When modelling credit migration probabilities (e.g. AAA to AA), research has indicated the use of the Beta Distribution simply because it fits empirical data. My question is;

What are some other pros and what are some cons of modelling using this distribution? Are there any other distributions that could possibly be used?


1 Answer 1


I would think it is because

  • it can be bound between 2 points
  • it can assume wide range shapes
  • It fits the data empirically (as you said)

On a related note Sometime back I read a paper which might give you more formal reason. It is for estimating and simulating recovery rates . I havnt used it to model credit migration probabilities . But I think one can extend the structural model mentioned in paper and explain why probabilities can me modelling similarly.


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