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The study's purpose is to measure the extent to which futures and option prices reflect the subjective price distribution of a subset of market participants, farmers, and grain merchandisers in Illinois. Findings suggest that in most instances the futures price is an appropriate proxy for expected price. However, volatilities implied by option premia usually overestimate the subjective variances of producers and merchandisers. These differences between individual and market expectations of variance are consistent with findings of overconfidence in the psychology literature and should be considered by analysts when making observations about hedging decisions and risk aversion.

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Investment benefits from trading live cattle, hog, com, and soybean futures contracts are considered under the assumption that the investor's risk/return evaluation is relative to a highly diversified stock portfolio. A mean-variance approach is used to find the "optimal" mix of investments for the initial stock portfolio and for portfolios which may include both stocks and futures. The addition of futures contracts to the portfolio rarely increases the portfolio return. This finding is consistent with risk-premium results of previous studies. However, investment benefits from agricultural futures are found in the form of a reduction in the portfolio's nonsystematic risk.

he newly traded options on agricultural futures contracts offer speculative T instruments to investors and additional pricing strategies to agricultural producers and agribusinesses. Regardless of how and why the options are used, the level and expected change in option premia are obviously of central importance in the decision-making process. Traditional option pricing models provide an excellent framework for initial evaluation of the premium. However, during the past decade, numerous variants of the traditional model have been developed to account for theoretical assumptions which may not hold in practice.The objectives of this article, corresponding respectively with the five following sections, are: (a) to describe the fundamental concepts leading to traditional optionpremium solutions through examples which assume a simple dynamic process for the underlying commodity price; (b) to identify potential factors causing market premia to differ from the traditional solution; (c) to describe criteria, given market premia, which can be used to identify which of these factors are important; (d) to exemplify a factor identification process by examining the first five months of trading in the soybean futures option market; and (e) to suggest research needs.

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Map 1. Primary inland waterway system for exportbound barge grain shipments 4 Map 2. Production regions of Pedeler, Heady, and Koo 21 Map 3. Demand regions of Pedeler, Heady, and Koo 22 Map 4. Supply and demand regions of Leath and Blakely 23 Map 5-Production regions of Taylor,

The behavior of a commodity's price-return variance over time is critical to both the theory and practice of commodity option valuation. In this paper three models are used to forecast soybean price variance for the period during which a seasonal increase in variance has been found in previous studies. A time-series model outperforms the ordinary least squares and naive models. The significance of the forecast error levels is then examined in terms of expected deviations above and below a price target for a put hedge. The resulting trade-off between risk and return is shown by strike price and variance expectation.