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Speculative Activity and Returns to Volatility of Chinese Major Agricultural Commodity Futures

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  • Martin T. Bohl, Pierre Siklos, Claudia Wellenreuther

    (Wilfrid Laurier University)

Abstract

Chinese futures markets for agricultural commodities are among the fastest growing futures markets in the world and trading behaviour in those markets is perceived as highly speculative. Therefore, we empirically investigate whether speculative activity in Chinese futures markets for agricultural commodities destabilizes futures returns. To capture speculative activity a speculation and a hedging ratio are used. Applying GARCH models we first analyse the influence of both ratios on the conditional volatility of eight heavily traded Chinese futures contracts. Additionally, VAR models in conjunction with Granger causality tests, impulse-response analyses and variance decompositions are used to obtain insight into the lead-lag relationship between speculative activity and returns volatility. For most of the commodities, we find a positive influence of the speculation ratio on conditional volatility. The results relying on the hedging ratio are inconclusive.

Suggested Citation

  • Martin T. Bohl, Pierre Siklos, Claudia Wellenreuther, 2018. "Speculative Activity and Returns to Volatility of Chinese Major Agricultural Commodity Futures," LCERPA Working Papers 0111, Laurier Centre for Economic Research and Policy Analysis, revised 30 Jan 2018.
  • Handle: RePEc:wlu:lcerpa:0111
    Note: LCERPA Working Paper No. 2018-5, January 2018.
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    Cited by:

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    2. Debopam Rakshit & Ranjit Kumar Paul & Md Yeasin & Walid Emam & Yusra Tashkandy & Christophe Chesneau, 2023. "Modeling Asymmetric Volatility: A News Impact Curve Approach," Mathematics, MDPI, vol. 11(13), pages 1-14, June.
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    5. Fan, John Hua & Mo, Di & Zhang, Tingxi, 2022. "The “necessary evil” in Chinese commodity markets," Journal of Commodity Markets, Elsevier, vol. 25(C).
    6. Bernardina Algieri & Matthias Kalkuhl, 2019. "Efficiency and Forecast Performance of Commodity Futures Markets," American Journal of Economics and Business Administration, Science Publications, vol. 11(1), pages 19-34, June.
    7. Algirdas Justinas Staugaitis & Bernardas Vaznonis, 2022. "Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security," Agriculture, MDPI, vol. 12(11), pages 1-27, November.
    8. Schmidt, Torsten & Kirsch, Florian & Dirks, Maximilian W., 2021. "Kurzfristige Perspektiven der Rohstoffpreisentwicklung," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 251878.
    9. 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.
    10. Mert Demir & Terrence F. Martell & Jun Wang, 2019. "The trilogy of China cotton markets: The lead–lag relationship among spot, forward, and futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(4), pages 522-534, April.
    11. Fang, Ming & Chang, Chiu-Lan & Zhang, Qi, 2023. "Impacts of trading restrictions on price volatilities and speculative activities: Evidence from CSI 300 futures," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 184-204.
    12. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
    13. 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.
    14. Yongmei Fang & Bo Guan & Shangjuan Wu & Saeed Heravi, 2020. "Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 877-886, September.
    15. Wimmer, Thomas & Geyer-Klingeberg, Jerome & Hütter, Marie & Schmid, Florian & Rathgeber, Andreas, 2021. "The impact of speculation on commodity prices: A Meta-Granger analysis," Journal of Commodity Markets, Elsevier, vol. 22(C).
    16. Algirdas Justinas Staugaitis & Bernardas Vaznonis, 2022. "Short-Term Speculation Effects on Agricultural Commodity Returns and Volatility in the European Market Prior to and during the Pandemic," Agriculture, MDPI, vol. 12(5), pages 1-26, April.
    17. Luo, Jiawen & Marfatia, Hardik A. & Ji, Qiang & Klein, Tony, 2023. "Co-volatility and asymmetric transmission of risks between the global oil and China's futures markets," Energy Economics, Elsevier, vol. 117(C).
    18. Akinbode, S. O. & Ojo, O. T., 2018. "The Effect of Exchange Rate Volatility on Agricultural Exports in Nigeria: An Autoregressive Distributed Lag (ARDL) Bounds Test Approach," Nigerian Journal of Agricultural Economics, Nigerian Journal of Agricultural Economics, vol. 8(1), October.

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    More about this item

    Keywords

    Speculation Ratio; Returns Volatility; Chinese Futures Markets; Agricultural Commodities;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F30 - International Economics - - International Finance - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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