Abstract
In this paper we propose a clustering procedure aimed at grou** time series with an association between extremely low values, measured by the lower tail dependence coefficient. Firstly, we estimate the coefficient using an Archimedean copula function. Then, we propose a dissimilarity measure based on tail dependence coefficients and a two-step procedure to be used with clustering algorithms which require that the objects we want to cluster have a geometric interpretation. We show how the results of the clustering applied to financial returns could be used to construct defensive portfolios reducing the effect of a simultaneous financial crisis.
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De Luca, G., Zuccolotto, P. A tail dependence-based dissimilarity measure for financial time series clustering. Adv Data Anal Classif 5, 323–340 (2011). https://doi.org/10.1007/s11634-011-0098-3
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DOI: https://doi.org/10.1007/s11634-011-0098-3