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FRM Part II · FRM Exam Part II · Credit Value at Risk

Two portfolio credit models are applied to the same loan book. Model A (a structural, asset-value factor model) and Model B (a reduced-form model using stochastic default intensities driven by macro variables) give different 99.9% VaR figures. Which statement best describes the key reason such models can differ even when calibrated to the same average default rates?

Models calibrated to the same average default rates can still give different 99.9% VaR because they embed different assumptions about default clustering. Structural models derive dependence from asset correlations, while intensity models derive it from common macro factors, and the tail loss is driven mainly by that dependence.

  1. AThey make different assumptions about how defaults cluster, i.e. the source and strength of default correlationCorrect
  2. BAverage default rates have no effect on either model
  3. COne model ignores loss given default entirely by construction
  4. DReduced-form models can only be used for a single obligor

Explanation

Matching mean PDs fixes the expected loss but not the tail. The tail depends on default dependence, which structural models obtain from asset correlations and intensity models from common macro factors and their volatility. Hence VaR can differ even with identical average default rates.

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