an image The task of this essay is to examine price discovery, volatility spillovers and impacts of speculation in the dairy sector. I have developed a method that allows for the calculation of implied cheese futures prices for a period before cheese futures actually started trading. Evidence for periods when cheese futures did trade suggests that utilized approximation methods perform very well. Examining the time series properties of cheese cash and implied futures price I find that the unit root hypothesis is strongly rejected for cash prices, while unit roots cannot be rejected for nearby futures prices in the framework that carefully controls for rollovers. To explain this result, I built a model that illustrates the time series properties of the nearby futures price series for a futures contract written on a second-order stationary cash series and identified the mean-reverting nonlinear dynamics that will occur at rollovers. Given the time series properties of the cash and futures series I propose an error-correction-type model using spreads between cash and the second nearby futures instead of the cointegration vector. To account for volatility dynamics I employ the GARCH-BEKK structure. I find that the flow of information in the mean model is predominantly from futures to cash, while volatility spillovers are bidirectional. It is possible that cash prices that include unfilled bid/offers react differently to increases in volatility in futures prices than sales cash prices, though this result may not be robust and further research is needed to identify if liquidity in the cash market is reduced with increase in conditional volatility of the futures price. I propose an extension of the BEKK variance model that I refer to as GARCH-MEX. That model does not restrict the sign of the additional regressors on the conditional variances, and can easily insure positive-definiteness of the conditional covariance matrix. Utilizing that model to evaluate the impact of speculation I find strong evidence against the hypothesis that excessive speculation is increasing the conditional variance of futures prices. If anything, speculation may in fact be inadequate, and further research with daily speculative positions and high-frequency futures prices is needed to identify the effect of increased speculation on realized volatility of futures prices, bid-ask spread and magnitude of slippage.

July, 2011

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an image Options on agricultural futures are popular financial instruments used for agricultural price risk management and to speculate on future price movements. Poor performance of Black's classical option pricing model has stimulated many researchers to introduce pricing models that are more consistent with observed option premiums. However, most models are motivated solely from the standpoint of the time series properties of futures prices and need for improvements in forecasting and hedging performance. In this paper I propose a novel arbitrage pricing model motivated from the economic theory of optimal storage, and consistent with implications of plant physiology on the importance of weather stress. I introduce a pricing model for options on futures based on a Generalized Lambda Distribution (GLD) that allows greater flexibility in higher moments of the expected terminal distribution of futures price. I use times and sales data for corn futures and options for the period 1995-2009 to estimate the implied skewness parameter separately for each trading day. An economic explanation is then presented for inter-year variations in implied skewness based on the theory of storage. After controlling for changes in planned acreage, I find a statistically significant negative relationship between ending stocks-to-use and implied skewness, as predicted by the theory of storage. Furthermore, intra-year dynamics of implied skewness reflect the fact that resolution of uncertainty in corn supply is resolved between late June and middle of October, i.e. during corn growth phases that encompass corn silking through grain maturity. Impacts of storage and weather on the distribution of terminal futures price jointly explain upward sloping implied volatility curves.

July, 2011

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an image This paper begins with an account of the evolution of dairy futures markets in the U.S. A partially overlapping time series (POTS) model is then estimated to examine price behavior in simultaneously traded Class III milk futures contracts. POTS is a latent factor model that measures price changes in futures as a linear combination of a common factor, i.e. information affecting all traded contracts, and an idiosyncratic term specific to each contract. This paper contributes to the literature by showing that the importance of a common factor in price volatility determination for dairy is related to capital production factors, i.e. the dairy herd. Finally, it is shown that Class III milk futures volatility decreases as contracts approach maturity. This "Inverse Samuelson Effect" comes from the fact that Class III milk futures are cash-settled contracts that settle against a formula-based price. The results suggest that the importance of the common factor declines as one approaches maturity, implying that individual contract months are poor substitutes in hedging a specific month's cash price risk. Thus, despite low liquidity in the market, it is useful to have 12 contract delivery months per year.

July, 2011


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an image The econometric analysis contained in this study adopts the modeling principle currently used by USDA for policy analysis whereby milk production is modeled as a product of dairy herd size and yield per cow, stochastic elements that are modeled separately (USDA 2007). We account for herd size dynamics along the lines of models proposed by Chavas and Klemme (1986) and Schmitz (1997). We contribute to the literature by introducing a mixed frequency approach that allows us to model yield using quarterly data, while dairy herd and replacement heifer pool is modeled using annual frequency. That allows us to model the yield as a trend-stationary process with the economic environment inducing both short-term shocks and impacting the speed of reversion to trend yields. In addition, we design and implement dynamic residual-based bootstrap technique that can be used in testing for changes in non-marginal simulated long-run supply responsiveness. We obtain strong in-sample predictive power and very high significance of key economic and herd structure variables. Several conclusions emerge from our study. First, given the large difference between short-run and long-run responses of production to price changes, policy makers should consider more than short run responses to future policy changes. What may in the short-run seem like a minor impact that does not disturb market equilibrium can indeed lead to large production surpluses after more time has passed and dairy herd size has had adequate time to adjust to the new policy environment. Second, despite dramatic yield improving technological change, improved genetics and the increasing importance of large farms, all of which we would expect to increase the milk supply price responsiveness, we find a declining trend in long-run supply responsiveness from 1975 through 2005. If such decline were to persist or continue that would be a major cause for worry, as ever larger price swings would be needed to quickly equilibrate the market in face of demand shocks. However, we find that milk supply is getting more responsive in recent several years both to milk and feed prices. We recommend extending this analysis using micro-level data to examine farm-level behavior in face of price-swings. Increasing responsiveness to feed prices further justifies focusing the next generation of dairy policy toolbox on managing dairy profit margins rather than just revenue streams. Further research needs to be done to develop a partial equilibrium model of the U.S. dairy sector based on insights on structural characteristics of the production presented in this article. We believe that when combined with a model of the demand for dairy products our work has a potential to improve reliability of long-run projections of the U.S. milk production.

July, 2011

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