Explanation:
One of the key advantages of using copulas is that the marginal distributions do not need to be normal. Copulas allow marginal distributions of any shape (e.g., normal, skewed, fat-tailed) to be joined together to form a multivariate distribution. Thus, we are not forced to accept a multivariate normal distribution of defaults. Options B, C, and D are valid pitfalls associated with the use of copulas in estimating portfolio credit risk as highlighted by Malz.
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311.3. In regard to using copulas to estimate portfolio credit risk, Malz writes that "copulas are a very attractive modeling technique since 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."⁷ However, he cites EACH of the following as a PITFALL (drawback) EXCEPT for which is not?
A
The marginal distributions must be normal such that 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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