CMAseparation: CMA separation. Decomposes arbitrary joint distributions...
In R-Finance/Meucci: Collection of functionality ported from the MATLAB code of Attilio Meucci.
Description
Usage
Arguments
Details
Value
Author(s)
References
Description
The CMA separation step attains from the cdf "F" for the
marginal "X", the scenario-probabilities representation
of the copula (cdf of U: "F") and the inter/extrapolation
representation of the marginal CDF's. It seperates this
distribution into the pure "individual" information
contained in the marginals and the pure "joint"
information contained in the copula.
Usage
1
CMAseparation(X, p)
Arguments
X
A matrix where each row corresponds to a
scenario/sample from a joint distribution. Each column
represents the value from a marginal distribution
p
A 1-column matrix of probabilities of the
Jth-scenario joint distribution in X
Details
Separation step of Copula-Marginal Algorithm (CMA)
Value
xdd a JxN matrix where each column consists of each
marginal's generic x values in ascending order
udd a JxN matrix containing the cumulative probability
(cdf) for each marginal by column - it is rescaled by 'l'
to be <1 at the far right of the distribution can
interpret 'udd' as the probability weighted grade
scenarios (see formula 11 in Meucci)
U a copula (J x N matrix) - the joint distribution of
grades defined by feeding the original variables X into
their respective marginal CDF
Author(s)
Ram Ahluwalia rahluwalia@gmail.com
References
Meucci A., "New Breed of Copulas for Risk and Portfolio
Management", Risk, September 2011 Most recent version of
article and code available at
http://www.symmys.com/node/335
R-Finance/Meucci documentation built on May 8, 2019, 3:52 a.m.
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Description Usage Arguments Details Value Author(s) References
Description
The CMA separation step attains from the cdf "F" for the marginal "X", the scenario-probabilities representation of the copula (cdf of U: "F") and the inter/extrapolation representation of the marginal CDF's. It seperates this distribution into the pure "individual" information contained in the marginals and the pure "joint" information contained in the copula.
Usage
1 | CMAseparation(X, p)
|
Arguments
X |
A matrix where each row corresponds to a scenario/sample from a joint distribution. Each column represents the value from a marginal distribution |
p |
A 1-column matrix of probabilities of the Jth-scenario joint distribution in X |
Details
Separation step of Copula-Marginal Algorithm (CMA)
Value
xdd a JxN matrix where each column consists of each marginal's generic x values in ascending order
udd a JxN matrix containing the cumulative probability (cdf) for each marginal by column - it is rescaled by 'l' to be <1 at the far right of the distribution can interpret 'udd' as the probability weighted grade scenarios (see formula 11 in Meucci)
U a copula (J x N matrix) - the joint distribution of grades defined by feeding the original variables X into their respective marginal CDF
Author(s)
Ram Ahluwalia rahluwalia@gmail.com
References
Meucci A., "New Breed of Copulas for Risk and Portfolio Management", Risk, September 2011 Most recent version of article and code available at http://www.symmys.com/node/335
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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