Abstract
A futures price forecasting model is presented which uses monthly futures prices, cash prices received, basis values (cash prices less futures) and marketing weights to forecast the season-average farm price for US corn. Performance of the model forecasts is examined using standard measures, such as mean absolute error, mean absolute percentage error and mean squared error. Tests for statistical differences between the futures model forecast and price projections from the US Department of Agriculture (USDA) are conducted using the Modified Diebold–Mariano test statistic. A measurement of price volatility identified the past 3 crop years, 2006/2007–2008/2009 with increased volatility compared to the prior 6 years, 2000/2001–2005/2006. Forecast errors from the futures forecast model increased during these volatile price years compared to the prior 6 year period which exhibited more stability. Suggestions are made to improve model price forecasts during periods of price volatility.
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Notes
- 1.
Counter-cyclical payments: Counter-cyclical payments are available to producers with historic program payment acres and yields of wheat, corn, barley, grain sorghum, oats, upland cotton, long-grain and medium-grain rice, soybeans, other oilseeds, peanuts and pulse crops (dry peas, lentils, small and large chickpeas). Payments are made whenever the current effective commodity price is less than the target price. The effective price is calculated by adding: (1) the national average farm price for the marketing year, or the commodity national loan rate, whichever is higher and (2) the direct payment rate for the commodity. For more information, see: http://www.ers.usda.gov/publications/FDS/JAN05/fds05a01/.
- 2.
Average crop revenue election (ACRE): An optional revenue-based program provision introduced in the 2008 farm legislation that replaces counter-cyclical payments for those producers who elect to participate in ACRE. Once producers elect to participate, participation continues until 2012. Producers continue to receive reduced direct payments and are eligible for reduced loan deficiency payments. For more information, see: http://www.ers.usda.gov/Publications/ERR84/.
- 3.
Future work will determine whether creating a composite forecast from futures model forecasts and WASDE projections provide improved forecasts compared to those from the futures price model. For example, see Sanders and Manfredo (2005).
- 4.
Adequate supplies of a commodity usually cause the more distant futures to trade at a higher price than the nearby futures; this is referred to as a normal market. In such a market situation, the amount of the difference, or spread, between the futures months for a stored commodity tells the trader what the market will pay, on a given day, for the costs of carrying the commodity over time storage, interest and insurance. This amount is rarely equal to the full carrying charge, total cost of storage, interest rates and insurance, and it varies among different commodities (Besant, 1985, p. 70).
- 5.
When supply and demand indicate that a shortage exists, the premium will narrow on the deferred contract months. If a scarcity develops, the carrying charges will disappear or actually “invert”. This situation is called an “inverted market” and it reflects negative carrying charges. Scarcity causes high prices in the cash and nearby futures contracts because the market gives priority to the present and discounts the future. Essentially the market is saying to the contract holder that it will pay a premium for the commodity if it is delivered now (Besant, 1985, p. 71).
- 6.
- 7.
The nearby futures price is always used except when the forecast month coincides with the closing month of the nearby futures contract. For this situation, the next nearby futures contract is selected. This procedure is followed because futures prices for the maturing contract may be affected by a decline in liquidity during the month of maturity. Also, a contract usually closes about the third week of the month, and using the current futures contract during its closing month would lower the number of observations that could be used to calculate the average monthly closing price and corresponding basis.
- 8.
This procedure provides a spot forecast based on the nearby or deferred contract, but the national average monthly price reported by the National Agricultural Statistics Service (NASS) is the price actually received for the crop that was delivered for the given month, which may be more than or less than the simple average of the daily average of prices posted by elevators for spot delivery. For example, July and August 2004 NASS prices were above the average of daily spot prices because farmers were delivering grain at prices that were contracted in the spring when corn prices were higher. Thus, there may be some error introduced by a time lag from when the farmer priced the grain to when it was actually delivered and recorded by NASS. Futures prices are based on “today’s” values.
- 9.
Several factors affect the basis and help explain why the basis varies from one location to another. Some of these factors include: local supply and demand conditions for the commodity and its substitutes, transportation and handling charges, transportation bottlenecks, availability and costs of storage, drying capacities, grain quality and market expectations.
- 10.
Future calculations will update these performance measures. However, for the time being it was determined that the present time span would be representative of any differences between the WASDE projections and the futures model forecasts.
- 11.
Since the futures model deviates from the original assumptions of the efficient market hypothesis, the futures model forecasts will not be used to test for futures market efficiency.
- 12.
These statistical tests are performed for crop years 1980 through 2005.
- 13.
The futures forecast is determined from the settlement futures prices on the day of the WASDE release. Mid-points of the projected price range from the WASDE report are used.
- 14.
This is a standard measurement of price volatility since its use in the Black–Scholes option pricing model (Black and Sholes, 1973).
- 15.
Statistical differences between the means of these two forecast methods will be tested in the next section.
- 16.
An Informa Economics (2008) study and a study by Aulerich et al. (2009) found increased volatility for grains and soybeans during their study periods of recent years. The Informa Economics study found no persuasive evidence that index traders or money managers caused increased volatility as some have alleged.
- 17.
The 5-year-average basis included two of the volatile basis years of 2006–2007 and 2007–2008.
- 18.
During the recent period of price volatility, some grain elevators have created new forward contracts that pass the futures margin and transportation costs to the producer, resulting in a quoted basis that may be adjusted downwards depending upon circumstances.
- 19.
See http://www.ers.usda.gov/Data/PriceForecast/Data/Futmodcorn.xls change forecast tab on the spreadsheet.
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Appendix: Tables 7.1 to 7.5
Appendix: Tables 7.1 to 7.5
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Hoffman, L.A. (2011). Using Futures Prices to Forecast US Corn Prices: Model Performance with Increased Price Volatility. In: Piot-Lepetit, I., M'Barek, R. (eds) Methods to Analyse Agricultural Commodity Price Volatility. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7634-5_7
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