  Data exhibiting heavy-tails in one or more dimensions is often studied using
the framework of regular variation. In a multivariate setting this requires
identifying specific forms of dependence in the data; this means identifying
that the data tends to concentrate along particular directions and does not
cover the full space. This is observed in various data sets from finance,
insurance, network traffic, social networks, etc. In this paper we discuss the
notions of full and strong asymptotic dependence for bivariate data along with
the idea of hidden regular variation in these cases. In a risk analysis
setting, this leads to improved risk estimation accuracy when regular methods
provide a zero estimate of risk. Analyses of both real and simulated data sets
illustrate concepts of generation and detection of such models.
