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Oil price volatility forecasting: Threshold effect from stock market volatility

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  • Chen, Yan
  • Qiao, Gaoxiu
  • Zhang, Feipeng

Abstract

Stock market volatility, which is usually considered a proxy for the general economy, contains important information for the crude oil market. In this paper, we investigate the incremental benefit of stock market volatility over oil volatility using the S&P 500 index and WTI oil prices for the period from January 1990 to December 2021. The threshold autoregressive regression (TAR) model is used to capture the nonlinear threshold effect of stock market shock on oil market volatility. From empirical analysis, both in-sample and out-of-sample results highlight the prediction superiority and effectiveness of the nonlinear threshold regression model, which indicates the valuable strong threshold effects of stock volatility for oil volatility forecasting. Moreover, the additional effects of stock volatility in terms of bad volatility forecasting further confirm the effectiveness of the nonlinear TAR model and the information content of stock volatility. This study will prove useful for policy-makers to formulate reasonable policies and for investors to avoid risk.

Suggested Citation

  • Chen, Yan & Qiao, Gaoxiu & Zhang, Feipeng, 2022. "Oil price volatility forecasting: Threshold effect from stock market volatility," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:tefoso:v:180:y:2022:i:c:s0040162522002311
    DOI: 10.1016/j.techfore.2022.121704
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    Cited by:

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    2. Liang, Ruibin & Cheng, Sheng & Cao, Yan & Li, Xinran, 2024. "Multi-scale impacts of oil shocks on travel and leisure stocks: A MODWT-Bayesian TVP model with shrinkage approach," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    3. Wang, Jia & Wang, Xinyi & Wang, Xu, 2024. "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    4. Tumala, Mohammed M. & Salisu, Afees A. & Gambo, Ali I., 2023. "Disentangled oil shocks and stock market volatility in Nigeria and South Africa: A GARCH-MIDAS approach," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 707-717.
    5. Chen, Yan & Liu, Yakun & Zhang, Feipeng, 2024. "Coskewness and the short-term predictability for Bitcoin return," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    6. Tumala, Mohammed M. & Salisu, Afees & Nmadu, Yaaba B., 2023. "Climate change and fossil fuel prices: A GARCH-MIDAS analysis," Energy Economics, Elsevier, vol. 124(C).
    7. Yingchao Zou & Kaijian He, 2022. "Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model," Mathematics, MDPI, vol. 10(14), pages 1-11, July.

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    More about this item

    Keywords

    Threshold autoregressive regression model; Oil price volatility; Out-of-sample forecasting; Threshold effect;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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