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Clustering of time series via non-parametric tail dependence estimation

Author

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  • Fabrizio Durante
  • Roberta Pappadà
  • Nicola Torelli

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. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2015. "Clustering of time series via non-parametric tail dependence estimation," Statistical Papers, Springer, vol. 56(3), pages 701-721, August.
    • Handle: RePEc:spr:stpapr:v:56:y:2015:i:3:p:701-721
    DOI: 10.1007/s00362-014-0605-7
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    Cited by:

    1. Chen Yang & Wenjun Jiang & Jiang Wu & Xin Liu & Zhichuan Li, 2018. "Clustering of financial instruments using jump tail dependence coefficient," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 491-513, August.
    2. Xin Liu & Jiang Wu & Chen Yang & Wenjun Jiang, 2018. "A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection," Risks, MDPI, vol. 6(4), pages 1-26, October.
    3. Jafar Rahmanishamsi & Ali Dolati & Masoudreza R. Aghabozorgi, 2018. "A Copula Based ICA Algorithm and Its Application to Time Series Clustering," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 230-249, July.
    4. Fuchs, Sebastian & Di Lascio, F. Marta L. & Durante, Fabrizio, 2021. "Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    5. Alessia Benevento & Fabrizio Durante, 2023. "Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information," Mathematics, MDPI, vol. 12(1), pages 1-15, December.
    6. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    7. Francesca Mariani & Gloria Polinesi & Maria Cristina Recchioni, 2022. "A tail-revisited Markowitz mean-variance approach and a portfolio network centrality," Computational Management Science, Springer, vol. 19(3), pages 425-455, July.
    8. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    9. De Luca Giovanni & Zuccolotto Paola, 2017. "A double clustering algorithm for financial time series based on extreme events," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 1-12, June.
    10. Giovanni De Luca & Paola Zuccolotto, 2021. "Regime dependent interconnectedness among fuzzy clusters of financial time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 315-336, June.
    11. Giovanni De Luca & Paola Zuccolotto, 2017. "Dynamic tail dependence clustering of financial time series," Statistical Papers, Springer, vol. 58(3), pages 641-657, September.
    12. Ji-Eun Choi & Dong Wan Shin, 2022. "Quantile correlation coefficient: a new tail dependence measure," Statistical Papers, Springer, vol. 63(4), pages 1075-1104, August.

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