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How does inter-industry spillover improve the performance of volatility forecasting?

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  • Liu, Bin
  • Xiao, Wen
  • Zhu, Xingting

Abstract

This study aims to investigate whether introducing inter-industry spillover information into the GARCH-MIDAS model improves out-of-sample forecasting attempts. We explore the transmission of volatility across sectors, as well as the reliance on inter-industry business links. Our findings demonstrate strong cross-industry volatility spillovers that are related to the degree of the industry-to-industry trading linkage. We compare the out-of-sample volatility forecasting performance of the spillovers-information-incorporated GARCH-MIDAS model with that of the traditional GARCH model. The empirical results show that the GARCH-MIDAS model outperforms traditional GARCH models. Notably, we discover that good (bad) news is always transferred from the back end of the production process to the front end, meaning that economic growth (decline) is driven by consumption expansion (shrinkage).

Suggested Citation

  • Liu, Bin & Xiao, Wen & Zhu, Xingting, 2023. "How does inter-industry spillover improve the performance of volatility forecasting?," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:ecofin:v:65:y:2023:i:c:s1062940823000013
    DOI: 10.1016/j.najef.2023.101878
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    Cited by:

    1. Yan, Han & Liu, Bin & Zhu, Xingting & Wu, Yan, 2024. "Systemic risk monitoring model from the perspective of public information arrival," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

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