The cumulative distribution function $F(x)=P(X\leq x)$ is a fixed probability number. I wonder what its variance is, if we let the argument be a random variable following the same distribution as $X$.
For continuous $X$, this variance can be readily computed because density and CDF are related through $f(x)=F'(x)$ (sorry for the odd notation for the definite integral, but due to a bug on StackExchange the vertical bar does not support subscripts but only superscripts): $$ Var(F(X)) = E(F(X)^2) - E(F(X))^2 = \int_{-\infty}^\infty F(x)^2f(x) dx - \left(\int_{-\infty}^\infty F(x) f(x) dx\right)^2 $$ $$= \frac{1}{3}F(x)^3\Big|^{-\infty,\infty} - \left( \frac{1}{2}F(x)^2\Big|^{-\infty,\infty} \right)^2 = \frac{1}{3} - \frac{1}{4} = \frac{1}{12}$$
This does not hold, though, for discrete random variables $X$, and I wonder whether there is a simple expression for this variance, too, for discrete variables. In this case, we have $$E(F(X)^2) = \sum_{x\in X(\Omega)} F(x)^2 P(X=x) = \sum_{i=1}^{|X(\Omega)|} F(x_i)^2 \Big(F(x_i)-F(x_{i-1})\Big)$$ where $x_0$ is set to $-\infty$, which means that $F(x_0)=0$. This looks like a candidate for Abel's summation by parts, but I could not see a way to simplify this expression.
Does someone know of other ways to compute $Var(F(X))$, or is there an interpretation of this variance that allows for a simple computation?