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Measuring Comonotonicity in M-Dimensional Vectors

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  • Koch, Inge
  • De Schepper, Ann

Abstract

In this contribution, a new measure of comonotonicity for m-dimensional vectors is introduced, with values between zero, representing the independent situation, and one, reflecting a completely comonotonic situation. The main characteristics of this coefficient are examined, and the relations with common dependence measures are analysed. A sample-based version of the comonotonicity coefficient is also derived. Special attention is paid to the explanation of the accuracy of the convex order bound method of Goovaerts, Dhaene et al. in the case of cash flows with Gaussian discounting processes.

Suggested Citation

  • Koch, Inge & De Schepper, Ann, 2011. "Measuring Comonotonicity in M-Dimensional Vectors," ASTIN Bulletin, Cambridge University Press, vol. 41(1), pages 191-213, May.
    • Handle: RePEc:cup:astinb:v:41:y:2011:i:01:p:191-213_00
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    Cited by:

    1. Rafał Wójcik & Charlie Wusuo Liu, 2022. "Bivariate Copula Trees for Gross Loss Aggregation with Positively Dependent Risks," Risks, MDPI, vol. 10(8), pages 1-24, July.
    2. Daniël Linders & Jan Dhaene & Wim Schoutens, 2015. "Option prices and model-free measurement of implied herd behavior in stock markets," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(02), pages 1-35.
    3. Rafał Wójcik & Charlie Wusuo Liu & Jayanta Guin, 2019. "Direct and Hierarchical Models for Aggregating Spatially Dependent Catastrophe Risks," Risks, MDPI, vol. 7(2), pages 1-22, May.
    4. Ying Zhang & Chuancun Yin, 2014. "A new multivariate dependence measure based on comonotonicity," Papers 1410.7845, arXiv.org.

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