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Properties of CoVaR based on tail expansions of copulas

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  • Li, Xiaoting
  • Joe, Harry

Abstract

The theoretical properties of two widely used CoVaR definitions are investigated under different dependence structures in joint distributions. By using copulas, the dependence is separated from marginal distributions, and CoVaR is expressed through an adjustment factor based solely on the copula. The primary contribution is to study the limiting behavior of the adjustment factor and its link to the strength of dependence in the tails of the joint distribution. We also provide asymptotic results for bivariate Archimedean copulas and extend these findings to extreme value copulas and their mixtures. These findings enhance the understanding of CoVaR in risk scenarios, particularly as the conditional event becomes more extreme.

Suggested Citation

  • Li, Xiaoting & Joe, Harry, 2026. "Properties of CoVaR based on tail expansions of copulas," Journal of Multivariate Analysis, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:jmvana:v:211:y:2026:i:c:s0047259x25001058
    DOI: 10.1016/j.jmva.2025.105510
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