Do Farmers Hedge Optimally or by Habit? A Bayesian Partial-Adjustment Model of Farmer Hedging

Hedging is one of the most important risk management decisions that farmers make and has a potentially large role in the level of profit eventually earned from farming. Using panel data from a survey of Georgia farmers that recorded their hedging decisions for 4 years on four crops, we examine the role of habit, demographics, farm characteristics, and information sources on the hedging decisions made by 57 different farmers. We find that the role of habit varies widely and that estimation of a single habit effect suffers from aggregation bias. Thus, modeling farmer-level heterogeneity in the examination of habit and hedging is crucial.Bayesian econometrics, habit formation, hedging decisions, information sources, Agribusiness, Agricultural Finance, Farm Management, Financial Economics, Labor and Human Capital, Production Economics, Productivity Analysis, Research Methods/ Statistical Methods, C11, Q12, Q14,

Components of Grain Futures Price Volatility

We analyze the determinants of daily futures price volatility in corn, soybeans, wheat, and oats markets from 1986 to 2007. Combining the information from simultaneously traded contracts, a generalized least squares method is implemented that allows us to clearly distinguish among time-to-delivery effects, seasonality, calendar trend, and volatility persistence. We find strong evidence of time-to-delivery (Samuelson) effects and systematic seasonal components with volatility increasing prior to harvest times— an indirect confirmation of the theory of storage.futures markets, Samuelson effect, seasonality, time to maturity, volatility, Crop Production/Industries, Risk and Uncertainty,

What Explains High Commodity Price Volatility? Estimating a Unified Model of Common and Commodity-Specific, High- and Low-Frequency Factors

We estimate a model of common and commodity-specific, high- and low-frequency factors, built on the spline-GARCH model of Engle and Rangel (2008) to explain the period of exceptionally high price volatility in commodity markets during 2006-2008. We find that decomposing realized volatility into high- and low-frequency components reveals the impact of slowly-evolving macroeconomic variables on the price volatility. Further, we find that while macroeconomic variables have similar effects within the same commodity category (e.g., storable agricultural), they have different effects across commodity groups (e.g., live stock versus energy).volatility, spline-GARCH, futures markets, Agricultural Finance, Demand and Price Analysis,

Do Farmers Hedge Optimally or by Habit? A Bayesian Partial-Adjustment Model of Farmer Hedging

Hedging is one of the most important risk management decisions that farmers make and has a potentially large role in the level of profit eventually earned from farming. Using panel data from a survey of Georgia farmers that recorded their hedging decisions for four years on three crops we examine the role of habit, demographics, farm characteristics, and information sources on the hedging decisions made by 106 different farmers. We find that the role of habit varies widely. Information sources are shown to have significant and large effects on the chosen hedge ratios. The farmer's education level, attitude toward technology adoption, farm profitability, and the ratio of acres owned to acres farmed also play important roles in hedging decisions.Bayesian econometrics, hedging decisions, habit formation, information sources, Agricultural Finance,

Volatility Persistence in Commodity Futures:Inventory and Time-to-Delivery Effects

Most financial asset returns exhibit volatility persistence. We investigate this phenomenon in the context of daily returns in commodity futures markets. We show that the time gap between the arrival of news to the markets and the delivery time of futures contracts is the fundamental variable in explaining volatility persistence in the lumber futures market. We also find an inverse relationship between inventory levels and lumber futures volatility.volatility persistence, theory of storage, volatility, futures markets, lumber, Agricultural Finance,

Is commodity price volatility persistent? Another look using improved, full-sample estimates

Crop Production/Industries, Risk and Uncertainty,

HIGH PRICE VOLATILITY AND SPILLOVER EFFECTS IN ENERGY MARKETS

Replaced with revised version of paper 07/22/11.Asymmetric shocks, energy markets, oil, spillover effects, volatility, Marketing, Resource /Energy Economics and Policy, GARCH,

Do Inventory and Time-to-Delivery Effects Vary Across Futures Contracts? Insights from a Smoothed Bayesian Estimator

Replaced with revised version of paper 07/15/08.volatility, theory of storage, futures markets, Bayesian econometrics, lumber, Marketing,

Does Futures Price Volatility Differ Across Delivery Horizon?

We study the difference in the volatility dynamics of CBOT corn, soybeans, and oats futures prices across different delivery horizons via the smoothed Bayesian estimator of Karali, Dorfman, and Thurman (2010). We show that the futures price volatilities in these markets are affected by the inventories, time to delivery, and the crop progress period. Some of these effects vary across delivery horizons. Further, it is shown that the price volatility is higher before the harvest starts in most of the cases compared to the volatility during the planting period. These results have implications for hedging, options pricing, and the setting of margin requirements.Bayesian econometrics, futures markets, seasonality, theory of storage, volatility, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Consumer/Household Economics, Demand and Price Analysis, Farm Management, Financial Economics, Marketing, Research Methods/ Statistical Methods, Risk and Uncertainty,

Assessing the Market for Poultry Litter in Georgia: Are Subsidies Needed to Protect Water Quality?

Concerns about nutrient loads into our waters have focused attention on poultry litter applications. Like many states with a large poultry industry, Georgia recently designed a subsidy program to facilitate the transportation of poultry litter out of vulnerable watersheds. This paper uses a transportation model to examine the necessity of a poultry litter subsidy to achieve water protection goals in Georgia. We also demonstrate the relationship between diesel and synthetic fertilizer prices and the value of poultry litter. Results suggest that a well functioning market would be able to remove excess litter from vulnerable watersheds in the absence of a subsidy.fertilizer, phosphorous, poultry litter, subsidy, transportation model, water quality, Environmental Economics and Policy, Marketing, Q12, Q13, Q25, Q53,