Clustering of time series via non-parametric tail dependence estimation

Abstract

We present a procedure for clustering time series according to their tail dependence behaviour as measured via a suitable copula-based tail coefficient, estimated in a non-parametric way. Simulation results about the proposed methodology together with an application to financial data are presented showing the usefulness of the proposed approach.

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Acknowledgments

The first and second author acknowledge the support of Free University of Bozen-Bolzano, School of Economics and Management, via the project MODEX. The second author would like to thank Claudia Czado and Eike Brechmann (TU Munich, Germany) for useful comments and discussions.

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Correspondence to Fabrizio Durante.

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Durante, F., Pappadà, R. & Torelli, N. Clustering of time series via non-parametric tail dependence estimation. Stat Papers 56, 701–721 (2015). https://doi.org/10.1007/s00362-014-0605-7

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Keywords

  • Cluster analysis
  • Copula
  • Extreme-value theory
  • Risk management
  • Tail dependence

Mathematics Subject Classification

  • 62H30
  • 62H20
  • 62M10