De Laurentis, Developing, Validating and Using Internal Ratings, Chapter 3 Study Notes contains 25 pages covering the following learning objectives:

* The key features of a good rating system.

* The experts-based approaches, statistical-based models, and numerical approaches to predicting default.

* Rating migration matrix and calculating the probability of default, cumulative probability of default, marginal probability of default, and annualized default rate.

* Rating agencies’ assignment methodologies for issue and issuer ratings.

* The relationship between borrower rating and probability of default.

* Comparing agencies’ ratings to internal experts-based rating systems.

* Distinguishing between the structural approaches and the reduced-form approaches to predicting default.

* Applying the Merton model to calculate default probability and the distance to default and describing the limitations of using the Merton model.

* Linear discriminant analysis (LDA), the Z-score and its usage, and applying LDA to classify a sample of firms by credit quality.

* The application of logistic regression model to estimate default probability.

* Defining and interpreting cluster analysis and principal component analysis.

* The use of cash flow simulation model in assigning rating and default probability, and explaining the limitations of the model.

* The application of heuristic approaches, numeric approaches, and artificial neural network in modeling default risk and their strengths and weaknesses.

* The role and management of qualitative information in assessing probability of default.

After reviewing the notes you will be able to apply what you learned with practice questions & answers.

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