Multivariate regular variation plays a role in 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 (2002) and Mitra and Resnick (2011)). We offer a more flexible definition of hidden regular variation that provides improved risk estimates for a larger class of tail risk regions.
"Living on the multidimensional edge: seeking hidden risks using regular variation." Adv. in Appl. Probab. 45 (1) 139 - 163, March 2013. https://doi.org/10.1239/aap/1363354106