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Modeling and forecasting volatility in global food commodity prices

Author

Listed:
  • Ibrahim A. ONOUR

    (Arab Planning Institute, Kuwait, Kuwait)

  • Bruno S. SERGI

    (Faculty of Political Science, University of Messina, Messina, Italy)

Abstract

To capture the volatility in the global food commodity prices, we employed two competing models, the thin tailed the normal distribution, and the fat-tailed Student t-distribution models. Results based on wheat, rice, sugar, beef, coffee, and groundnut prices, during the sample period from October 1984 to September 2009, show the t-distribution model outperforms the normal distribution model, suggesting that the normality assumption of residuals which are often taken for granted for its simplicity may lead to unreliable results of the conditional volatility estimates. The paper also shows that the volatility of food commodity prices characterized with the intermediate and short memory behavior, implying that the volatility of food commodity prices is mean reverting.

Suggested Citation

  • Ibrahim A. ONOUR & Bruno S. SERGI, 2011. "Modeling and forecasting volatility in global food commodity prices," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 57(3), pages 132-139.
    • Handle: RePEc:caa:jnlage:v:57:y:2011:i:3:id:28-2010-agricecon
    DOI: 10.17221/28/2010-AGRICECON
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    1. Chris Brooks & Gita Persand, 2003. "The Effect of Asymmetries on Stock Index Return Value‐at‐Risk Estimates," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 4(2), pages 29-42, January.
    2. Ronald A. Babula & Agapi Somwaru, 1992. "Dynamic impacts of a shock in crude oil price on agricultural chemical and fertilizer prices," Agribusiness, John Wiley & Sons, Ltd., vol. 8(3), pages 243-252.
    3. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    4. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Vilasuso, Jon, 2002. "Forecasting exchange rate volatility," Economics Letters, Elsevier, vol. 76(1), pages 59-64, June.
    7. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    8. Diebold, Francis X. & Rudebusch, Glenn D., 1991. "On the power of Dickey-Fuller tests against fractional alternatives," Economics Letters, Elsevier, vol. 35(2), pages 155-160, February.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Baillie, Richard T. & King, Maxwell L., 1996. "Editors' introduction: Fractional differencing and long memory processes," Journal of Econometrics, Elsevier, vol. 73(1), pages 1-3, July.
    11. Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
    12. 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.
    13. Cunado, J. & Gil-Alana, L.A. & de Gracia, F. Perez, 2005. "A test for rational bubbles in the NASDAQ stock index: A fractionally integrated approach," Journal of Banking & Finance, Elsevier, vol. 29(10), pages 2633-2654, October.
    14. Barone-Adesi, Giovanni & Whaley, Robert E, 1987. "Efficient Analytic Approximation of American Option Values," Journal of Finance, American Finance Association, vol. 42(2), pages 301-320, June.
    15. Uri, Noel D., 1996. "Changing crude oil price effects on US agricultural employment," Energy Economics, Elsevier, vol. 18(3), pages 185-202, July.
    16. Shephard, Neil (ed.), 2005. "Stochastic Volatility: Selected Readings," OUP Catalogue, Oxford University Press, number 9780199257201.
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