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Bayesian Estimation of Generalized Partition of Unity Copulas

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  • Andreas Masuhr

Abstract

This paper proposes two (Metropolis-Hastings) algorithms to estimate Generalized Partition of Unity Copulas (GPUC), a new class of nonparametric copulas that includes the versatile Bernstein Copula as a special case. Additionally a prior distribution for the parameter Matrix of GPUCs is established via Importance Sampling and an algorithm to sample such matrices is introduced. Finally, simulation studies show the effectiveness of the presented algorithms.

Suggested Citation

  • Andreas Masuhr, 2018. "Bayesian Estimation of Generalized Partition of Unity Copulas," CQE Working Papers 7318, Center for Quantitative Economics (CQE), University of Muenster.
    • Handle: RePEc:cqe:wpaper:7318
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    References listed on IDEAS

    as
    1. Cornelia Savu & Mark Trede, 2010. "Hierarchies of Archimedean copulas," Quantitative Finance, Taylor & Francis Journals, vol. 10(3), pages 295-304.
    2. Dietmar Pfeifer & Andreas Mandle & Olena Ragulina, 2017. "Data driven partition-of-unity copulas with applications to risk management," Papers 1703.05047, arXiv.org, revised Nov 2020.
    3. Baker, Rose, 2008. "An order-statistics-based method for constructing multivariate distributions with fixed marginals," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2312-2327, November.
    4. Dou, Xiaoling & Kuriki, Satoshi & Lin, Gwo Dong & Richards, Donald, 2016. "EM algorithms for estimating the Bernstein copula," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 228-245.
    5. Chib, Siddhartha & Ramamurthy, Srikanth, 2010. "Tailored randomized block MCMC methods with application to DSGE models," Journal of Econometrics, Elsevier, vol. 155(1), pages 19-38, March.
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