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2014 | OriginalPaper | Chapter

Dynamic Clustering of Financial Assets

Authors : Giovanni De Luca, Paola Zuccolotto

Published in: Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Publisher: Springer International Publishing

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Abstract

In this work we propose a procedure for time-varying clustering of financial time series. We use a dissimilarity measure based on the lower tail dependence coefficient, so that the resulting groups are homogeneous in the sense that the joint bivariate distributions of two series belonging to the same group are highly associated in the lower tail. In order to obtain a dynamic clustering, tail dependence coefficients are estimated by means of copula functions with a time-varying parameter. The basic assumption for the dynamic pattern of the copula parameter is the existence of an association between tail dependence and the volatility of the market. A case study with real data is examined.

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previous chapter The Estimation of the Parameters in Multi-Criteria Classification Problem: The Case of the Electre Tri Method
next chapter A Comparison of χ 2 Metrics for the Assessment of Relational Similarities in Affiliation Networks
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Metadata
Title
Dynamic Clustering of Financial Assets
Authors
Giovanni De Luca
Paola Zuccolotto
Copyright Year
2014
Book
Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Print ISBN: 978-3-319-06691-2

Electronic ISBN: 978-3-319-06692-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-06692-9_12

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