The COPULA Procedure (Experimental)
- Overview
- Getting Started
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Syntax
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Details
Sklar’s Theorem Dependence Measures Normal Copula Student’s t copula Archimedean Copulas Canonical Maximum Likelihood Estimation (CMLE) Exact Maximum Likelihood Estimation (MLE) Calibration Estimation Nonlinear Optimization Options Displayed Output OUTCOPULA= Data Set OUTPSEUDO=, OUT=, and OUTUNIFORM= Data Sets ODS Table Names ODS Graph Names
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Examples
- References
| Normal Copula |
Let 
for 
, where 
represents the uniform distribution on the 
interval. Let 
be the correlation matrix with 
parameters satisfying the positive semidefiniteness constraint. The normal copula can be written as
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where 
is the distribution function of a standard normal random variable and 
is the 
-variate standard normal distribution with mean vector 
and covariance matrix . That is, the distribution is 
.
Simulation
For the normal copula, the input of the simulation is the correlation matrix . The normal copula can be simulated by the following steps in which 
denotes one random draw from the copula:
Generate a multivariate normal vector
where is an -dimensional correlation matrix. Transform the vector
into 
, where is the distribution function of univariate standard normal.
The first step can be achieved by Cholesky decomposition of the correlation matrix 
where 
is a lower triangular matrix with positive elements on the diagonal. If 
, then 
.
Fitting
To fit a normal copula is to estimate the covariance matrix from an input sample data set. Given a random sample 
where 
, the log-likelihood function is
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Here 
is the joint density of the multivariate normal with mean zero and variance , and 
is the univariate density of the standard normal distribution. Note that the second term is not related to the parameters and, therefore, can be ignored during the optimization. The restriction that is a correlation matrix is very inconvenient, and it is common practice to circumvent this problem by first assuming that has the covariance form. Therefore, can be estimated by
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where
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This estimate is consistent with the form of a covariance matrix but not necessarily with the form of a correlation matrix. The approximation to the original MLE problem can be obtained using the normalizing operator defined as follows:
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