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Information Demand and Agriculture Commodity Prices

Author

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  • Peri, Massimo
  • Vandone, Daniela
  • Baldi, Lucia

Abstract

This paper investigates the effect of information flow on corn futures price variability for the period January 2004 -July 2011. The theoretical framework is the Mixture Distribution Hypothesis, that posits a joint dependence of return volatility and information. The main contribution of this article is that we use two different proxy for the observed component of information flow that allow to separate the effect of supply (News) and demand (Internet Search Volume) of information. Empirical estimates highlight that: i) results support the MDH since observed volatility persistence appears to be related to the information flow; ii) variation in information demand has a significant effect on volatility of futures corn returns even controlling for variation in information supply and such result can be interpreted in light of behavioural finance.

Suggested Citation

  • Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2012. "Information Demand and Agriculture Commodity Prices," 2012 International European Forum, February 13-17, 2012, Innsbruck-Igls, Austria 144973, International European Forum on Innovation and System Dynamics in Food Networks.
  • Handle: RePEc:ags:iefi12:144973
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    References listed on IDEAS

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    1. Mitchell, Mark L & Mulherin, J Harold, 1994. " The Impact of Public Information on the Stock Market," Journal of Finance, American Finance Association, vol. 49(3), pages 923-950, July.
    2. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    3. Lamoureux, Christopher G & Lastrapes, William D, 1990. " Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-229, March.
    4. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    5. Laura L. Veldkamp, 2006. "Media Frenzies in Markets for Financial Information," American Economic Review, American Economic Association, vol. 96(3), pages 577-601, June.
    6. Ederington, Louis H & Lee, Jae Ha, 1993. " How Markets Process Information: News Releases and Volatility," Journal of Finance, American Finance Association, vol. 48(4), pages 1161-1191, September.
    7. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    8. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    9. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
    10. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
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    12. Harris, Lawrence, 1987. "Transaction Data Tests of the Mixture of Distributions Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(02), pages 127-141, June.
    13. Kalev, Petko S. & Liu, Wai-Man & Pham, Peter K. & Jarnecic, Elvis, 2004. "Public information arrival and volatility of intraday stock returns," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1441-1467, June.
    14. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
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    More about this item

    Keywords

    commodity prices; information; futures markets; Mixture Distribution Hypothesis; egarch; Agribusiness; Agricultural Finance; Demand and Price Analysis; Food Consumption/Nutrition/Food Safety; C32; G13; G14; Q11;

    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
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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