A network autoregressive model with GARCH effects and its applications

Shih-Feng Huang; Hsin-Han Chiang; Yu-Jun Lin

doi:10.1371/journal.pone.0255422

A network autoregressive model with GARCH effects and its applications

PLoS One. 2021 Jul 29;16(7):e0255422. doi: 10.1371/journal.pone.0255422. eCollection 2021.

Authors

Shih-Feng Huang¹, Hsin-Han Chiang², Yu-Jun Lin²

Affiliations

¹ Dept. Applied Mathematics, National University of Kaohsiung, Kaohsiung, Taiwan.
² Institute of Statistics, National University of Kaohsiung, Kaohsiung, Taiwan.

Abstract

In this study, a network autoregressive model with GARCH effects, denoted by NAR-GARCH, is proposed to depict the return dynamics of stock market indices. A GARCH filter is employed to marginally remove the GARCH effects of each index, and the NAR model with the Granger causality test and Pearson's correlation test with sharp price movements is used to capture the joint effects caused by other indices with the most updated market information. The NAR-GARCH model is designed to depict the joint effects of nonsynchronous multiple time series in an easy-to-implement and effective way. The returns of 20 global stock indices from 2006 to 2020 are employed for our empirical investigation. The numerical results reveal that the NAR-GARCH model has satisfactory performance in both fitting and prediction for the 20 stock indices, especially when a market index has strong upward or downward movements.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Causality
Investments
Models, Economic*

Grants and funding

This study was supported by Ministry of Science and Technology, Taiwan, with grant number MOST 108-2118-M-390-003-MY2 of the first author. There was no additional external funding received for this study.