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Estimating standard errors in regular vine copula models

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  • Jakob Stöber
  • Ulf Schepsmeier

Abstract

We describe a new algorithm for the computation of the score function and observed information in regular vine (R-vine) copula models. R-vine copulas are constructed hierarchically from bivariate copulas as building blocks only, and the algorithm exploits this hierarchical nature for subsequent computation of log-likelihood derivatives. This allows to routinely estimate standard errors of parameter estimates, and overcomes reliability and accuracy issues associated with numerical differentiation in multidimensional models. Results obtained using the proposed methods are discussed in the context of the asymptotic efficiency of different estimation methods and of an application to exchange rate data. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Jakob Stöber & Ulf Schepsmeier, 2013. "Estimating standard errors in regular vine copula models," Computational Statistics, Springer, vol. 28(6), pages 2679-2707, December.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:6:p:2679-2707
    DOI: 10.1007/s00180-013-0423-8
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    References listed on IDEAS

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    1. Anastasios Panagiotelis & Claudia Czado & Harry Joe, 2012. "Pair Copula Constructions for Multivariate Discrete Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1063-1072, September.
    2. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    3. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    4. Smith, Michael & Min, Aleksey & Almeida, Carlos & Czado, Claudia, 2010. "Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1467-1479.
    5. Stöber, Jakob & Joe, Harry & Czado, Claudia, 2013. "Simplified pair copula constructions—Limitations and extensions," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 101-118.
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    Cited by:

    1. Li, Haihe & Wang, Pan & Huang, Xiaoyu & Zhang, Zheng & Zhou, Changcong & Yue, Zhufeng, 2021. "Vine copula-based parametric sensitivity analysis of failure probability-based importance measure in the presence of multidimensional dependencies," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Mohd Sabri Ismail & Nurulkamal Masseran & Mohd Almie Alias & Sakhinah Abu Bakar, 2024. "Modeling Asymmetric Dependence Structure of Air Pollution Characteristics: A Vine Copula Approach," Mathematics, MDPI, vol. 12(4), pages 1-23, February.
    3. Stöber, Jakob & Hong, Hyokyoung Grace & Czado, Claudia & Ghosh, Pulak, 2015. "Comorbidity of chronic diseases in the elderly: Patterns identified by a copula design for mixed responses," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 28-39.
    4. Brechmann, Eike C. & Hendrich, Katharina & Czado, Claudia, 2013. "Conditional copula simulation for systemic risk stress testing," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 722-732.
    5. 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.

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