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SAFEX maize price volatility scrutinised

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  • Geyser, Mariette
  • Cutts, Michela
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    Abstract

    Commodity prices in general are known to have a high volatility. This is in fact what attracts speculators. The South African futures exchange (SAFEX) is not immune to this volatility. Volatility increases the risk of paying higher prices for a specific commodity, and it also makes the use of derivative instruments to hedge against price risk more expensive. Given the importance of South Africa as a regional supplier of maize and price discovery mechanism, investigations into the volatility of the maize price are not only important, but also indispensable if all parties involved are to manage this risk. The question therefore is whether the SAFEX maize price volatility can be explained by using fundamental factors or whether this volatility is unexplainably high.

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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)
    Pages:

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    Handle: RePEc:ags:agreko:8009

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    Web page: http://www.aeasa.org.za/
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    Related research

    Keywords: Derivative; price volatility; call option; hedging; food risk; SAFEX; CBOT; Demand and Price Analysis;

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    1. Louis Eeckhoudt & Harris Schlesinger, 2006. "Putting Risk in Its Proper Place," American Economic Review, American Economic Association, vol. 96(1), pages 280-289, March.
    2. Anderson, Kym & Martin, Will & Valenzuela, Ernesto, 2006. "The Relative Importance of Global Agricultural Subsidies and Market Access," CEPR Discussion Papers 5569, C.E.P.R. Discussion Papers.
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    5. Cohen, Benjamin H, 1999. "Derivatives, Volatility and Price Discovery," International Finance, Wiley Blackwell, vol. 2(2), pages 167-202, July.
    6. Koray, Faik & Lastrapes, William D, 1989. "Real Exchange Rate Volatility and U.S. Bilateral Trade: A VAR Approach," The Review of Economics and Statistics, MIT Press, vol. 71(4), pages 708-12, November.
    7. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    8. Szego, Giorgio, 2002. "Measures of risk," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1253-1272, July.
    9. Kroner, Kenneth F. & Kneafsey, Devin P. & Claessens, Stijn & DEC, 1993. "Forecasting volatility in commodity markets," Policy Research Working Paper Series 1226, The World Bank.
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