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arXiv · 2605.10303

Measuring Tail Dependence in Linear Processes: Theory and Empirics

Debanjana Datta·Diganta Mukherjee
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Abstract

The quantitative analysis of financial time series often reveals two distinct features that standard Gaussian frameworks fail to capture: heavy-tailed marginal distributions and the phenomenon of extreme co-movements.While extreme value theory characterizes marginal behavior, Copulas provide a functional bridge to describe the dependence structure independently of the marginals. We are proposing a different way of looking at the joint extremes on the basis of a dependence measure. The proposed idea incorporates both the non-identical and identical regularly varying distributions. Informed by the analysis of some high-frequency cryptocurrency datasets, the effect of persistence property have been thoroughly studied under these setups. A detailed simulation study confirms our intuition and findings.

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Debanjana Datta, Diganta Mukherjee. 2026-05-11. Measuring Tail Dependence in Linear Processes: Theory and Empirics. https://arxiv.org/abs/2605.10303

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