Open AccessA network autoregressive model with GARCH effects and its applications
Author(s) -
ShihFeng Huang,
HsinHan Chiang,
YuehJaw Lin
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0255422
Subject(s) - autoregressive conditional heteroskedasticity , autoregressive model , econometrics , stock market index , granger causality , stock market , index (typography) , stock (firearms) , correlation , economics , mathematics , statistics , computer science , volatility (finance) , biology , engineering , world wide web , mechanical engineering , paleontology , geometry , horse
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.