price information in producer markets: an evaluation of futures and spot cotton price relationships...

Pr ice Information in Producer Markets: An Evaluation of Fu tu res and Spot Cotton

Price Rela t ionships i n the Southwest Region Using Coint egrat i on

Darren Hudson Emmett Elam Don Ethridge

J e f Brown

Producer spot (cash) prices of cotton from the Southwest region were compared to futures prices for cotton to examine the cashJutures price relationship using the cointegration technique. The results showed that the cash producer price and the futures price were not consistently related. The futures and cash prices were cointegrated in 2 of 4 years, while not cointegrated in the other 2 years. The inconsistency indicates that the reliability of the futures price as a source of price information to producers of cotton in the Southwest is questionable. This relationship may be arising from quality uncertainty in the producer market. 01996 John Wiley & Sons, Inc.

Introduction

Price information is a vital part of the efficient operation of commodity markets. The futures ......................................................

Requests for reprints should be sent to Dr. Don Ethridge, Dept. of Agricultural Economics, Box 42132, Texas Tech University, Lubbock, TX 79409.

market is used by market participants to shift risk, facilitate equity financing, and discover prices. Price information is transferred through market mechanisms so that traders in both cash and futures markets can assess their respective positions and formulate current and expected prices based on current information. The process of using all available information to formulate prices is often referred to as price disc0very.l

The cash market for cotton can be effectively divided into different levels, depending on which point in the marketing channel (i.e., producer level, merchant to merchant, merchant to textile mill, etc.) that a transaction takes place. For this analysis, the focus is on the first pricing point, or the producer market.” In the producer market, participants need price

and other information, such as supply and de- mand, to facilitate price discovery. There are two primary sources of daily price information to producer market participants in cotton. These are

LG

............................................................................................................... This article benefited from the comments of Carl Anderson, Kary Mathis, and an anonymous reviewer. The research was supported by Cotton Incorporated through the Texas State Support Committee. Texas Tech College of Agricultural Sciences and Natural Resources Manuscript

The authors are Research Assistant, Associate Professor, Professor, and Former Research Associate, respectively, Texas Tech University.

T-1-395.

............................................................................................................... Agribusiness, Vol. 12, No. 4, 363-369 (1996) 0 1996 by John Wiley & Sons, Inc.

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Hudson a t al.

the Daily Spot Cotton Quotations (DSCQ) and the futures market (New York Cotton Exchange) price reports. The DSCQ are daily spot market price quotations published by the Agricultural Market- ing Service (AMS), US Department of Agriculture. The DSCQ consist of a base price and quality premiums and discounts for each of seven US marketing regions.2 The futures market price differs from the DSCQ in that it only represents the base quality of cotton that is deliverable at a specific place and time in the future. A primary role of the futures market is its contribution to the price discovery p r o ~ e s s . ~ Both the DSCQ and the futures market represent widely disseminated sources of price information, and thus are the usual channels through which price information is available to market participants.

The futures market and producer cash market differ in terms of structure and composition. In terms of composition, the producer (cash) market is primarily composed of merchants, producers, textile mill buyers, exporters, etc. The futures market is composed of these participants as well as the added component of speculators.a In terms of market structure, the futures market has a higher concentration of buyers and sellers than the producer (cash) market^.^ Given this, the forces and expectations that drive price move- ments may differ to some extent between the cash and futures market. The different composition and structure of the markets may lead to an a priori expectation of different price behaviors. How- ever, deliverability of the commodity is expected to prevent prices from becoming too divergent because of arbitrage.

The deliverability factor led to an early idea that futures and cash prices should be related. Arbi- trage dictates that prices not diverge by large amounts because of the opportunity to earn excess profits." Additionally, the cash market tends to derive much of its price information from the futures market.3 Thus, cash price changes may appear to "mirror" changes in the futures price.

...................................................... =It can he argued that many cash market participants are also

speculators. However, in the sense used here, speculators refer to

those who trade in the futures market with no positions in the cash

market.

This would lead to a lead-lag relationship between producer (cash) and futures prices.5 That is, price information is transferred from the futures market to the cash market, with the cash market responding to changes in the futures market. This deals with the price discovery aspect discussed earlier.

Previous research dealing with price discovery in cotton markets gave some indication of the relationship between cash and futures prices. Brorsen et a1.6 found that the cotton futures market leads the cash market, which suggests that cotton prices are discovered in the futures market. However, the cash price data used in this study were ob- tained from the DSCQ (price quotations provided by USDA), whose accuracy in reflecting producer market prices in the Southwest region (Texas and Oklahoma) is in doubt.7.8 Hudson et a1.8 found that the DSCQ did not effectively move with the daily producer market price over a 4-year period, nor did it appear to accurately reflect the level of prices, premiums, and discounts. Additionally, al- though the general results of Brorsen et al." are plausible and consistent with expectations, the ro- bustness of the Granger causality test used by the authors has been questioned.g

The objective of this study was to analyze the producer market (cash)/futures price relationship in the Southwest region. The study extends previous research of the producer market in that the cash price data for this study is more objective and reliable than other data sources used in previous studies (discussed below). The analysis is con- centrated on the producer market to give producers and producer market participants more reliable information about cash/futures price relationships. The results of this analysis give an indication of how closely related the futures and Southwest producer cotton prices are, which can be used as a measuring mechanism for the useful- ness of the futures market as price information for the producer cash market in the Southwest.

Methods and Procedures

Two methods were used to determine producer (cash)/futures price relationships: a cointegration

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Price

test of the futures and producer markets and error correction (causality) models to determine the nature of lead-lag relationships when the price series were cointegrated. Data, as well as each approach, are discussed in the following sections.

Data Considerations

Spot (cash) producer market prices were estimated using a hedonic approach for measuring daily cotton prices and quality premiums and discounts in Texas and Oklahoma (referred to as the Southwest regi~n).~>lO This system of estimating prices, called the Daily Price Estimation System (DPES) , 7 1 1 ~ has been under development since 1988. It provides an objective set of market prices derived directly from actual market transactions, and the prices are reproducible. Tests have shown that the DPES estimated prices, when compared to actual sales prices, explained 95% of the variation in actual prices with no systematic error.7 In contrast, the price quotes provided in the DSCQ explained 84% of the variation in price and were shown to have a systematic bias with respect to actual sales prices. The statistical reliability of the DSCQ cannot be directly determined because the sample characteristics (i.e., size, distributions, etc.) are not known and the sampling procedure is not documented.8 Thus, the DPES provides a useful data set of producer market (cash) prices that is reliable for use in other analyses.

Because the bulk of producer transactions for spot sales in the southwest region occur in the period of November through the end of February, this period was used for this analysis. The data set provided 4 years of cash cotton prices (1989/1990 through 1992/1993), 4 months in each year (No- vember 1-February 28), and two market regions (west Texas and east Texas/Oklahoma).b Because the cotton in the Southwest region can differ from the base quality (grade 41, staple 34, strength 24 and 25, micronaire 3.5-4.9)" in any year (and of ......................................................

bThe Southwest region is officially divided into the two marketing

regions shown for purposes of price reporting by AMS-USDA. cThis represents the official base grade before the 1993 crop year

(1993/1994 marketing year), which is the period of time for this

study. The grading system has since changed.

ten does), the average quality from each year was included for analysis with the futures price (Table I). The prices for the average qualities were collected for the same period as above, resulting in approximately 81 observations per year. This represents a cross-hedge relationship, and should accurately represent the buying and selling behavior of the Southwest producer market.

Futures prices were collected using the nearby contract for the same period as above. December futures were used for the period from November 1 through the expiration of the December contract (approximately December 6). After that date, the March contract was used for the remainder of each season.

Cointegration Test

A cointegration test was used to evaluate the price relationship between the producer market and futures prices. Before applying the cointegration test, each price series was tested using an aug- mented Dickey-Fuller test.12 This test showed that each individual series was a unit root (nonstationary), and this justifies the application of the cointegration test (which is applied to nonstationary series only1").

nonstationary by themselves, but a linear combination of those series may result in a stationary error term. A cointegration regression was formu- lated as follows:

Engle and Grange+ state that two series may be

where F, is the futures price at time t, M , is the producer market price at time t, a and p are parameters, and E , is the error term. To analyze the stationarity of the error term, Eq. (1) is rewritten as:

If the linear combination of the nonstationary series results in a stationary error,d they are said

...................................................... dNonstationary economic time series can generally be differenced

once to obtain stationarity.16

Hudson e t al.

Table 1. Average Qualities for 1989/1993 Period.

Year Grade Staple Strength Micronaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198911990 42 32 24 3.34 199011991 41 32 25 3.60 199111992 62 32 26 3.56 199211993 42 33 26 3.58

to be ~0integrated.I~ If they do not result in a stationary series, they are not cointegrated. This suggests that no lead-lag relationship exists and the level of one series cannot be predicted from the level of the other ~ e r i e s . ~ . ’ ~

There are several methods of analyzing the stationarity of the error term.l3 However, a simple method is to use the Durbin-Watson statistic from the regression analysis in Eq. (1). This was the test employed in this analysis. Results from the cointegration test give an indication of the price relationship between the producer and futures markets. If the series are cointegrated, this provides evidence that the producer market and futures market are related. The nature of that relationship is explored in the next section. How- ever, if they are not cointegrated, there are two possible conclusions: there is no relationship between cash and futures prices or prices are simul- taneously determined.3 This first conclusion would seem unlikely given the inherent interrelationships in cotton markets because of common market participants, delivery, etc. However, transportation costs may make markets operate relatively independent of one another.’ The second conclusion is plausible because of the rapid transmission of information.

Error Correction Models

A variant of the Granger causality test was employed to check the direction of causality when the price series were cointegrated. This test employed two regressions. The first was the “unrestricted” regression, and is expressed as follows:

(3)

where p is the error correction parameter. This formulation regresses the lagged price changes in the futures market and the lagged price changes in the producer market on current price changes in the futures market. The error correction parameter accounts for errors coming from the existence of cointegration. The value of m was set to 10 because it represents 2 marketing weeks.

The second equation in the causality test is the restricted regression,” which is as follows: bb

rn

AFi = -p(F a - P M ) , - , + c OiAFtPi + vt. (4) i= 1

Equation (4) “restricts” the independent variables to only past price changes in the futures market. This allows the test of the hypothesis that the cash market does not lead the futures market. This is evaluated through a combined F test as follows:

where N is the total number of observations, k is the number of parameters in the unrestricted regression, ESSR is the error sum of squares from the restricted regression, ESSUR is the error sum of squares from the unrestricted regression, and q is the number of parameter restrictions. If the calculated F value is significant, it is concluded that past changes in the cash market price are impor- tant in explaining the change in the futures price, which is equivalent to saying that the cash market price leads the futures market price and that Granger causality exists.

A second test was also conducted using Eqs. (3) and (4) except that the cash price was used as the dependent variable and the futures price was used as the independent variable. This test was conducted to examine if the futures market leads the cash market.

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"able 11. Results of Cointegration Analysis.

Year West Texas East Texas/Oklahoma

19891 I990 0.486" 0.466" 19901 1991 0.059 0.061 19911 1992 0.118 0.267

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1992 / 1993 0.719" 0.6618

PDurbin-Watson statistic indicates cointegration is present.'",'?

Results of the cointegration analysis are summarized in Table 11. The Durbin-Watson statistic indicates that cointegration was present in 1989 and 1992 in both west Texas and east Texas/Oklahoma. This says that there was a significant interrelationship between producer cash prices and the futures price of cotton in those years. However, in 1990 and 1991, the hypothesis of no cointegration could not be rejected by the Durbin-Watson criteria at the 0.10 level of significance. When cointegration is present, this indicates that

there is a lead-lag relationship present through the Granger representation theorem. When cointegration is not present, no lead-lag relationship exists. Thus, in 198911990 and 1992/1993, one market was leading the other; in 199011991 and 1991/1992, no lead-lag relationship was detected through the cointegration test.

which market is the dominant market (i.e., which market is leading). Thus, the error correction causality model was estimated. These results are summarized in Table 111. Generally, when cointegration was present, the futures market was found to lead the cash market. This tends to support the findings of Brorsen et a1.6 That is, when there was an interrelationship between the producer cash price and the futures price, the information appeared to be flowing from the futures market to the cash market.

The results presented here are similar, but not identical to those found by Brorsen et al.'j: Brorsen et a1.6 found a general tendency of the futures market to lead the cash market. While the

The existence of cointegration tells nothing about

Table 111. Results of Error Corrected Causality Tests for Years Showing Cointegration.

Year West Texas East Texas/Oklahoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198911990 ( N = 72)

Cash lead 1.30 1.09 Futures lead 2.95" 3.838

Cash lead 0.91 1.01 Futures lead 2.16" 2.75"

199211993 ( N = 82)

Numbers presented are calculated F values. aSignificant at the a = 0.05 level.

direction of causality here tends to support the findings of Brorsen et al. ,6 the relationship does not appear as strong (at least in terms of the producer market in the Southwest region). The results indicate that a strong link between the futures market and the Texas-Oklahoma producer cash market has not been consistent over the past 4 years. One plausible explanation for this may be the existence of TELCOT, an electronic spot cotton market operating in Texas and Oklahoma,14 which provides producers and buyers in the producer market with ready access to price information on sales of cotton within their region. Because TELCOT is widely accessible and has information on sales occurring in Texas and Oklahoma, producers from the Southwest region may find it eas- ier to base decisions on past sales in their own region rather than analyzing futures prices. This may reduce reliance on the futures market for derivation of price information. This needs further analysis before conclusions can be drawn.

Other forces may also be driving this relationship. For example, changes in the proportion of cotton going to export versus domestic markets may influence how the producer market responds to changes in the futures price. One other explanation for the inconsistent relationship between cash and futures prices is changes in crop quality; that is, as crop quality significantly diverges from the base quality, the a priori expectation would be that the cash and futures prices would not be significantly related. This appears consistent with the

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Hudson et al .

results for 1991/1992. In 1990/1991, however, the quality was near the base, but the cash and futures prices were not related. This suggests that quality plays a role in determining the cash/futures relationship but is not the sole determinant. As more data are accumulated on this point, more definitive conclusions can be offered.

Conclusions and Implications

The general conclusion from this analysis is that the futures market is the dominant market in terms of price information in the Southwest region. However, this relationship is not as strong as once believed, at least in terms of the producer market in Texas and Oklahoma. This raises ques- tions as to how good a source of price information the futures market is to those markets. If it is assumed that the USDA price reports are

representative of the cotton market as a whole (i.e., across the entire market channel from producers to textile manufacturers), as some have as-

serted,l5 this analysis suggests that the producer market has weaker ties to the futures market than the cotton market as a whole (as shown by Brorsen et al.“). In other words, Brorsen et al.“ found a stronger general relationship between USDA reported prices and the futures market than found here. This may be suggesting that the interrelationship between the cotton market in general (producer to textile manufacturer) is stronger than the producer market only, which may be because producers have more quality uncertainty, meaning producers do not know the quality of their cotton before harvest and grading, making hedging more difficult. Knowing this, the producer may tend to not hedge and rely on local information (e.g., TELCOT or other market intel- ligence sources) for the formation of price expectations. This question needs further analysis. Nevertheless, this analysis points to the conclusion that the futures market and producer cash markets are linked, with the futures being dominant, but that relationship is not as strong as previous studies suggest.

References

1. W.G. Tomek, “Price Behavior in a Declining Terminal Market,” American Journal of Agricultural Economics, 62, 434 (1980).

2. US Dept. of Agriculture. “Daily Spot Cotton Quotations,” US Dept. of Agriculture, Agricultural Marketing Service, Memphis, TN, Daily Issues.

3. R. Leuthold, P. Garcia, and N. Chaherli, “Information, Pricing, and Efficiency in Cash and Futures Markets: The Case of Hogs,” The Economic Record [Supplemental Edition on Futures Markets], 1992, 27 (1992).

Commercial and Personal Pro&, Commodity Research Bureau, Inc., New York, 1971.

5. R. MacDonald and M. Taylor, “Metals Prices, Efficiency and Co-integration: Some Evidence for the London Met- als Exchange,” Bulletin of Economic Research, 40, 235 (1988).

6. B.W. Brorsen, D. Bailey, and J. Richardson, “Investiga- tion of Price Discovery and Efficiency for Cash and Fu- tures Cotton Prices,” Western Journal of Agricultural Economics, 9 , 170 (1984).

7. J. Brown, D. Ethridge, D. Hudson, and C. Engels, “An

4. T.A. Hieronymus, Economics of Futures Trading for

Automated, Econometric Approach for Estimating and Reporting Daily Cotton Market Prices,” Journal of Agri- cultural and Applied Economics, 27, (1995).

Prices in Cotton Markets: An Evaluation of Reported Price Information Accuracy,” Agribusiness: An lnterna- tional Journal, 12, (1996).

9. C.W.J. Granger, “Testing for Causality: A Personal View- point,” Journal of Economic Dynamics and Control, 2 , 329 (1980).

10. D.E. Ethridge, C. Engels, and J. Brown, “An Economet- ric Approach for Estimating Daily Market Prices,” in 1992 Beltwide Cotton Conferences, Proceedings, Cotton Economics and Marketing Conference, National Cotton Council, Memphis, TN, 1992, p. 399.

Price Analysis in Cotton: An Alternative Approach for Providing Market Information,” in Proceedings of the NCR-134 Conference on Commodity Price and Risk Analysis, Chicago, IL, 1995, p. 233.

Macmillan Publishing Co., New York, 1992.

8. D. Hudson, D. Ethridge, and J. Brown, “Producer

11. D. Hudson, D. Ethridge, and J. Brown, “Daily Hedonic

12. G.S. Maddala, Introduction to Econometrics, 2nd ed.,

Prlce

13. R.F. Engle and C.W.J. Granger, “Co-integration and Er- ror Correction: Representation, Estimation, and Test- ing,” Econornetrica, 55, 251 (1987).

14. D.E. Ethridge, “A Computerized Remote-Access Com- modity Market: Telcot,” Southern Journal of Agri- cultural Economics, 10, 177 (1978).

15. T. Kuehlers, “1993 Crop Spot Market Quotations,” in 1994 Beltwide Cotton Conferences, Proceedings, Cotton

Economics and Marketing Conference, National Cotton Council, Memphis, TN, 1994, p. 457.

16. C.W.J. Granger and P. Newbold, Forecasting Economic Time Series, 2nd ed., Academic Press, New York, 1986.

17. R. Pindyk and D. Rubinfeld, Econometric Models and Economic Forecasting, 3rd ed., McGraw-Hill Co., New York, 1991.

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price information in producer markets: an evaluation of futures and spot cotton price relationships...

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