Model-Based Filtering via Finite Skew Normal Mixture for Stock Data

Abstract

This paper proposes a flexible finite mixture model framework using multivariate skew normal distribution for banking and credit institutions’ stock data in Iran. This method clusters time series stocks data of Iranian banks and credit institutions to filter those data into four groups. The proposed model estimates matrices of time-varying parameter for skew normal distribution mixture using EM algorithm, updating the estimated parameters via generalized autoregressive score (GAS) model. Empirical studies are conducted to examine the effect of the proposed model in clustering, estimating, and updating parameters for real data from 12 sets of stocks. Our stock data were filtered in four trade clusters with best performance.