IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v33y2014i7p751-771.html

A Goodness-of-fit Test for Copulas

Author

Listed:
  • Wanling Huang
  • Artem Prokhorov

Abstract

We propose a new rank-based goodness-of-fit test for copulas. It uses the information matrix equality and so relates to the White (1982) specification test. The test avoids parametric specification of marginal distributions, it does not involve kernel weighting, bandwidth selection, or any other strategic choices, it is asymptotically pivotal with a standard distribution, and it is simple to compute compared to available alternatives. The finite-sample size of this type of tests is known to deviate from their nominal size based on asymptotic critical values, and bootstrapping critical values could be a preferred alternative. A power study shows that, in a bivariate setting, the test has reasonable properties compared to its competitors. We conclude with an application in which we apply the test to two stock indices.

Suggested Citation

  • Wanling Huang & Artem Prokhorov, 2014. "A Goodness-of-fit Test for Copulas," Econometric Reviews, Taylor & Francis Journals, vol. 33(7), pages 751-771, October.
    • Handle: RePEc:taf:emetrv:v:33:y:2014:i:7:p:751-771
    DOI: 10.1080/07474938.2012.690692
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07474938.2012.690692
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07474938.2012.690692?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 look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Ding, Wei & Song, Peter X.-K., 2016. "EM algorithm in Gaussian copula with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 1-11.
    2. Jean-David Fermanian, 2012. "An overview of the goodness-of-fit test problem for copulas," Papers 1211.4416, arXiv.org.
    3. Nguyen, Cuong & Bhatti, M. Ishaq & Komorníková, Magda & Komorník, Jozef, 2016. "Gold price and stock markets nexus under mixed-copulas," Economic Modelling, Elsevier, vol. 58(C), pages 283-292.
    4. Eling, Martin & Jung, Kwangmin, 2020. "Risk aggregation in non-life insurance: Standard models vs. internal models," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 183-198.
    5. Yali Dou & Haiyan Liu & Georgios Aivaliotis, 2019. "Dynamic Dependence Modeling in financial time series," Papers 1908.05130, arXiv.org.
    6. Qian M. Zhou, 2024. "Information matrix equivalence in the presence of censoring: a goodness-of-fit test for semiparametric copula models with multivariate survival data," Statistical Papers, Springer, vol. 65(7), pages 4677-4713, September.
    7. Zhang, Shulin & Okhrin, Ostap & Zhou, Qian M. & Song, Peter X.-K., 2016. "Goodness-of-fit test for specification of semiparametric copula dependence models," Journal of Econometrics, Elsevier, vol. 193(1), pages 215-233.
    8. Shulin Zhang & Qian M. Zhou & Huazhen Lin, 2021. "Goodness-of-fit test of copula functions for semi-parametric univariate time series models," Statistical Papers, Springer, vol. 62(4), pages 1697-1721, August.
    9. Huang, Wanling & Mollick, André Varella & Nguyen, Khoa Huu, 2016. "U.S. stock markets and the role of real interest rates," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 231-242.
    10. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
    11. repec:hum:wpaper:sfb649dp2013-041 is not listed on IDEAS
    12. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2016. "Generalized Information Matrix Tests for Detecting Model Misspecification," Econometrics, MDPI, vol. 4(4), pages 1-24, November.
    13. Okhrin Ostap, 2013. "Editorial to the special issue on Copulae of Statistics & Risk Modeling," Statistics & Risk Modeling, De Gruyter, vol. 30(4), pages 281-286, December.
    14. Shofiqul Islam & Sonia Anand & Jemila Hamid & Lehana Thabane & Joseph Beyene, 2020. "A copula-based method of classifying individuals into binary disease categories using dependent biomarkers," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 871-897, December.
    15. Pavel Krupskii & Harry Joe, 2015. "Tail-weighted measures of dependence," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 614-629, March.
    16. Gong Chen & Hartmut Fricke & Ostap Okhrin & Judith Rosenow, 2022. "Importance of Weather Conditions in a Flight Corridor," Stats, MDPI, vol. 5(1), pages 1-27, March.
    17. Wanling Huang & André Varella Mollick & Khoa Huu Nguyen, 2017. "Dynamic responses and tail-dependence among commodities, the US real interest rate and the dollar," Empirical Economics, Springer, vol. 53(3), pages 959-997, November.
    18. Huang, Wanling & Mollick, Andre Varella, 2020. "Tight oil, real WTI prices and U.S. stock returns," Energy Economics, Elsevier, vol. 85(C).
    19. Schepsmeier, Ulf, 2015. "Efficient information based goodness-of-fit tests for vine copula models with fixed margins: A comprehensive review," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 34-52.
    20. Nguyen, Cuong & Ishaq Bhatti, M. & Henry, Darren, 2017. "Are Vietnam and Chinese stock markets out of the US contagion effect in extreme events?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 480(C), pages 10-21.
    21. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

    Statistics

    Access and download statistics

    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:taf:emetrv:v:33:y:2014:i:7:p:751-771. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.