52 research outputs found

    Corn-Crush Hedging – Does Location Matter?

    No full text
    At the end of 2018, 200 ethanol refineries were operating in the U.S. and processing nearly 40 percent of U.S. corn production. These refineries are widely dispersed and the typical ethanol refining firm operates several plants. Hedging is widely used as a price risk management strategy. The dispersion of a given firm’s plants leads us to ask the question posed by the title of this paper – should the plant’s location be considered in constructing a hedging program? We tested this notion by drawing a sample from the plants operated by a multi-state/multi-plant ethanol refiner. We interviewed plant managers to get information about input-output coefficients, inventory turnover, plant efficiency comparisons, and hedging horizons. This information informed our modelling. To test our premise, we sought plant location prices for corn, natural gas, ethanol, dried distiller gran, and distiller corn oil. Local corn prices were obtained but we had to use proxies and state averages for the other prices. Standard hedging methodology was applied as we examined two hedging strategies: (a) hedging the crush margin, and (b) hedging individual commodity transactions then combining these hedges according to the input-output coefficients to hedge the crushing margin. Approach (b) produced better results but the data limitations hindered testing our main hypothesis. In addition to variations in hedge ratios for each location, we also discovered that (a) storage periods for input and output inventories are short (1 to 2 weeks), (b) input-output coefficient variability across plants creates opportunities for location specific hedging strategies, and (c) previous studies that are based on aggregated cash prices likely overstate the effectiveness of local hedging strategies

    Is “Good Enough” Good Enough When Hedging Agricultural Commodities?

    No full text
    This paper explores the returns to grain producers and processors from expending efforts to determine hedge ratios. We use cash and futures prices from Barchart.com and multi-location cash prices from the Daily Grain Review to determine if location-specific hedge ratios are superior to hedge ratios estimated for a central location then used for hedging at the specific location. We find generally that the price-risk management capabilities of central market hedge ratios computed from central market data perform well in hedging corn, oats, soybeans, and soybean products at non-central market locations. This finding does not generally apply to wheat. Producers and processors of the commodities covered by our general findings will see only modest price-risk management gains from determining precise hedge ratios that apply to their location. In other words, “good enough” hedge ratios are, in fact, good enough for these agents so that cooperative efforts to find central market hedge ratios will benefit all market participants

    Automation in the Hedge-Ratio Estimation Cottage Industry

    No full text
    Futures markets can be used to minimize a firm’s financial exposure to cash price fluctuations, but it’s costly to determine the futures position size that minimizes this risk. We present survey results that indicate that finding the risk-minimizing futures position requires 160 hours of skilled market analysts’ time spread over 60 days and costs between 15,000and15,000 and 25,000. This process can be automated so that optimal futures positions can be determined in minutes at a fraction of this cost. We introduce HedgeSmart, software that determines the optimal hedging strategy by combining user-supplied, business-specific data with the generally accepted price-risk minimization model and an up-to-date database containing more than 10 million records on commodity price movements. The user can incorporate his/her own historical commodity prices to insure that the analysis reflects specific location, grade, and pricing characteristics as appropriate to your firm. The time and cost savings that HedgeSmart achieves enables analysts to ask “what-if” questions, to explore alternative hedging approaches

    Ethanol Futures: Thin but Effective? —Why?

    No full text
    This study examines the paradox where the ethanol futures market provides effective hypothetical hedges yet the use of this market is shunned by those with ethanol cash market positions because of its limited volume and open interest. Examining this issue requires describing ethanol cash, futures, and swaps markets, and ethanol contracting practices. We observe that ethanol futures open interest is about two percent of annual U.S. usage compared to nine percent in gasoline markets. We also observe that an attempt by a single refiner to fully hedge its production would significantly alter the volume/open interest profile of the ethanol futures market. In this respect, the ethanol futures market is thin. The ethanol futures market is nonetheless efficient except in the final month of a contract’s life. We examine causality relationships between the ethanol futures and swaps markets and find that the futures market adjusts to swaps market disequilibrium but the converse does not hold. The implications of these findings are (1) because futures equilibrium open interest adjusts to changes in swaps equilibrium open interest, the futures price reflects conditions in the deeper swaps market as well as in the futures market, (2) because of (1) using the futures settlement price for marking swaps to market provides secure bonding in the over-the-counter ethanol derivatives (swaps) market, and (3) inefficiencies in the futures market during the last month of a contract’s life are likely due to the swaps market’s use of the cumulative average of the futures prices during the last month of the swap contract’s life

    Tests of the Difference between In-Sample and Post-Sample Hedging Effectiveness

    No full text
    Hedging effectiveness is proportional price-risk reduction achieved by hedging. Typically, hedging studies estimate hedging effectiveness for the sample period then use estimated hedge ratios to simulate hedging and estimate hedging effectiveness in a “post-sample” period. This paper derives the statistical properties of the sample-period effectiveness estimator and the statistical properties of the difference between the sample-period and the post-sample period estimators. We find that the bias associated with the sample-period estimator is negligible and that a difference between the sample estimator and the post-sample estimator ties directly to changes in the structural parameters of the hedge-ratio regression. We develop tests for structural change and demonstrate those tests with an empirical example

    You Know It's Going to Be a Bad Day When a 60 Minutes Camera Crew Is Waiting for You at Work - A Case Study of Chicken Contamination Publicity

    No full text
    Adverse publicity about food contamination can depress demand, causing lost producer revenue. TV and print news coverage of bacterial contamination of chicken in the U.S. is incorporated into an inverse demand for chicken which is estimated using 1982 and 1991 data. A beta binomial audience-exposure distribution is used to estimate net reach and average frequency of exposure to contamination publicity. It was found that for each unit of increase in weekly publicity frequency, prices were depressed by 1.2 percent, leading to a $760 million retail loss to the chicken industry. This amounts to less than one-quarter of one percent of revenue over the ten years studied
    • …
    corecore