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Futures Price Volatility in Commodities Markets: The Role of Short Term vs Long Term Speculation

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  • Matteo Manera

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

  • Marcella Nicolini

    (University of Pavia, Pavia, and Fondazione Eni Enrico Mattei, Milan)

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, 2013. "Futures Price Volatility in Commodities Markets: The Role of Short Term vs Long Term Speculation," Working Papers 2013.45, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2013.45
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    References listed on IDEAS

    as
    1. Arjun Chatrath & Sanjay Ramchander & Frank Song, 1996. "The role of futures trading activity in exchange rate volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 16(5), pages 561-584, August.
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    Citations

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    Cited by:

    1. Algieri, Bernardina & Kalkuhl, Matthias & Koch, Nicolas, 2015. "A Tale for Two Tails: Explaining Extreme Events in Financialized Agricultural markets," 2015 Conference (59th), February 10-13, 2015, Rotorua, New Zealand 202529, Australian Agricultural and Resource Economics Society.
    2. Karol Szafranek, 2015. "Financialisation of the commodity markets. Conclusions from the VARX DCC GARCH," EcoMod2015 8554, EcoMod.
    3. 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).
    4. repec:eee:jfpoli:v:69:y:2017:i:c:p:256-269 is not listed on IDEAS
    5. 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.
    6. Bosch, David & Pradkhan, Elina, 2015. "The impact of speculation on precious metals futures markets," Resources Policy, Elsevier, vol. 44(C), pages 118-134.
    7. repec:spr:fininn:v:3:y:2017:i:1:d:10.1186_s40854-017-0066-9 is not listed on IDEAS
    8. 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.
    9. 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.

    More about this item

    Keywords

    Commodities Futures Markets; Speculation; Scalping; Working’s T; Data Frequency; GARCH Models;

    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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