# VineCopulaRand

Generating (pseudo-)random variables from a copula or vine copula

## Purpose

The function draws N pseudo-random d-dimensional tuples from a C-Vine or D-Vine copula. The vine copulas can be of the simplified and non-simplified form. In the case of a non-simplified Vine copula, the parameters of the conditional copulas have to be given as functionals of the variables on which the conditioning is done.

## Usage

```
Standard C-Vine copula (simplified & non-simplified, where the
parameters of the conditional copulas have to be given as
functionals of the variables on which the conditioning is done)
simplified
U = CopulaRand('C-Vine',N,d,families,thetas,ones(1,(d-1)*(d-2)/2))
non-simplified
U = CopulaRand('C-Vine',N,d,families,thetas,simplified,condparameterfunctionals)
Standard D-Vine copula (simplified & non-simplified, where the
parameters of the conditional copulas have to be given as
functionals of the variables on which the conditioning is done)
simplified
U = CopulaRand('D-Vine',N,d,families,thetas,ones(1,(d-1)*(d-2)/2))
non-simplified
U = CopulaRand('D-Vine',N,d,families,thetas,simplified,condparameterfunctionals)
```

## Inputs

```
type = The vine copula type.
N = The number of pseudo-random tuples to be drawn.
d = The dimension of the C- or D-Vine.
rotation = A vector of the same dimension as families in
which one can specify rotation levels.
families = A cell array of the pair-copula families, which are
part of the PCC. The cell array has to have the length
(d-1)*d/2. The first d-1 entries are the copula
families in the first tree and the next d-2 entries are
the copula families in the second tree and so on. That
means, for d=4 the array should look similar to this
{'Frank', 'Frank', 'Frank', 'AMH', 'AMH', 'Clayton'},
which is the special case where all copulas in the
second tree are AMH copulas and all copulas in the
third tree are Clayton copulas. The order of the
families is (exemplarily for the case d=4): C12, C13,
C14, C23|1, C24|1, C34|12.
Note: If families is a simple string/character, e.g.,
'Clayton', then all pair-copulas are specified to be
from this copula family.
thetas = The values of the parameters for the (d-1)*d/2 pair-
copulas. These parameter values have to be given in the
same order as the families vector, but in a row-
vector. If a pair-copula is a independence copula, then
there is no parameter needed. Furthermore, if a pair-
copula has to or more parameters, the parameters have
to be given in same order as they have to be provided
if the pair-copula is considered only. For example for
a t-copula, the first parameter is rho and the second
parameter is the degrees of freedom parameter nu.
Note: If thetas is a skalar then all parameters (i.e.,
the parameters of all pair-copulas) are set to the same
value (i.e. to the one given).
simplified
= A vector consisting of (d-1)*(d-2)/2 zeros or ones
(either 1 (for an unconditional bivariate pair-copula)
or 0 for a conditional copula) specifying, whether
the copulas in the second and higher tree are pair-copulas
(unconditional bivariate copulas) or conditional copulas.
condparameterfunctionals
= A cell array consisting of (# non-partial (i.e.
conditional) pair-copulas) cell array, which consist of
as many parameter functionals as the copula families
has parameters. Therefore, if one of the parameters is
not varying in the conditioning set, then the function
handle has to give back constant (e.g., @(w) c, where c
is a scalar constant. Functionals for conditional
copulas beeing part of the second tree of the C-Vine
have to be functions of one variable (i.e., they should
be evaluatable for column vectors). Functionals of the
third tree of the C-Vine have to be functionals of two
variables (i.e. they should be evaluatable for matrices
of the size N x 2). And so on, for conditional copulas,
where the conditioning is done on three or more
variables. The ordering of the variables in the
conditioning set corresponds to the ordering of the
root nodes of the different trees (e.g., in the fourth
tree, the first conditional copula is the C_45|123
copula and the conditioning set is three-dimensional,
where the first column of observations corresponds to
observations of the root node in the first tree (i.e.
variable X_1).
Note: Of course it is possible, that the parameter of a
conditional copula only depends on some of the
variables, but then the functions have to be specified
in an appropriate way (e.g., if a conditional copula in
the third tree, where the conditioning is done on two
variables, only depends on the second variable, the
function could look similar to this @(u) 3.*u(:,2),
when the parameter of the conditional copula is exactly
three times the second variable on which the
conditioning is done.
```

## Outputs

```
U = (N x d) Matrix of simulated tuples from the specified
copula, where every row is a simulated d-dimensional
tuple from the d-dimensional C-Vine or D-Vine copula.
```