De Laurentis, Chapter 3: Ratings Assignment Methodologies Practice Question set contains 23 pages covering the following learning objectives:

* 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.

We have also provided individual links for each question to their respective forum discussion.

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