  Hidden regular variation defines a subfamily of distributions satisfying
multivariate regular variation on $\mathbb{E} = [0, \infty]^d \backslash
\{(0,0, ..., 0) \} $ and models another regular variation on the sub-cone
$\mathbb{E}^{(2)} = \mathbb{E} \backslash \cup_{i=1}^d \mathbb{L}_i$, where
$\mathbb{L}_i$ is the $i$-th axis. We extend the concept of hidden regular
variation to sub-cones of $\mathbb{E}^{(2)}$ as well. We suggest a procedure
for detecting the presence of hidden regular variation, and if it exists,
propose a method of estimating the limit measure exploiting its semi-parametric
structure. We exhibit examples where hidden regular variation yields better
estimates of probabilities of risk sets.
