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  1. M

    Definitions of probability of default vs. cumulative or marginal probability of default

    Hi, in book 2, chapter 4 (or the BT study notes), the following definitions are presented: I do not understand how to read the second definition. [t, t+k) is a continuous set. Therefore, I do not see how the numerator is supposed to be defined here. Even if I assume that we simply iterate...
  2. Nicole Seaman

    P2.T6.706. Heuristic approach versus neural networks (De Laurentis)

    Learning objectives: 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...
  3. Nicole Seaman

    P2.T6.705. Logistic regression and principal component analysis (PCA, De Laurentis)

    Learning objectives: Describe the application of a logistic regression model to estimate default probability. Define and interpret cluster analysis and principal component analysis. Questions: 705.1. Logistic regression is often used to predict whether a loan will default. For example, the...
  4. Nicole Seaman

    P2.T6.704 Linear discriminant analysis (LDA according to De Laurentis)

    Learning objectives: 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...
  5. Nicole Seaman

    P2.T6.703 Structural versus Reduced-form credit risk approaches (DeLaurentis)

    Learning outcomes: 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...
  6. Nicole Seaman

    P2.T6.702. Credit rating assignment methodologies (De Laurentis)

    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...
  7. Nicole Seaman

    P2.T6.701. Unexpected loss and return on risk-adjusted capital (RARORAC) (De Laurentis)

    Learning objectives: Explain expected loss, unexpected loss, VaR, and concentration risk, and describe the differences among them. Evaluate the marginal contribution to portfolio unexpected loss. Define risk-adjusted pricing and determine risk-adjusted return on risk-adjusted capital (RARORAC)...
  8. Nicole Seaman

    P2.T6.700. Credit risk classifications (De Laurentis)

    Learning objectives: Describe the role of ratings in credit risk management. Describe classifications of credit risk and their correlation with other financial risks. Define default risk, recovery risk, exposure risk and calculate exposure at default. Questions: 700.1. In contrasting...