Regime dependent interconnectedness among fuzzy clusters of financial time series

We analyze the dynamic structure of lower tail dependence coefficients within groups of assets defined such that assets belonging to the same group are characterized by pairwise high associations between extremely low values. The groups are identified by means of a fuzzy cluster analysis algorithm. The tail dependence coefficients are estimated using the Joe–Clayton copula function, and the 75th percentile within clusters is used as a measure of each cluster’s overall tail dependence. The interdependence structure of the clusters’ tail dependence dynamics is then analyzed in order to determine whether the pattern of a cluster can be predicted based on the past values of the others, using a Granger causality approach. The hypothesis of a possible regime switching dynamics in tail dependence is also investigated by means of a threshold vector autoregressive model and the results are compared to those obtained with a linear autoregression. The whole procedure is described with reference to a case study dealing with the assets composing Eurostoxx 50, but it can be viewed as the proposal of a general method, that can be relevantly applied to whatever set of asset returns time series.