Time Series Clustering on Lower Tail Dependence for Portfolio Selection

Giovanni De Luca () and Paola Zuccolotto ()
Additional contact information
Giovanni De Luca: University of Naples Parthenope, Department of Business and Quantitative Sciences
Paola Zuccolotto: University of Brescia, Department of Economics and Management

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2014, pp 131-140 from Springer

Abstract: Abstract In this paper we analyse a case study based on the procedure introduced by De Luca and Zuccolotto [8], whose aim is to cluster time series of financial returns in groups being homogeneous in the sense that their joint bivariate distributions exhibit high association in the lower tail. The dissimilarity measure used for such clustering is based on tail dependence coefficients estimated using copula functions. We carry out the clustering using an algorithm requiring a preliminary transformation of the dissimilarity index into a distance metric by means of a geometric representation of the time series, obtained with Multidimensional Scaling. We show that the results of the clustering can be used for a portfolio selection purpose, when the goal is to protect investments from the effects of a financial crisis.

Keywords: Portfolio Selection; Efficient Frontier; Sharpe Ratio; Tail Dependence; Dissimilarity Matrix (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:sprchp:978-3-319-02499-8_12

Ordering information: This item can be ordered from
http://www.springer.com/9783319024998

DOI: 10.1007/978-3-319-02499-8_12

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().