  We analyze the joint extremal behavior of $n$ random products of the form
$\prod_{j=1}^m X_j^{a_{ij}}, 1 \leq i \leq n,$ for non-negative, independent
regularly varying random variables $X_1, \ldots, X_m$ and general coefficients
$a_{ij} \in \mathbb{R}$. Products of this form appear for example if one
observes a linear time series with gamma type innovations at $n$ points in
time. We combine arguments of linear optimization and a generalized concept of
regular variation on cones to show that the asymptotic behavior of joint
exceedance probabilities of these products is determined by the solution of a
linear program related to the matrix $\mathbf{A}=(a_{ij})$.
