Explanation:
Copulas are specifically utilized because they allow the separation of marginal distributions from the dependence structure. Therefore, the marginal distributions do not have to be normal; copulas can link any marginal distributions (e.g., skewed or heavy-tailed default probabilities) using a selected dependence structure. Statement A is incorrect and thus the correct answer to the "not cited as a pitfall" question. Options B, C, and D are valid criticisms and potential pitfalls of using copulas in credit risk modeling as highlighted by Malz.
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24.12.2. Malz praises copulas for estimating portfolio credit risk as they "permit the model to generate quite detailed results—the entire probability distribution of portfolio credit outcomes—with a very light theoretical apparatus and requiring the estimation of only one additional parameter, the correlation, beyond those used in single-credit modeling." Despite these benefits, Malz highlights several potential drawbacks associated with using copulas. However, one of these options is not cited by him as a pitfall:
A
The marginal distributions must be normal, so we are forced to accept a multivariate normal of defaults.
B
The choice of copula is arbitrary, and we simply do not know enough to reliably estimate the copula correlation.
C
It is difficult enough to estimate default correlations and the copula correlation is only related to, not identical to, it.
D
Once a copula parameter value is assigned, it is tempting to rely on a wide range of consequently generated model results, but this is dangerous.
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