Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models

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.


Issue Date:
2003-04
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/30714
Published in:
Journal of Agricultural and Resource Economics, Volume 28, Number 1
Page range:
71-85
Total Pages:
15




 Record created 2017-04-01, last modified 2017-08-24

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