Entropy weighted model-based clustering of skewed and heavy tailed time series

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties such as heavy tails and skewness. Thanks to its flexibility, the Skewed Exponential Power Distribution (also called Skewed Generalized Error Distribution) ensures a unified and general framework for clustering possibly skewed and heavy-tailed time series. A clustering procedure of model-based type is developed, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. Moreover, by applying skewness or kurtosis-based projection pursuit, the resulting interesting projections can be used as the input of the clustering procedure with a different distributional assumption. The usefulness of the proposal is showed by means of application to financial time series.