the impact of multiple volatilities on import demand for u.s. commodities: the case of soybeans

The Impact of Multiple Volatilities on Import Demandfor U.S. Commodities: The Case of Soybeans

Qiang Zhang�

Department of Agricultural Economics, University of Kentucky, Lexington,KY 40546-0276. E-mail: [email protected] R. ReedDepartment of Agricultural Economics, University of Kentucky, Lexington,KY 40546-0276. E-mail: [email protected]

Sayed H. SaghaianDepartment of Agricultural Economics, University of Kentucky, Lexington,KY 40546-0276. E-mail: [email protected]

ABSTRACT

The focus of this study is the effects of exchange rate, commodity price, and ocean freight costrisks on import demand with forward-futures markets. The case of U.S. and Braziliansoybeans is analyzed empirically using monthly data. A two-way error component two-stageleast squares procedure for panel data is used for the analysis. Risk for these three effects ismeasured by the moving average of the standard deviation. Major soybean importers aresensitive to exchange rate risk. Importing countries in general are not sensitive to soybeanprice and ocean shipping cost risks for Brazilian or U.S. soybeans. [JEL classifications: Q13,Q17]. r 2010 Wiley Periodicals, Inc.

1. INTRODUCTION

Foreign exchange rates have been highly volatile since the end of the Bretton Woodssystem. This volatility has led researchers to investigate their impact on internationaltrade. Commodity markets are also unstable, and the volatility in commoditymarkets fluctuates over time. Price volatility in cash markets increases cash flowvariability and affects the profits for exporters and importers. Traders must also facemuch risk from the volatility of spot shipping rates because energy prices aregenerally more volatile than prices of other commodities.The general objective of this study is to investigate the impacts of multiple risks

that firms encounter when they import U.S. commodities. These multiple risks areexchange rate, commodity price, and ocean freight costs. We focus on regionaldifferences among the main U.S. soybean export markets. Brazil is the mostimportant competitor for the United States in the world soybean market, and theeffects of these risks on import demand for Brazil soybeans also will be considered inthis study.Bilateral data between the United States and its trade partners are used in a panel

data analysis to investigate the effects of exchange rate, soybean price, and oceanfreight costs on import demand with forward-futures markets. An error componenttwo-stage least squares methodology (EC2SLS) is estimated for the United Statesand its major competitor, Brazil. Due to its pegged currency system, China is

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Agribusiness, Vol. 26 (2) 202–219 (2010) rr 2010 Wiley Periodicals, Inc.

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/agr.20214

202

considered as a specific case without exchange rate risk. Because of physical distance,the volatility of ocean freight costs is omitted for Mexico, another importantimporting country. These two countries are excluded from the panel data analysisand investigated as two specific case analyses.During the last 15 years, global soybean production has increased continuously.

The quantity was 107 million metric tons (MTs) in 1990 and 229 million MTs in2006. Among global soybean producers, the United States, Brazil, Argentina, andChina accounted for 89% of the global total in 2006 (U.S. Department ofAgriculture Foreign Agricultural Service [USDA FAS], 2007). Among them, Chinahas become the largest soybean importing country in the world since 2002 becauseChina’s soybean production cannot satisfy its huge domestic consumption. Figures 1to 4 show soybean production, domestic consumption, exports (imports), and endingstocks for Argentina, Brazil, the United States, and China from 1991 to 2006.The United States, Brazil, and Argentina, account for 91% of world exports. Their

soybean production and exports have upward trends, especially for Argentina andBrazil. Prior to 1970, the United States accounted for 90% of soybean exports. Since1970, the United States has steadily lost its export market share with the emergenceof Brazil and Argentina in these markets. The export market compositions havechanged from being dominated by a single country to multiple countries. In 2006, themarket shares in the world soybean market for Argentina, Brazil, and the UnitedStates were 10%, 37%, and 43%, respectively (USDA FAS, 2007). The U.S. soybeanexport share in the world market has been decreasing from 66% in 1991 to 43% in2006. In contrast, the Brazilian soybean export market share has increased from14% in 1991 to 37% in 2006. Figure 1 demonstrates that most Argentinean soybeansare used for domestic consumption and exports are relatively low. But Argentineansoybean ending stocks are relatively high.An important factor that impacts competitiveness of soybean exports of the

United States and South America in the world market is cost, including productionand transportation costs. Schnepf, Dohlman, and Bolling (2001) compared soybeanexport costs and found that soybean production cost favors Brazil and Argentina;

44.00

36.57

7.25

18.36

0.00

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30.00

40.00

50.00

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Mill

ion

Met

ric

Ton

s

production Consumption Exports Ending Stocks

Figure 1 Soybean Production, Consumption, Exports, and Ending Stocks for Argentina,

1991 to 2006. Source: USDA-FAS, PS & D, 2007.

203IMPORT DEMAND FOR U.S. COMMODITIES: THE CASE OF SOYBEANS

Agribusiness DOI 10.1002/agr

however, this production cost advantage was offset by higher internal marketing andtransportation costs compared with the United States.Soybean production in Brazil has grown rapidly and it has been the leading

producer in South America. Figure 2 shows that Brazilian soybean stocks increasedfrom 4.6 million MTs in 1991 to 16.1 million MTs in 2005, and Brazilian soybeanstocks have been more than U.S. soybean stocks since 1999.1 Large stocks in Brazilmay extend their soybean exporting season from April to October every year andincrease Brazil’s competitiveness in the international market. Brazil’s soybean

57.00

30.37

25.90

16.10

0.00

10.00

20.00

30.00

40.00

50.00

60.00

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Mill

ion

Met

ric

Ton

s

Production Consumption Exports Ending Stocks

Figure 2 Soybean Production, Consumption, Exports, and Ending Stocks for Brazil, 1991

to 2006. Source: USDA-FAS, PS & D, 2007.

86.77

52.97

29.94

16.200.00

20.00

40.00

60.00

80.00

100.00

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Mil

lio

n M

etri

c To

ns

Production Consumption Exports Ending Stocks

Figure 3 U.S. Soybean Production, Consumption, Exports, and Ending Stocks, 1991 to

2006. Source: USDA-FAS, PS & D, 2007.

1Ending stocks based on Market Year are not fully comparable for the U.S. and Brazil because of the

difference of the production seasons between these two countries.

204 ZHANG, REED, AND SAGHAIAN


exports increased dramatically from less than 4 million MTs in 1991 to 26 millionMTs in 2006, a 550% increase.From 1991 to 2006, annual world exports of soybean were 45.7 million MTs,

about 28% of global soybean production. U.S. soybean production and exportshave increased steadily over the past decade and the export volumes are relativelystable; Brazil and Argentina have experienced dramatic increases in soybeanproduction and exports due to a series of policy changes and new technologies, suchas the introduction of genetically modified organisms (GMO), the depreciation oftheir currency, and strong government support.On the importer’s side, China, the European Union (EU), Japan, Mexico, Taiwan,

Thailand, and Indonesia are major importers in the world. China’s soybean importshave increased dramatically over the last decade, going from 0.15 million MTs in1994 to 31.5 million MTs in 2006. Soybean imports to developed countries andregions (including South Korea and Taiwan) remain relatively stable in the lastdecade. However, China and Mexico have expanded their imports from the UnitedStates dramatically. China is an important market where the U.S. and Brazilcompete. In 2006, China’s soybean imports from Brazil were larger than its importsfrom the United States. All Mexican soybean imports have been from the UnitedStates since 1996.China adjusted soybeans trade policies in 1996. A tariff-rate quota system was

established for soybeans and the tariff rate was 3% for imports within the quota.However, the overquota tariff for soybeans was as high as 180%. That year markedChina’s shift from a net exporter to a net importer of soybeans. This adjustment ofsoybean trade policies was considered the beginning of China’s open trade regimewith respect to nontariff barriers in agriculture. In the U.S.–China Bilateral WorldTrade Organization (WTO) agreement, China’s import tariffs for soybeans werelowered to 3% and import quotas were eliminated in 2000, so the soybean sector inChina has been liberalized. China has been the world’s largest importing country forsoybeans since 2002. China’s domestic demand for soybeans continues to be strongdue to per capita income growth and urbanization, which stimulates meat

16.20

47.85

31.50

3.93

0.00

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50.00

60.00

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Mil

lio

n M

etri

c To

ns

Production Consumption Imports Ending Stocks

Figure 4 Chinese soybean production, consumption, imports, and ending stocks, 1991 to

2006. Source: USDA-FAS, PS & D, 2007.



consumption in China. In 2006, China’s soybean imports from Brazil were largerthan its imports from the United States.The specific objectives of the study are (a) to obtain import demand equations for

soybeans by certain soybean importing countries of the world; (b) to investigate theeffects of multiple volatilities that influence the import demand for U.S. soybeans; (c)to extend the analysis to the import demand for Brazil soybeans and to comparethese effects with the findings for U.S. soybeans. The model will include othervariables for importing countries that make the demand equations for soybeansmore accurate for achieving the main objectives.

2. BACKGROUND

Much of the empirical work in international trade has focused on the impact ofexchange rate risk on total imports or total exports of a country. The traditionalhypothesis is that unexpected exchange rate volatility reduces the incentives to tradefor risk-averse traders. In an early study, Hooper and Kohlhagen (1978) use anonlinear reduced form equation for market equilibrium price and quantity toconclude that exchange rate volatility negatively affects risk-averse traders.They argue that the currency denomination of the contracts, the risk preference oftraders, and the proportion of hedging are three important factors impactingcurrency risk.Many other studies have extended Hooper and Kohlhagen’s model (Cushman,

1983, 1988; De Grauwe, 1988; Franke, 1991; Viaene & Casper De Vries, 1992). Thesestudies indicate that the effect of currency risk is quite sensitive to the sample period,model specification, sectors/commodities, and considered countries.Many previous studies in agricultural economics investigate the influence of the

exchange rate and its fluctuation on aggregate or individual commodity trade. Pick(1990) follows Cushman’s model by using bilateral real exchange rates and he findsthat exchange rate risk has an effect on U.S. agricultural trade to developedcountries and has a significantly negative effect on developing countries. Andersonand Garcia (1989) research the impact of exchange rate uncertainty on U.S.soybeans exporting to Japan, France, and Spain by using a demand function that isderived from a risk-averse firm maximizing profits. They find all three countries havesignificantly negative responses to exchange rate variability. Sun and Zhang (2003)analyze the impact of exchange rate uncertainty on U.S. forest commodity exportsby using an error-correction model for time series data. They find the impact ofcurrency risk is negative in the long-run, but the short-run impact varies bycommodity. Cho, Sheldon, and McCorriston (2002) develop a gravity model, whichincludes real exchange rate uncertainty. They conclude that exchange rateuncertainty has a more adverse effect on the agricultural sector than for other tradesectors.Due to the price volatility and the shipping characteristics of agricultural

commodities, some researchers focus on the effects of these uncertainties on exports(Haigh & Holt, 2000; Thoung &Visscher, 1990). Theoretically, Kawai and Zicha(1986) analyze international trade with forward-futures markets under exchange rateand price uncertainty from either the importers’ or the exporters’ perspective.However, none of these models analyzed the impacts of exchange rate, commodityprice, and shipping cost risks jointly.



For the model specification, there is no unique way to measure exchange risk orthe impact of this risk on trade flow. There are two traditional ways to measureexchange risk; one is based on the standard deviation of the level or percentage of theactual exchange rate, the other is based on the difference between the actual andforward exchange rate. Measures based on the ARCH or GARCH model have beenused to study financial risk premium; others have employed regression models.Traditionally, a reduced form demand function derived from microeconomic theoryis an important method to measure the impact of exchange rate volatility on trade(Anderson & Garcia, 1989; Cushman, 1983; Hooper & Kohlhagen, 1978; Pick,1990). Some time series techniques, such as VAR, GARCH in mean, and Error-Correction, are also used (Korary & Lastrapes, 1989; Kroner & Lastrapes, 1993; Sun& Zhang, 2003).Most of the previous studies use quarterly or annual data, though Klein (1990),

Korary and Lastrapes (1983), Kroner and Lastrapes (1989), and McKenzie andBrook (1997) apply monthly data to evaluate the effects of exchange rate volatilityon trade flows. No research employs monthly data to study the impacts of marketvolatilities on individual commodities which have futures markets. Data frequency isan important factor that could impact the empirical results so it is important to selectthe appropriate data frequency that reflects the characteristics in the cash market andfutures market.In the case of international commodity trade, ocean freight rates definitely have an

effect on the importing costs and ocean freight rates are quite volatile. For example,the average freight rate from a U.S. gulf port to Japan was $60.83 per metric ton inthe fourth quarter of 2004 and was $46.75 in the fourth quarter of 2005, a 23%change in one year. For the fourth quarter, ocean freight rates have varied widelyduring the 1996–2005 periods with a high of $60.83 in 2004 and a low of $13.33 in1998. In the empirical model, the ocean freight costs should be taken intoconsideration in the individual commodity demand for importing countries.Overall, for the individual commodity trade, especially, for those commodities which

have the futures market to hedge the commodity price risk, it is more comprehensive toevaluate the impacts of exchange rate, commodity price, and other quantifiable marketrisks jointly when considering the effects of a futures-forward market.

3. MODEL DESCRIPTION

This study employs a modified version of Hooper and Kohlhagen’s trade model,which assumes the demand for commodity import is a derived demand. We developa model of a competitive firm importing a commodity under exchange rate, price,and ocean freight cost uncertainty. Suppose the importing firm produces final goodsusing an imported commodity as an input. The exchange rate, foreign currency priceof the imported commodity, and the ocean freight rate are random variables,whereas the domestic currency price of final goods is known with certainty. Theimporter can hedge foreign exchange risk by purchasing foreign exchange forwardand hedge commodity price and freight rate price by going long in the futuresmarkets.An import firm faces a domestic demand for its output (Qo), which is a function

of its own price (P), prices of substitutes and complements (PD), and domestic



income (Y):

Qo ¼ aP1bPD1cY ð1Þ

A risk-averse importing firm’s optimization problem may be formulated as:

maxEU ðpÞ ¼ EðpÞ � gðV ðpÞÞ1=2 ð2Þ

where E is the expected value operator, U is total utility, V is the variance of profitoperator, and g is the relative measure of risk preference (g 40 is risk aversion, go0is risk lover, and g5 0 is risk neutral). Utility is an increasing, differentiable functionof profits and a decreasing, differentiable function of standard deviation of profits.The firm receives orders for its output and places the orders for its imported inputs inthe first period, and it pays for and ships imports and receives payments for itsoutput in the second period. This firm’s profit in domestic currency is:

p ¼ Qo � PðQoÞ �UC �Qo �HMq�HNq ð3Þ

where UC is per unit production cost, H is the foreign exchange variable, M is theprice of the imported input, N is the freight cost, and q is the imported quantity.A fixed ratio of imports, i, are needed to produce output

q ¼ iQo ð4Þ

The imported commodity is invoiced in the foreign currency (i.e., U.S. dollars) andthe firm has access to both foreign exchange and commodity futures contracts. Thefirm hedges some constant proportion (a) in the forward market at the futuresexchange rate F; some constant proportion (b) in the commodity futures market atthe priceF 0; and some constant proportion (D) in the freight rate futures market atthe pricef 0

H ¼ ð1� aÞR1aF ð5Þ

M ¼ pdð1� bÞ1ðF 0 � ~F1pdÞb ð6Þ

N ¼ poð1� dÞ1ðf 0 � ~f 1poÞd ð7Þ

R is the spot exchange rate on the payment date; pd is the foreign currency price ofimports; F 0 is the commodity futures market price in the first period; ~F is thecommodity futures market price in the second period; po is the foreign currency priceof freight rate; f 0 is the commodity futures market price in the first period; and ~f isthe freight rate futures market price in the second period. The importer’s profit isobtained by substituting Equations 4, 5, 6 and 7 into Equation 3,

p ¼ Qo � PðQoÞ �UC �Qo � ½ð1� aÞR1aF �f½pd ð1� bÞ1ðF0

� ~F1pdÞb�

1½poð1� dÞ1ðf 0 � ~f 1poÞd�g � iQo ð8Þ

For simplification, covariances between the variables are assumed to be zero. All thevariables above, except R; ~F ; ~f , are assumed known with certainty on the contract



date. Therefore, the variance in the importing firm’s profits is

V ðpÞ ¼ ½ð1� aÞ2ðPd1Po1F 0b1f 0dÞ2s2R1ðaFbÞ2s2~F 1ðaFdÞ2s2~f

1ð1� aÞ2b2s2R ~F 1ð1� aÞ2d2s2

R ~f� � ðiQoÞ

2 ð9Þ

where s2R;s2~F; s2~f ; s

2R ~F; s2

R ~fare the variances of R; ~F ; ~f ;R ~F ;R ~f , respectively.

Hooper and Kohlhagen derive reduced-form import demands by substitutingEquations 8 and 9 into Equation 2 and differentiating with respect to Qo to obtainfirst-order conditions. Then, they substitute @P=@Qo ¼ 1

afrom (1) and assume the

importing firm is a price taker in the import market. The resulting firm-level importfunction has many variables and coefficients, but its derivation is quite straightforward. When the firm-level import demands are summed the aggregate importdemand function is obtained:

Qd ¼ gðUC; PD; Y ; Eð ~F Þ; Eð ~f Þ; EðRÞ; EðR ~F Þ; EðR ~f Þ; sR; s ~F ;s ~f ;sR ~F ;sR ~f Þ

ð10Þ

where sR;s ~F ; s ~f ;sR ~F ;sR ~f are standard deviations for each random variable; PD isthe importing country’s price of a competitive product (the price of soybeans of U.S.competitors may be considered as a proxy because the grain is typically invoiced inU.S. dollars in the world market). The relationships between quantity and UC, PD,Y, Eð ~F Þ, Eð ~f Þ, E(R), EðR ~F Þ, EðR ~f Þ are linear; a nonlinear relationship exists betweenquantity and the standard deviations.The exporter’s production decision is determined by the world market price, the

volatility of the world market price, the exporter’s unit cost of production and riskpreference, and the exporter’s hedging position in the commodity futures market.This implies that the export supply curve is infinitely elastic, so imports are demand-determined.

4. EMPIRICAL ANALYSIS

Because soybeans are selected for analysis, the U.S.’s competitors are Argentina andBrazil. The data for Argentina are not available, and it is reasonable that only Brazilis involved in this study because Brazil is the most important competitor for theUnited States.

4.1. Explanatory Variable Measures

The empirical model includes expected values and standard deviations for the threerandom variables and the competitors’ price (which is Brazil’s price in thisapplication). Consistent with Hooper and Kohlhagen, the expected values of ~F ; ~f areconsidered as the next period futures market price for simplicity. For the variableR,it is reasonable to assume that importers consider the current futures market price astheir expected value. The exchange rate from the futures market is used to measureexchange rate volatility. The expected value of R ~F and R ~f can be calculated basedon corresponding values of R; ~F and ~f . Volatility is defined by a moving samplestandard deviation around the random variable (Arize, Osang, & Slottje, 2000;



Chowdhury, 1993; Korary & Lanstapes, 1989).

Vt ¼1

m

Xmi¼1

ðlnXj;t1i � 1� lnXj;t1i � 2Þ2

" #1=2ð11Þ

where m is the order of moving average, and Xj represents R; ~F ; ~f ;R ~F ;R ~f , and R ~f ,respectively. Empirically, m is specified as 2 in this study for measuring short-run risk.Soybeans are selected in this analysis of risk. All of the main U.S. soybeans export

markets are included in the analysis: the EU, China, Mexico, Japan, Taiwan, SouthKorea, Indonesia, and Thailand. China is excluded from the panel data analysisbecause of its pegged currency system in the selected period. Mexico is excluded fromthe panel data analysis because ocean freight rate volatility has no significant effecton Mexico’s import from the United States. Individual models were fitted for thesetwo countries because of these special cases.

4.2. Data and Sources

The data used are based on U.S monthly value (1000 U.S. dollar) and quantity (1000MT) of soybean exports to selected destination markets from January 1996 toAugust 2006. Export prices of soybeans are obtained by dividing the export value byquantity exported. The data source is the USDA FAS. The monthly futures marketprices for soybeans, heating oil, and exchange rate of every destination (importingcountry’s currency per U.S. dollar) market are obtained from the published CD-ROM of the Commodity Research Bureau (CRB). Yearly nominal per capita GDPfor every destination market is used as the measure of each market’s income, which isavailable from USDA, Economic Research Service (USDA ERS, 2007). Themonthly data for income (per capita GDP) are derived from yearly data based on itsaverage growth rate.2

China is a specific case for Brazil as well. The monthly quantity (1000 MT) andunit price of Brazilian soybean exports (F.O.B.) are obtained from the BrazilianDepartment of Agriculture (accessed from http://www.aliceweb.desenvolvimento.gov.br).

4.3. Estimation Procedure

Equation 10 has a linear relationship with income, competitor’s price, and theexpected value of each random variable, but it has a nonlinear relationship with thestandard deviation of each random variable. Unfortunately, there is no statisticalpackage to handle a linear–nonlinear regression model for such a panel data.Because the purpose of this article is to investigate the impact of the risk of eachrandom variable on commodity trade simultaneously, the empirical models arelinearized. Because the purpose of this article is to investigate the impact of the riskof each random variable on commodity trade, the empirical models are linearized.Unit costs of production are not available for importing countries, so this variable is

2The monthly data for income (per capital GDP) are derived from yearly data based on its average

growth rate. First, the annual growth rate of income is calculated. Second, monthly growth rate is assumed

to be the annual growth rate divided by twelve. Third, an average value of annual income was set as the

June income. The calculated average values of income and its annual growth rate were used to estimate the

income for the other months.



excluded. High collinearity among EðR ~F Þ, EðR ~f Þ, and the expected exchange ratesuggests that these cross product variables and their standard deviations be droppedfrom the empirical model.The Baltic International Freight Futures Exchange and its Baltic Freight Index

were established in 1986. It was modestly successful in some years, but the indexceased trading in 2001 due to lack of liquidity. As the largest component of variableshipping cost, the price of fuel oil is volatile and it impacts ocean ship costssignificantly. Fuel oil futures can be considered as a substitute for hedging oceanfreight risk in practice. In this study, heating oil futures are used to hedge diesel fuel.Because the theoretical model is based on a two-period framework, all empirical

equations are estimated with lags3 on all of the explanatory variables for monthlydata. All the variables which are used in this study are nominal values.4 The finalempirical model is

Q ¼ b01b1PD1b2Y1b3EðRÞ1b4Eð ~F Þ1b5Eð ~f Þ1b6sR1b7s ~F1b8s ~f 1b9iDi1b10T ð12Þ

where Di identifies the month, i ¼ 1; . . . ; 11 and T represents the time trend from 1up to t.A Durbin–Wu–Hausman test found that the competitor’s price is endogenous for

the U.S. model, so the competitor’s price is instrumented using its own lagged value,expected exchange rate, expected futures price of soybeans, exporting country’sincome, and dummy variables for seasonality.Baltagi’s two-way error component two-stage least squares methodology

(EC2SLS) is employed for the panel data with the endogeneity problem. Baltagi(1981) applies the two-way error component model to the simultaneous equationcase and derives the EC2SLS estimator. This estimator is a weighted combination ofbetween cross-section, between time periods, and within two-stage least squaresestimates for the panel data. Furthermore, Baltagi (1984) shows the EC2SLS methodis superior to 2SLS method based on root mean square error (RMSE). The variablesfor China were transformed to eliminate problems with nonstationarity, whereas thevariables for Mexico were found to be stationary. After the necessary transforma-tions, ordinary least squares were used for the estimations.

5. EMPIRICAL RESULTS

5.1. U.S. Soybean Exports

Table 1 presents the results for U.S. soybean exports. As the expected price ofsoybeans increases, U.S. exports decline as expected; and the coefficient is significantat the 10% level. The export price for Brazilian soybeans does not significantly affectU.S. soybeans exports. These findings reflect the competitiveness of the world

3A lag of three periods was selected for the analysis. These findings are similar to results based on one,

two, and four period lags.4The debate on whether real or nominal exchange rate volatility enters into the decision making function

of traders is inconclusive. The empirical studies suggest that the results achieved based on both real and

nominal measures of volatility are not significantly different (McKenzie, 1999). In this study, exchange

rate, commodity prices, and oil price, which are used to measure the volatilities, are from futures markets,

and it is quite difficult to conceptualize real values for these three variables. Nominal values are more

appropriate to enter into the decision making function of traders in this study for short-run data.



soybean market. Quantity competition or availability of soybeans are major factorsfor the United States and its competitors’ soybeans exports. The results areconsistent with the findings from export competitiveness analysis between the UnitedStates and Brazil. Even though the United States has a big advantage over Brazil intransportation cost and Brazil has a large advantage in production cost, the exportprices are similar for these two countries. Thus, availability of soybeans plays abigger role in determining export volumes.The time trend is negative and statistically significant, while income is negative and

not statistically significant. The time trend shows that over time, U.S. exports havediminished consistently and significantly. Even though the United States is still thelargest exporting country for soybeans, South American soybean exports continue togain market share relative to the United States. Between the time trend and theincome effect, it is clear that the United States has been selling fewer soybeans asBrazil has expanded its production capacity.The expected exchange rate has a significantly negative effect on U.S. soybean

exports. Theoretically, when an importing country’s currency depreciates or the U.S.dollar appreciates the demand for importing goods should decrease, so these resultsare expected. Recent downward pressure on the U.S. dollar should increase U.S.export prospects. Shipping costs are not found to be an important variable thatimpacts U.S. soybean exports.The market risk variables are the focus of this study. Exchange rate volatility has a

negative effect on U.S. soybean exports that is statistically significant. It seems thatthe importers cannot find an appropriate timing or magnitude for hedging their

TABLE 1. Empirical Results for the U.S. Soybean Export Model

Variable Coefficient

Intercept 294.1��

(9.58)

Per capita GDP �0.0023

(�0.16)

Competitor’s price 0.03

(0.29)

Expected exchange rate �0.32��

(�3.36)

Expected soybean price �0.15�

(�1.69)

Expected shipping costs 2.87

(0.25)

Exchange rate volatility �244.84��

(�3.11)

Soybean price volatility 83.39

(0.97)

Shipping cost volatility �22.58

(�0.03)

Time trend �0.64��

(�4.52)

Note. Numbers in parentheses are z-statistics from the EC2SLS method. GDP5Gross domestic product.� and �� denote significantly different from zero at the 10% and 1% levels, respectively.



exchange rate risk for soybean deliveries. On the other hand, an effective commodityhedge or heating oil hedge cannot ignore changes in the exchange rate, which impactgains significantly on futures contracts denominated in U.S. dollars. So the exchangerate volatility is more sensitive and it is more difficult to manage than the commodityrisk and this risk has reduced exports.Soybean and heating oil price volatility does not appear to significantly impact

U.S. soybean exports. Importing firms can apparently manage soybean price riskand heating oil volatility effectively by using the futures or forward markets. Becausethe heating oil variable is a proxy for the ocean shipping cost, it suggests that U.S.soybean exports are not affected much by the ocean shipping cost volatility.Soybean trade has a high seasonal characteristic for exporting countries due to the

differences in harvesting dates in the U.S. and South America. Monthly dummyvariables were included in the empirical model and the coefficients for Marchthrough September were negative and statistically significant, as one would expectfor the U.S. due to the storage that must take place in the U.S. and the availability ofnewly-harvested soybeans from South America. These results are available from theauthors by request.

5.2. Brazil Model

Table 2 presents the results for Brazilian soybean exports. The insignificant effect ofthe own price and the competitor’s price on Brazilian soybean exports further verifiesthe idea that price is not an important factor in determining competitiveness in theworld soybean market. Increased Brazilian soybean production and stock holdingcapacity has played a role in increasing Brazil’s competitiveness in this market. Since1999, Brazil has had enough soybean stocks to supply the world market throughoutthe year when the world market price or its competitor’s price is high. This plays amajor role in increasing Brazilian soybean exports.Exchange rate volatility has a positive effect on Brazilian export demand,

indicating exchange rate volatility favors Brazilian exports. It is possible theBrazilian export price changes to compensate for exchange rate changes so thatimporters move to Brazilian soybeans when the U.S. dollar fluctuates (buying fromBrazil when the dollar appreciates). Results show that the expected exchange rate,expected futures market price and its volatility, and heating oil and its volatility haveno significant impact on Brazilian exports.The time trend and the income coefficients show that there is an increasing trend

toward Brazilian soybean exports, though the income coefficient is not statisticallysignificant. This is the exact opposite of the findings for the United States, showingthat the U.S. loss is Brazil’s gain.

5.3. China and Mexico Models

Table 3 presents the results for U.S. soybean exports to China. The expected U.S.soybean price is an important factor in determining the U.S. market share in Chinawith the expected soybean price having a significantly negative effect. The Brazilianprice has no significant impact on the import demand for U.S. soybeans. The impactof the expected shipping cost is significantly positive too. This result could bebecause ocean shipping costs for transporting soybeans from the United States toChina is much lower than from Brazil to China.5



TABLE 2. Empirical Results for the Brazilian Soybean Export Model


Intercept �116.5�

(1.80)

Per capita GDP 0.04

(1.52)

Competitor’s price �0.02

(�0.16)

Expected exchange rate 0.45

(1.10)

Expected soybean price 0.04

(0.27)

Expected shipping costs 12.28

(0.84)

Exchange rate volatility 350.75��

(2.26)


(0.42)

Shipping cost volatility �90.41

(�0.99)

Time trend 0.41��

(2.36)

Note. Numbers in parentheses are z-statistics from the EC2SLS method. GDP5Gross domestic product.�, ��, and �� denote significantly different from zero at the 10%, 5%, and 1% levels, respectively.

TABLE 3. Empirical Results for U.S. Soybean Exports to China


Intercept 8.38�

(1.94)

Per capita GDP 4.37��

(2.23)


(�0.54)

Expected soybean price �6.26��

(�3.28)

Expected shipping costs 1.96��

(2.06)


(0.48)

Shipping cost volatility 0.02

(0.01)

Note. Numbers in parentheses are z-statistics from the EC2SLS method. GDP5Gross domestic product.�, ��, and �� denote significantly different from zero at the 10%, 5%, and 1% levels, respectively.



For the soybean price volatility, the effect on China’s imports is positive, but notsignificantly different from zero, indicating that soybean imports might be hedged.China might prefer volatility in soybean prices because they can adjust their importquantities as prices change. Chinese importers may exercise their monopsony powerto maximize their soybean import profits when world market price changes becausethey can choose among United States and South America suppliers. The oceanshipping cost volatility has no significant effect on soybean import demand forChina.The results from the Brazilian model are different than the U.S. results (Table 4).

The impact of the competitor’s price is insignificant on the import demand, and itfurther confirms that competitor’s price is not a significant factor impacting soybeanexports. The impact of the expected soybean price is positive (though notsignificant), indicating China prefers to import soybeans from Brazil when theworld price is high. China might be willing to pay a slight premium to diversify itssupplies.The coefficient of expected fuel oil price is significantly negative, indicating China

will decrease imports from Brazil when ocean shipping cost is high. A shortershipping route and lower transportation cost from the United States to China mightinduce this result and it is consistent with the finding from the U.S. model. Theimpact of soybean price volatility is positive, but not statistically significant.Comparing this effect in the U.S. model, it further verifies that China might managesoybean price risk effectively. Shipping cost volatility does not significantly impactChinese imports of Brazilian soybeans, which confirms the findings that oceanshipping cost volatility is not an important factor on export volumes.

TABLE 4. Empirical Results for Brazilian Soybean Exports to China


Intercept �16.67��

(�5.84)

Per capita GDP 9.08�

(8.84)


(�0.59)

Expected soybean price 0.47

(0.35)

Expected shipping costs �2.22�

(�3.49)


(0.74)

Shipping cost volatility 0.45

(0.25)


5According to USDA-AMS reports, the ocean shipping cost of transporting soybeans from Minnesota,

MN, U.S. was $43.69/MT in the fourth quarter of 2005, and $49.13/MT in Mato Grosso, Brazil; In the

fourth quarter of 2006, this cost was $50.24 for the U.S. and $73.32 for Brazil.



When soybean prices fluctuate a lot, China may adjust its soybean imports to takeadvantage of those price fluctuations. The result from the Brazilian model isconsistent with the finding from the U.S. model that China might exercise itsmonopsony power when the world market price changes. The impact of income onsoybean imports from Brazil is significantly positive; it also can be considered as atime trend.Finally, the results of the model for U.S. soybean exports to Mexico are presented

in Table 5. They show that the expected soybean price has significantly negativeeffects on the import demand. The expected exchange rate, the volatilities of theexpected exchange rate and soybean price have no significant impacts on Mexicanimported demand from the United States. Obviously, the most important factor thatimpacts Mexico soybean imports is the soybean price. The United States is the onlysoybean supply source to Mexico, and it may have market power in the Mexicanmarket, so exchange rate changes can affect the United States’ exporting price. Whenthe soybean exporting price is high, Mexican importers have to adjust their exportsto maximize their import profits. Strategically, Mexican importers might importmore corn from the United States as a substitute to soybeans.

6. SUMMARY AND CONCLUSION

The world is in an era where commodity prices, exchange rates, and other variablesare more volatile. This study constructed a theoretical model for analyzing importdemand with forward-futures markets under exchange rate, commodity price, andocean freight risk. Exchange rate and its volatility have generally been identified asone of the major determinants of international trade, which is also true in the worldsoybean trade for the United States. The model results show that the volumes tradedare sensitive to volatilities in nominal bilateral exchange rates. U.S. soybean exportsfall significantly when the U.S. dollar appreciates or the foreign currency depreciates.Major importers reduce soybean imports from the United States significantly whenexchange rates have large fluctuations. This study verified an overall reduction in

TABLE 5. Empirical Results for U.S. Soybean Exports to Mexico


Intercept 406.55�

(3.97)

Per capita GDP 0.11

(0.87)

Expected exchange rate �6.71

(�0.42)

Expected soybean price �0.51��

(�3.15)

Exchange rate volatility 946.15

(1.33)

Soybean price volatility �70.73

(�0.28)




world market share for U.S. soybeans. In contrast, Brazil’s export to its majormarkets has substantially increased.The Brazilian soybean price and expected soybean price (average monthly price in

the CBOT futures market) do not play very important roles in deciding U.S. soybeanexports. Soybean price volatility has no significant effect on U.S. soybean exports.This effect is also insignificant on Brazilian soybean exports. The soybean pricevolatility may be hedged in the futures market. Thus, the quantity competitiveness ismore important than price competitiveness in the world soybean market.The ocean shipping cost and its volatility have very little impact on the soybean

demand for both countries. The results can be explained based on three reasons:the proportion of freight cost is low in the C.I.F. soybean price; the difference of thefreight cost from the United States to a destination market and from Brazil to thesame market is small and this difference does not impact an importers’ decision;freight cost can be successfully hedged through heating oil futures market. Yet itmight be that the heating oil price is not a suitable measure of ocean freight rates.For China, the expected soybean price and heating oil price play important roles in

determining U.S. soybean exports to China. As a leading importing country, Chinawill reduce imports from the United States when the world market price is high andimport significantly more soybeans when the fuel oil price is high. A high fuel pricewill induce China to reduce imports from Brazil. Chinese importers might prefersoybean price volatility because they have some monopsony power to adjust thesupply sources for their soybean imports; heating oil price volatility has nosignificant effect on China’s soybean imports from both the United States and Brazil.The United States dominates the soybean import market for Mexico. This study

also measures the effects of the expected soybean price and exchange rate as well astheir volatility on import demand for U.S. soybeans. Significant negative effects ofsoybean price on import demand further verify that when the futures market priceincreases, all major importers will reduce their soybean imports from the UnitedStates. Unlike other importing countries, Mexico seldom imports soybeans fromBrazil. However, it might import more soybean substitutes, for example corn fromthe United States, when the soybean price is high. Market risks have no significantimpacts on the Mexican demand for U.S. soybeans.Overall, the empirical results presented make a contribution to understanding the

implications of some important factors in soybean export competition. Compared toimporting soybeans from Brazil, major importing countries are more sensitive toexchange rate risk when they import soybeans from the United States, and theseimporting countries are not sensitive to the soybean price and ocean shipping costrisk. For U.S. exporters, this study gives evidence that Brazil’s soybean exportingprice is not the threat to U.S. exports. What has really helped Brazilian soybeanexports is its stockholding capacity. This increases Brazil’s competitiveness in theworld market and allows it to supply more soybeans out of its soybean harvestseason. Quantity competitiveness is becoming a more important characteristic in theworld soybean market.Although the theoretical and empirical analyses have presented some interesting

issues, further research is necessary based on problems identified here. First, someassumptions adopted need to be further analyzed. The assumption about theaccessibility of the importing firm to foreign exchange, commodity futures, andfreight rate contracts impacts the empirical conclusions about the role of market



risks on import demand. The importing country is considered a price taker; however,it is possible that the major importing countries have some degree of monopsonypower. For specific analysis of the exporting supply from Brazil, it is necessary toconsider the effects of exchange rate changes between the U.S. dollar and Brazilianreal on export price and supply because the exporters from Brazil need to convertU.S. dollars to Brazilian real when their transactions occur.This analysis shows the impacts of the exchange rate, commodity price, ocean

shipping cost, and their corresponding volatility on specific commodities can bemuch different from aggregate level results. Furthermore, some specific factors thatare related to individual commodities and countries (for example, commoditymarket structures, invoicing currencies in world markets, and forward-futuresmarket access for traders) and the empirical model specification can impact theabove effects significantly. More detailed research about these factors and theireffects on export patterns can provide better support for U.S. exporters to remaincompetitive in the world market.

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Qiang Zhang is currently a Strategic Modeling Manager at Walgreens Co. in Deerfield, Illinois.

Zhang holds a Ph.D. in Agricultural Economics from University of Kentucky (2008), a M.A. in

Economics M.B.A. from Western Kentucky University (2003), and a B.A. in Finance from East

China Normal University (2000). His current research interests include quantitative marketing

research, marketing strategy analysis in the retail industry, and pharmaceutical economics and

policy.

Michael R. Reed is a Professor in the Department of Agricultural Economics, University of

Kentucky, Lexington. He received his B.S. degree in Economics in 1974 from Kansas State

University, his M.S. in Economics in 1976 from Iowa State University, and his Ph.D. in

Economics (with a minor in Statistics) in 1979 from Iowa State University. His current research

interests are research and teaching international trade in agricultural products, the effects of

macroeconomic policies and exchange rates on U.S. food exports, the dynamics of consumer

demand in various countries, and the effects of competition patterns on world agricultural trade

patterns.

Sayed H. Saghaian is an Associate Professor in the Department of Agricultural Economics,

University of Kentucky, Lexington. He received his B.S. in Mechanical Engineering in 1983 from

University of Kentucky, his M.S. in Operations Research in 1986 from University of Kentucky,

and his Ph.D. in Agricultural Economics in 1992 from University of Kentucky. His current

research interests are food safety concerns, firm-level strategic decision making and response to

food safety issues, product differentiation, health and wellness functions of food, quality-

assurance provision, and consumer responsiveness.



the impact of multiple volatilities on import demand for u.s. commodities: the case of soybeans

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