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Futures price volatility in commodities markets: The role of short term vs long term speculation

Author

Listed:
  • Matteo Manera

    (University of Milan-Bicocca and Fondazione Eni Enrico Mattei)

  • Marcella Nicolini

    (Department of Economics and Management, University of Pavia)

  • Ilaria Vignati

    (Fondazione Eni Enrico Mattei)

Abstract

This paper evaluates how different types of speculation affect the volatility of commodities’ futures prices. We adopt four indexes of speculation: Working’s T, the market share of non-commercial traders, the percentage of net long speculators over total open interest in future markets, which proxy for long term speculation, and scalping, which proxies for short term speculation. We consider four energy commodities (light sweet crude oil, heating oil, gasoline and natural gas) and seven non-energy commodities (cocoa, coffee, corn, oats, soybean oil, soybeans and wheat) over the period 1986-2010 analyzed at weekly frequency. Using GARCH models we find that speculation significantly affects volatility of returns: short term speculation has a positive and significant impact on volatility, while long term speculation generally has a negative effect. The robustness exercise shows that: i) scalping is positive and significant also at higher and lower data frequencies; ii) results remain unchanged through different model specifications (GARCH-in-mean, EGARCH, and TARCH); iii) results are robust to different specifications of the mean equation.

Suggested Citation

  • Matteo Manera & Marcella Nicolini & Ilaria Vignati, 2013. "Futures price volatility in commodities markets: The role of short term vs long term speculation," DEM Working Papers Series 042, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:demwp0042
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    References listed on IDEAS

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    Cited by:

    1. Gurdip Bakshi & Xiaohui Gao & Alberto G. Rossi, 2019. "Understanding the Sources of Risk Underlying the Cross Section of Commodity Returns," Management Science, INFORMS, vol. 65(2), pages 619-641, February.
    2. Karol Szafranek, 2015. "Financialisation of the commodity markets. Conclusions from the VARX DCC GARCH," EcoMod2015 8554, EcoMod.
    3. 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.
    4. 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.
    5. Bharat Kumar Meher & Iqbal Thonse Hawaldar & Mathew Thomas Gil & Deebom Zorle Dum, 2021. "Measuring Leverage Effect of Covid 19 on Stock Price Volatility of Energy Companies Using High Frequency Data," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 489-502.
    6. Bharat Kumar Meher & Iqbal Thonse Hawaldar & Latasha Mohapatra & Adel M. Sarea, 2020. "The Impact of COVID-19 on Price Volatility of Crude Oil and Natural Gas Listed on Multi Commodity Exchange of India," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 422-431.
    7. Bosch, David & Pradkhan, Elina, 2015. "The impact of speculation on precious metals futures markets," Resources Policy, Elsevier, vol. 44(C), pages 118-134.
    8. 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.
    9. Bohl, Martin T. & Siklos, Pierre L. & Wellenreuther, Claudia, 2018. "Speculative activity and returns volatility of Chinese agricultural commodity futures," Journal of Asian Economics, Elsevier, vol. 54(C), pages 69-91.
    10. Haase, Marco & Seiler Zimmermann, Yvonne & Zimmermann, Heinz, 2016. "The impact of speculation on commodity futures markets – A review of the findings of 100 empirical studies," Journal of Commodity Markets, Elsevier, vol. 3(1), pages 1-15.
    11. Algieri, Bernardina & Kalkuhl, Matthias & Koch, Nicolas, 2017. "A tale of two tails: Explaining extreme events in financialized agricultural markets," Food Policy, Elsevier, vol. 69(C), pages 256-269.
    12. Algieri, Bernardina & Kalkuhl, Matthias, 2014. "Back to the Futures: An Assessment of Commodity Market Efficiency and Forecast Error Drivers," Discussion Papers 187159, University of Bonn, Center for Development Research (ZEF).
    13. Suranjana Joarder, 2018. "The Commodity Futures Volatility and Macroeconomic Fundamentals - The Case of Oil and Oilseed Commodities in India," International Econometric Review (IER), Econometric Research Association, vol. 10(2), pages 33-50, September.
    14. Isita Mukherjee & Bhaskar Goswami, 2017. "The volatility of returns from commodity futures: evidence from India," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-23, December.
    15. Siami-Namini, Sima & Hudson, Darren, 2017. "Volatility Spillover Between Oil Prices, Us Dollar Exchange Rates And International Agricultural Commodities Prices," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252845, Southern Agricultural Economics Association.
    16. Kumar SANTOSH & Meher Kumar BHARAT & Ramona BIRAU & Mircea Laurentiu SIMION & Anand ABHISHEK & Singh MANOHAR, 2023. "Quantifying Long-Term Volatility for Developed Stock Markets: An Empirical Case Study Using PGARCH Model on Toronto Stock Exchange (TSX)," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-68.
    17. Fan, John Hua & Fernandez-Perez, Adrian & Indriawan, Ivan & Todorova, Neda, 2020. "Internationalization of futures markets: Lessons from China," Pacific-Basin Finance Journal, Elsevier, vol. 63(C).

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    More about this item

    Keywords

    Commodities futures markets; Speculation; Scalping; Working’s T; Data frequency; GARCH models;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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