IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v9y2021i4p68-d530986.html
   My bibliography  Save this article

Matrix-Tilted Archimedean Copulas

Author

Listed:
  • Marius Hofert

    (Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada)

  • Johanna F. Ziegel

    (Institute of Mathematical Statistics and Actuarial Science, University of Bern, Alpeneggstrasse 22, 3012 Bern, Switzerland)

Abstract

The new class of matrix-tilted Archimedean copulas is introduced. It combines properties of Archimedean and elliptical copulas by introducing a tilting matrix in the stochastic representation of Archimedean copulas, similar to the Cholesky factor for elliptical copulas. Basic properties of this copula construction are discussed and a further extension outlined.

Suggested Citation

  • Marius Hofert & Johanna F. Ziegel, 2021. "Matrix-Tilted Archimedean Copulas," Risks, MDPI, vol. 9(4), pages 1-24, April.
  • Handle: RePEc:gam:jrisks:v:9:y:2021:i:4:p:68-:d:530986
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/9/4/68/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/9/4/68/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paul Embrechts & Marius Hofert, 2011. "Comments on: Inference in multivariate Archimedean copula models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 263-270, August.
    2. Quessy, Jean-François & Rivest, Louis-Paul & Toupin, Marie-Hélène, 2016. "On the family of multivariate chi-square copulas," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 40-60.
    3. J. Rosco & Harry Joe, 2013. "Measures of tail asymmetry for bivariate copulas," Statistical Papers, Springer, vol. 54(3), pages 709-726, August.
    4. Krupskii, Pavel & Joe, Harry, 2015. "Structured factor copula models: Theory, inference and computation," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 53-73.
    5. Krupskii, Pavel & Joe, Harry, 2013. "Factor copula models for multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 85-101.
    6. Cambanis, Stamatis & Huang, Steel & Simons, Gordon, 1981. "On the theory of elliptically contoured distributions," Journal of Multivariate Analysis, Elsevier, vol. 11(3), pages 368-385, September.
    7. Quessy, Jean-François & Durocher, Martin, 2019. "The class of copulas arising from squared distributions: Properties and inference," Econometrics and Statistics, Elsevier, vol. 12(C), pages 148-166.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mogens Steffensen, 2022. "Special Issue “Risks: Feature Papers 2021”," Risks, MDPI, vol. 10(3), pages 1-2, March.

    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. Jean-François Quessy, 2021. "On nonparametric tests of multivariate meta-ellipticity," Statistical Papers, Springer, vol. 62(5), pages 2283-2310, October.
    2. Tachibana, Minoru, 2022. "Safe haven assets for international stock markets: A regime-switching factor copula approach," Research in International Business and Finance, Elsevier, vol. 60(C).
    3. Benedikt Schamberger & Lutz F. Gruber & Claudia Czado, 2017. "Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting," Econometrics, MDPI, vol. 5(2), pages 1-23, May.
    4. Pavel Krupskii, 2017. "Copula-based measures of reflection and permutation asymmetry and statistical tests," Statistical Papers, Springer, vol. 58(4), pages 1165-1187, December.
    5. Jonas Moss & Steffen Grønneberg, 2023. "Partial Identification of Latent Correlations with Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 241-252, March.
    6. Quessy, Jean-François & Durocher, Martin, 2019. "The class of copulas arising from squared distributions: Properties and inference," Econometrics and Statistics, Elsevier, vol. 12(C), pages 148-166.
    7. Oh, Rosy & Jeong, Himchan & Ahn, Jae Youn & Valdez, Emiliano A., 2021. "A multi-year microlevel collective risk model," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 309-328.
    8. Zhang, Xi & Li, Jian, 2018. "Credit and market risks measurement in carbon financing for Chinese banks," Energy Economics, Elsevier, vol. 76(C), pages 549-557.
    9. Nguyen, Hoang & Ausín, M. Concepción & Galeano, Pedro, 2020. "Variational inference for high dimensional structured factor copulas," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    10. Billio Monica & Frattarolo Lorenzo & Guégan Dominique, 2021. "Multivariate radial symmetry of copula functions: finite sample comparison in the i.i.d case," Dependence Modeling, De Gruyter, vol. 9(1), pages 43-61, January.
    11. Hua, Lei & Joe, Harry, 2017. "Multivariate dependence modeling based on comonotonic factors," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 317-333.
    12. Nasri, Bouchra R., 2020. "On non-central squared copulas," Statistics & Probability Letters, Elsevier, vol. 161(C).
    13. Chen Tong & Peter Reinhard Hansen, 2023. "Characterizing Correlation Matrices that Admit a Clustered Factor Representation," Papers 2308.05895, arXiv.org.
    14. Ackerer Damien & Vatter Thibault, 2017. "Dependent defaults and losses with factor copula models," Dependence Modeling, De Gruyter, vol. 5(1), pages 375-399, December.
    15. Minoru Tachibana, 2020. "Flight-to-quality in the stock–bond return relation: a regime-switching copula approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(4), pages 429-470, December.
    16. Kreuzer, Alexander & Czado, Claudia, 2021. "Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 130-150.
    17. Sayed H. Kadhem & Aristidis K. Nikoloulopoulos, 2023. "Bi-factor and Second-Order Copula Models for Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 132-157, March.
    18. Perreault, Samuel & Duchesne, Thierry & Nešlehová, Johanna G., 2019. "Detection of block-exchangeable structure in large-scale correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 400-422.
    19. Savinov, Evgeniy & Shamraeva, Victoria, 2023. "On a Rosenblatt-type transformation of multivariate copulas," Econometrics and Statistics, Elsevier, vol. 25(C), pages 39-48.
    20. Verhoijsen Alex & Krupskiy Pavel, 2022. "Fast inference methods for high-dimensional factor copulas," Dependence Modeling, De Gruyter, vol. 10(1), pages 270-289, January.

    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:gam:jrisks:v:9:y:2021:i:4:p:68-:d:530986. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.