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.
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