Price Volatility Forecasts for Agricultural Commodities: An Application of Historical Volatility Models, Option Implieds and Composite Approaches for Futures Prices of Corn and Wheat

There has been substantial research effort aimed to forecast futures price return volatilities of financial and commodity assets. Some part of this research focuses on the performance of time-series models (in particular ARCH models) versus option implied volatility models. A significant part of the literature related to this topic shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of volatility forecast models for the case of corn and wheat futures price returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), an option implied and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. The results show that the option implied model is superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-square-errors. Given these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied. In addition, the results of this paper are consistent to that part of the literature that emphasizes the difficulty on being accurate about forecasting asset price return volatility. This is because the explanatory power (coefficient of determination) calculated in the forecast regressions were relatively low.