Examining Volatility Persistence and News Asymmetry in Soybeans Futures Returns

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.