use and the requirements of regulators and validation teams.
• The discussion gives no empirical comparison or detailed model validation method.

Tags

# Generalized Linear Mixed Model (GLMM) for the probability of default of corporates


# Generalized Linear Mixed Model (GLMM) for the probability of default of corporates












I work in the financial industry and we want to implement an internal rating model for our clients (think corporates large or mid , banks etc. some listed on an exchange some others not).

We want to give ratings (A,B,C etc.) according the probability of default each client will be assigned -say pd's of $0-0.05$ goes to credit category A $0.06-0.1$ to category B and so on.-

I have studied the literature quite well and my background is qfin and maths.

I ended up thinking that a simple logistic regression is quite good but adding random effects would be interesting since they can capture dependence structures among defaults (i.e. in the firms in the same sector). My data will be financial ratios for each firm for a period of time (say 5-10 years) and if defaulted (1) or not (0) I though of having the financial ratios as fixed effects and the sector and the year as random. Do you think this approach makes sense?

PS i am using R.

## Answer by Kch (score 2)

https://quant.stackexchange.com/a/63284

Merton models and their progeny are likely the route you want to take. There's a ton of research out there on this, model selection largely depends on the use for your product (eg, loan or firm risk management, trading, etc).

Regression here is likely insufficient given the amount of very public research on the topic. Regulators care a great deal about this topic, so your real answer to this should be in context of discussion with regulator-facing business partners and your model validation team. Unless, of course, this is a type of side project that has limited business implication.

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