Rating Assignment Methodologies (FRM Part 2 2023 - Book 2 - Chapter 4)

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After completing this reading you should be able to:
- Explain the key features of a good rating system.
- Describe the experts-based approaches, statistical-based models and numerical approaches to predicting default.
- Describe a rating migration matrix and calculate the probability of default, cumulative probability of default, marginal probability of default and annualized default rate.
- Describe rating agencies’ assignment methodologies for issue and issuer ratings.
- Describe the relationship between borrower rating and probability of default.
- Compare agencies’ ratings to internal experts-based rating systems.
- Distinguish between the structural approaches and the reduced-form approaches to predicting default.
- Apply the Merton model to calculate default probability and the distance to default and describe the limitations of using the Merton model.
- Describe linear discriminant analysis (LDA), define the Z-score and its usage and apply LDA to classify a sample of firms by credit quality.
- Describe the application of a logistic regression model to estimate default probability.
- Define and interpret cluster analysis and principal component analysis.
- Describe the use of a cash flow simulation model in assigning rating and default probability and explain the limitations of the model.
- Describe the application of heuristic approaches, numeric approaches and artificial neural networks in modeling default risk and define their strengths and weaknesses.
- Describe the role and management of qualitative information in assessing probability of default.

[@mybui2812]

It's a world-class lecture. All sophisticated concepts that I've faced and not totally understood for many years have been simply and clearly explained thanks to Jim!

[@benpanyenful]

Around 14:29 , should the accumulated PD 5 be 4% instead of 0.4%? (40/1000=0.04)

[@zedricktorres]

27:50, I think that the probability of default is the normsdist of -5.5 which gives almost 0% PD

[@Amir-ln5qm]

this is a very good explanations
thank you very much for your efforts.

[@analystprep]

You are welcome! Good luck on the exam!