In this paper, we propose a new fuzzy clustering of time series with entropy regularization. Following a model-based approach, the dissimilarity measure is based on the bivariate lower tail dependence coefficients estimated for each pair of assets using a copula function. We apply the clustering procedure to the time series of price returns of the assets composing the Dow Jones Sustainability Europe Index and to the time series of 23 Morgan Stanley Capital International (MSCI) Developed Markets indices. We identify the classification structures according to the value selected for the exponent α which enters the Fuzzy Silhouette index formula.

Tail dependence-based fuzzy clustering of financial time series / D’Urso, Pierpaolo; De Luca, Giovanni; Vitale, Vincenzina; Zuccolotto, Paola. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - (2023). [10.1007/s10479-023-05744-1]

Tail dependence-based fuzzy clustering of financial time series

D’Urso, Pierpaolo;Vitale, Vincenzina;
2023

Abstract

In this paper, we propose a new fuzzy clustering of time series with entropy regularization. Following a model-based approach, the dissimilarity measure is based on the bivariate lower tail dependence coefficients estimated for each pair of assets using a copula function. We apply the clustering procedure to the time series of price returns of the assets composing the Dow Jones Sustainability Europe Index and to the time series of 23 Morgan Stanley Capital International (MSCI) Developed Markets indices. We identify the classification structures according to the value selected for the exponent α which enters the Fuzzy Silhouette index formula.
2023
Fuzzy clustering · Partitioning around medoids; Copula functions; Tail dependence; Time series; Financial returns; Dow Jones Sustainability Europe Index; Entropy regularization
01 Pubblicazione su rivista::01a Articolo in rivista
Tail dependence-based fuzzy clustering of financial time series / D’Urso, Pierpaolo; De Luca, Giovanni; Vitale, Vincenzina; Zuccolotto, Paola. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - (2023). [10.1007/s10479-023-05744-1]
File allegati a questo prodotto
File Dimensione Formato  
s10479-023-05744-1.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.1 MB
Formato Adobe PDF
2.1 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1708859
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 0
social impact