However, in his analysis, the basis variability during the contract maturity month is calculated assuming that all basis fluctuations in this period reflect basis risk. Presuming that any basis variation during contract maturity month is random and, therefore, risky, this can lead to incorrect results.
Cash and futures prices convergence is expected during the contract maturity month and can be used by market participants in their marketing strategies. Therefore, the convergence process does not represent basis risk. For this reason, Leuthold , cited by Garcia et alii , suggested that basis fluctuations should be viewed in terms of a systematic component and an unsystematic or random component.
Kenyon et alii analyzed the impact of cash settlement on basis variability and predictability on the CME feeder cattle futures contract, based on feeder cattle futures price before and after cash settlement introduction. Estimated basis equations in which basis was specified as a function of breed, sex, weight, grade, and season were used to predict termination basis and to determine the impact of cash settlement on the ability to forecast basis.
The results suggested that hedgers ability to forecast basis in general was not improved significantly under cash settlement compared to physical delivery. Rich and Leuthold expanded the sample size to a larger number of markets and analyzed how cash settlement issues have influenced hedging conditions at the individual market level and for the feeder cattle industry in general. The study analyzed basis behavior and hedging risk for 27 feeder cattle futures contracts before and after cash settlement.
Hedging risk represented by basis risk was represented by basis standard deviations in the sample. A regression model was developed to isolate the impact of cash settlement on basis risk from other contract specification changes and to gain better insight into hedging risk across regions and between sexes. Using ordinary least squares OLS , delivery week basis standard deviation was regressed on dummy variables to represent cash settlement, sex, and location effects.
Although not statistically linked to cash settlement, basis risk was found to be reduced at contract expiration for most individual markets. The regression model on weekly data suggested that basis variability was reduced after cash settlement, since the coefficient of the dummy variable for cash settlement was negative. Before , fed cattle futures market in Brazil have had very low liquidity, therefore the sample is composed by contracts from onwards. Fed male and female animals have been considered in the analysis, although the IBG calculation only includes male regional prices.
Regional fed cattle cash prices are available only from onwards, and futures trading months during this period were: October and December ; March, May, August, October, and December ; February, April, June, August, September, October, November, and December ; February Hence, the period considered in this analysis is from October to February , due to data availability.
The period before cash settlement includes four contracts October and December ; March and May and the period after cash settlement includes 12 contracts August, October, and December ; February, April, June, August, September, October, November, and December ; February Leuthold , cited by Garcia et alii , developed models to explain live cattle basis variation using variables which reflect current and expected supplies.
He concluded that basis movements for distant contracts could be explained by factors affecting shifts in supply. However, during the delivery period, he attributed that the basis was more random due to increased speculative activity, reflecting commodity trading between cash and futures markets.
Based on the work of Leuthold , Garcia et alii suggested that basis fluctuations should be viewed in terms of a systematic component and an unsystematic or risky one.
According to Garcia et alii , basis shows a systematic component represented by cash and futures prices convergence, and, if there are cash price seasonal patterns, they can be reflected in seasonal patterns from the basis fluctuations.
Since this component does not represent risk, because it can be forecasted and used by market agents in their strategies, it is necessary to isolate the random component which represents the risk during the contract maturity month.
The authors isolated the systematic and the random components of the basis series to assess basis risk for selected cattle and hogs markets in the USA. The variate difference approach was used, which assumes that the mathematical expectation can be approximated by polynomials of the variable time. The expectation from the time series can be eliminated by differencing, leaving only the random element the deviations from expectation which represents risk.
Garcia et alii used the variance of the random element as a measure of basis risk, which is the dependent variable of the estimated model. The exogenous variables included are the consumer index price and a livestock cycle indicator to represent the impact of long term price fluctuations on the unsystematic basis component , dummy variables to isolate the effect of the contract maturity month and different markets , and average basis level.
As suggested by Garcia et alii , basis variance is analyzed in terms of a systematic and a random component in this paper. Basis risk is represented by the standard deviation of the random element of the basis series during the contract maturity month.
However, in order to assess if and how cash settlement introduction has affected basis risk during the contract maturity month in each region and for each sex, and to identify seasonal effects on basis risk, only the contract maturity months have been analyzed and only dummies are included in the model as exogenous variables.
During each contract maturity month, the random component of the basis series is isolated by successive lags in the original basis series until every one becomes stationary and the errors result in a white noise series. These criteria are used to compare different lags in order to determine the best autoregressive model for the basis series. All the basis series contract maturity months are submitted to AIC and SC in each considered region, for male and female animals.
Thus, for each region there are 16 contract maturity months for males likewise for females, so that 32 basis series are submitted to AIC and SC for each region. After determining the basis series autoregressive process orders through these criteria, each basis series corresponding to each contract maturity month is autoregressed according to its determined lag, to isolate the white noise series from which standard deviations are calculated.
Each of the nine regions considered in this study contains male and female fed cattle cash prices. Since there are 16 contract maturity months, there are likewise 16 basis series for males and 16 basis series for females in each region. The standard deviations of the random element are determined for every contract maturity month.
Therefore, for each region correspond 16 standard deviations of the random component for males and 16 standard deviations of the random component for females. Using 16 observations, the regression model represented by 5 has been estimated for every region and sex by ordinary least squares OLS :. The coefficient for the seasonal effect variable is expected to be negative and significant due to the seasonal patterns in cattle supply for slaughtering because of the extensive raising system used in Brazil.
Moreover, the coefficient for the cash settlement variable is expected to be negative and significant because delivery costs which hinder cash and futures prices convergence and increase basis risk have been eliminated. The basis series autoregressive process orders determined through AIC and SC criteria ranged from 1 to 5 for male and female, being 1 the most common order for both sexes in all nine regions. The presence of autocorrelation in the residual series was tested through the Ljung Box statistic Q test and none of the estimated regression presented this problem, indicating that those series are white noise.
The standard deviations of the random component of the basis series were larger during the period before cash settlement introduction compared to the period after that for male and female in all regions.
Tables 1 and 2 present the regression results in each region for male animals. Table 1 shows through F statistics that all regressions are highly statistically significant. The dummy variables for seasonal effects are significant in all regressions, indicating that basis risk is different for trading months placed in the first and in the second semester of the year. Since all the coefficients are negative, basis risk shows to be lower for trading months placed in the first semester of the year relative to those placed in the second semester of the year.
This result is probably linked to the flow and quality of new information and its effects on cash and futures prices. During the first semester of the year, when cattle slaughtering supply increases, more information about fed cattle demand and supply conditions is available, improving the price discovery process.
Thus, futures prices forecasts are more accurate and subject to less unexpected fluctuations. On the other hand, during the second semester of the year, when cattle slaughtering supply decreases, there is less available information about shifts in demand and supply conditions, which can lead to a more inaccurate price forecast, increasing basis risk.
In all regions, cash settlement dummy variables are highly significant and show negative signs, indicating that basis risk during contract maturity month has been reduced after cash settlement introduction. According to Halvorsen and Palmquist , to obtain a dummy variable effect on a semi-logarithmic equation it is necessary to calculate the antilog of the dummy variable coefficient and subtract from 1. This calculation gives the percentage change in the dependent variable attributable to the structural change.
Thus, transforming coefficients of the cash settlement dummy variables, the relative changes in basis risk due to cash settlement are obtained in every region. The results are showed in table 2 , according to which basis risk reductions due to cash settlement introduction are close in all concerned regions.
The regression results for females are presented in table 3. All the dummy variables added to detect seasonal effects show negative signs and are statistically significant, indicating that basis risk for females is also lower for trading months placed in the first semester of the year in relation to the second. Cash settlement dummy variables also show negative signs and are statistically significant, indicating that basis risk has been reduced for females during contract maturity months, after cash settlement introduction in all regions concerned.
It could be argued that these results are possibly linked to regional cash market conditions, since each of those three regions belongs to different states. Further researches should be conducted to explain this point. It suggests that hedging performance has been improved through cash settlement introduction. The dummy variables for seasonal effects were found to be highly significant in every considered region, indicating that hedges placed in the first semester of the year show lower basis risk in relation to those placed in the second semester.
Therefore, the time of placing a hedge may influence its success. Hence, market agents need to be specially careful in adopting their marketing strategies when taking a futures market position for contract maturity months placed in the second semester of the year.
It is worthwhile to make some considerations before concluding this paper. First, the results obtained through the analysis are based on a small sample because of lack of available data. He calls it a primary problem in the industry. It no longer represents the American producers, it is a globalized average. The producer is taking care of the livestock.
The speculators oversell and take it way below the market, never having to worry about receiving or delivering a perishable product.
They are buying a piece of paper. Ulmer also said that the value of feeder cattle futures is based on cattle commingled weighing pounds, and of every flesh score — from fleshy to green and from gant to full. While the live cattle contracts do provide for a delivery option, the rules are so complex and fat cattle are so perishable, that delivery rarely happens. The futures system can provide feeders the ability to limit losses, he said, but the system is set up to benefit speculators, not cattle feeders.
Ulmer feeds cattle and has used the board of trade since the early s off and on. He said 90 percent of the time it has cost him money and he has never felt like it was a protection tool, but rather a speculation game. The inventory was 4 percent above July 1, This is the highest July 1 inventory since the series began in The inventory included 7. This group accounted for 63 percent of the total inventory.
Heifers and heifer calves accounted for 4. Ranchers are keeping less heifers, so there will be more heifers on feed and more pounds of meat. With more beef supplying the market, it would be natural to assume beef prices would drop; however, beef demand is robust. Hog basis is calculated using the the Western Combelt price for 51 to 52 percent lean pound carcass.
The futures contract delivery month used to compute the basis during each period is shown in column 2 of the basis tables inInformation File Lean Hog Basis and Information File Live Cattle Basis. Two criteria are used in choosing the futures contract delivery month. The delivery month must be actively traded during the time when the livestock are marketed. Trading of a delivery month for cattle futures ceases at the end of the futures delivery month.
Lean hog futures trade until the 10th business day of the month. Also, the delivery month used is the one with delivery closest to the time when the livestock are marketed. Basis tables are often report the average or expected basis and some measure of basis risk. The column on the far right in the table is the standard deviation calculated daily for the two week period over the three years that make up the average basis.
The standard deviation is a measure of variability. The actual basis is expected to fall in a range from one standard deviation above and one below the average approximately two-thirds of the time. Basis is less variable than price cash or futures price. Basis levels remain relatively constant because the cash and futures prices react to similar conditions.
If hog supplies decline relative to demand, both cash and futures prices tend to rise. If supplies increase, both cash and futures prices tend to fall. Similar but different The two markets - cash and futures are similar but not identical. The cash market is a current market reflecting today's supply and demand conditions. Conversely, the futures market is an anticipatory market reflecting expectations of future supply and demand conditions. For example, at contract maturity, basis during early March reflects the difference between the cash price during early March and the April futures contract price with delivery during April.
The two markets do not react exactly the same way to market factors. This difference leads to changing basis levels. Basis variability due to comparing current market conditions with future conditions disappears during contract maturity. For example, at contract maturity both the cash market and the April live cattle futures contract represent market conditions during the April 1 to 20 period. Here the futures market becomes a current market like the cash market.
The only difference is the futures price represents delivery of a specific type of cattle to a delivery point whereas the cash market might be cattle delivered to a specific packing plant or a local auction. The lean hog futures uses cash settlement rather than live delivery see Information File Lean Hog Basis.
The basis is expected to be less variable and trading should be more orderly at the end of the contract. Seasonal hog price patterns Seasonal hog price patterns affect hog basis because the two markets reflect supply and demand conditions during two different time periods as discussed above. During a period of increasing supplies, prices are expected to decline.
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