IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v155y2021ics0167947320302000.html
   My bibliography  Save this article

Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation

Author

Listed:
  • Górecki, Jan
  • Hofert, Marius
  • Okhrin, Ostap

Abstract

Outer power (OP) transformations of Archimedean generators are suggested to increase the modeling flexibility and statistical fitting capabilities of classical Archimedean copulas restricted to a single parameter. For OP-transformed Archimedean copulas, a formula for computing tail dependence coefficients is obtained, as well as two feasible OP Archimedean copula estimators are proposed and their properties studied by simulation. For hierarchical extensions of OP-transformed Archimedean copulas under the sufficient nesting condition, a new construction principle, efficient sampling and parameter estimation for models based on a single one-parameter Archimedean family are addressed. Special attention is paid to the case where the sufficient nesting condition simplifies to two types of restrictions on the corresponding parameters. By simulation, the convergence rate and standard errors of the proposed estimator are studied. Excellent tail fitting capabilities of OP-transformed hierarchical Archimedean copula models are demonstrated in a risk management application. The results show that the OP transformation is able to improve the statistical fit of exchangeable Archimedean copulas, particularly of those that cannot capture upper tail dependence or strong concordance, as well as the statistical fit of hierarchical Archimedean copulas, especially in terms of tail dependence and higher dimensions. Given how comparably simple it is to include OP transformations into existing exchangeable and hierarchical Archimedean copula models, OP transformations provide an attractive trade-off between computational effort and statistical improvement.

Suggested Citation

  • Górecki, Jan & Hofert, Marius & Okhrin, Ostap, 2021. "Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
  • Handle: RePEc:eee:csdana:v:155:y:2021:i:c:s0167947320302000
    DOI: 10.1016/j.csda.2020.107109
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947320302000
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2020.107109?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rafael Schmidt & Ulrich Stadtmüller, 2006. "Non‐parametric Estimation of Tail Dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 307-335, June.
    2. Marius Hofert & Matthias Scherer, 2011. "CDO pricing with nested Archimedean copulas," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 775-787.
    3. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    4. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    5. Rezapour, Mohsen, 2015. "On the construction of nested Archimedean copulas for d-monotone generators," Statistics & Probability Letters, Elsevier, vol. 101(C), pages 21-32.
    6. Hofert, Marius, 2011. "Efficiently sampling nested Archimedean copulas," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 57-70, January.
    7. 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.
    8. Okhrin, Ostap & Okhrin, Yarema & Schmid, Wolfgang, 2013. "On the structure and estimation of hierarchical Archimedean copulas," Journal of Econometrics, Elsevier, vol. 173(2), pages 189-204.
    9. Capéraà, Philippe & Fougères, Anne-Laure & Genest, Christian, 2000. "Bivariate Distributions with Given Extreme Value Attractor," Journal of Multivariate Analysis, Elsevier, vol. 72(1), pages 30-49, January.
    10. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    11. Vladimir Batagelj, 1981. "Note on ultrametric hierarchical clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 46(3), pages 351-352, September.
    12. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    13. 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.
    14. Charpentier, A. & Fougères, A.-L. & Genest, C. & Nešlehová, J.G., 2014. "Multivariate Archimax copulas," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 118-136.
    15. Ressel, Paul, 2013. "Homogeneous distributions—And a spectral representation of classical mean values and stable tail dependence functions," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 246-256.
    16. Cossette, Hélène & Gadoury, Simon-Pierre & Marceau, Etienne & Robert, Christian Y., 2019. "Composite likelihood estimation method for hierarchical Archimedean copulas defined with multivariate compound distributions," Journal of Multivariate Analysis, Elsevier, vol. 172(C), pages 59-83.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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.
    3. Hofert, Marius & Huser, Raphaël & Prasad, Avinash, 2018. "Hierarchical Archimax copulas," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 195-211.
    4. Hofert, Marius, 2021. "Right-truncated Archimedean and related copulas," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 79-91.
    5. Górecki J. & Hofert M. & Holeňa M., 2017. "Kendall’s tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas," Dependence Modeling, De Gruyter, vol. 5(1), pages 75-87, January.
    6. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    7. 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.
    8. Nathan Uyttendaele, 2018. "On the estimation of nested Archimedean copulas: a theoretical and an experimental comparison," Computational Statistics, Springer, vol. 33(2), pages 1047-1070, June.
    9. Bedoui, Rihab & Braiek, Sana & Guesmi, Khaled & Chevallier, Julien, 2019. "On the conditional dependence structure between oil, gold and USD exchange rates: Nested copula based GJR-GARCH model," Energy Economics, Elsevier, vol. 80(C), pages 876-889.
    10. Durante Fabrizio & Sánchez Juan Fernández & Sempi Carlo, 2018. "A note on bivariate Archimax copulas," Dependence Modeling, De Gruyter, vol. 6(1), pages 178-182, October.
    11. Hofert, Marius & Mächler, Martin & McNeil, Alexander J., 2012. "Likelihood inference for Archimedean copulas in high dimensions under known margins," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 133-150.
    12. Víctor Adame-García & Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2017. "“Resolution of optimization problems and construction of efficient portfolios: An application to the Euro Stoxx 50 index"," IREA Working Papers 201702, University of Barcelona, Research Institute of Applied Economics, revised Feb 2017.
    13. Federico Pasquale Cortese, 2019. "Tail Dependence in Financial Markets: A Dynamic Copula Approach," Risks, MDPI, vol. 7(4), pages 1-14, November.
    14. Diers, Dorothea & Eling, Martin & Marek, Sebastian D., 2012. "Dependence modeling in non-life insurance using the Bernstein copula," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 430-436.
    15. Grothe, Oliver & Hofert, Marius, 2015. "Construction and sampling of Archimedean and nested Archimedean Lévy copulas," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 182-198.
    16. Charpentier, A. & Fougères, A.-L. & Genest, C. & Nešlehová, J.G., 2014. "Multivariate Archimax copulas," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 118-136.
    17. Jäschke, Stefan, 2014. "Estimation of risk measures in energy portfolios using modern copula techniques," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 359-376.
    18. Yuri Salazar Flores & Adán Díaz-Hernández, 2022. "The General Tail Dependence Function in the Marshall-Olkin and Other Parametric Copula Models with an Application to Financial Time Series," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 146-187, May.
    19. Uyttendaele, Nathan, 2016. "On the estimation of nested Archimedean copulas: A theoretical and an experimental comparison," LIDAM Discussion Papers ISBA 2016005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    20. Vettori, Sabrina & Huser, Raphael & Segers, Johan & Genton, Marc, 2017. "Bayesian Clustering and Dimension Reduction in Multivariate Extremes," LIDAM Discussion Papers ISBA 2017017, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:155:y:2021:i:c:s0167947320302000. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.