[R-SIG-Finance] Copula and Multivariate distribution

[Samuel Le]

Hello,

The copula function will give you random numbers that are marginally following a uniform distribution on [0,1]. Then you need to apply to those numbers the inverse of the cumulative distribution you think that mimic the best your data.
For example, if you want to simulate n reduced centred normal distribution using a non Gaussian copula -i.e each variable follows a normal distribution of mean 0 and variance 1 but their joint distribution isn't the Gaussian copula but something else (it can be Clayton, Gumbel, etc.)- you need to apply to each random variable the function R qnorm.

HTH,

Samuel

-----Original Message-----
From: r-sig-finance-bounces at r-project.org [mailto:r-sig-finance-bounces at r-project.org] On Behalf Of salmajj
Sent: 19 January 2011 20:50
To: r-sig-finance at stat.math.ethz.ch
Subject: [R-SIG-Finance] Copula and Multivariate distribution


Hi all,
I understand that rmvdc generates random number from mvdc object. But the
mvdc object can only be used if we define the marginals! So my question is
suppose we don't find any distribution which fit marginals so we use the
Canonical Maximum Likelihood method (This approach uses the empirical CDF of
each marginal distribution to transform the observations into pseudo
observations with uniform margins) SO after finding the copula which fit the
dependancy HOW i can generate random number which mimic the data?
Hope my question is clear, please if someone have an idea help me!
THANKS

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