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A network autoregressive model with GARCH effects and its applications

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

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.

Suggested Citation

  • Shih-Feng Huang & Hsin-Han Chiang & Yu-Jun Lin, 2021. "A network autoregressive model with GARCH effects and its applications," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-18, July.
    • Handle: RePEc:plo:pone00:0255422
    DOI: 10.1371/journal.pone.0255422
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    References listed on IDEAS

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    5. Chi‐Hsiang Chu & Mong‐Na Lo Huang & Shih‐Feng Huang & Ray‐Bing Chen, 2019. "Bayesian structure selection for vector autoregression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(5), pages 422-439, August.
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    Cited by:

    1. Yao, Yuan & Zhao, Yang & Li, Yan, 2022. "A volatility model based on adaptive expectations: An improvement on the rational expectations model," International Review of Financial Analysis, Elsevier, vol. 82(C).

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