Sklar’s Extension Theorem and support restrictions This question is about an application of the Sklar's Extension Theorem, whose proof can be found in Sklar, A. (1996), "Random variables, distribution functions, and copulas: A personal look backward and forward." Institute of Mathematical Statistics Lecture Notes-Monograph Series, 28, 1–14. [106, 133].
I will firstly briefly (and, due to space constraints, non exhaustively) summarise the content   of the Sklar's Extension Theorem and then I will present my question.

Review of Sklar's Extension Theorem
For simplicity, I will focus on 3-dimensional copulas. Note that the Sklar's Extension Theorem applies to any dimension $N\geq 1$.
Let $\mathcal{I}\equiv \mathcal{I}_1\times \mathcal{I}_2 \times  \mathcal{I}_3\subseteq [0,1]^3$, where  $\mathcal{I}_n$ is such that $\{0,1\}\in \mathcal{I}_n$ for each $n=1,2,3$.
An $3$-dimensional subcopula is a function $\bar{C}:\mathcal{I}\rightarrow [0,1]$ such that:

*

*$\bar{C}$ is non decreasing.


*$\bar{C}(u) = 0$ for any $u \in \mathcal{I}$ that has at least one component equal to 0.


*$\bar{C}(u) = u_n$ for any $u \in  \mathcal{I}$ that has all components, except the $n$-th, equal to 1.
A $3$-dimensional copula  is a $3$-dimensional subcopula for which $\mathcal{I}=[0,1]^3$ .
Sklar's Lemma: Let $\bar{C}$ be a $3$-dimensional subcopula with domain $\mathcal{I}$. Then, there exists a   proper $3$-dimensional copula $C$ such that $C(u) = \bar{C}(u)$ for all $u\in \mathcal{I}$.

Question: when extending the copula, can we make sure that the CDF associated with the copula satisfies some desired support restrictions?

Reading Nelsen's 2006 "Introduction to Copulas":

*

*$k$-dimensional sub-copulas are defined in a subset of $[0,1]^k$ (their domain). The same is said in Sklar's paper that the OP mentioned.

*A Copula is the extension of a sub-copula to the whole $[0,1]^k$ (its domain).

*Sklar's Theorem (the "core" one) then asserts that every "usual" multivariate distribution function can be represented by some Copula, uniquely (continuous rv's), or not (discrete rv's).

The OP talks about the "support" of the distribution function, and I suspect they meant the support of the rv's that have this distribution function. This "support" does not enter the picture nor is affected by all the above, because for the Copula what matters is to have as arguments distribution functions, no matter what goes on with the rv's being represented by them.
So place any kind of restrictions you want on $(X,Y)$ that have the joint DF $H(x, y)$ and marginals $F(x), G(x)$, because these marginal DFs will range in $[0,1]$ no matter what restrictions you apply on the joint support -and the Copula will be some
$C(F(x), G(x)) = H(x,y)$ for the designated support $S_X \times S_Y$ that incorporates your restrictions.
