Performance of shrimp futures markets as price discovery and hedging mechanisms

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Performance of shrimp futuresmarkets as price discovery andhedging mechanismsLeigh J. Maynard a , Sam Hancock b & Heath Hoagland ca Assistant Professor, Dept. of Agricultural Economics,University of Kentucky, 319 Agricultural Engineering Bldg.,Lexington, KY, 40546–0276, USA Phone: ++1 (859) 257–7286Fax: ++1 (859) 257–7286 E-mail:b Dept. of Agricultural Economics, University of Georgia,Athens, USAc Dept. of Agricultural Economics, University of Kentucky,Lexington, USAPublished online: 13 Nov 2008.

To cite this article: Leigh J. Maynard , Sam Hancock & Heath Hoagland (2001): Performance ofshrimp futures markets as price discovery and hedging mechanisms, Aquaculture Economics &Management, 5:3-4, 115-128

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Performance of shrimp futures markets as price discoveryand hedging mechanisms

LEIGH J. MAYNARD1, SAM HANCOCK2 & HEATH HOAGLAND1

1 Dept. of Agricultural Economics, University of Kentucky, Lexington, USA2 Dept. of Agricultural Economics, University of Georgia, Athens, USA

Abstract

Granger causality tests revealed leading indicators of shrimp futures prices, implying that futuresprices do not reflect all available market information and potentially fail to be an exemplary pricediscovery mechanism. Trading simulations confirmed that the use of some leading indicatorsallowed profitable arbitrage in shrimp futures trading. Shrimp futures were deficient as ahedging tool, as well. Correlations between futures and wholesale cash prices were often low,and basis risk rivaled price risk. Lack of liquidity is a likely explanation for shrimp futures'shortcomings as a hedging tool and price discovery mechanism.

Keywords: shrimp, futures markets, arbitrage, price discovery, hedging

Background and Objectives

A futures market provides a centralized, highly visible venue in which new information can bealmost immediately incorporated into a commodity's market price, and arbitrage incentivesshould ensure that a futures market is an effective price discovery mechanism. However, theexistence of persistent arbitrage opportunities (i.e., predictable opportunities to earn risklessprofits) indicates the failure of a futures price series to reflect all information currently availablethat will influence forward prices.

The Black Tiger and Ecuador White shrimp futures contracts offered by the MinneapolisGrain Exchange are thinly traded, potentially offering persistent arbitrage opportunities. Dailytrading volume for the nearby futures contract has not exceeded 50 contracts since 1997 foreither Black Tiger or Ecuador White shrimp, and liquidity appears to be declining over time. Forexample, on August 20,1999 white shrimp trading volume consisted of one contract, with openinterest of 14 contracts. This pattern suggests that speculative day trading is almost nonexistentin shrimp futures markets, and that most participants use shrimp futures for hedging. Contrastthis with the Chicago Board of Trade corn contracts, where trading volume exceeded 44,000contracts on August 20,1999.

Lack of liquidity may disrupt the effective functioning of a futures market as a hedging tooland price discovery mechanism. One would expect cash and futures prices to be less integratedin a thin market, thus reducing the benefits of replacing price risk with basis risk through hedg-

Correspondence Leigh J. Maynard, Assistant Professor, Dept. of Agricultural Economics, 319Agricultural Engineering Bldg., University of Kentucky, Lexington KY 40546-0276, USA, Tel: ++1(859) 257-7286, Fax: ++1 (859) 257-7290, email: [email protected]

Aquaculture Economics and Management 5(3/4) 2001 115

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116 Performance of Shrimp Futures Markets • L.J. Maynard et al.

ing. Hedgers and speculators may experience difficulty filling orders in thin futures markets,leading to yet lower levels of participation and potentially the demise of a thinly-traded futurescontract. Given the considerable resources that futures exchanges devote to launching andmaintaining futures contracts, assessments of how thinly-traded contracts perform with respect toprice discovery and hedging effectiveness are warranted. As the only seafood commodity tradedon a futures exchange, the fate of the shrimp contracts is relevant to marketers and marketinstitutions throughout the seafood and aquaculture sector.

Shrimp futures are not thinly traded for lack of economic importance. The U.S. shrimpsupply is primarily imported, with 1998 imports of 893.6 million pounds (heads-off weight)compared to 1998 domestic landings of 173.3 million pounds (U.S. Department of Commerce1999). Thailand supplied 35 percent of 1998 U.S. shrimp imports, Ecuador supplied 18 percent,and Mexico supplied 12 percent. Per capita consumption reached record levels in 1998 at 2.8pounds (U.S. Department of Commerce 1999), and during the mid-1990's U.S. shrimpconsumption grew at an annual rate of approximately nine percent versus two percent annualgrowth in U.S. seafood consumption (Chauvin 1998). Shrimp leads the U.S. seafood sector invalue of landings (Chauvin 1998), with the Gulf region supplying 83 percent of the 1998 U.S.catch (U.S. Department of Commerce 1999). Forward contracting is common between theexvessel, wholesale, and processing market levels. Although the U.S. shrimp industry is highlyconcentrated at the processor level, Adams et al. (1987) found no evidence of monopsonisticpricing, and determined that price responses were symmetric across market levels. Given thedominant market share of imported shrimp, opportunities to exert market power may be rare.

The objective of the study is to test the hypothesis that persistent arbitrage opportunities donot exist even in thinly traded futures markets such as Black Tiger and Ecuador White shrimp.Identification of leading indicators of futures prices provides tentative evidence of arbitrageopportunities. In heavily traded markets such as the S&P 500 Index contract, futures prices leadspot markets by less than a day (Herbst et al. 1987). Price responses may be less prompt inthinly traded markets such as shrimp. If evidence of persistent arbitrage opportunities is found, asecondary objective is to determine if the potential profits from arbitrage have economicsignificance in addition to statistical significance. Ma and Soenen (1988) document the existenceof profitable arbitrage opportunities in diverse futures markets, including T-bills and T-bonds,the S&P 500 Index, gold, silver, and newly-created futures and options contracts. In this study,the performance of shrimp futures markets is evaluated with emphasis on their primary roles ashedging tools and price discovery mechanisms. The impetus for this work arose from theexpressed need of Kentucky freshwater shrimp producers for more information about marketingand risk management.

Data and Methods

The analysis relies on a panel of weekly wholesale cash price data for thirteen commercialvarieties of marine shrimp provided by Shrimp World, Inc., and weekly closing futures price dataprovided by the Minneapolis Grain Exchange, which offers futures contracts for Black Tiger andEcuador White shrimp. Black Tiger and Ecuador White are the leading shrimp varietiesimported into the U.S. from Asia and Latin America, respectively. The futures data reflect thenearby contract price at any given date. The study period runs from November 15,1994 to June23, 1998. All price data were deflated by the Consumer Price Index. Cash prices representwholesale prices for headless shrimp as charged by importers and processors for volumeshipments. Data are collected by Shrimp World, Inc. via surveys of market cross-sectionsconsidered to be reliable indicators of market conditions (Shrimp World, Inc. 1999). With theexception of Bangladesh Black Tiger cash prices, the set of cash price series provided by ShrimpWorld, Inc. relates to varieties produced in the Western hemisphere (including Ecuador White).

Aquaculture Economics and Management 5(3/4) 2001

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L.J. Mayñard et al. • Performance of Shrimp Futures Markets 117

Thailand and Indonesia are leading suppliers of Black Tiger shrimp, and future analysis wouldbenefit from additional Black Tiger cash price data.

Table 1 provides descriptive statistics of the real price series. Coefficients of variationtended to be approximately ten percent, reflecting moderate price volatility. Black Tiger futuresprices were highly correlated (i.e., Pearson correlation coefficient exceeding 75 percent) withonly two cash price series: Brazil Pink and Panama Pink. Oddly, the Bangladesh Black Tigercash price series exhibited almost no correlation with Black Tiger futures prices. None of thecash price series was highly (i.e., more than 75 percent) correlated with Ecuador White futuresprices.

Table 1 Descriptive statistics of selected weekly average real cash and futures prices for the periodNovember 15,1994 - June 23,1998

. . „ Std. Coeff. M .M e a n Dev. ofVar. M i n "

Max. Corr. withBlack Tiger

Futures

Corr. withEcuadorWhite

Futures

Black Tiger FuturesEcuador White FuturesBrazil Pink Cash8

Ecuador White CashGulf Brown Cash

Gulf White CashGulf Peeled CashMexican #1 Brown CashMexican #2 Brown CashMexican #1 White CashMexican #2 White Cash

Mexican Pink P&D CashPanama Pink CashPanama White Cash

Bangladesh Black TigerCash

5.823.765.495.265.04

5.074.775.145.005.335.20

6.384.394.72

4.48

0.630.400.530.53

0.53

0.530.390.540.540.530.54

0.850.410.49

0.27

10.8910.619.6110.08

10.60

10.438.2010.5310.8910.00

10.4513.349.3010.38

6.00

4.742.984.494.10

4.15

4.114.414.033.904.264.085.443.833.82

3.88

7.084.586.345.90

5.70

5.755.435.785.585.965.83

7.614.995.76

5.03

1.00-0.140.820.470.67

0.65-0.170.650.680.57

0.590.410.790.44

0.02

-0.141.00-0.140.64

0.46

0.490.030.440.390.540.52-0.140.250.57

0.23

a All cash prices reflect the 31-35 shrimp/lb. size category

The low correlation coefficients between cash and futures prices raise concerns about shrimpfutures markets= role as a price discovery and hedging mechanism. Comparison with othercommodities provides a useful perspective. For example, weekly 1996 feeder cattle futuresprices and Kentucky average cash prices exhibited a 95 percent correlation, despite the fact thatfeeder cattle futures settlement prices are determined by market activity far from Kentucky. Onewould expect even higher correlations between cash and futures prices for commodities such ascorn and soybeans.

In this application, model choice and specification depend on the existence ofnonstationarity, cointegration, and serial correlation. Granger and Newbold ( 1974) demonstratedthat regressing a nonstationary series upon another nonstationary series can produce spuriousresults. Many time series are nonstationary when expressed in levels, but are stationary whenexpressed as first differences. Augmented Dickey-Fuller tests applied to the 15 price seriesexamined in this study (denoted pi) relied on the following unrestricted and restrictedregressions:


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118 Performance of Shrimp Futures Markets • LJ. Maynard et al.

kp¡, = a + ßt + (p-1)/?,,_, + AA/>,.,_, (unrestricted)

Ap¡, = a + AAp,v_, (restricted),

where A denotes a first difference and / denotes time. If the resulting F-ratio allows one to rejectthe null hypothesis that ß=0 and p=l, then one concludes that the series is unit-root stationary.Critical values of/'-ratios sufficient to reject the null hypothesis of unit-root nonstationarityappear in Dickey and Fuller (1981, p. 1063, Table VI). Table 2 shows computed F-statisticsfrom the Augmented Dickey-Fuller tests applied to each price series in levels and in firstdifferences. Based on a critical F-value of 5.39 at a .10 significance level, all of the price serieswere deemed nonstationary in levels, and all were deemed stationary in first differences. Toavoid spurious results, all subsequent analyses relied on first-differenced price series.

Table 2 Augmented Dickey-Fuller tests imply first-differenced prices are stationary

Estimated F (186,4,2)"

Price Series Levels First Differences

Black Tiger Futures

Ecuador White Futures

Brazil Pink Cash

Ecuador White Cash

Gulf Brown Cash

Gulf White Cash

Gulf Peeled Cash

Mexican #1 Brown Cash


Mexican #1 White Cash


Mexican Pink P&D Cash

Panama Pink Cash

Panama White Cash

Bangladesh Black Tiger Cash

" An estimated F-statistic exceeding the critical value of 5.39 at a .10 significance level rejects the nullhypothesis of nonstationarity.

If two series are each nonstationary, but a linear combination of the series is stationary, thenthe two series are said to be cointegrated (Engle & Granger 1987). First differencing data toremedy nonstationarity implies a loss of long-run information if series are cointegrated and thedifference operator is not recognized in the error process (Johansen & Juselius 1990). Errorcorrection terms may be specified, when necessary, that exploit the long-run informationotherwise lost by first differencing cointegrated time series. Engle and Granger ( 1987) suggesteda commonly used test for cointegration: regress one nonstationary series on another, thenperform a Dickey-Fuller test on the estimated residuals v, based on the following regression:


1.23

1.97

1.56

1.01

0.93

0.54

2.13

1.25

1.66

0.79

0.80

1.88

1.09

1.09

3.21

48.71

34.91

60.38

10.01

9.22

10.42

45.23

30.11

30.42

20.79

23.63

46.77

33.92

25.15

45.47

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L.J. Maynard et al. • Performance of Shrimp Futures Markets 119

Av, = {a- l)v,_, + e,. (2)

If the null hypothesis that a =1 is rejected, then the residuals are deemed stationary, implying thatthe series are cointegrated. Pairwise Engle-Granger tests were applied to all combinations ofshrimp price series with Black Tiger or Ecuador White futures prices, and the estimated t-ratiosappear in Table 3. At a. 10 significance level (implying a 2.57 critical value), only Brazil Pinkcash prices appear to be cointegrated with Black Tiger futures prices, and none of the price seriesappear to be cointegrated with Ecuador White futures prices. The nonstationarity andcointegration tests thus suggested that further analysis should rely on first differenced data,without the use of error correction terms.

Table 3 Engle-Granger tests suggest that cash and futures prices for shrimp are rarely cointegrated

Estimated (-ratio

Price Series Black Tiger Futures Ecuador White Futures


Brazil Pink Cash

Ecuador White Cash

Gulf Brown Cash

Gulf White Cash

Gulf Peeled Cash





Mexican Pink P&D Cash

Panama Pink Cash

Panama White Cash


" estimated i-ratios exceeding 2.57 imply cointegration at a .10 significance level

Two nonstationary price series for the same asset should display a stationary equilibriumrelationship if the markets generating the price series are efficient. The results shown in Table 3suggest either that cash and futures markets for shrimp do not represent the same assets, or thatthe cash and/or futures markets for shrimp are not efficient. The lack of cointegration betweenBlack Tiger futures and Bangladesh Black Tiger cash prices, and between Ecuador White cashand futures prices, is most surprising. Schroeder and Goodwin (1991) encountered a similar lackof cointegration between live hog cash and futures prices, and determined that one source ofasset disparity was the time remaining until expiration of futures contracts. Frozen shrimp aremore storable than live hogs, and shrimp futures are traded for all calendar months, which shouldmitigate the issue of disparate delivery dates. The alternative possibility that inefficient shrimpmarkets exist, and may offer arbitrage opportunities via systematic lead-lag relationships, bearsfurther exploration.

If a futures price series reflects all available information useful in predicting forward prices,one would expect it to be a leading indicator of related cash prices. Conversely, if a leading

Aqitaculttfre Economics and Management 50/4) 2001

-1.395-3.523 a

-1.233

-1.659

-1.481

-1.259

-1.910

-2.164

-1.547

-1.662

-1.771

-2.412

-1.490

-1.256

n/a-1.895

-2.072

-1.896

-1.994

-1.775

-1.753

-1.592

-2.054

-1.976

-1.997

-1.705

-1.992

-1.802

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indicator of a futures price series were identified, arbitragers could exploit the relationship toearn riskless profits. Speculative activity in heavily-traded futures contracts would tend toeliminate persistent arbitrage opportunities. The existence of arbitrage opportunities, or the lackthereof, in the thinly-traded shrimp futures markets are a measure of the contracts ' performanceas a price discovery mechanism.

Previous studies have used a variety of methods to evaluate lead-lag relationships amongcash and futures price series. Garbade and Silber (1983) proposed a system of simultaneouspartial adjustment equations that test for price leadership and convergence to equilibrium.Studies relying on this dynamic equilibrium model include Schwarz and Laatsch (1991) andSchroeder and Goodwin (1991). Alternatively, error correction models offer a convenientmethod of testing for Granger causality among price series (Wahab & Lashgari 1993, Schroeder& Goodwin 1991), and have the advantage of providing information on the duration of lead-lagrelationships. The lack of evidence supporting cointegration in this application, however, impliesthat an error correction model would be inappropriate. Several alternative methods of testing forGranger causality exist; the method selected for this study is a two-sided distributed lag modeldeveloped by Sims (1972).

Granger causality tests are a useful tool in identifying leading indicators, and are widely usedto assess agricultural price relationships (e.g., Bessler & Schrader 1980, Bessler & Brandt 1982,Maynard 1997). Adams et al. (1987) used three popular Granger causality tests to examineshrimp price determination at selected market levels. Granger causality refers to a predictive (notnecessarily causal) time series relationship between two variables Xand Y. If current values of Xcan be better predicted with knowledge of past values of y than without such knowledge, then Y"causes" X. Specifically, Sims' (1972) method tests the hypothesis that price series Y leadsseries X via the following regression:

AY, = ft + ̂ AX,_4+...+P9AX,+A + s , , (3)

where X and Y are whitened price series, and A denotes a first difference. Joint significance offuture lags of AX offers evidence that Y leads X. The model incorporated lags of up to fourweeks to allow ample time for recognition of responses to cash or futures price shocks. Two setsof Granger causality tests were performed: the first set tested if each price series was a leadingindicator of Black Tiger and Ecuador White futures prices, and the second set tested if BlackTiger and Ecuador White futures prices were leading indicators of each price series.

Regarding the use of whitened price series, most of the price series exhibited serialcorrelation, the presence of which renders inferential statistics invalid. Four series (Black Tigerfutures, Gulf Peeled, Mexican Pink P&D, and Panama Pink) were not serially correlated in first-differenced form. Serial correlation was addressed by estimating the most parsimonious ARIMAprocess sufficient to produce white noise innovations (i.e., residuals), and retaining theinnovations for further analysis. In each case, an integrated process with no more than threeautoregressive terms and no moving average terms was sufficient to whiten the price series.After removing the time series properties from a series, the remaining innovations retain anyGranger causality relationships (Pierce 1977).

Statistical significance defines the results of Granger causality tests, but economicsignificance is also relevant in evaluating futures contract performance. A futures tradingsimulation assessed the potential profits earned through arbitrage during the study period.Suppose in the example above that Y leads X(where Xis a futures price series). A speculativetrading strategy based on series Y can generate signals indicating when to buy and sell futurescontracts of X. The profits from using Y to guide trades in X was compared to basing tradingdecisions only on knowledge of X. No more than one futures contract was open at any giventime, and the simulation assumed the use of stop-loss orders if a position's losses exceeded


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$0.10/lb.Numerous techniques exist to generate buy/sell signals in futures trading. The relative

strength index (RSI) was selected for this study because it is a widely used measure of marketmomentum (Purcell & Koontz 1999) that can be uniformly applied to each of the tradingscenarios. To calculate the five-week relative strength index used in this study, compute theaverage weekly upward change (denoted AUp) in futures prices during the most recent five-weekperiod, and compute the average weekly downward change (denoted ADown) during the five-week period. Define the relative strength index as:

RSI = — *100 . (4)AUp+ bDown

The speculator chooses upper and lower thresholds of the RSI that trigger trading activity. Anindex value exceeding the upper threshold indicates an overbought market, at which point thetrader would liquidate long positions and establish short positions by selling futures. An indexvalue below the lower threshold suggests an oversold market, and the trader would respond byliquidating short positions and establishing long positions by buying futures. In this study,various pairs of thresholds were examined, ranging from 70/30 to 90/10. The 70/30 pair ofthresholds, for example, implies that a short futures position would be established when the RSIrises above 70, and a long futures position would be established if the RSI falls below 30. Anunavoidable shortcoming of the trading simulation was that the study period was too brief toallow a reliable out-of-sample simulation. As a longer time series of cash and futures pricesbecomes available, out-of-sample testing will be appropriate.

Results

Table 4 demonstrates that leading indicators of shrimp futures prices existed during the studyperiod. The table shows calculated F-statistics used to test the null hypothesis that futuremovements in series X do not help explain movements in series Y. Calculated F-statisticsexceeding a selected critical F-value reject the null hypothesis and suggest that Y leads X. Twocash price series, Brazil Pink and Panama White, appeared to lead Black Tiger futures prices.Black Tiger futures led Ecuador White futures prices, as did the Gulf Brown and Mexican #1Brown cash price series. These results offer tentative evidence of persistent arbitrageopportunities.

As shown in the bottom half of Table 4 and in Table 5, the Sims tests revealed strongerunidirectional relationships when futures prices were the hypothesized leading indicator. BlackTiger futures led seven of the thirteen cash price series, and Ecuador White futures led four cashprice series. The magnitudes of lead-lag relationships were consistently greater when futuresprices led cash prices, and the relationships were often significant at a higher confidence level.In all but two cases, the signs of the leading indicator coefficients were positive, as would beexpected if economic forces influenced the price of all shrimp varieties similarly and if shrimpvarieties were substitutes in consumption. The strongest causality from futures to cash pricesexisted among the Mexican Brown and Mexican White varieties. Black Tiger futuresconsistently led the Mexican Brown and White varieties by three weeks, as indicated in Table 5by statistical significance of the third future lag in the unrestricted regressions. No such patternwas discernible among the other shrimp varieties.


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Table 4 Sims tests of Granger causality among selected cash and futures shrimp prices

Test if: Selected prices lead Black Tiger and Ecuador White futures prices

Potential Leading Series Black Tiger Futures Ecuador White Futures

Black Tiger Futures n/a 1.995* a

Ecuador White Futures 0.486 n/aBrazil Pink Cash 2.076 * 0.479Ecuador White Cash 0.849 1.585Gulf Brown Cash 0.561 2.352 *Gulf White Cash 0.336 0.849Gulf Peeled Cash 1.152 0.097Mexican #1 Brown Cash 1.458 2.115*Mexican #2 Brown Cash 1.462 1.792Mexican #1 White Cash 0.810 0.200Mexican #2 White Cash 1.709 0.167Mexican Pink P&D Cash 0.023 0.311Panama Pink Cash 0.418 1.809Panama White Cash 2.385 * 0.554Bangladesh Black Tiger Cash 0.690 0.553

Test if: Black Tiger and Ecuador White futures prices lead selected pricesPotential Trailing Series Black Tiger Futures Ecuador White Futures

Black Tiger Futures n/a 0.486Ecuador White Futures 1.995* n/aBrazil Pink Cash 0.791 0.373Ecuador White Cash 1.978 * 1.533Gulf Brown Cash 1.522 1.807Gulf White Cash 0.956 1.725Gulf Peeled Cash 3.090** 0.417Mexican #1 Brown Cash 2.831 ** 2.269 *Mexican #2 Brown Cash 3.545 *** 2.529 **Mexican #1 White Cash 3.206 ** 4.196 ***Mexican #2 White Cash 3.085 ** 4.414 ***Mexican Pink P&D Cash 0.848 0.104Panama Pink Cash 1.605 0.315Panama White Cash 3.563*** 1.244

Bangladesh Black Tiger Cash 0.913 0.570a Table entries are estimated F-statistics with 4 df in the numerator and 170 df in the denominator.

Granger causality is indicated by an estimated F-statistic exceeding 1.97 (. 10 level), 2.42 (.05 level),or 3.41 (.01 level).

*, **, and *** denote statistical significance at a .10 level, .05 level, and .01 level, respectively.

The results suggested weak relationships between futures prices and the corresponding cashprice of the same shrimp variety. Black Tiger futures prices failed to lead Bangladesh BlackTiger cash prices, and Ecuador White futures prices failed to lead Ecuador White cash prices.Black Tiger futures, however, led Ecuador White cash prices.

Statistical significance in the Granger causality tests may not guarantee that a leadingindicator is a useful tool for arbitrage; thus, a measure of economic significance is warranted.The futures trading simulation examined the profitability of using leading price series as signals

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for futures trading. The benchmark for comparison was the profit resulting from using onlyfutures prices to generate buy/sell signals. Specifically, a benchmark RSI was calculated fromBlack Tiger futures prices, and used to generate signals for Black Tiger futures trading.Similarly, a benchmark RSI was calculated from Ecuador White futures prices and used togenerate signals for Ecuador White futures trading.

Table 5 Statistically significant parameter estimates indicating duration and sign of unidirectional lead-lagrelationships

LeadingSeries

Brazil Pink CashBlack Tiger FuturesGulf Brown CashBlack Tiger FuturesBlack Tiger FuturesBlack Tiger FuturesBlack Tiger FuturesBlack Tiger FuturesBlack Tiger FuturesBlack Tiger FuturesEcuador White FuturesEcuador White FuturesEcuador White FuturesEcuador White Futures

LaggingSeries

Black Tiger FuturesEcuador White FuturesEcuador White FuturesEcuador White CashGulf Peeled CashMexican #1 Brown CashMexican #2 Brown CashMexican #1 White CashMexican #2 White CashPanama White CashMexican #1 Brown CashMexican #2 Brown CashMexican #1 White CashMexican #2 White Cash

1

-0.166*

0.632**-0.457***

0.249**

Lead (weeks)2 3

0.179*0.067**

0.436***0.436***0.498***0.555***

0.474***

0.280"

4

0.161**

0.230*

0.371***0.507***

*, **, and *** denote statistical significance at the .10, .05, and .01 levels, respectively; insignificantparameter estimates not shown

RSI's were also calculated from each leading indicator of the two futures price series,trading signals were generated from the RSI's, and the resulting profits were compared to thebenchmark profit levels. Table 6 shows the profits calculated from each pair of "signal" and"trading" series. Three sets of upper and lower RSI thresholds (70/30, 80/20, and 90/10) wereexamined to gain a more comprehensive view of trading profitability under each scenario.

Table 6 Profitability of using leading indicators to provide buy/sell signals for futures trading under threetrading strategies

Ecuador White Arbitrage Profits Relative Strength Index Thresholds

Signal SeriesEcuador White Futures (benchmark)Black Tiger FuturesGulf Brown Cash


Trading Series 70/30 80/20 90/10Ecuador White Futures $12,831 " $6,769 $4,643Ecuador White Futures $5,465 $11,192 $11,693Ecuador White Futures $2,055 $1,326 -$856

Ecuador White Futures $6,662 $6,639 $6,976

Black Tiger Arbitrage Profits Relative Strength Index Thresholds

Signal SeriesBlack Tiger Futures (benchmark)Brazil Pink Cash

Panama White Cash

Trading Series 70/30 80/20 90/10Black Tiger Futures -$2,016 -$4,717 -$5,539Black Tiger Futures -$7,818 -$9,128 -$9,869

Black Tiger Futures -$4,376 -$3,954 -$4,874

a Table entries reflect profit from the trading strategy specified by the Relative Strength Index parameters(sell at the higher level, buy at the lower level), assuming no more than one futures contract is open atany time, and assuming a stop-loss order takes effect when losses reach $0.10/lb.

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Table 6 confirms that using Black Tiger futures prices to guide futures trading in EcuadorWhite shrimp was profitable during the study period when the 80/20 and 90/10 RSI thresholdswere used. For example, selling Ecuador White futures when the Black Tiger RSI exceeded 80,and buying Ecuador White futures when the Black Tiger RSI dipped below 20, produced profitsof $ 11,192 compared to benchmark profits of $6,769 when trading was based only on knowledgeof Ecuador White futures prices. Figure 1 illustrates the profitability of using Black Tiger futuresprices to guide Ecuador White futures trading over a more detailed range of RSI thresholds. Twonoteworthy findings emerge from the results: (1) use of a leading indicator can lead toeconomically significant arbitrage opportunities in the Ecuador White futures market, and (2) theprofitability of using a leading indicator is sensitive to the trading strategy employed (in thiscase, the choice of RSI thresholds).

16000

Relative Strength Index Parameters

Fig. 1 Black Tiger shrimp futures prices offer arbitrage opportunities for Ecuador White Shrimp futurestrading over a wide range of Relative Strength Index parameters ** EW/EW denotes use of Ecuador White futures prices as buy/sell signals for Ecuador White futurestrading; BT/EW denotes use of Black Tiger futures prices as signals for Ecuador White futures trading.

In general, Gulf Brown and Mexican #1 Brown cash prices were not profitable guides toEcuador White futures trading, despite the evidence offered by the Granger causality tests.Using Mexican #1 Brown cash prices to generate trading signals was more profitable than thebenchmark only in the 90/10 RSI threshold scenario. In the case of Black Tiger futures trading,the RSI strategy was uniformly unprofitable during the study period. Alternative strategies (e.g.,basing signals on pairs of moving averages) may have been more effective and could be tested,but the primary issue of interest is the profitability of using leading indicators relative to thebenchmark. Only Panama White cash prices showed modest promise as a tool for arbitrage,yielding higher profits (i.e., lower losses) than the benchmark in the 80/20 and 90/10 RSIthreshold scenarios.


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LJ. Maynard et al. • Performance of Shrimp Futures Markets 125

Discussion

This study was motivated by the expressed need of shrimp industry decision makers forinformation on marketing and risk management opportunities. Black Tiger and Ecuador Whiteshrimp futures markets are intended to offer an effective hedging tool with which to manageprice risk, as well as a price discovery mechanism that enhances industry-wide performance.Shrimp futures contracts currently lack liquidity, however, which may disrupt the contracts'effectiveness as a hedging instrument and price discovery mechanism. The purpose of this studywas to empirically test if leading indicators of shrimp futures prices exist, and test if persistentarbitrage opportunities exist in shrimp futures markets. Evidence of leading indicators andpersistent arbitrage opportunities would suggest deficiencies in shrimp futures markets.

Granger causality tests indicated that Brazil Pink and Panama White cash prices wereleading indicators of Black Tiger futures prices during the period from late-1994 to mid-1998. Inaddition, Black Tiger futures prices, Gulf Brown cash prices, and Mexican # 1 Brown cash priceswere leading indicators of Ecuador White futures prices during the study period. The resultssuggest that shrimp futures prices fail to reflect all available market information affectingforward prices, and therefore suffer shortcomings as a price discovery mechanism. The findingsfrom the Granger causality analysis are tempered somewhat by the results of futures tradingsimulations indicating that Granger causality does not imply persistent arbitrage opportunities inall cases. Sufficient evidence of persistent arbitrage opportunities existed, however, to warrantconcern that the Ecuador White futures contract in particular fails to serve as an exemplary pricediscovery mechanism.

Price discovery is an important issue at the industry level, but individual decision makersmay be more concerned with the effectiveness of shrimp futures contracts as hedginginstruments. Three related findings offer evidence useful in evaluating hedging prospects. First,the correlation between shrimp futures prices and cash prices ranged from -17 percent to 82percent. Ecuador White cash and futures prices, for example, exhibited a correlation coefficientof only 64 percent. Hedging replaces price risk with basis risk. Basis levels (cash prices minusfutures prices) are typically much less volatile than price levels, thus offering risk managementopportunities from hedging. Low correlation between cash and futures prices, however, impliesthat basis risk may not be sufficiently lower than price risk to justify hedging.

Second, basis levels were often more variable than price levels. The standard deviation ofbasis was calculated for each cash price series against each futures price series. The standarddeviation of each variety's price provides a comparable measure of price risk. Table 7 showsthat in five of the thirteen cases, Black Tiger basis risk exceeded price risk, and Ecuador Whitebasis risk exceeded price risk in five of the thirteen cases, as well. Even in the best-case scenarioinvolving Brazil Pink cash prices and Black Tiger futures prices, the 0.69 ratio of basis risk toprice risk implies limited risk management opportunities from hedging.

Table 7 also illustrates a third related finding that the ex post, optimal hedge ratio for mostshrimp varieties was zero. Optimal (i.e., risk minimizing) hedge ratios were estimated byregressing first-differenced cash prices on a constant and first differenced futures prices (Stoll &Whaley 1993, p. 56). The coefficient on the first-differenced futures price variable represents thequantity of shrimp futures (in pounds) that should be traded to hedge one pound of shrimp in thespot market. The highest significant hedge ratio was 0.13 (Ecuador White shrimp hedged withEcuador White futures). Hedging effectiveness is measured by the adjusted R-squared (Stoll &Whaley 1993, p. 56). Ecuador White shrimp hedged with Ecuador White futures returned thehighest adjusted R-squared of only 0.06, implying that hedging would fail to eliminate 94 percentof price risk. The combination of low correlations between cash and futures prices, basis riskthat often rivals or exceeds price risk, and the results of the optimal hedge ratio analysis make acompelling argument that shrimp futures markets were ineffective hedging tools for many shrimp

Aquaailftire Economics and Management 5(3/4) 2001

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varieties during the period examined in this study.Lack of liquidity, particularly speculative activity, is a likely cause of the deficiencies

suggested by this analysis. Speculators may prefer to trade in other commodities if there is a lackof volatility in shrimp futures prices, or if information is lacking with which to forecast marketdevelopments. Neither of these explanations, however, obviously applies to shrimp futuresmarkets. U.S. shrimp prices are moderately volatile, as shown in Table 1, and sensitive tosupply-side factors such as foreign exchange rate volatility, import levels, cold storage stocks,virus outbreaks worldwide, and the growth of farmed shrimp production (Chauvin 1998).Although cash shrimp markets are not as transparent as many agricultural markets (Martinez-Garmendia & Anderson 1999), organizations such as Urner Barry and Shrimp World, Inc.provide detailed market information, and the fundamental factors influencing prices areamenable to forecasting.

Table 7 Risk-minimizing hedge ratios suggest poor shrimp hedging effectiveness

Cash Series

Brazil Pink Cash

Ecuador White CashGulf Brown CashGulf White CashGulf Peeled CashMexican #1 Brown CashMexican #2 Brown CashMexican #1 White CashMexican #2 White CashMexican Pink P&D Cash

Panama Pink CashPanama White Cash


Black Tiger Futures

HedgeRatioa

-0.109

0.069**0.068**0.046

0.0280.041

0.0220.0630.0540.018

0.060*0.043

0.014

R2b

0.01

0.030.020.01

-0.000.00

-0.000.010.01

-0.01

0.010.00

-0.00

S-d-basis '

S-d-price

0.69

1.150.910.94

2.050.92

0.881.03

0.990.97

0.971.24

2.54


HedgeRatioa

0.069

0.131***0.0660.106**

- 0.0090.009

0.0240.057

0.0730.146

0.088**0.102**

0.029

R2b

-0.00

0.060.010.03

-0.01-0.01

-0.000.00

0.010.00

0.020.02

-0.00

s-d.^sisC

S-d-price

1.34

0.780.930.911.400.94

0.980.87

0.881.16

1.210.85

1.59a Reflects the estimated parameter ai in the regression APcash = a0 + at APfutures + ebR2 denotes the adjusted R-squared statistic, a measure of hedging effectiveness0 Denotes the ratio of standard deviations of basis and cash price*, **, and *** denote statistical significance at the .10, .05, and .01 levels, respectively

Martinez-Garmendia and Anderson (1999) argue that shrimp markets satisfy most of therequirements for a successful futures contract, with the possibly debilitating exception of producthomogeneity. Contract specifications allow several non-par categories of shrimp to be delivered,at the seller's discretion. Fixed premiums and discounts for non-par categories fail to capturefluctuations in the value of various categories, thus causing market failures that discourageliquidity. Given that shrimp is a final product for which buyers have specific requirements, theuncertainty concerning delivered grades further discourages long hedging. A tendency forspeculators to avoid contracts with low liquidity, simply due to the lack of other traders withwhom to trade, aggravates the problem. For example, scalpers (traders who simultaneously postoffers one price tick above bids) have few incentives to participate in a market where fewer thanten contracts change hands daily.


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Directions for future research include further verification of persistent arbitrageopportunities. The conclusions drawn from this analysis would be stronger if daily cash pricedata were available, and if the data series were sufficiently long (relative to the frequency ofround turns in the trading simulations) to permit reliable out-of-sample testing. Extensions toother thinly traded futures contracts would be appropriate. For example, the butter contract onthe Chicago Mercantile Exchange is so thinly traded that its usefulness as a hedging tool iscompromised, accordbg to some experts (Prosi 1999).

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Bessler, D.A. & Schrader, L.F. (1980) Relationship Between Two Price Quotes for Eggs.American Journal of Agricultural Economics, 62, 766-771.

Chauvin, W.D. (1998) All About the Shrimp Market - A Tutorial. Shrimp World, Inc.,http://www.shrimpcom.com.

Dickey, D.A. & Fuller, W.A. (1981) Likelihood Ratio Statistics for Autoregressive Time Serieswith a Unit Root. Econometrica, 49,1057-1072.

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Martinez-Garmendia, J. & Anderson, J.L. (1999) Hedging Performance of Shrimp FuturesContracts with Multiple Deliverable Grades. The Journal of Futures Markets, 19,957-990.

Maynard, L.J. (1997) Price Discovery in the Egg Industry. Agricultural and ResourceEconomics Review, 26,23-30.

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Pierce, D.A. (1977) Relationships - and Lack Thereof - Between Economic Time Series, withSpecial Reference to Money and Interest Rates. Journal of the American StatisticalAssociation, 72,11-23.

Prosi, R. (1999) Daily Price Discovery and Futures Market Efficiency. 1999 Dairy Forum,Naples, Fl., January 17-20.

Purcell, W.D. & Koontz, S.R. (1999) Agricultural Futures and Options: Principles andStrategies, 2nd ed., Prentice-Hall, Upper Saddle River, N.J.

Schroeder, T.C. & Goodwin, B.K. (1991) Price Discovery and Cointegration for Live Hogs. The


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Journal of Futures Markets, 11,685-696.Schwartz, T.V. & Laatsch, F.E. (1991) Dynamic Efficiency and Price Leadership in Stock

Market Index Cash and Futures Markets. The Journal of Futures Markets, 11,669-683.Shrimp World, Inc. (1999) Market Price and Index. 28 December, http://www.shrimpcom.com.Sims, C.A. (1972) Money, Income, and Causality. American Economic Review, 62, 540-552.Stoll, H.R. & Whaley, R.E. (1993) Futures and Options: Theory and Applications. South-

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Performance of shrimp futures markets as price discovery and hedging mechanisms

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