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Measuring the Price Volatility of Certain Field Crops in South Africa using the ARCH/GARCH Approach


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  • Jordaan, Henry
  • Grove, Bennie
  • Jooste, Andre
  • Alemu, A.G.
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    The conditional volatility in the daily spot prices of the crops traded on the South African Futures Exchange (yellow maize, white maize, wheat, sunflower seed and soybeans) is determined. The volatility in the prices of white maize, yellow maize and sunflower seed have been found to vary over time, suggesting the use of the GARCH approach in these cases. Using the GARCH approach, the conditional standard deviation is the measure of volatility, and distinguishes between the predictable and unpredictable elements in the price process. This leaves only the stochastic component and is hence a more accurate measure of the actual risk associated with the price of the crop. The volatility in the prices of wheat and soybeans was found to be constant over time; hence the standard error of the ARIMA process was used as the measure of volatility in the prices of these two crops. When comparing the medians of the conditional standard deviations in the prices of white maize, yellow maize and sunflower seed to the constant volatilities of wheat and soybeans, the price of white maize was found to be the most volatile, followed by yellow maize, sunflower seed, soybeans, and wheat respectively. These results suggest that the more risk-averse farmers will more likely produce wheat, sunflower seed and to a lesser extent soybeans, while maize producers are expected to utilise forward pricing methods, especially put options, at a high level to manage the higher volatility.

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    Bibliographic Info

    Article provided by Agricultural Economics Association of South Africa (AEASA) in its journal Agrekon.

    Volume (Year): 46 (2007)
    Issue (Month): 3 (September)

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    Handle: RePEc:ags:agreko:8013

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    Keywords: Price volatility; field crops; SAFEX; time series analysis; ARCH/GARCH; Demand and Price Analysis;


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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
    4. Moledina, Amyaz A. & Roe, Terry L. & Shane, Mathew, 2004. "Measuring Commodity Price Volatility And The Welfare Consequences Of Eliminating Volatility," 2004 Annual meeting, August 1-4, Denver, CO 19963, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Heifner, Richard G. & Kinoshita, Randal, 1994. "Differences Among Commodities in Real Price Variability and Drift," Journal of Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, issue 3.
    6. Robert Engle, 2004. "Risk and Volatility: Econometric Models and Financial Practice," American Economic Review, American Economic Association, vol. 94(3), pages 405-420, June.
    7. Offutt, Susan E. & Blandford, David, 1986. "Commodity market instability : Empirical techniques for analysis," Resources Policy, Elsevier, vol. 12(1), pages 62-72, March.
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    Cited by:
    1. Larisa Nicoleta POP & Flavius ROVINARU & Mihaela ROVINARU, 2013. "Assessing The Price Risk On The Romanian Agricultural Market: Analyses And Implications," Interdisciplinary Management Research, Faculty of Economics in Osijek, Croatia, vol. 9, pages 469-479.
    2. Sukati, Mphumuzi, 2013. "Measuring Maize Price Volatility in Swaziland using ARCH/GARCH approach," MPRA Paper 51840, University Library of Munich, Germany.


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