Credit Risk: Estimating Default Probabilities
8 important questions on Credit Risk: Estimating Default Probabilities
Name two main areas of application of (credit) risk modeling. Which is static and which dynamic?
1. CR management models for: (static)
- the determination of the loss distribution of a loan or bond portfolio oer a fixed time horizon (e.g. 1 yr)
- the computation of (loan) loss-distributuin-based risk measures.
- the optimal allocation of risk-capital.
2. Analysis of credit securities. (dynamic)
Challenges in modeling CR (1/4). Explain the issue of lack of public information and data.
Scarce inormation about the credit quality of corporations and, often, individuals. There's few data for calibration.
qa-answernotes-contentChallenges in modeling CR (2/4). Explain the issue of informational asymmetries.
Te management of a firm has more information about the firm itself than every other bank or fin. institution, which creates mistrust and volatility on the market.
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qa-answernotes-contentChallenges in modeling CR (3/4). Explain the issue of a skewed (loss) distribution.
The world is not Gaussian. Loss distributions are often highly skewed and possess relatively heavy upper tails. There's need for EVT in determining risk capital.
qa-answernotes-contentChallenges in modeling CR (4/4). Explain the issue of dependence.
Companies may be linked by systemic risk, commercial relations or other forms of dependence. This may cause the distribution of defaults to ' spread' and we see a larger number of defaults with a higher probability. The more defaults are dependent, the more the loss distributions shifts to the left and shows a fatter right tail.
Why is the gamma exposure minor in comparison to the delta exposure?
What is a principle component? (PCA)
When we don't want to make the normality assumption, what to do?
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