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Regime dependent interconnectedness among fuzzy clusters of financial time series

Author

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  • Giovanni De Luca

    (University of Naples Parthenope)

  • Paola Zuccolotto

    (University of Brescia)

Abstract

We analyze the dynamic structure of lower tail dependence coefficients within groups of assets defined such that assets belonging to the same group are characterized by pairwise high associations between extremely low values. The groups are identified by means of a fuzzy cluster analysis algorithm. The tail dependence coefficients are estimated using the Joe–Clayton copula function, and the 75th percentile within clusters is used as a measure of each cluster’s overall tail dependence. The interdependence structure of the clusters’ tail dependence dynamics is then analyzed in order to determine whether the pattern of a cluster can be predicted based on the past values of the others, using a Granger causality approach. The hypothesis of a possible regime switching dynamics in tail dependence is also investigated by means of a Threshold Vector AutoRegressive model and the results are compared to those obtained with a linear autoregression. The whole procedure is described with reference to a case study dealing with the assets composing Eurostoxx 50, but it can be viewed as the proposal of a general method, that can be relevantly applied to whatever set of asset returns time series.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:advdac:v:15:y:2021:i:2:d:10.1007_s11634-020-00405-8
    DOI: 10.1007/s11634-020-00405-8
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    References listed on IDEAS

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    1. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," 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. 5(4), pages 323-340, December.
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    1. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2021. "A spatially-weighted AMH copula-based dissimilarity measure to cluster variables in panel data," BEMPS - Bozen Economics & Management Paper Series BEMPS89, Faculty of Economics and Management at the Free University of Bozen.

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