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has there been any interesting work or advances on the copula-marginal algorithm (CMA) as proposed by Attilio Meucci. I am unable to find anything on the web other then the original article, here is the original article that i am referring too. I'm just curious if any one has built on his research or if this may be of any use at all

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The algorithm is certainly useful in that it is non-parametric, fast, and versatile. Meucci summarizes the advantages nicely:

Unlike traditional copula techniques, CMA a) is not restricted to few parametric copulas such as elliptical or Archimedean; b) never requires the explicit computation of marginal cdf’s or quantile functions; c) does not assume equal probabilities for all the scenarios, and thus allows for advanced techniques such as importance sampling or entropy pooling; d) allows for arbitrary transformations of copulas. Furthermore, the implementation of CMA is also computationally very efficient in arbitrary large dimensions.

The paper was published 3Q last year so there are not many citations yet. Hard to see what features are lacking -- if anything, the algorithm corrects for the weaknesses of parametric copulas and offers far more versatility for stress-testing and mixing arbitrary copulas and marginals.

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Is it possible to give more citation that Meucci's article as requested in the question? This will be very much appreciated. –  Suminda Sirinath Salpitikorala Feb 25 '12 at 18:28

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