TailCoR: A new and simple metric for tail correlations that disentangles the linear and nonlinear dependencies that cause extreme co-movements

Sladana Babić, Christophe Ley, Lorenzo Ricci and David Veredas

PLOS ONE, 2023, vol. 18, issue 1, 1-23

Abstract: Economic and financial crises are characterised by unusually large events. These tail events co-move because of linear and/or nonlinear dependencies. We introduce TailCoR, a metric that combines (and disentangles) these linear and non-linear dependencies. TailCoR between two variables is based on the tail inter quantile range of a simple projection. It is dimension-free, and, unlike competing metrics, it performs well in small samples and no optimisations are needed. Indeed, TailCoR requires a few lines of coding and it is very fast. A Monte Carlo analysis confirms the goodness of the metric, which is illustrated on a sample of 21 daily financial market indexes across the globe and for 20 years. The estimated TailCoRs are in line with the financial and economic events, such as the 2008 great financial crisis and the 2020 pandemic.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0278599

DOI: 10.1371/journal.pone.0278599

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