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Copulas, Tail Dependence and Applications to the Analysis of Financial Time Series

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Aggregation Functions in Theory and in Practise

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 228))

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Abstract

Tail dependence is an important property of a joint distribution function that has a huge impact on the determination of risky quantities associated to a stochastic model (Value-at-Risk, for instance). Here we aim at presenting some investigations about tail dependence including the following aspects: the determination of suitable stochastic models to be used in extreme scenarios; the notion of threshold copula, that helps in describing the tail of a joint distribution. Possible applications of the introduced concepts to the analysis of financial time series are presented with particular emphasis on cluster methods and determination of possible contagion effects among markets.

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Author information

Authors and Affiliations

  1. School of Economics and Management, Free University of Bozen-Bolzano, Bolzano, Italy

    Fabrizio Durante

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  1. Fabrizio Durante
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Correspondence to Fabrizio Durante .

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Editors and Affiliations

  1. Department of Automatica y Computacion, Universidad Publica de Navarra, Pamplona, Spain

    Humberto Bustince

  2. Department of Automatica y Computacion, Universidad Publica de Navarra, Pamplona, Spain

    Javier Fernandez

  3. Department of Mathematics and, Slovak University of Technology, Bratislava, Slovakia

    Radko Mesiar

  4. Department of Computer Science, Universidad de Alcalá de Henares, Madrid, Spain

    Tomasa Calvo

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Durante, F. (2013). Copulas, Tail Dependence and Applications to the Analysis of Financial Time Series. In: Bustince, H., Fernandez, J., Mesiar, R., Calvo, T. (eds) Aggregation Functions in Theory and in Practise. Advances in Intelligent Systems and Computing, vol 228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39165-1_3

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  • DOI: https://doi.org/10.1007/978-3-642-39165-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39164-4

  • Online ISBN: 978-3-642-39165-1

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