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Volatility transmission in the South African white maize futures market

  • Ayesha SayedEmail author
  • Christo Auret
Original Paper
  • 11 Downloads

Abstract

Research in the United States’ agricultural futures markets have found maize (what they refer to as corn) to be the commodity that most broadly received and transmitted volatility transmissions. South Africa is the main emerging market for price discovery of maize in Africa, with white maize being the largest and most liquid agricultural commodity futures contract traded on the South African Futures Exchange (SAFEX). This paper examines volatility spillover effects in white maize futures against several other domestic grain and external market futures listed on SAFEX. Using daily return data, a multivariate GARCH approach is employed to study spillover effects between grain futures (white maize, yellow maize, wheat and sunflower seed), currency futures (Dollar/Rand and Euro/Rand), equity futures (JSE Top 40 Index) and interest rate futures (JIBA). A Dynamic Conditional Correlation (DCC) model is used to evaluate the degree of interdependence between futures markets which is measured through a time-variant conditional correlation matrix. The results indicate that the South African futures markets analyzed are highly interrelated, with significant dependence and volatility transmissions being observed. Furthermore, the results also highlight that these interrelations are changing over time. The findings have important implications for portfolio allocations, hedging strategies and policy and regulatory initiatives.

Keywords

Volatility spillover Volatility transmission Multivariate GARCH Dynamic conditional correlations White maize futures Grain price volatility 

JEL classification

C32 G13 G14 G32 Q13 

Notes

References

  1. Adelegan, O. J. (2009). The derivatives market in South Africa: Lessons for sub-Saharan African countries. International Monetary Fund 2009, IMF Working Paper WP/09/196, 1–19.Google Scholar
  2. Alexander, C. (2008). Market risk analysis: Practical financial econometrics (II ed.). Chichester: Wiley.Google Scholar
  3. Andersen, T. G., & Bollerselv, T. (1997). Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance, 4(2–3), 115–158.  https://doi.org/10.1016/S0927-5398(97)00004-2.CrossRefGoogle Scholar
  4. Auret, C. J., & Schmitt, C. C. (2008). An explanatory model of South African white maize futures prices. Studies in Economics and Econometrics, 32(1), 103–131.Google Scholar
  5. Beckmann, J., & Czudaj, R. (2014). Volatility transmission in agricultural futures markets. Economic Modelling, 36, 541–546.  https://doi.org/10.1016/j.econmod.2013.09.036.CrossRefGoogle Scholar
  6. Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. The Review of Economics and Statistics, 72(3), 498–505.CrossRefGoogle Scholar
  7. Booth, G. G., & Ciner, C. (1997). International transmission on information in corn futures markets. The Journal of Multinational Financial Management., 7(3), 175–187.  https://doi.org/10.1016/S1042-444X(97)00012-1.CrossRefGoogle Scholar
  8. CIMMYT. (2018). Spotlight on the international maize and wheat improvement center. Cereal Foods World, 63(5), 226–227.Google Scholar
  9. Conrad, J., Gultekin, M. N., & Kaul, G. (1991). Asymmetric predictability of conditional variances. The Review of Financial Studies, 4(1), 597–622.  https://doi.org/10.1093/rfs/4.4.597.CrossRefGoogle Scholar
  10. Engle, R. (2002). Dynamic conditional correlation—a simple class of multivariate GARCH models. Journal of Business & Economic Statistics, 20(3), 339–350.  https://doi.org/10.1198/073500102288618487.CrossRefGoogle Scholar
  11. Engle, R. F., Ito, T., & Lin, W. L. (1990). Meteor showers or heat waves? Heteroskedastic intra-daily volatility in the foreign exchange market. Econometrica, 58(3), 525–542.  https://doi.org/10.3386/w2609.CrossRefGoogle Scholar
  12. Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory, 11(1), 122–150.  https://doi.org/10.1017/S0266466600009063.CrossRefGoogle Scholar
  13. Engle, R. F., & Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. NBER Working Paper 8554, National Bureau of Economic Research.  https://doi.org/10.3386/w8554.
  14. Felipe S. P., & Diranzo C. F. (2006). Volatility transmission models: A survey. Revista De Economia Financiera, 10, 32–81.Google Scholar
  15. Gallagher, L. A., & Twomey, C. E. (1998). Identifying the source and of mean and volatility spillovers in Irish equities: A multivariate GARCH analysis. The Economic and Social Review, 29(4), 341–356.Google Scholar
  16. Gardebroek, C., Hernandez, M. A., & Robles, M. (2016). Market interdependence and volatility transmission among major crops. Agricultural Economics, 47(2), 141–155.  https://doi.org/10.1111/agec.12184.CrossRefGoogle Scholar
  17. Geyser, M., & Cutts, M. (2007). SAFEX maize price volatility scrutinized. Agrekon, 46(3), 291–305.  https://doi.org/10.1080/03031853.2007.9523773.CrossRefGoogle Scholar
  18. Ghalanos, A. (2018). rmgarch: Multivariate GARCH models. R package version 1.3.5. https://cran.r-project.org/web/packages/rmgarch/citation.html. Accessed 19 Oct 2018.
  19. Grieb, T. (2015). Mean and volatility transmission for commodity futures. Journal of Economics and Finance, 39(1), 100–118.  https://doi.org/10.1007/s12197-012-9425-8.CrossRefGoogle Scholar
  20. Hamao, Y., Masulis, R. W., & Ng, V. (1990). Correlation in price changes and volatility across international stock markets. The Review of Financial Studies, 3(2), 281–307.  https://doi.org/10.1093/rfs/3.2.281.CrossRefGoogle Scholar
  21. Hernandez, M. A., Ibarra, R., & Trupkin, D. R. (2014). How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets. European Review of Agricultural Economics, 41(2), 301–325.  https://doi.org/10.1093/erae/jbt020.CrossRefGoogle Scholar
  22. Hurditt, P. (2004). An assessment of volatility transmission in the Jamaican financial system. Journal of Business, Finance and Economics in Emerging Economies, 1(1), 1–28.Google Scholar
  23. Kalu, E. (2014). Volatility transmission between stock and foreign exchange markets: Evidence from Nigeria. Journal of Banking and Financial Economics, 1(1), 59–72.  https://doi.org/10.7172/2353-6845.jbfe.2014.1.4.CrossRefGoogle Scholar
  24. King, M., & Wadhwani, S. (1990). Transmission of volatility between stock markets. The Review of Financial Studies, 3(1), 5–33.  https://doi.org/10.1093/rfs/3.1.5.CrossRefGoogle Scholar
  25. Kotze, A. (2017). FTSE/JSE Top 40 index long-term returns (May 31, 2017).  https://doi.org/10.2139/ssrn.2978093.
  26. Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315–13350.  https://doi.org/10.2307/1913210.CrossRefGoogle Scholar
  27. Le Pen, Y., & Sevi, B. (2010). Revisiting the excess co-movements of commodity prices in a data-rich environment. Working paper, France: Université d’Angers and Université de Nantes.Google Scholar
  28. Liu, Y. A., & Pan, M. S. (1997). and volatility spillover effects in the U.S. and Pacifin-Basin stock markets. Multinational Finance Journal, 1(1), 47–62.CrossRefGoogle Scholar
  29. Monk, M. J., Jordaan, H., & Grove, B. (2010). Factors affecting the price volatility of July futures contracts for white maize in South Africa. Agrekon, 49(4), 446–458.  https://doi.org/10.1080/03031853.2010.526420.CrossRefGoogle Scholar
  30. Protopapadakis, A., & Stoll, H. R. (1983). Spot and futures prices and the law of one price. The Journal of Finance, 38(5), 1431–1455.  https://doi.org/10.1111/j.1540-6261.1983.tb03833.x.CrossRefGoogle Scholar
  31. RStudio (2018). RStudio: Integrated development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com
  32. Sayed, A., & Auret, C. J. (2018). The effectiveness of price limits in the South African white maize futures markets. Investment Analysts Journal, 47(3), 200–212.  https://doi.org/10.1080/10293523.2018.1475591.CrossRefGoogle Scholar
  33. The Maize Trust (2013). Prospectus on the South African Maize Industry, 1-20. https://www.jse.co.za/content/JSEBrochureItems/Prospectus%20on%20the%20South%20African%20Maize%20Industry.pdf. Accessed 3 March 2019.
  34. USDA WASDE. (2018). United States Department of Agriculture World Agricultural Supply and Demand Estimates. In: Released 11 th October 2018 by the National Agricultural Statistics Service (NASS), Agricultural Statistics Board, United States Department of Agriculture (USDA) Google Scholar
  35. Wright, B. D. (2011). The economics of grain price volatility. Applied Economic Perspectives and Policy, 33(1), 32–58.  https://doi.org/10.1093/aepp/ppq033.CrossRefGoogle Scholar
  36. Yang, J., Bessler, D. A., & Leatham, D. J. (2001). Asset storability and price discovery in commodity futures markets: A new look. The Journal of Futures Markets, 21(3), 279–300.  https://doi.org/10.1002/1096-9934(200103)21:3%3c279:AID-FUT5%3e3.0.CO;2-L.CrossRefGoogle Scholar
  37. Zhao, J., & Goodwin, B. (2011). Volatility spillovers in agricultural commodity markets: An application involving implied volatilities from options markets. In: Paper prepared for presentation at the Agricultural and Applied Economics Association’s 2011 Annual Meeting, Pittsburgh, Pennsylvania, July 24–26, 2011.Google Scholar

Copyright information

© Eurasia Business and Economics Society 2019

Authors and Affiliations

  1. 1.School of Economic and Business SciencesUniversity of the WitwatersrandJohannesburgSouth Africa

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