Abstract
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.
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B. Das was supported by MOE-2013-T2-1-158 and IDG31300110. B. Das also acknowledges hospitality from Cornell University during visits in June 2015 and January 2016.
S. Resnick was supported by Army MURI grant W911NF-12-1-0385 to Cornell University.
Hospitality and support from SUTD during the week of January 23-27, 2017 is gratefully acknowledged.
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Das, B., Resnick, S.I. Hidden regular variation under full and strong asymptotic dependence. Extremes 20, 873–904 (2017). https://doi.org/10.1007/s10687-017-0290-8
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DOI: https://doi.org/10.1007/s10687-017-0290-8