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Flexible and Dynamic Modeling of Dependencies via Copulas

  • Irène GijbelsEmail author
  • Klaus Herrmann
  • Dominik Sznajder
Conference paper
  • 1.7k Downloads
Part of the Lecture Notes in Statistics book series (LNS, volume 217)

Abstract

In this chapter we first review recent developments in the use of copulas for studying dependence structures between variables. We discuss and illustrate the concepts of unconditional and conditional copulas and association measures, in a bivariate setting. Statistical inference for conditional and unconditional copulas is discussed, in various modeling settings. Modeling the dynamics in a dependence structure between time series is of particular interest. For this we present a semiparametric approach using local polynomial approximation for the dynamic time parameter function. Throughout the chapter we provide some illustrative examples. The use of the proposed dynamical modeling approach is demonstrated in the analysis and forecast of wind speed data.

Keywords

Dependence Structure Copula Function Association Measure Rolling Window Joint Distribution Function 
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.

Notes

Acknowledgements

The authors thank the organizers of the “Second workshop on Industry Practices for Forecasting” (wipfor 2013) for a very simulating meeting. This research is supported by IAP Research Network P7/06 of the Belgian State (Belgian Science Policy), and the project GOA/12/014 of the KU Leuven Research Fund. The third author is Postdoctoral Fellow of the Research Foundation – Flanders, and acknowledges support from the foundation.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Irène Gijbels
    • 1
    Email author
  • Klaus Herrmann
    • 1
  • Dominik Sznajder
    • 1
  1. 1.Department of Mathematics and Leuven Statistics Research Centre (LStat)KU LeuvenLeuven (Heverlee)Belgium

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