Dynamic clustering of financial assets

In this work we propose a procedure for time-varying clustering of financial time series. We use a dissimilarity measure based on the lower tail dependence coefficient, so that the resulting groups are homogeneous in the sense that the joint bivariate distributions of two series belonging to the same group are highly associated in the lower tail. In order to obtain a dynamic clustering, tail dependence
coefficients are estimated by means of copula functions with a time-varying parameter. The basic assumption for the dynamic pattern of the copula parameter is the existence of an association between tail dependence and the volatility of the market. A case study with real data is examined.