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Forecasting interval-valued crude oil prices using asymmetric interval models

Author

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  • Quanying Lu
  • Yuying Sun
  • Yongmiao Hong
  • Shouyang Wang

Abstract

Practitioners and policy makers rely on accurate crude oil forecasting to avoid price risks and grasp investment opportunities, but the core of existing predictive models for such prices is based on point-valued inputs and outputs, which may suffer from informational loss of volatility. This paper addresses this issue by proposing a modified threshold autoregressive interval-valued models with interval-valued factors (MTARIX), as extended by Sun et al. [Threshold autoregressive models for interval-valued time series. J. Econom., 2018, 206, 414–446], to analyze and forecast interval-valued crude oil prices. In contrast to point-valued data methods, MTARIX models simultaneously capture nonlinear features in price trend and volatility, and this informational gain can produce more accurate forecasts. Several interval-valued factors and point-valued threshold variables are analyzed, including supply and demand, speculation, stock market, monetary market, technical factor, and search query data. Empirical results suggest that MTARIX models with appropriate threshold variables outperform other competing forecast models (ACIX, CR-SETARX, ARX, and VARX). The findings indicate that oil price range information is more valuable than oil price level information in forecasting crude oil prices.

Suggested Citation

  • Quanying Lu & Yuying Sun & Yongmiao Hong & Shouyang Wang, 2022. "Forecasting interval-valued crude oil prices using asymmetric interval models," Quantitative Finance, Taylor & Francis Journals, vol. 22(11), pages 2047-2061, November.
  • Handle: RePEc:taf:quantf:v:22:y:2022:i:11:p:2047-2061
    DOI: 10.1080/14697688.2022.2112065
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    Cited by:

    1. Wu, Dan & Dai, Xingyu & Zhao, Ruikun & Cao, Yaru & Wang, Qunwei, 2023. "Pass-through from temperature intervals to China's commodity futures’ interval-valued returns: Evidence from the varying-coefficient ITS model," Finance Research Letters, Elsevier, vol. 58(PA).
    2. Hou, Guolian & Wang, Junjie & Fan, Yuzhen, 2024. "Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction," Energy, Elsevier, vol. 286(C).
    3. Piao Wang & Shahid Hussain Gurmani & Zhifu Tao & Jinpei Liu & Huayou Chen, 2024. "Interval time series forecasting: A systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 249-285, March.
    4. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).

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