Clustering of time series via non-parametric tail dependence estimation

Fabrizio Durante (), Roberta Pappadà () and Nicola Torelli ()

Statistical Papers, 2015, vol. 56, issue 3, 721 pages

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

Keywords: Cluster analysis; Copula; Extreme-value theory; Risk management; Tail dependence; 62H30; 62H20; 62M10 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://hdl.handle.net/10.1007/s00362-014-0605-7 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:56:y:2015:i:3:p:701-721

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-014-0605-7

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().