Conditional Monte Carlo replaces a naive estimate $Z$ of a number $z$ by its conditional expectation given a suitable piece of information. It always reduces variance and its traditional applications are in that vein. We survey here other potential uses such as density estimation and Value-at-Risk calculations, going in part into the implementation in various copula structures. Also the interplay between these different aspects comes into play.

Keywords: Archimedean copula; Density estimation; Expected shortfall; Lognormal sums; Rare event simulation; Value-at-Risk