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Clustering of financial time series in risky scenarios

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  • Fabrizio Durante
  • Roberta PappadÃ
  • Nicola Torelli

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. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2014. "Clustering of financial time series in risky scenarios," 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. 8(4), pages 359-376, December.
    • Handle: RePEc:spr:advdac:v:8:y:2014:i:4:p:359-376
    DOI: 10.1007/s11634-013-0160-4
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    5. 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.
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    7. 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.
    8. Yuzhen Zhao & Xiyu Liu & Xiufeng Li, 2018. "An improved DBSCAN algorithm based on cell-like P systems with promoters and inhibitors," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-17, December.
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