Dynamic Clustering of Financial Assets

In this work we discuss the clustering procedure of 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 by means of 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. The results of the clustering could be used for a portfolio selection purpose, when the goal is to protect investments from the effects of a financial crisis. The clustering of the time series has been considered under the hypotheses of constant and dynamic tail dependence coefficients.