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Clustering of financial time series in risky scenarios

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Abstract

A methodology is presented for clustering financial time series according to the association in the tail of their distribution. The procedure is based on the calculation of suitable pairwise conditional Spearman’s correlation coefficients extracted from the series. The performance of the method has been tested via a simulation study. As an illustration, an analysis of the components of the Italian FTSE–MIB is presented. The results could be applied to construct financial portfolios that can manage to reduce the risk in case of simultaneous large losses in several markets.

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Acknowledgments

We would like to thank the Editor, the Associate Editor and two anonymous Reviewers for their comments that have improved significantly a previous version of the manuscript. The second author would like to thank Claudia Czado and Eike Brechmann (TU Munich, Germany), and Massimiliano Caporin (University of Padua, Italy) for useful comments and discussions. The first author acknowledges the support of School of Economics and Management, Free University of Bozen-Bolzano, via the project “MODEX”.

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Authors and Affiliations

  1. School of Economics and Management, Free University of Bozen-Bolzano, Piazza Università 1, 39100 , Bolzano, Italy

    Fabrizio Durante

  2. Department of Statistical Sciences, University of Padua, Via Cesare Battisti 241, 35121 , Padova, Italy

    Roberta Pappadà

  3. Department of Economics, Business, Mathematics and Statistics “Bruno De Finetti”, University of Trieste, Via Valerio 4/1, 34127 , Trieste, Italy

    Nicola Torelli

Authors
  1. Fabrizio Durante
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  2. Roberta Pappadà
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  3. Nicola Torelli
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Correspondence to Roberta Pappadà.

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Durante, F., Pappadà, R. & Torelli, N. Clustering of financial time series in risky scenarios. Adv Data Anal Classif 8, 359–376 (2014). https://doi.org/10.1007/s11634-013-0160-4

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  • DOI: https://doi.org/10.1007/s11634-013-0160-4

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