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Examining Volatility Persistence and News Asymmetry in Soybeans Futures Returns

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  • Naveen Musunuru

    (Murray State University)

Abstract

Four alternative generalized autoregressive conditional heteroscedasticity (GARCH), and three asymmetric GARCH models (EGARCH, TGARCH and APARCH) are used to examine the presence of volatility persistence and news asymmetry in soybeans futures data. Presence of fat tails in the data series resulted in applying Student’s-t and generalized error distributions in addition to Gaussian normal distribution. The results reveal that soybean return series exhibit volatility characteristics typical of a financial time series. The findings of this study indicate that the leverage effect was absent for soybeans suggesting that positive news causes more volatility to the commodity than negative news. Results further suggest that the fit of the GARCH models is improved by applying t-distribution errors. The diagnostic tests reveal that GARCH models are correctly specified and among all the competing models, APARCH (1,3) model with t-distribution performed best in capturing the volatility.

Suggested Citation

  • Naveen Musunuru, 2016. "Examining Volatility Persistence and News Asymmetry in Soybeans Futures Returns," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 44(4), pages 487-500, December.
    • Handle: RePEc:kap:atlecj:v:44:y:2016:i:4:d:10.1007_s11293-016-9517-3
    DOI: 10.1007/s11293-016-9517-3
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    1. Jian Yang & Michael Haigh & David Leatham, 2001. "Agricultural liberalization policy and commodity price volatility: a GARCH application," Applied Economics Letters, Taylor & Francis Journals, vol. 8(9), pages 593-598.
    2. Wang, Nanying & Houston, Jack, 2015. "The Comovement between Non-GM and GM Soybean Price in China: Evidence from Dalian Futures Market," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196775, Southern Agricultural Economics Association.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. 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.
    5. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    6. Trujillo-Barrera, Andres & Mallory, Mindy L. & Garcia, Philip, 2012. "Volatility Spillovers in U.S. Crude Oil, Ethanol, and Corn Futures Markets," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), pages 1-16, August.
    7. Hens, Thorsten & Steude, Sven C., 2009. "The leverage effect without leverage," Finance Research Letters, Elsevier, vol. 6(2), pages 83-94, June.
    8. Nicholas Apergis & Anthony Rezitis, 2003. "Agricultural price volatility spillover effects: the case of Greece," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 30(3), pages 389-406, September.
    9. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
    10. Wang, Nanying & Houston, Jack E., 2015. "The Co-movement between Non-GM and GM Soybean Price in China: Evidence from China Futures Market," 2015 Conference, August 9-14, 2015, Milan, Italy 211914, International Association of Agricultural Economists.
    11. 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.
    12. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    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. Jondeau, Eric & Rockinger, Michael, 2003. "Conditional volatility, skewness, and kurtosis: existence, persistence, and comovements," Journal of Economic Dynamics and Control, Elsevier, vol. 27(10), pages 1699-1737, August.
    17. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
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