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Bayesian estimation of generalized partition of unity copulas

Author

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  • Masuhr Andreas

    (Institute of Econometrics, Department of Economics, University of Münster, Am Stadtgraben 9, 48143Münster, Germany)

  • Trede Mark

    (Institute of Econometrics, Department of Economics, University of Münster, Am Stadtgraben 9, 48143Münster, Germany)

Abstract

This paper proposes a Bayesian estimation algorithm to estimate Generalized Partition of Unity Copulas (GPUC), a class of nonparametric copulas recently introduced by [18]. The first approach is a random walk Metropolis-Hastings (RW-MH) algorithm, the second one is a random blocking random walk Metropolis-Hastings algorithm (RBRW-MH). Both approaches are Markov chain Monte Carlo methods and can cope with ˛at priors. We carry out simulation studies to determine and compare the efficiency of the algorithms. We present an empirical illustration where GPUCs are used to nonparametrically describe the dependence of exchange rate changes of the crypto-currencies Bitcoin and Ethereum.

Suggested Citation

  • Masuhr Andreas & Trede Mark, 2020. "Bayesian estimation of generalized partition of unity copulas," Dependence Modeling, De Gruyter, vol. 8(1), pages 119-131, January.
    • Handle: RePEc:vrs:demode:v:8:y:2020:i:1:p:119-131:n:7
    DOI: 10.1515/demo-2020-0007
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    References listed on IDEAS

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