Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models

Abstract

The performance of a proposed asymmetric-error GARCH model is evaluated in comparison to the normal-error- and Student-t-GARCH models through three applications involving forecasts of U.S. soybean, sorghum, and wheat prices. The applications illustrate the relative advantages of the proposed model specification when the error term is asymmetrically distributed, and provide improved probabilistic forecasts for the prices of these commodities.

Keywords

• garch
• nonnormality
• skewness
• time-series forecasting
• u.s. commodity prices