Living on the multi-dimensional edge: seeking hidden risks using
regular variation

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Multivariate regular variation plays a role assessing tail risk in
diverse applications such as finance, telecommunications, insurance
and environmental science. The classical theory, being based on an
asymptotic model, 
sometimes leads to inaccurate and useless estimates of probabilities
of joint tail regions. This problem can be partly ameliorated by
using hidden regular variation; see Resnick(2000), Mitra and Resnick
(2010). We offer a more flexible definition of hidden regular
variation that provides improved risk estimates for a larger class 
of risk tail regions and unifies concepts such as asymptotic
independence and asymptotic full dependence.