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Fitting High-Dimensional Copulae to Data

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Handbook of Computational Finance

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

This paper make an overview of the copula theory from a practical side. We consider different methods of copula estimation and different Goodness-of-Fit tests for model selection. In the GoF section we apply Kolmogorov-Smirnov and Cramer-von-Mises type tests and calculate power of these tests under different assumptions. Novating in this paper is that all the procedures are done in dimensions higher than two, and in comparison to other papers we consider not only simple Archimedean and Gaussian copulae but also Hierarchical Archimedean Copulae. Afterwards we provide an empirical part to support the theory.

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Acknowledgements

The financial support from the Deutsche Forschungsgemeinschaft through SFB 649 “Ökonomisches Risiko”, Humboldt-Universität zu Berlin is gratefully acknowledged.

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Authors and Affiliations

  1. Ladislaus von Bortkiewitcz Chair of Statistics, C.A.S.E. – Center of Applied Statistics and Economics, Humboldt-Universität zu Berlin, 10178, Berlin, Germany

    Ostap Okhrin

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  1. Ostap Okhrin
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Correspondence to Ostap Okhrin .

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Editors and Affiliations

  1. , Risk Management Institute, National University of Singapore, 21 Heng Mui Keng Terrace, Level 4, Singapore, 119613, Singapore

    Jin-Chuan Duan

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. , Department of Computational and Data Sci, George Mason University, University Drive 4400, Fairfax, 22030, Virginia, USA

    James E. Gentle

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Okhrin, O. (2012). Fitting High-Dimensional Copulae to Data. In: Duan, JC., Härdle, W., Gentle, J. (eds) Handbook of Computational Finance. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17254-0_17

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