Conditional excess risk measures and multivariate regular variation
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Bikramjit Das
und
Vicky Fasen-Hartmann
Abstract
Conditional excess risk measures like Marginal Expected Shortfall and Marginal Mean Excess are designed to aid in quantifying systemic risk or risk contagion in a multivariate setting. In the context of insurance, social networks, and telecommunication, risk factors often tend to be heavy-tailed and thus frequently studied under the paradigm of regular variation. We show that regular variation on different subspaces of the Euclidean space leads to these risk measures exhibiting distinct asymptotic behavior. Furthermore, we elicit connections between regular variation on these subspaces and the behavior of tail copula parameters extending previous work and providing a broad framework for studying such risk measures under multivariate regular variation. We use a variety of examples to exhibit where such computations are practically applicable.
Funding statement: Bikramjit Das gratefully acknowledges support from AcRF Tier 2 Singapore Ministry of Education grant MOE2017-T2-2-161, and hospitality of the Department of Mathematics, Karlsruhe Institute of Technology during his visit in 2018.
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