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

DOI

10.2991/jsta.d.200827.001How to use a DOI?

Keywords

Stock of banks and credit institutions; Mixture model; Clustering time series; Multivariate skew normal; GAS model

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.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Solmaz Yaghoubi
AU  - Rahman Farnoosh
PY  - 2020
DA  - 2020/09/08
TI  - Model-Based Filtering via Finite Skew Normal Mixture for Stock Data
JO  - Journal of Statistical Theory and Applications
SP  - 391
EP  - 396
VL  - 19
IS  - 3
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.200827.001
DO  - 10.2991/jsta.d.200827.001
ID  - Yaghoubi2020
ER  -