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Copulas

  • David Ruppert
  • David S. Matteson
Part of the Springer Texts in Statistics book series (STS)

Abstract

Copulas are a popular framework for both defining multivariate distributions and modeling multivariate data. A copula characterizes the dependence—and only the dependence—between the components of a multivariate distribution; they can be combined with any set of univariate marginal distributions to form a full joint distribution. Consequently, the use of copulas allows us to take advantage of the wide variety of univariate models that are available.

References

  1. Cherubini, U., Luciano, E., and Vecchiato, W. (2004) Copula Methods in Finance, John Wiley, New York.CrossRefzbMATHGoogle Scholar
  2. Duffie, D. and Singleton, K. J. (2003) Credit Risk, Princeton University Press, Princeton and Oxford.Google Scholar
  3. Joe, H. (1997) Multivariate Models and Dependence Concepts, Chapman & Hall, London.CrossRefzbMATHGoogle Scholar
  4. Li, D (2000) On default correlation: A copula function approach, Journal of Fixed Income, 9, 43–54.CrossRefGoogle Scholar
  5. Mari, D. D. and Kotz, S. (2001) Correlation and Dependence, World Scientific, London.CrossRefzbMATHGoogle Scholar
  6. McNeil, A., Frey, R., and Embrechts, P. (2005) Quantitative Risk Management, Princeton University Press, Princeton and Oxford.zbMATHGoogle Scholar
  7. Nelsen, R. B. (2007) An Introduction to Copulas, 2nd ed., Springer, New York.Google Scholar
  8. Salmon, F. (2009) Recipe for Disaster: The Formula That Killed Wall Street, Wired http://www.wired.com/techbiz/it/magazine/17-03/wp_quant?currentPage=all

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • David Ruppert
    • 1
  • David S. Matteson
    • 2
  1. 1.Department of Statistical Science and School of ORIECornell UniversityIthacaUSA
  2. 2.Department of Statistical Science Department of Social StatisticsCornell UniversityIthacaUSA

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