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Measurement Errors in Index Trader Positions Data: Is the Price Pressure Hypothesis Still Invalid?

Author

Listed:
  • Martin T. Bohl
  • Nicole Branger
  • Mark Trede

Abstract

In this paper, we examine whether the repeated rejection of Masters' price pressure hypothesis is robust with respect to measurement errors in index trader position data. We allow for autocorrelated errors and a potential impact of index trader positions on the level and volatility of commodity returns. The resulting state-space model is estimated via particle MCMC. The empirical investigation relies on weekly data for eleven commodities contained in the SCoT reports. Our empirical findings show that the rejection of the price pressure hypothesis is robust concerning the inclusion of measurement errors in index trader positions data.

Suggested Citation

  • Martin T. Bohl & Nicole Branger & Mark Trede, 2019. "Measurement Errors in Index Trader Positions Data: Is the Price Pressure Hypothesis Still Invalid?," CQE Working Papers 8019, Center for Quantitative Economics (CQE), University of Muenster.
    • Handle: RePEc:cqe:wpaper:8019
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    References listed on IDEAS

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    1. Christopher L. Gilbert, 2010. "Speculative Influences On Commodity Futures Prices 2006-2008," UNCTAD Discussion Papers 197, United Nations Conference on Trade and Development.
    2. Kenneth J. Singleton, 2014. "Investor Flows and the 2008 Boom/Bust in Oil Prices," Management Science, INFORMS, vol. 60(2), pages 300-318, February.
    3. Sanders, Dwight R. & Boris, Keith & Manfredo, Mark, 2004. "Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports," Energy Economics, Elsevier, vol. 26(3), pages 425-445, May.
    4. Sanders, Dwight R. & Irwin, Scott H., 2014. "Energy futures prices and commodity index investment: New evidence from firm-level position data," Energy Economics, Elsevier, vol. 46(S1), pages 57-68.
    5. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(5), pages 933-956, October.
    6. Susanne M. Schennach, 2016. "Recent Advances in the Measurement Error Literature," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 341-377, October.
    7. Martin T. Bohl & Martin Stefan & Claudia Wellenreuther, 2019. "An Introduction to ESMA’s Commitments of Traders Reports: Do Hedgers Really Hedge?," CQE Working Papers 8619, Center for Quantitative Economics (CQE), University of Muenster.
    8. Dwight R. Sanders & Scott H. Irwin, 2011. "New Evidence on the Impact of Index Funds in U.S. Grain Futures Markets," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 59(4), pages 519-532, December.
    9. Louis Ederington & Jae Ha Lee, 2002. "Who Trades Futures and How: Evidence from the Heating Oil Futures Market," The Journal of Business, University of Chicago Press, vol. 75(2), pages 353-374, April.
    10. Yan, Lei & Irwin, Scott H. & Sanders, Dwight R., 2018. "Mapping algorithms, agricultural futures, and the relationship between commodity investment flows and crude oil futures prices," Energy Economics, Elsevier, vol. 72(C), pages 486-504.
    11. Dwight R. Sanders & Scott H. Irwin & Robert P. Merrin, 2010. "The Adequacy of Speculation in Agricultural Futures Markets: Too Much of a Good Thing?," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 32(1), pages 77-94.
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    Cited by:

    1. Robe, Michel A. & Roberts, John S., 2024. "Four Commitments of Traders Reports puzzles, revisited: Answers from grains and oilseeds futures markets," Journal of Commodity Markets, Elsevier, vol. 34(C).
    2. Sania Wadud & Robert D. Durand & Marc Gronwald, 2021. "Connectedness between the Crude Oil Futures and Equity Markets during the Pre- and Post-Financialisation Eras," CESifo Working Paper Series 9202, CESifo.

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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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