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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

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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    References listed on IDEAS

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    More about this item

    Keywords

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

    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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