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Investigating rapeseed price volatilities in the course of the food crisis

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  • Busse, S.
  • Brümmer, B.
  • Ihle, R.

Abstract

This paper investigates the development of volatilities in agricultural commodity prices during and after the food crisis with a focus on rapeseed future prices at the MATIF. We apply a dynamic conditional correlation model belonging to the class of multivariate GARCH models on price returns for rapeseed, crude oil and related agricultural commodity prices. Volatility developments on a daily basis between 1999 and 2009 are investigated with a focus on the period during the 2007/08 food crisis. An increasing correlation between the returns in rapeseed and crude oil price is found. Additionally, this correlation did not only increase during the food crisis but further rose afterwards. This implies that rapeseed prices react in an increasing manner to the same information as crude oil prices. Furthermore, rapeseed prices show high sensitivity to shocks and low persistency in volatilities and thus, bear the risk of overreactions in volatile phases. The increased correlation introduces the potential of even more pronounced volatilities in agricultural commodity prices during the next price boom since crude oil prices exhibited a higher volatility level versus agricultural commodity prices in the past. Furthermore, due to the difficulty in distinguishing commodity price trends, caused by changes in supply and demand, from volatilities, stemming from expectations and speculations, optimal production schemes are difficult to set up. Therefore they bear the risk of more pronounced price level changes in the long-run.
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  • Busse, S. & Brümmer, B. & Ihle, R., 2011. "Investigating rapeseed price volatilities in the course of the food crisis," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 46, March.
  • Handle: RePEc:ags:gewipr:260269
    DOI: 10.22004/ag.econ.260269
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    1. Meyers, William H. & Meyer, Seth D., 2008. "Causes and Implications of the Food Price Surge," FAPRI-MU Report Series 47533, Food and Agricultural Policy Research Institute (FAPRI).
    2. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    3. Bekkerman, Anton & Pelletier, Denis, 2009. "Basis Volatilities of Corn and Soybean in Spatially Separated Markets: The Effect of Ethanol Demand," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49281, Agricultural and Applied Economics Association.
    4. Du, Xiaodong & Yu, Cindy L. & Hayes, Dermot J., 2011. "Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis," Energy Economics, Elsevier, vol. 33(3), pages 497-503, May.
    5. Serra, Teresa & Zilberman, David & Gil, Jose Maria & Goodwin, Barry K., 2008. "Nonlinearities in the US corn-ethanol-oil price system," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6512, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    7. Kelvin Balcombe & George Rapsomanikis, 2008. "Bayesian Estimation and Selection of Nonlinear Vector Error Correction Models: The Case of the Sugar-Ethanol-Oil Nexus in Brazil," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(3), pages 658-668.
    8. 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.
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    3. M. Thenmozhi & Shipra Maurya, 2020. "Crude Oil Volatility Transmission Across Food Commodity Markets: A Multivariate BEKK-GARCH Approach," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 20(2), pages 131-164, August.
    4. Leonardo Chaves Borges Cardoso & Maurício Vaz Lobo Bittencourt, 2016. "Price Volatility Transmission From Oil To Energy And Non-Energy Agricultural Commodities," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 181, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    5. Algieri, Bernardina, 2014. "The influence of biofuels, economic and financial factors on daily returns of commodity futures prices," Energy Policy, Elsevier, vol. 69(C), pages 227-247.
    6. Basak Bayramoglu & Jean-François Jacques, 2016. "The economic and environmental effects of a biofuel mandate policy: the case of France [Les effets économiques et environnementaux d’une politique d’incorporation obligatoire de biocarburants : le ," Post-Print hal-02877013, HAL.
    7. Wanti Fitrianti & Yusman Syaukat & Sri Hartoyo & Anna Fariyanti, 2019. "The Spillover Effect of Shocks of Fundamental Factors and Speculative Activity on Prices Volatility of World Vegetable Oil," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 230-240.
    8. Serra, Teresa & Zilberman, David, 2013. "Biofuel-related price transmission literature: A review," Energy Economics, Elsevier, vol. 37(C), pages 141-151.
    9. Han, Liyan & Jin, Jiayu & Wu, Lei & Zeng, Hongchao, 2020. "The volatility linkage between energy and agricultural futures markets with external shocks," International Review of Financial Analysis, Elsevier, vol. 68(C).
    10. Annastiina Silvennoinen & Susan Thorp, 2016. "Crude Oil and Agricultural Futures: An Analysis of Correlation Dynamics," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(6), pages 522-544, June.
    11. Algirdas Justinas Staugaitis & Bernardas Vaznonis, 2022. "Short-Term Speculation Effects on Agricultural Commodity Returns and Volatility in the European Market Prior to and during the Pandemic," Agriculture, MDPI, vol. 12(5), pages 1-26, April.
    12. Serra, Teresa, 2012. "Biofuel-related price volatility literature: a review and new approaches," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126057, International Association of Agricultural Economists.
    13. Tiago Silveira Gontijo & Alexandre de C ssio Rodrigues & Cristiana Fernandes De Muylder & Jefferson Lopes la Falce & Thiago Henrique Martins Pereira, 2020. "Analysis of Olive Oil Market Volatility using the ARCH and GARCH techniques," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 423-428.

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