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Volatility forecasting in the Chinese commodity futures market with intraday data

Author

Listed:
  • Ying Jiang

    (University of Nottingham Ningbo)

  • Shamim Ahmed

    (University of Nottingham)

  • Xiaoquan Liu

    (University of Nottingham Ningbo)

Abstract

Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically explore volatility forecasting for aluminum, copper, fuel oil, and sugar at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results suggest that the long memory property is an essential feature in the commodity futures volatility dynamics and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in statistical terms.

Suggested Citation

  • Ying Jiang & Shamim Ahmed & Xiaoquan Liu, 2017. "Volatility forecasting in the Chinese commodity futures market with intraday data," Review of Quantitative Finance and Accounting, Springer, vol. 48(4), pages 1123-1173, May.
    • Handle: RePEc:kap:rqfnac:v:48:y:2017:i:4:d:10.1007_s11156-016-0570-4
    DOI: 10.1007/s11156-016-0570-4
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    Cited by:

    1. Fan, John Hua & Mo, Di & Zhang, Tingxi, 2022. "The “necessary evil” in Chinese commodity markets," Journal of Commodity Markets, Elsevier, vol. 25(C).
    2. Haidong Cai & Shamim Ahmed & Ying Jiang & Xiaoquan Liu, 2020. "The impact of US macroeconomic news announcements on Chinese commodity futures," Quantitative Finance, Taylor & Francis Journals, vol. 20(12), pages 1927-1966, December.
    3. Bernard Ben Sita, 2019. "Crude oil and gasoline volatility risk into a Realized-EGARCH model," Review of Quantitative Finance and Accounting, Springer, vol. 53(3), pages 701-720, October.
    4. Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
    5. Li, Jianping & Li, Guowen & Liu, Mingxi & Zhu, Xiaoqian & Wei, Lu, 2022. "A novel text-based framework for forecasting agricultural futures using massive online news headlines," International Journal of Forecasting, Elsevier, vol. 38(1), pages 35-50.
    6. Phillip A. Cartwright & Natalija Riabko, 2019. "Do spot food commodity and oil prices predict futures prices?," Review of Quantitative Finance and Accounting, Springer, vol. 53(1), pages 153-194, July.
    7. Chuxuan Xiao & Winifred Huang & David P. Newton, 2024. "Predicting expected idiosyncratic volatility: Empirical evidence from ARFIMA, HAR, and EGARCH models," Review of Quantitative Finance and Accounting, Springer, vol. 63(3), pages 979-1006, October.
    8. Bruno Deschamps & Tianlun Fei & Ying Jiang & Xiaoquan Liu, 2022. "Procyclical volatility in Chinese stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 58(3), pages 1117-1144, April.
    9. John Hua Fan & Tingxi Zhang, 2020. "The untold story of commodity futures in China," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(4), pages 671-706, April.

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    More about this item

    Keywords

    Out-of-sample predictability; Long memory time series; Futures market regulation; Realized volatility; Econometric models;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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