IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v8y2020i4p106-d427624.html
   My bibliography  Save this article

New Families of Bivariate Copulas via Unit Lomax Distortion

Author

Listed:
  • Fadal Abdullah-A Aldhufairi

    (Department of Statistics, Actuarial and Data Sciences, Central Michigan University, Mount Pleasant, MI 48859, USA)

  • Ranadeera G.M. Samanthi

    (Department of Statistics, Actuarial and Data Sciences, Central Michigan University, Mount Pleasant, MI 48859, USA)

  • Jungsywan H. Sepanski

    (Department of Statistics, Actuarial and Data Sciences, Central Michigan University, Mount Pleasant, MI 48859, USA)

Abstract

This article studies a new family of bivariate copulas constructed using the unit-Lomax distortion derived from a transformation of the non-negative Lomax random variable into a variable whose support is the unit interval. Existing copulas play the role of the base copulas that are distorted into new families of copulas with additional parameters, allowing more flexibility and better fit to data. We present general forms for the new bivariate copula function and its conditional and density distributions. The properties of the new family of the unit-Lomax induced copulas, including the tail behaviors, limiting cases in parameters, Kendall’s tau, and concordance order, are investigated for cases when the base copulas are Archimedean, such as the Clayton, Gumbel, and Frank copulas. An empirical application of the proposed copula model is presented. The unit-Lomax distorted copula models outperform the base copulas.

Suggested Citation

  • Fadal Abdullah-A Aldhufairi & Ranadeera G.M. Samanthi & Jungsywan H. Sepanski, 2020. "New Families of Bivariate Copulas via Unit Lomax Distortion," Risks, MDPI, vol. 8(4), pages 1-19, October.
  • Handle: RePEc:gam:jrisks:v:8:y:2020:i:4:p:106-:d:427624
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/8/4/106/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/8/4/106/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin, Feng & Peng, Liang & Xie, Jiehua & Yang, Jingping, 2018. "Stochastic distortion and its transformed copula," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 148-166.
    2. Nelsen, Roger B., 1997. "Dependence and Order in Families of Archimedean Copulas," Journal of Multivariate Analysis, Elsevier, vol. 60(1), pages 111-122, January.
    3. Nguyen, Thu Thuy & Tran, T.N. & Nguyen, V.C., 2020. "Oil price shocks against stock return of oil- and gas-related firms in the economic depression: A new evidence from a copula approach," OSF Preprints 4cm7b, Center for Open Science.
    4. Di Bernardino Elena & Rullière Didier, 2013. "On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators," Dependence Modeling, De Gruyter, vol. 1, pages 1-36, October.
    5. 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.
    6. repec:hal:wpaper:hal-00834000 is not listed on IDEAS
    7. Thu Thuy Nguyen & Van Chien Nguyen & Trong Nguyen Tran & David McMillan, 2020. "Oil price shocks against stock return of oil- and gas-related firms in the economic depression: A new evidence from a copula approach," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1799908-179, January.
    8. Yang, Xipei & Frees, Edward W. & Zhang, Zhengjun, 2011. "A generalized beta copula with applications in modeling multivariate long-tailed data," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 265-284, September.
    9. Edward Frees & Emiliano Valdez, 1998. "Understanding Relationships Using Copulas," North American Actuarial Journal, Taylor & Francis Journals, vol. 2(1), pages 1-25.
    10. Sepanski, Jungsywan H., 2020. "A note on distortion effects on the strength of bivariate copula tail dependence," Statistics & Probability Letters, Elsevier, vol. 166(C).
    11. Joe, Harry & Hu, Taizhong, 1996. "Multivariate Distributions from Mixtures of Max-Infinitely Divisible Distributions," Journal of Multivariate Analysis, Elsevier, vol. 57(2), pages 240-265, May.
    12. Ranadeera G. M. Samanthi & Jungsywan Sepanski, 2019. "A bivariate extension of the beta generated distribution derived from copulas," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(5), pages 1043-1059, March.
    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. 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.
    2. Fadal A.A. Aldhufairi & Jungsywan H. Sepanski, 2020. "New families of bivariate copulas via unit weibull distortion," Journal of Statistical Distributions and Applications, Springer, vol. 7(1), pages 1-20, December.
    3. Roman Matkovskyy, 2019. "Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 667-698, September.
    4. Sadeghi, Abdorasoul & Roudari, Soheil, 2022. "Heterogeneous effects of oil structure and oil shocks on stock prices in different regimes: Evidence from oil-exporting and oil-importing countries," Resources Policy, Elsevier, vol. 76(C).
    5. Hobæk Haff, Ingrid, 2012. "Comparison of estimators for pair-copula constructions," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 91-105.
    6. Włodzimierz Wysocki, 2015. "Kendall's tau and Spearman's rho for n -dimensional Archimedean copulas and their asymptotic properties," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(4), pages 442-459, December.
    7. Hua, Lei, 2015. "Tail negative dependence and its applications for aggregate loss modeling," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 135-145.
    8. Bouye, Eric & Durlleman, Valdo & Nikeghbali, Ashkan & Riboulet, Gaël & Roncalli, Thierry, 2000. "Copulas for finance," MPRA Paper 37359, University Library of Munich, Germany.
    9. 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.
    10. Abdullah M. H. Alharbi, 2023. "Oil Shocks, Monetary Policy, and Stock Returns: A Case of Oil-based Economy," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 56-63, November.
    11. Emura, Takeshi & Lin, Chien-Wei & Wang, Weijing, 2010. "A goodness-of-fit test for Archimedean copula models in the presence of right censoring," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3033-3043, December.
    12. Durante Fabrizio & Puccetti Giovanni & Scherer Matthias & Vanduffel Steven, 2016. "Stat Trek," Dependence Modeling, De Gruyter, vol. 4(1), pages 109-122, May.
    13. Liang Zhu & Christine Lim & Wenjun Xie & Yuan Wu, 2017. "Analysis of tourism demand serial dependence structure for forecasting," Tourism Economics, , vol. 23(7), pages 1419-1436, November.
    14. Szego, Giorgio, 2005. "Measures of risk," European Journal of Operational Research, Elsevier, vol. 163(1), pages 5-19, May.
    15. Zhu, Mingxue & Zhang, Hua & Xing, Wanli & Zhou, Xuanru & Wang, Lu & Sun, Haoyu, 2023. "Research on price transmission in Chinese mining stock market: Based on industry," Resources Policy, Elsevier, vol. 83(C).
    16. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    17. 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.
    18. Bücher, Axel & Dette, Holger, 2010. "Some comments on goodness-of-fit tests for the parametric form of the copula based on L2-distances," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 749-763, March.
    19. Genest, Christian & Masiello, Esterina & Tribouley, Karine, 2009. "Estimating copula densities through wavelets," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 170-181, April.

    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:8:y:2020:i:4:p:106-:d:427624. 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.