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Volatility transmission in the South African white maize futures market

Author

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  • Ayesha Sayed

    (University of the Witwatersrand)

  • Christo Auret

    (University of the Witwatersrand)

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.

Suggested Citation

  • Ayesha Sayed & Christo Auret, 2020. "Volatility transmission in the South African white maize futures market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 10(1), pages 71-88, March.
  • Handle: RePEc:spr:eurase:v:10:y:2020:i:1:d:10.1007_s40822-019-00128-y
    DOI: 10.1007/s40822-019-00128-y
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    Cited by:

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    2. Sarasty, Oscar & Amin, Modhurima & Badruddoza, Syed, 2022. "Impact of the COVID-19 pandemic on agricultural commodity prices," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322240, Agricultural and Applied Economics Association.
    3. Umar, Muhammad & Ji, Xiangfeng & Mirza, Nawazish & Li, Haiping, 2022. "Crypto swings and the performance of carbon-intensive equity funds in China," Resources Policy, Elsevier, vol. 78(C).
    4. Achraf Ghorbel & Ahmed Jeribi, 2021. "Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 449-467, September.
    5. Daehyeon Park & Jiyeon Park & Doojin Ryu, 2020. "Volatility Spillovers between Equity and Green Bond Markets," Sustainability, MDPI, vol. 12(9), pages 1-12, May.

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    More about this item

    Keywords

    Volatility spillover; Volatility transmission; Multivariate GARCH; Dynamic conditional correlations; White maize futures; Grain price volatility;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness

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