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Tail Dependence in Multivariate Data — Review of Some Problems

  • Krzysztof Jajuga
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Tail dependence is understood as the dependence between the variables assuming that these variables take the values from the tails of univariate distributions. In the paper two approaches of tail dependence determination are discussed: conditional correlation coefficient and tail dependence coefficients. It can be argued that both approaches are the generalizations of the well-known univariate approach, based on conditional excess distribution. In the paper the proposal is also given to extend tail dependence coefficients to the general multivariate case and to represent these coefficients through copula function.

Keywords

Generalize Pareto Distribution Tail Dependence Bivariate Normal Distribution Copula Function Conditional Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Krzysztof Jajuga
    • 1
  1. 1.Department of Financial Investments and InsuranceWroclaw University of EconomicsWroclawPoland

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