Pair-copula constructions of multiple dependence
AbstractBuilding 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 InfoArticle provided by Elsevier in its journal Insurance: Mathematics and Economics.
Volume (Year): 44 (2009)
Issue (Month): 2 (April)
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Web page: http://www.elsevier.com/locate/inca/505554
Pair-copulae Vines Conditional distribution Decomposition Multivariate distribution;
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- 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.
- 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.
- Niall Whelan, 2004. "Sampling from Archimedean copulas," Quantitative Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 4(3), pages 339-352.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series, Economics and Econometrics Research Institute (EERI), Brussels EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- 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.
- 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.
- 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.
- 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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