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A Study of Seasonality on the Safex Wheat Market

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  • Chris Motengwe
  • Angel Pardo

Abstract

This paper examines seasonality in returns and volatilities in the South African Futures Exchange (SAFEX) wheat futures contract in order to seek market inefficiencies that can be exploited for financial gain. Non-parametric and parametric-based techniques are used to study sample regimes before and after the peak in wheat prices that occurred during the global economic crisis in 2008. Findings of the study indicate that wheat returns on Mondays and Kansas City Board of Trade (KCBT) holidays are significant and positive while Tuesday returns are negative and significant. These seasonal patterns occur largely in the second sample of the wheat dataset. Furthermore, it is observed that volatility diminished after the global financial crisis. Finally, based on the return seasonality detected and by applying Monte Carlo simulation in an out-of-sample period, some trading rules are developed that yield higher returns than any trading approach based on chance.

Suggested Citation

  • Chris Motengwe & Angel Pardo, 2015. "A Study of Seasonality on the Safex Wheat Market," Agrekon, Taylor & Francis Journals, vol. 54(4), pages 45-72, November.
    • Handle: RePEc:taf:ragrxx:v:54:y:2015:i:4:p:45-72
    DOI: 10.1080/03031853.2015.1116398
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    1. Nabil Khoury & Pierre Yourougou, 1993. "Determinants of agricultural futures price volatilities: Evidence from winnipeg commodity exchange," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(4), pages 345-356, June.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    4. Taufiq Choudhry, 2000. "Day of the week effect in emerging Asian stock markets: evidence from the GARCH model," Applied Financial Economics, Taylor & Francis Journals, vol. 10(3), pages 235-242.
    5. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    6. Auer, Benjamin R., 2014. "Daily seasonality in crude oil returns and volatilities," Energy Economics, Elsevier, vol. 43(C), pages 82-88.
    7. Ronald W. Anderson, 1985. "Some determinants of the volatility of futures prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 5(3), pages 331-348, September.
    8. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    9. Seung‐Ryong Yang & B. Wade Brorsen, 1993. "Nonlinear dynamics of daily futures prices: Conditional heteroskedasticity or chaos?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(2), pages 175-191, April.
    10. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    11. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    12. Fabozzi, Frank J & Ma, Christopher K & Briley, James E, 1994. "Holiday Trading in Futures Markets," Journal of Finance, American Finance Association, vol. 49(1), pages 307-324, March.
    13. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    14. Jordaan, Henry & Grove, Bennie & Jooste, Andre & Alemu, A.G., 2007. "Measuring the Price Volatility of Certain Field Crops in South Africa using the ARCH/GARCH Approach," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 46(3), pages 1-17, September.
    15. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    16. Nikolaos T. Milonas, 1986. "Price variability and the maturity effect in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 6(3), pages 443-460, September.
    17. Nikolaos T. Milonas, 1991. "Measuring seasonalities in commodity markets and the half‐month effect," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 11(3), pages 331-345, June.
    18. Phukubje, M.P. & Moholwa, Motlatjo B., 2006. "Testing for weak-form efficiency in South African futures market for wheat and sunflower seeds," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 45(2), pages 1-16, June.
    19. David Kenyon & Kenneth Kling & Jim Jordan & William Seale & Nancy McCabe, 1987. "Factors affecting agricultural futures price variance," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 7(1), pages 73-91, February.
    20. 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.
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