I’m having trouble getting understanding how to actual construct a copula, from my understanding it captures the purely joint features of a joint distribution. I’ve been working with the following example.

Let X,Y be random variables with joint distribution function $$H(x,y) = (1 + e^{-x} + e^{-y})^{-1}$$ I have got by letting x and y tend to infinity respectively that the marginals of X and Y are standard logistic distributions given by $$F(x) = (1 + e^{-x})^{-1}, G(y) = (1 + e^{-y})^{-1}$$ I have to show that the copula of X and Y is $$C(u,v) = \frac{uv}{u + v - uv}$$ but I don’t know how to go about doing this? I’ve tried working with Sklars theorem but I can’t seem to get my head around this.

Aside from this specific example what is the approach in general to retrieve copulas from joint distributions? Any help would be great, thanks!


1 Answer 1


I will take you through a set of simple steps that will work for continuous distributions. (A little extra care is needed to handle the jumps that occur in non-continuous $F$ or $G,$ but no new concepts are involved.)

By definition, a copula is the joint distribution you get after you re-express the original variables in a particular way.

For continuous variables, as in this case, the re-expression is the Probability Integral Transform that replaces each possible value $x$ of a random variable $X$ (governed by a distribution $F$) by its quantile $u=F(x).$ You have already found the two quantile functions $$F,G:t\to \frac{1}{1 + e^{-t}}.\tag{1}$$

Let the re-expressed variables be $$U=F(X)\text{ and }V=G(Y).\tag{2}$$ The joint distribution of any pair of variables $(U,V)$ is

$$F_{(U,V)}(u,v) = \Pr(U \le u\text{ and } V \le v).\tag{3}$$

This is the copula. Note that this definition also means

$$ H(x,y) = \Pr(X\le x\text{ and } Y\le y)=\frac{1}{1 + e^{-x} + e^{-y}}.\tag{4}$$

Combining $(1),(2),(3)$ and simple algebraic manipulation of the descriptions of these events gives

$$\eqalign{F_{(U,V)}(u,v) &= \Pr(F(X) \le u\text{ and } F(Y) \le v)\\ &= \Pr\left(\frac{1}{1+e^{-X}} \le u \text{ and } \frac{1}{1+e^{-Y}} \le v\right) \\ &= \Pr\left(X \le \log\left(\frac{u}{1-u}\right) \text{ and } Y \le \log\left(\frac{v}{1-v}\right) \right) \\ &= H\left(\log\left(\frac{u}{1-u}\right), \log\left(\frac{v}{1-v}\right)\right). }$$

Plug this result into $(4)$ and do the algebra.

  • $\begingroup$ Thank you for this. So the marginals are the actual quantile functions themselves? Also could you explain how the definition (3) implies (4) ? $\endgroup$
    – gb4
    May 27, 2019 at 18:42
  • $\begingroup$ $H$ is the (bivariate) distribution function--as you stipulate--so $(4)$ is merely a special case of $(3).$ $\endgroup$
    – whuber
    May 27, 2019 at 21:46

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