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The Financialization of Food?

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  • Valentina G. Bruno
  • Bahattin Buyuksahin
  • Michel A. Robe

Abstract

Commodity-equity and cross-commodity return co-movements rose dramatically after the 2008 financial crisis. This development took place following what has been dubbed the “financialization” of commodity markets. We first document changes since 2000 in the intensity of speculative activity in grain and livestock futures. We then use a structural VAR model to establish the role of speculative activity in explaining the strength of co-movements between grain, livestock and equity returns. We find that speculative intensity does not in itself affect the extent to which grain markets move in sync with the stock market. Rather, pre-crisis, financial speculators’ futures positions facilitated the transmission of macroeconomic shocks into grain markets. Strikingly, in the post-crisis period, this transmission channel weakened to the point of statistical insignificance. The role of speculative activity is less evident in livestock markets, where only macroeconomic conditions have a statistically significant impact on return co-movements with equities.

Suggested Citation

  • Valentina G. Bruno & Bahattin Buyuksahin & Michel A. Robe, 2013. "The Financialization of Food?," Staff Working Papers 13-39, Bank of Canada.
    • Handle: RePEc:bca:bocawp:13-39
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    1. Wolfram Schlenker & Sofia B. Villas-Boas, 2009. "Consumer and Market Responses to Mad Cow Disease," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(4), pages 1140-1152.
    2. Wolfram Schlenker & Sofia B. Villas-Boas, 2009. "Consumer and Market Responses to Mad Cow Disease," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(4), pages 1140-1152.
    3. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    4. 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.
    5. Yiuman Tse & James C. Hackard, 2006. "Holy mad cow! Facts or (mis)perceptions: A clinical study," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(4), pages 315-341, April.
    6. Xiaodong Du and Lihong Lu McPhail, 2012. "Inside the Black Box: the Price Linkage and Transmission between Energy and Agricultural Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    7. Devadoss, Stephen & Holland, David W. & Stodick, Leroy & Ghosh, Joydeep, 2006. "A General Equilibrium Analysis of Foreign and Domestic Demand Shocks Arising from Mad Cow Disease in the United States," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 31(2), pages 1-13, August.
    8. Pozo, Veronica F. & Schroeder, Ted C., 2012. "Price and Volatility Spillover between Livestock and Related Commodity Markets," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124798, Agricultural and Applied Economics Association.
    9. Witsanu Attavanich & Bruce A. McCarl & David Bessler, 2011. "The Effect of H1N1 (Swine Flu) Media Coverage on Agricultural Commodity Markets," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(2), pages 241-259.
    10. Ding, Yulian & Veeman, Michele M. & Adamowicz, Wiktor L., 2009. "BSE and the Dynamics of Beef Consumption: Influences of Habit and Trust," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49284, Agricultural and Applied Economics Association.
    11. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    12. Matteo Manera, Marcella Nicolini, and Ilaria Vignati, 2013. "Financial Speculation in Energy and Agriculture Futures Markets: A Multivariate GARCH Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    13. Thomsen, Michael R. & McKenzie, Andrew M. & Power, Gabriel J., 2009. "Volatility Surface and Skewness in Live Cattle Futures Price Distributions with Application to North American BSE Announcements," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49354, Agricultural and Applied Economics Association.
    14. Lehecka, Georg V., 2013. "The Reaction of Corn and Soybean Futures Markets to USDA Crop Progress and Condition Information," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 142491, Southern Agricultural Economics Association.
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    More about this item

    Keywords

    International topics; Recent economic and financial developments;

    JEL classification:

    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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