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Pair-copula constructions of multiple dependence

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  • Aas, Kjersti
  • Czado, Claudia
  • Frigessi, Arnoldo
  • Bakken, Henrik
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    Abstract

    Building on the work of Bedford, Cooke and Joe, we show how multivariate data, which exhibit complex patterns of dependence in the tails, can be modelled using a cascade of pair-copulae, acting on two variables at a time. We use the pair-copula decomposition of a general multivariate distribution and propose a method for performing inference. The model construction is hierarchical in nature, the various levels corresponding to the incorporation of more variables in the conditioning sets, using pair-copulae as simple building blocks. Pair-copula decomposed models also represent a very flexible way to construct higher-dimensional copulae. We apply the methodology to a financial data set. Our approach represents the first step towards the development of an unsupervised algorithm that explores the space of possible pair-copula models, that also can be applied to huge data sets automatically.

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    Bibliographic Info

    Article provided by Elsevier in its journal Insurance: Mathematics and Economics.

    Volume (Year): 44 (2009)
    Issue (Month): 2 (April)
    Pages: 182-198

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    Handle: RePEc:eee:insuma:v:44:y:2009:i:2:p:182-198

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    Web page: http://www.elsevier.com/locate/inca/505554

    Related research

    Keywords: Pair-copulae Vines Conditional distribution Decomposition Multivariate distribution;

    References

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    1. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 51(6), pages 2836-2850, March.
    2. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, Econometric Society, vol. 50(4), pages 987-1007, July.
    3. Niall Whelan, 2004. "Sampling from Archimedean copulas," Quantitative Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 4(3), pages 339-352.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 31(3), pages 307-327, April.
    5. Kurowicka, D. & Cooke, R.M., 2007. "Sampling algorithms for generating joint uniform distributions using the vine-copula method," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 51(6), pages 2889-2906, March.
    6. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification," Journal of Econometrics, Elsevier, Elsevier, vol. 135(1-2), pages 125-154.
    7. W. Breymann & A. Dias & P. Embrechts, 2003. "Dependence structures for multivariate high-frequency data in finance," Quantitative Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 3(1), pages 1-14.
    8. Fang, Hong-Bin & Fang, Kai-Tai & Kotz, Samuel, 2002. "The Meta-elliptical Distributions with Given Marginals," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 82(1), pages 1-16, July.
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