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Constructing hierarchical Archimedean copulas with Lévy subordinators

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  • Hering, Christian
  • Hofert, Marius
  • Mai, Jan-Frederik
  • Scherer, Matthias

Abstract

A probabilistic interpretation for hierarchical Archimedean copulas based on Lévy subordinators is given. Independent exponential random variables are divided by group-specific Lévy subordinators which are evaluated at a common random time. The resulting random vector has a hierarchical Archimedean survival copula. This approach suggests an efficient sampling algorithm and allows one to easily construct several new parametric families of hierarchical Archimedean copulas.

Suggested Citation

  • Hering, Christian & Hofert, Marius & Mai, Jan-Frederik & Scherer, Matthias, 2010. "Constructing hierarchical Archimedean copulas with Lévy subordinators," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1428-1433, July.
  • Handle: RePEc:eee:jmvana:v:101:y:2010:i:6:p:1428-1433
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    References listed on IDEAS

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    1. Niall Whelan, 2004. "Sampling from Archimedean copulas," Quantitative Finance, Taylor & Francis Journals, vol. 4(3), pages 339-352.
    2. Hofert, Marius, 2008. "Sampling Archimedean copulas," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5163-5174, August.
    3. Li, Haijun, 2009. "Orthant tail dependence of multivariate extreme value distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(1), pages 243-256, January.
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    Cited by:

    1. Quanrui Song & Jianxu Liu & Songsak Sriboonchitta, 2019. "Risk Measurement of Stock Markets in BRICS, G7, and G20: Vine Copulas versus Factor Copulas," Mathematics, MDPI, vol. 7(3), pages 1-16, March.
    2. Mai, Jan-Frederik & Scherer, Matthias, 2012. "H-extendible copulas," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 151-160.
    3. Chaoubi, Ihsan & Cossette, Hélène & Marceau, Etienne & Robert, Christian Y., 2021. "Hierarchical copulas with Archimedean blocks and asymmetric between-block pairs," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    4. David Blake & Marco Morales & Enrico Biffis & Yijia Lin & Andreas Milidonis, 2017. "Special Edition: Longevity 10 – The Tenth International Longevity Risk and Capital Markets Solutions Conference," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(S1), pages 515-532, April.
    5. Segers, Johan & Uyttendaele, Nathan, 2014. "Nonparametric estimation of the tree structure of a nested Archimedean copula," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 190-204.
    6. Zhu, Wenjun & Wang, Chou-Wen & Tan, Ken Seng, 2016. "Structure and estimation of Lévy subordinated hierarchical Archimedean copulas (LSHAC): Theory and empirical tests," Journal of Banking & Finance, Elsevier, vol. 69(C), pages 20-36.
    7. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2015. "Clustering of time series via non-parametric tail dependence estimation," Statistical Papers, Springer, vol. 56(3), pages 701-721, August.
    8. ManWo Ng & Hong Lo, 2013. "Regional Air Quality Conformity in Transportation Networks with Stochastic Dependencies: A Theoretical Copula-Based Model," Networks and Spatial Economics, Springer, vol. 13(4), pages 373-397, December.
    9. Cossette, Hélène & Gadoury, Simon-Pierre & Marceau, Étienne & Mtalai, Itre, 2017. "Hierarchical Archimedean copulas through multivariate compound distributions," Insurance: Mathematics and Economics, Elsevier, vol. 76(C), pages 1-13.
    10. Ressel Paul, 2019. "Copulas, stable tail dependence functions, and multivariate monotonicity," Dependence Modeling, De Gruyter, vol. 7(1), pages 247-258, January.
    11. Mai Jan-Frederik, 2019. "Simulation algorithms for hierarchical Archimedean copulas beyond the completely monotone case," Dependence Modeling, De Gruyter, vol. 7(1), pages 202-214, January.
    12. Segers, Johan & Uyttendaele, Nathan, 2013. "Nonparametric estimation of the tree structure of a nested Archimedean copula," LIDAM Discussion Papers ISBA 2013009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    13. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    14. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    15. Gonzalez-Fernandez, Yasser & Soto, Marta, 2014. "copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i09).

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