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Modelling total tail dependence along diagonals

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  • Zhang, Ming-Heng

Abstract

An approach to modelling total tail dependence beyond the main diagonals is proposed. The concept introduced combines the principal and minor diagonals to describe total extreme dependence. A framework is introduced for the measurement of total tail dependence under model mixture. Illustrations are presented using empirical data on stock market indices and exchange rates. An extension is provided to the multivariate case and total tail dependence is considered for model mixtures.

Suggested Citation

  • Zhang, Ming-Heng, 2008. "Modelling total tail dependence along diagonals," Insurance: Mathematics and Economics, Elsevier, vol. 42(1), pages 73-80, February.
    • Handle: RePEc:eee:insuma:v:42:y:2008:i:1:p:73-80
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    References listed on IDEAS

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    1. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
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    Cited by:

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    2. Wu, Shaomin, 2014. "Construction of asymmetric copulas and its application in two-dimensional reliability modelling," European Journal of Operational Research, Elsevier, vol. 238(2), pages 476-485.
    3. González-Sánchez, Mariano & Nave Pineda, Juan M., 2023. "Where is the distribution tail threshold? A tale on tail and copulas in financial risk measurement," International Review of Financial Analysis, Elsevier, vol. 86(C).
    4. Kuang-Liang Chang, 2021. "A New Dynamic Mixture Copula Mechanism to Examine the Nonlinear and Asymmetric Tail Dependence Between Stock and Exchange Rate Returns," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 965-999, December.
    5. Ojea Ferreiro, Javier, 2020. "Disentangling the role of the exchange rate in oil-related scenarios for the European stock market," Energy Economics, Elsevier, vol. 89(C).
    6. Jianxi Su & Edward Furman, 2016. "Multiple risk factor dependence structures: Copulas and related properties," Papers 1610.02126, arXiv.org.
    7. Ferreiro Javier Ojea, 2019. "Structural change in the link between oil and the European stock market: implications for risk management," Dependence Modeling, De Gruyter, vol. 7(1), pages 53-125, January.
    8. Karl Friedrich Siburg & Christopher Strothmann & Gregor Wei{ss}, 2022. "Comparing and quantifying tail dependence," Papers 2208.10319, arXiv.org.
    9. Sun, Xiaolei & Liu, Chang & Wang, Jun & Li, Jianping, 2020. "Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    10. Girard Stéphane, 2018. "Transformation of a copula using the associated co-copula," Dependence Modeling, De Gruyter, vol. 6(1), pages 298-308, December.
    11. Siburg, Karl Friedrich & Strothmann, Christopher & Weiß, Gregor, 2024. "Comparing and quantifying tail dependence," Insurance: Mathematics and Economics, Elsevier, vol. 118(C), pages 95-103.
    12. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.
    13. Su, Jianxi & Furman, Edward, 2017. "Multiple risk factor dependence structures: Copulas and related properties," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 109-121.
    14. Furman, Edward & Kuznetsov, Alexey & Su, Jianxi & Zitikis, Ričardas, 2016. "Tail dependence of the Gaussian copula revisited," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 97-103.
    15. Chang, Kuang-Liang, 2017. "Does REIT index hedge inflation risk? New evidence from the tail quantile dependences of the Markov-switching GRG copula," The North American Journal of Economics and Finance, Elsevier, vol. 39(C), pages 56-67.

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