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Time Series Clustering on Lower Tail Dependence for Portfolio Selection

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

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  • Giovanni De Luca

    (University of Naples Parthenope, Department of Business and Quantitative Sciences)

  • Paola Zuccolotto

    (University of Brescia, Department of Economics and Management)

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.

Suggested Citation

  • Giovanni De Luca & Paola Zuccolotto, 2014. "Time Series Clustering on Lower Tail Dependence for Portfolio Selection," Springer Books, in: Marco Corazza & Claudio Pizzi (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 131-140, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-02499-8_12
    DOI: 10.1007/978-3-319-02499-8_12
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