A tail dependence-based dissimilarity measure for financial time series clustering

In this paper we propose a clustering procedure aimed at grouping 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.