Forecasts of economic time series are often evaluated according to their accuracy as measured by either quantitative precision or qualitative reliability. We argue that consumers purchase forecasts for the potential utility gains from utilizing them, not for their accuracy. Using Monte Carlo techniques to incorporate the temporal heteroskedasticity inherent in asset returns, the expected utility of a set of qualitative forecasts is simulated for corn and soybean futures prices. Monetary values

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... or forecasts of various reliability levels are derived. The method goes beyond statistical forecast evaluation, allowing individuals to incorporate their own utility function and trading system into valuing a set of asset price forecasts.