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Futures Trading and the Excess Co-movement of Commodity Prices
[On the comovement of commodity prices]

Author

Listed:
  • Yannick Le Pen
  • Benoît Sévi

Abstract

We empirically reinvestigate the issue of the excess co-movement of commodity prices initially raised in Pindyck and Rotemberg (1990). Excess co-movement appears when commodity prices remain correlated even after adjusting for the impact of fundamentals. We use recent developments in large approximate factor models to consider a richer information set and adequately model these fundamentals. We consider a set of eight unrelated commodities along with 184 real and nominal macroeconomic variables, from developed and emerging economies, from which nine factors are extracted over the 1993–2013 period. Our estimates provide evidence of time-varying excess co-movement which is particularly high after 2007. We further show that speculative intensity is a driver of the estimated excess co-movement, as speculative trading is both correlated across the commodity futures markets and correlated with the futures prices. Our results can be taken as direct evidence of the significant impact of financialization on commodity-price cross-moments.

Suggested Citation

  • Yannick Le Pen & Benoît Sévi, 2018. "Futures Trading and the Excess Co-movement of Commodity Prices [On the comovement of commodity prices]," Review of Finance, European Finance Association, vol. 22(1), pages 381-418.
    • Handle: RePEc:oup:revfin:v:22:y:2018:i:1:p:381-418.
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    References listed on IDEAS

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    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    3. Chunrong Ai & Arjun Chatrath & Frank Song, 2006. "On the Comovement of Commodity Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(3), pages 574-588.
    4. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    5. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
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    Cited by:

    1. Tom Dudda & Tony Klein & Duc Khuong Nguyen & Thomas Walther, 2022. "Common Drivers of Commodity Futures?," Working Papers 2207, Utrecht School of Economics.
    2. Aka Messouma Catherine & Wawa Zadi Yann & Aka Joseph, 2024. "The Evolution and Profitability of China's Futures Markets: A Comprehensive Review," International Journal of Science and Business, IJSAB International, vol. 34(1), pages 132-143.
    3. Bohl, Martin T. & Irwin, Scott H. & Pütz, Alexander & Sulewski, Christoph, 2023. "The impact of financialization on the efficiency of commodity futures markets," Journal of Commodity Markets, Elsevier, vol. 31(C).
    4. Banerjee, Ameet Kumar & Sensoy, Ahmet & Goodell, John W. & Mahapatra, Biplab, 2024. "Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period," Finance Research Letters, Elsevier, vol. 59(C).
    5. Abricha, Amal & Ben Amar, Amine & Bellalah, Makram, 2024. "Commodity futures markets under stress and stress-free periods: Further insights from a quantile connectedness approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 93(C), pages 229-246.

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