Joseph P. Janzen

Colin A. Carter

Aaron D. Smith

Michael K. Adjemian

Economic

Research

Service

Economic 

Research

Report

Number 165

April 2014

United States Department of Agriculture

Deconstructing Wheat Price Spikes: 

A Model of Supply and Demand, 

Financial Speculation, and 

Commodity Price Comovement
Economic Research Service 

www.ers.usda.gov

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Recommended citation format for this publication:

Janzen, Joseph P., Carter, Colin A., Smith, Aaron D. Smith, and Michael K. Adjemian. 

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial 

Speculation, and Commodity Price Comovement, ERR-165, U.S. Department of Agriculture, 

Economic Research Service, April 2014.
United States Department of Agriculture

Economic 

Research 

Service

Economic

Research

Report

Number 165

April 2014

Abstract

In 2008, wheat futures prices spiked and then crashed along with prices for other agricultural and nonagricultural commodities. Market observers offered several theories to 

explain this common movement, or comovement, in prices, and have proposed policies to 

address the perceived problem of excessive price volatility. The design of an appropriate 

policy response would benefit from a better understanding of the cause of the observed 

price movements. This study uses an econometric model to decompose observed wheat 

prices into a set of economic factors and measure the relative contribution of each factor 

to observed price changes. Findings show that market-specific shocks related to supply 

and demand for wheat were the dominant cause of price spikes in the three U.S. wheat 

futures markets. Fluctuations in the global macroeconomy associated with broadbased 

demand shocks were relatively less significant for wheat than for other commodities like 

crude oil and corn. Finally, little evidence suggests commodity index trading contributed 

to recent price spikes.

Keywords: Commodity prices, comovement, futures, index funds, speculation, wheat

Acknowledgments

The authors thank the following reviewers: Mark Jekanowski and Danny Pick, USDA’s, 

Economic Research Service (ERS); William Chambers, USDA’s Farm Service Agency; 

Andrew McKenzie, Professor of Agricultural Economics, University of Arkansas; Gabriel 

Power, Assistant Professor of Finance, Université Laval; Daniel Sumner, Professor of 

Agricultural Economics, University of California, Davis; Jeffrey Williams, Professor 

of Agricultural Economics, University of California, Davis; and anonymous reviewers. 

Thanks to John Weber and Cynthia A. Ray of ERS for editorial and design assistance.

Joseph P. Janzen, Colin A. Carter, Aaron D. Smith, 

and Michael K. Adjemian

Deconstructing Wheat Price Spikes: 

A Model of Supply and Demand, 

Financial Speculation, and Commodity 

Price Comovement
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Economic Research Service/USDA

About the Authors

Joseph P. Janzen is an assistant professor in the Department of Agricultural Economics and 

Economics at Montana State University. Colin A. Carter is a professor and Aaron D. Smith is an 

associate professor in the Department of Agricultural and Resource Economics at the University 

of California, Davis. Carter and Smith are members of the Giannini Foundation of Agricultural 

Economics. Michael K. Adjemian is an economist at USDA’s Economic Research Service. 
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Contents

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Structural Factors Driving Wheat Price Variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Modeling Wheat Price Dynamics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Data and Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Appendix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
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providing timely information on economic and policy 

issues related to agriculture, 

food, the environment,and 

rural America. www.ers.usda.gov

Find the full report 

at www.ers.usda.

gov/publications/erreconomic-researchreport/err165.aspx

April 2014

Deconstructing Wheat Price Spikes: 

A Model of Supply and Demand, 

Financial Speculation, and Commodity 

Price Comovement

Joseph P. Janzen, Colin A. Carter, Aaron D. Smith, 

and Michael K. Adjemian

Joseph P. Janzen

Colin A. Carter

Aaron D. Smith

Michael K. Adjemian

Economic

Research

Service

Economic 

Research

Report

Number 165

April 2014

United States Department of Agriculture

Deconstructing Wheat Price Spikes: 

A Model of Supply and Demand, 

Financial Speculation, and 

Commodity Price Comovement

What Is the Issue?

Over the last 5 years, wheat futures prices spiked and then crashed along with prices for other 

agricultural and nonagricultural commodities. Market observers offered theories to explain 

this common movement, or comovement, in prices: macroeconomic shocks, correlated supply 

disruptions, and the influence of nontraditional speculative firms known as commodity index 

traders (CITs). The perceived role of speculators in the recent price spikes led market participants, policymakers, and economists to call for restrictions on the trading activity of CITs. 

This study examines the degree to which a comprehensive set of economic factors, including 

the trading behavior of CITs, contributed to recent dynamics in wheat futures prices.

More acreage is planted to wheat than any other commodity in the world, and wheat is used 

to make flour, bread products, cookies, cakes, and pasta. Wheat is also a significant source of 

animal feed. So, understanding wheat price spikes is an important step toward understanding 

food price spikes more generally. Since food in developed countries is heavily processed, 

wheat price shocks have only a small effect on U.S. consumers. However, these shocks can 

affect wheat producers everywhere, as well as consumers in developing countries who spend a 

large share of their household budgets on less processed cereals.

What Did the Study Find?

Supply-and-demand shocks specific to the wheat market were the dominant cause of price 

spikes between 1991 and 2011 in the three U.S. wheat futures markets (hard red winter, hard 

red spring, and soft red winter wheat). Focusing specifically on the February 2008 wheat 

price spike, the study finds that, depending on the market, wheat prices in that month would 

have been 40-62 percent lower in the absence of current crop year supply-and demand-shocks, 

such as unexpected weather events that lower wheat yields. Wheat prices would have been 
www.ers.usda.gov

11-36 percent lower in the absence of precautionary demand shocks—which represent an anticipatory 

buildup of commodity inventories—associated with expectations about future prices. 

To put these estimates in context, at the same time, per-bushel wheat prices increased by 300 percent on 

the Minneapolis Grain Exchange, from around $8 to $24. Alternative explanations for wheat price spikes 

had minimal impact in February 2008. Broadbased demand shocks associated with fluctuations in global 

economic activity had a 9- to 12-percent price impact. In contrast, similar studies have found that changes 

in real economic activity (inflation-adjusted production and consumption of goods and services) accounted 

for most of the recent price spikes for corn and crude oil. CITs contributed minimally to recent price spikes: 

the peak wheat price in February 2008 would have been only 1 percent lower in the absence of shocks 

attributable to financial speculators like CITs. Even at its maximum price impact between 2006 and 2011, 

financial speculation increased wheat prices by only 5-8 percent. These findings suggest that wheat futures 

markets have performed efficiently in the sense that wheat futures prices have reflected fundamental factors. 

Consequently, restrictions on commodity index trading are not likely to prevent future price spikes.

How Was the Study Conducted?

The study uses a structural vector autoregession (SVAR) econometric model to decompose observed wheat 

prices into a set of factors and to explain the relative contribution of each factor to observed price changes. 

These factors include: (1) real economic activity affecting the demand for all commodities, (2) passive or 

financial speculation by CITs based on a desire to hold baskets of commodities as part of a larger investment portfolio, (3) precautionary speculation related to expected prices in future periods and the incentive to hold wheat inventories, and (4) current crop-year supply-and-demand shocks specific to the wheat 

market. Structural shocks associated with each explanation represent the change in the wheat price caused 

by that factor, conditional on all past information about prices and other relevant economic variables. The 

model allows CIT-driven shocks to take precedence over wheat market-specific shocks; if the latter are 

found to drive more of the variation in prices, one can be confident that the CIT effect was given the best 

chance to reveal itself in the data.
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Deconstructing Wheat Price Spikes: 

A Model of Supply and Demand, 

Financial Speculation, and Commodity 

Price Comovement

Introduction

Wheat futures prices spiked in early 2008 before rapidly crashing, though this hardly makes the 

wheat market unique; commodities such as crude oil, natural gas, corn, and cotton exhibited price 

spikes and crashes around the same time. But the magnitude of the wheat price spike combined 

with the coincidence of these other extreme price movements raises important questions. Were 

wheat prices driven by the same set of macroeconomic factors as the other commodities? Did wheat 

markets suffer from supply disruptions at the same time that the other commodities faced disruptions? Did the actions of futures market speculators and commodity index traders play a role? Past 

studies have provided divergent answers to these questions. For example, analysts at J.P. Morgan, 

an investment bank, suggest that wheat markets during this period “reflect(ed) unique fundamental 

storylines” and generated “rational and corresponding price signals”: observed wheat price fluctuations were due to supply-and-demand factors specific to the wheat market (J.P. Morgan, 2010). In 

contrast, some observers claim that “excessive speculation” by large financial traders caused wheat 

futures prices to diverge from levels justified by supply-and-demand forces. This study provides new 

answers to these questions using an econometric model of price dynamics.

As part of the analysis, this study identifies economic mechanisms that could account for observed 

wheat futures price movement and quantifies their importance in explaining price behavior from 

1991 to 2011, with particular emphasis on the 2008 period when prices for many commodities 

spiked. Price impacts are estimated for four factors: (1) macroeconomic influences on the demand 

for all commodities; (2) wheat-market-specific forces, such as the supply of wheat by farmers 

and demand from users such as millers and bakers; (3) the passive speculation associated with 

commodity index traders (CITs); and (4) more traditional speculation based on anticipation of future 

supply-and demand shocks in a single market. Macroeconomic shocks affecting all commodity 

markets could account for empirically observed common movement, or comovement, among agricultural and nonagricultural commodity prices. Some recent economic studies link comovement to 

speculative futures trading, particularly passive financial speculation, such as by CITs who trade 

simultaneously in many commodity markets. Unlike previous studies of commodity price comovement, this study distinguishes between two types of speculative demand for wheat and wheat futures 

contracts, one due to passive speculative trading associated with commodity index traders and one 

due to anticipation of future supply-and-demand shocks in a single market, in this case wheat. 

Most recent criticism of the wheat futures price discovery process has focused on the impact of 

financial speculation and commodity index trading. Proposed policy interventions, including 

speculative position limits (Masters, 2011), virtual grain reserves (Von Braun and Torero, 2009), 

transaction taxes, and proprietary trading bans (UNCTAD, 2011), are motivated by the suggestion 

that price levels and price volatility in commodity futures markets did not reflect market-specific 

supply-and-demand conditions during the 2008 price spike due to the effects of commodity index 
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trading. Identifying the causes of wheat price fluctuations and their relative importance is necessary 

to evaluate proposed policy interventions in commodity futures markets. 

This study focuses on wheat for three reasons. First, more land is planted to wheat than any other 

crop in the world, and only corn and soybeans account for more acreage in the United States 

(USDA, Foreign Agricultural Service, 2011). So, understanding wheat price spikes is an important 

step in understanding food price spikes more generally. Second, the three major classes of wheat 

grown in the United States are traded on three separate futures markets (hard red winter, Kansas 

City; hard red spring, Minneapolis; and soft red winter, Chicago). These distinct but connected 

markets serve as a laboratory in which to analyze price volatility.1 Third, the performance of 

these related futures markets has come under increasing scrutiny in the last decade. Price patterns 

in these markets have been very volatile and have not followed traditional relationships. In addition, expiring futures prices, particularly in Chicago and Kansas City, routinely far exceeded the 

prevailing cash price in their respective delivery markets, a phenomenon known as non-convergence. 

Some observers have linked non-convergence to the trading activities of large, passive speculators. 

Indeed, index trader holdings of Chicago wheat futures positions increased dramatically from 2004 

to 2008 and accounted for more than 30 percent of open positions by 2008. But Garcia et al. (2012) 

and Adjemian et al. (2013) find that recent non-convergence can be attributed to storage market 

dynamics; they find no evidence linking index trader behavior to non-convergence. Still, the empirical question of commodity index trader impacts on wheat prices remains unanswered.

1For example, the Chicago futures contract is much more widely traded and has much greater index trader 

participation than the other two contracts in Kansas City and Minneapolis. 
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Structural Factors Driving Wheat Price Variation

To construct a tractable econometric model, this study divides the structural or fundamental forces 

that drive wheat futures prices into four groups.2 Descriptions follow for each structural factor and 

the likely sources of variation in these factors driving unexpected wheat price changes.

Global Real Economic Activity and Commodity Demand

Rapid economic growth and intense industrial activity tend to coincide, especially in less developed 

nations. This increased activity spurs demand for commodities and raises commodity prices.3

Clearly, prices of energy and industrial commodities, such as crude oil and copper, rise in response 

to economic growth and industrialization. Why would prices for agricultural commodities, such as 

wheat, respond similarly? First, higher energy prices imply higher costs of agricultural production 

and higher costs of transporting agricultural commodities. Second, greater real economic activity 

(measured production of goods and services adjusted for inflation) leads to rising incomes, which, 

along with population growth, constitute the standard set of demand shifters for food commodities like wheat (Antle et al., 1999). Rising incomes drive greater wheat consumption, particularly 

in developing countries where caloric intake is more responsive to income growth. Moreover, 

consumers in developing countries adjust their diets in response to newfound wealth, eating proportionately more meat and less grain. This change in diet diverts grain to animal feed use. Many 

pounds of grain are needed to produce a single pound of meat, so demand for feed grains increases 

and prices rise (Trostle, 2008). As large and developing countries, such as China and India, increase 

their consumption of wheat and meat, global economic activity, not just economic activity in wealthy 

nations, becomes the more appropriate price driver.

Other factors may also contribute to the link between global economic activity and wheat prices. 

Low interest rates make grain storage relatively inexpensive, which increases the incentive to hold 

wheat off the market, thereby raising prices. Frankel (1986) argues that, because the prices of grains 

such as wheat tend to be more flexible than retail prices, grain prices may overshoot in response to 

monetary stimulus. This phenomenon causes pronounced procyclicality in commodity prices, that is, 

prices may rise more than measured economic activity.

Wheat-Specific Supply-and-Demand Factors

Wheat-specific factors that contribute to price volatility may have either supply or demand origins, 

but consistent with price variation for most agricultural commodities, supply shocks appear to 

dominate shortrun variation in wheat prices. Once the crop is planted, supply is virtually perfectly 

inelastic, and shocks that shift the supply curve cause most price changes (Adjemian and Smith, 

2012). In this light, wheat-specific supply-and-demand variation is termed a “net supply” shock. 

Production is a function of acreage harvested and crop yield. Growth in global wheat production 

since 1960 has come from yield gains rather than acreage increases. Over the period, world wheat 

2We focus on major U.S. wheat classes: hard red winter, hard red spring, and soft red winter milling wheats; each of 

these has established U.S. benchmark futures prices (Antle and Smith, 1999).

3Carter et al. (2011) show that commodity price booms in both 1973-74 and 2007-08 were preceded by unusually high 

world economic growth, especially in emerging economies.
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acreage has grown at an average annual rate of 0.04 percent, whereas yield has increased about 2 

percent annually (table 1). Growth in world wheat yields rebounded in the last decade after falling in 

the 1990s when agricultural productivity dropped in Kazakhstan, Russia, and Ukraine. (For a review 

of the spatial and temporal distribution of world wheat supply, see box “Global Wheat Production.”) 

Wheat yields in the United States have increased at a slower rate than global yields, leading to 

concerns about future productivity growth. Specifically, wheat-breeding efforts may have fallen 

behind the pace of genetic improvement for other crops (Carter, 2001).

Longrun trends in wheat production can be anticipated, so wheat prices remain volatile because of 

shortrun production variability. Weather, pests, and disease drive much of the variation in wheat 

yield, and their effects are difficult to anticipate, so prices adjust in response to quantity shocks 

(Adjemian and Smith, 2012). Both U.S. and world wheat supply vary considerably from year to year 

(see table 2). Production in a single country (like the United States) is more volatile than overall 

production worldwide. In the United States, planted acreage is particularly variable relative to yield. 

Globally, acreage and yield exhibit similar levels of year-to-year variation. Variability in acreage 

mainly reflects opportunities for substitution in production between wheat and other crops but also 

the potential for new land to be brought into production (Antle et al., 1999).

Wheat may be milled domestically for human consumption, used for animal feed, or exported to 

countries where demand exceeds production. In the 2011/12 marketing year, U.S. domestic wheat 

consumption was 32.1 million metric tons (mmt), of which just 14 percent was used for feed. In the 

same period, the United States exported 28.6 mmt of wheat. U.S. food use of wheat grew steadily 

from the 1960s until the late 1990s, leveling off at approximately 27 mmt annually, while world food 

use continued to increase (fig. 1). U.S. and world feed use of wheat exhibited a somewhat similar 

pattern during the period; world feed use, however, continues to grow though U.S. use has actually 

declined since the early 1990s (fig. 2). Annual growth rates in wheat consumption, both for feed and 

food, have been lower in the United States than in the rest of the world (see table 1). As noted earlier, 

population and income growth are the main drivers of global wheat consumption, and continued 

growth in global consumption has come from emerging economies where populations and incomes 

are rising the fastest (Carter, 2001; Alexandratos and Bruinsma, 2012).

Table 1

Growth in U.S. and world wheat production and consumption measures, 

1960/61 to 2011/12 (average annual percentage change)

United States World

Period Area Yield Feed use Food use Area Yield Feed use Food use

1960s 0.76 1.77 18.38 0.55 1.05 3.06 10.04 2.46

1970s 4.25 0.02 -7.71 2.00 1.20 2.07 1.47 3.05

1980s -3.54 -0.27 6.28 2.05 -0.90 2.76 2.21 2.29

1990s -1.28 1.30 -0.48 1.56 -0.41 1.07 -2.09 1.43

2000s 0.35 0.94 -9.45 -0.09 0.52 1.38 0.88 1.16

1960-2010 -0.02 1.10 2.15 1.46 0.04 2.01 2.29 2.04

Note: Growth rates are estimated using log-linear regressions of the form: ln(Y) = a + b*time, where the coefficient b 

represents a constant growth rate.

Source: USDA, Economic Research Service using data from USDA, Foreign Agricultural Service (2011).
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Global Wheat Production

For the 2011/12 marketing year, the global wheat harvest measured 697 million metric tons (mmt). Production 

in the United States was 8 percent of this total, or about 54.4 mmt (see box figure 1a). Since 1960, the global 

wheat supply has grown steadily, 

nearly tripling during the period. The 

U.S. share of global wheat production 

has shrunk over time, but the United 

States remains a major producer. The 

remainder of global production is 

concentrated in a number of countries; 

the European Union (EU), China, 

the former Soviet Union (mainly 

Kazakhstan, Russia, and Ukraine), 

India, Canada, Australia, Pakistan, and 

Argentina. On average, over the period 

2002-11, these nations and the United 

States produced over 84 percent of 

the world’s wheat. Production in most 

of these regions has remained relatively steady, with the notable exception of China, Kazakhstan, Russia, and 

Ukraine, where production has grown 

by approximately 25 percent in the last 

decade (USDA, Foreign Agricultural 

Service, 2011).

Regional production is specialized by class, both in the United States and abroad. The United States produces 

all classes of wheat, but production of each class is generally confined to specific U.S. regions. Hard red winter 

(HRW) wheat accounted for the largest share of production (39 percent) in the United States in 2011. U.S. 

production of HRW is concentrated in the Southern Great Plains. Soft red winter (SRW) wheat and hard red 

spring (HRS) wheat made up 23 and 20 percent of U.S. 2011 production, respectively. SRW is generally grown 

in States east of the Mississippi River and north of the Ohio River, but SRW production also occurs across 

the South from Arkansas to the Atlantic Coast. HRS is mainly grown in the Northern Great Plains (USDA, 

Economic Research Service, 2011). Canada primarily produces HRS. Most EU production is soft wheat; France, 

the United Kingdom, and Germany are the EU’s largest producers. Australia grows winter wheat, much of it a 

white wheat with milling qualities similar to U.S. HRW. Argentina produces both hard and soft wheat (Taylor 

and Koo, 2011). Kazakhstan, Russia, and Ukraine grow both winter and spring red wheat, though wheat production and wheat quality are more variable in these countries than in North America (Liefert et al., 2010). China 

mainly produces white and red winter wheats, while in India, white wheats are predominant (Curtis, 2002).

World regions also differ in planting and harvest timing. Regional price seasonality is dampened by global 

supply available throughout the year. Little wheat of any type is harvested globally in January or February, but 

wheat is harvested in significant quantity somewhere in every other month. India generally begins its harvest in 

March. U.S. and European winter wheat harvest generally begins in late May as Northern Hemisphere spring 

wheat planting is completed. Winter wheat harvest runs into the spring wheat harvest that begins in Canada and 

the United States in late July and continues through October. Harvest in major Southern Hemisphere producers 

Argentina and Australia occurs mainly in November and December. There, winter wheat crops are grown 

through the mild winters, with harvest occurring in the late spring and early summer (USDA, Office of the Chief 

Economist, 2011).

Box figure 1a

U.S. and world wheat production

Source: USDA, Economic Research Service using data from USDA, Foreign 

Agricultural Service (2011), “Production, Supply, and Distribution Online,” 

www.fas.usda.gov/psonline/

World wheat production, 

millions of metric tons

0

10

20

30

40

50

60

70

80

0

100

200

300

400

500

600

700

800

1960 1970 1980 1990 2000 2010

World

United States

U.S. wheat production,

millions of metric tons
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A comparison of figures 1 and 2 shows that wheat use for feed is smaller and more volatile from 

year to year than wheat use for human consumption. The proportion of the wheat crop used for 

animal feed depends on the quality of the wheat crop and the price of wheat relative to other feed 

grains, such as corn. Thus, wheat competes with other agricultural products both in production and 

consumption. The feed market is an outlet for lower quality wheat, and prices of other feed grains 

place a lower bound on potential wheat prices (Chambers, 2004). When wheat prices are high and 

Table 2

Variability in U.S. and world wheat production and consumption measures, 

1960/61 to 2011/12 (coefficient of variation (%) relative to linear trend)

United States World

Area 15.65 3.94

Yield 6.74 4.19

Production 17.66 6.36

Feed use 57.70 15.46

Food use 5.30 3.36

Exports 24.12 10.86

Source: USDA, Economic Research Service using data from USDA, Foreign Agricultural Service (2013).

Figure 1

U.S. and world consumption of wheat for food use

Source: USDA, Economic Research Service using data from USDA, Foreign Agricultural Service (2011), “Production, 

Supply, and Distribution Online,” www.fas.usda.gov/psonline/

World food use, millions of metric tons U.S. food use, millions of metric tons

0

10

20

30

40

50

60

0

100

200

300

400

500

600

1960 1970 1980 1990 2000 2010

World

United States
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crop quality is good, very little wheat is used for feed, as in the 2007-08 marketing year when wheat 

prices reached record-high levels. Crop quality issues may force wheat into the feed channel even 

when wheat prices are relatively high, as was the case in early 2011. 

Supply fluctuations in exporting countries are particularly relevant to U.S. prices because these 

supplies represent direct competition for U.S. exports. Annually, U.S. wheat exports account for 

approximately 50 percent of U.S. wheat production and 25 percent of global wheat trade. For the 

world, trade accounts for approximately 20 percent of annual global consumption (USDA, Foreign 

Agricultural Service, 2011). The United States is therefore an integral player in the global wheat 

market and is the world’s largest wheat exporter, competing with Canada, the European Union (EU), 

Australia, and Argentina. In addition, Kazakhstan, Russia, and Ukraine have also emerged as major 

wheat exporters. While their wheat production quality and their political climates are more volatile 

than those of other major exporters, their vast productive capacity gives them the potential to be 

major participants in wheat export markets (Liefert et al., 2010). 

Table 2 shows the coefficient of variation for feed use, food use, and exports for both the United 

States and the world. Since 1960, U.S. feed use has been more variable than exports, which in turn 

have been more volatile than domestic food consumption. Variability in food use, which constitutes 

most of consumption, is lower than variability in production, so one might expect that price changes 

reflect supply variability more than demand variability.

Figure 2

U.S. and world consumption of wheat for feed use

Source: USDA, Economic Research Service using data from USDA, Foreign Agricultural Service (2011), “Production, 

Supply, and Distribution Online,” www.fas.usda.gov/psonline/

World feed use, millions of metric tons U.S. feed use, millions of metric tons

0

2

4

6

8

10

12

14

0

20

40

60

80

100

120

140

160

1960 1970 1980 1990 2000 2010

World

United States
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Government intervention can be a source of unexpected variation in prices as policies alter the flow 

of wheat from production to end use. U.S. farm bill subsidy programs may affect wheat production 

and wheat prices, but the overall impact of current U.S. commodity subsidy programs on wheat 

prices is relatively small (Babcock, 2007). Trade controls by major wheat importing and exporting 

countries are more likely than farm subsidies to cause large, unanticipated commodity price fluctuations. Examples of trade controls include export taxes, restrictions, and bans on wheat imports 

in Argentina, Russia, Kazakhstan, Ukraine, Serbia, and India during the runup in wheat prices 

in 2007-08 (Trostle, 2008). Subsequently, Russia and other countries of the former Soviet Union 

continued to use export restrictions and state-trading enterprises to control domestic prices and the 

movement of wheat to export markets under a rationale of food security and economic nationalism 

(Wegren, 2011). In general, political objectives may supersede economic objectives in many major 

wheat-trading countries, with consequences for world prices. (For examples of economic activity 

and wheat-specific supply-and-demand driven price movements, see box “Recent Impacts of Global 

Economic Activity and Wheat-Specific Supply and Demand.”)

Speculative or Precautionary Demand

When commodities are storable, as in the case of wheat, the contemporaneous fundamentals 

represented by the factors mentioned earlier do not fully explain price levels or price changes. 

Rather, storability implies that price volatility can be smoothed over time if speculative storers buy 

commodities when prices are low and bring inventories to market when prices are high. Merchants, 

users, governments, and other entities may hold such inventories based on their own expectations of 

future prices to provide a buffer against fluctuations in supply and demand. 

Economists have formalized the relationship between current and expected future prices in rational 

expectations competitive storage models such as those described by Williams and Wright (1991). 

The competitive storage model essentially states that firms will hold inventories of storable commodities such that the current price equals the discounted value of the expected price in the future. This 

is a no-arbitrage condition. If discounted expected future prices exceed current prices, firms will add 

to current inventories and reduce the supply available for current consumption, raising the current 

price relative to the expected future price so that this condition holds. Thus, through stockholding 

behavior, expectations about future prices affect prices today. 

The competitive storage model recognizes an important component of commodity demand related 

to expectations about prices in the future. Because this demand component is anticipatory in nature, 

it is referred to as “precautionary demand.” In the presence of a futures market, the price of a 

futures contract normally represents the expected cash price in the delivery period for that contract.4

Therefore, traders may profit from expectations about future supply-and-demand conditions by 

speculating on the price of futures contracts, in addition to adjusting inventories. (For a review of 

speculative trading in wheat futures markets, see box “Comparing the Three U.S. Wheat Futures 

Markets: Specifications and Trading Volume.”) Futures trading motivated by precautionary demand 

is a form of speculative demand in the sense that “anyone buying not for current consumption but for 

future use is a speculator from an economic point of view” (Fattouh et al., 2012). However, informed 

speculation associated with precautionary demand is not the target of recent popular criticism, as 

4Garcia et al. (2012) and Adjemian et al. (2013) point out that that for most domestic grain futures markets, the futures 

contract price actually represents the price of the delivery instrument used to satisfy the contract. Most of the time, this 

price is equal to the commodity cash price, but these prices can diverge under non-convergence.
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discussed later in this section. That type of speculation is understood by economists as instrumental 

to the operation of a futures market (Working, 1970). Distinguishing the impact of precautionary 

demand on observed prices from current supply and demand and other speculative influences is the 

primary challenge in this study.

Two aspects of competitive storage models help to identify the effect of precautionary demand. The 

first relates to the physical limit restricting negative inventories. Inventories must be non-negative 

because future production cannot be brought forward to meet shortages in the present. Once inventories go to zero, demand for current consumption must equal current supply. If current supply is 

low, perhaps because of a poor crop, then prices for nearby delivery rise substantially to allocate 

the supplies to those users willing to pay the most. Price behavior in these low-inventory periods is 

quite different than that in high-inventory periods. When inventories are low, the spot price of the 

commodity may be much higher than the price for delivery after the next harvest has replenished 

stocks. In contrast, high inventories bring less volatile nearby prices and cause nearby prices to move 

in concert with distant prices because inventory management by speculative storage firms spreads 

the impact of supply-and-demand shocks over time (Wright, 2011). Of importance to this analysis, 

storable commodity prices are relatively more volatile during low inventory periods.

 The second aspect of competitive storage models is an implied relationship between current prices, 

futures prices for deferred delivery, and the level of inventories. Suppose that the futures price in 

period t for delivery in some distant period T represents an unbiased estimate of the spot price at 

T. Then competitive storage behavior implies that the calendar spread (i.e., the difference between 

the price for deferred delivery in T and the price for delivery today) is positively related to the level 

of inventories. This relationship was empirically confirmed by Working (1933) and is accordingly 

known as the “Working curve.” It is widely acknowledged as a stylized fact in storable commodity 

markets such as wheat (Carter and Revoredo-Giha, 2007). The non-negativity constraint and the 

Working curve help identify the econometric model of wheat futures markets.

Financial Speculation, Commodity Index Trading, and Comovement

Critics of futures market speculation generally are not concerned with traditional precautionary 

demand related to expectations about future prices. Instead, they suggest that alternative motives 

for speculative trading impede the ability of markets to discover the price levels justified by market 

fundamentals. The recent financialization of commodity futures markets and the presence of 

commodity index traders has been associated with these alternative motives for commodity market 

speculation. Financialization has been defined as the increased presence of “financial motives, financial markets, and financial actors in the operations of commodity markets” (UNCTAD, 2011) and 

the “increased acceptance of (commodity) derivatives as a financial asset” (Fattouh et al., 2012).

Many financial investors who participate in commodity markets do not have directional views on 

the prices of a specific commodity, such as wheat. Rather, they wish to gain exposure to the broad 

movement of commodity prices because of perceived portfolio diversification benefits. Influential 

studies such as Gorton and Rouwenhorst (2004) noted a negative correlation between returns from 

an index of commodity prices and equity or bond returns. Major indexes, such as the Standard 

and Poor’s-Goldman Sachs Commodity Index (GSCI) and the Dow Jones-UBS Commodity Index 

(DJ-UBSCI), measure broad commodity price movement, and many managed funds and exchangetraded funds track these popular indexes. Energy commodity prices, especially crude oil prices, 

are heavily weighted in these indexes. Financial firms have developed exchange-traded funds, swap 

contracts, and other vehicles to allow individual and institutional investors to track these and similar 
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Recent Impacts of Global Economic Activity and Wheat-Specific 

Supply and Demand 

In the model used in this study, two types of fundamental factors influence U.S. wheat prices: those that affect 

commodity markets generally through global real economic activity and factors specific to the wheat market. 

Prior to 2004, wheat prices had been in a long-term decline in real (inflation-adjusted) terms (see box fig. 

2a). This decline in real prices was associated with a reduction in wheat acres in the United States and a 

general decline in profitability of wheat production relative to other crops (Taylor and Koo, 2011). This 

movement was generally true of prices for many agricultural commodities relative to corn and soybeans, as 

identified by Timmer (2009) in the case of rice. Since 2004, wheat prices have risen and remained at historically elevated levels. Beginning in 2004, wheat prices rose steadily, culminating in spikes in 2008 and 2011 

(see box fig. 2b). During this period, Minneapolis Grain Exchange (MGEX) wheat futures reached a record 

level of $24 per bushel on February 28, 2008. Chicago Board of Trade (CBOT) and Kansas City Board of 

Trade (KCBT) prices hit more modest highs of $13.35 and $13.70, respectively, on February 27, 2008.

Over the period of observation, several events related to contemporaneous market fundamentals corroborate 

major price movements:

• Adverse weather shocks often accompany rising prices. Wheat prices began to rise in mid-2007 

as Australia’s wheat crop was planted amidst the second year of a severe drought (Flood, 2008). 

Wheat production was approximately two-thirds of normal in Australia that year (USDA, Foreign 

Agricultural Service, 2011), a shortfall that contributed to rising prices.

Box figure 2a

Nearby U.S. wheat futures prices in real terms

Note: CBOT = Chicago Board of Trade. KCBT = Kansas City Board of Trade. MGEX = Minneapolis Grain Exchange.

Source: USDA, Economic Research Service using data from Commodity Research Bureau (2011), 

“Futures Price Database.”

Price (Real 2011 $/bushel)

0

5

10

15

20

25

30 1/1/70

3/1/715/1/727/1/739/1/74

11/1/75

1/1/773/1/785/1/797/1/809/1/81

11/1/82

1/1/843/1/855/1/867/1/879/1/88

11/1/89

1/1/913/1/925/1/937/1/949/1/95

11/1/96

1/1/983/1/995/1/007/1/019/1/02

11/1/03

1/1/053/1/065/1/077/1/089/1/09

11/1/10

CBOT

KCBT

MGEX
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• As wheat prices fell from their peak in early 2008, macroeconomic shocks quickened the slide. The 

2008 financial crisis, centered on the bankruptcy of the investment bank Lehman Brothers, was a 

signal to commodity markets that economic conditions, particularly in the developed world, were not 

as strong as once thought. Indeed, wheat prices fell sharply to around $5 per bushel in September and 

October 2008, near their level prior to the price spike, just as news of the financial crisis was being 

fully disseminated (Hurt and Boehlje, 2008).

• In August 2010, Russia enacted a ban on wheat exports in the midst of drought-related production 

shortfalls in the former Soviet Union. After prices rose in July, largely in response to the effects of the 

drought, prices increased further in the first week of August as the Russian Government made the ban 

official. That week, CBOT wheat prices rose by more than $1 per bushel (Kramer, 2010).

These are examples of both wheat-specific and general economic supply-and-demand-type forces affecting 

wheat prices. However, one cannot infer that these events caused changes in wheat prices simply by observing 

correlation between news and prices. First, such events may not cause contemporaneous shifts in prices 

because the market may anticipate the outcomes so that prices adjust earlier. Second, other factors may cause 

prices to deviate from the level justified by current supply-and-demand fundamentals. 

Box figure 2b

Nearby U.S. wheat futures prices with selected events highlighted

Note: MGEX = Minneapolis Grain Exchange. KCBT = Kansas City Board of Trade. CBOT = Chicago Board of Trade.

Source: USDA, Economic Research Service using data from Commodity Research Bureau (2011), 

“Futures Price Database.”

Dollars per bushel

Sept. - Oct. 2008: 

Lehman bankruptcy

and financial crisis

June - Dec. 2007: 

Second year of 

Australian drought 

-

0

4

8

12

16

20

1/1/04 1/1/05 1/2/06 1/3/07 1/4/08 1/4/09 1/5/10 1/6/11 1/7/12

CBOT

MGEX

KCBT

August 2010:

Russian export ban
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Comparing the Three U.S. Wheat Futures Markets: 

Specifications and Trading Volume

For a given commodity, trading volume will tend to consolidate on a single exchange because, all else equal, 

traders will be attracted to the most liquid market (Silber, 1981). The United States has not one but three wheat 

futures contracts because in the case of wheat, all else is not equal. Each market serves as a benchmark for a 

specific class of wheat: (in decreasing order of trading volume) Chicago Board of Trade (CBOT) - soft red winter 

contract; Kansas City Board of Trade (KCBT) - hard red winter contract; and Minneapolis Grain Exchange - 

hard red spring contract. Each contract calls for 5,000 bushels of wheat to be delivered in 1 of 5 months (March, 

May, July, September, and December) of each year. Contract prices are quoted in U.S. cents per bushel. Wheat 

deliverable against CBOT, KCBT, and MGEX futures contracts is distinguished by class and by protein content. 

The CBOT contract has no protein requirement, the KCBT contract requires 11 percent protein content, and the 

MGEX contract requires 13.5 percent protein content. 

The presence of three wheat futures markets establishes price differentials across classes and allows these differentials to vary over time. Relative prices between contracts for nearby delivery reflect the current availability 

and value of wheat protein. Stiegert and Balzer (2001) frame price differentials between classes and contracts as 

the premium for protein above an “intrinsic level” that is generally available among all classes at a point in time. 

The intrinsic level fluctuates from year to year and is determined by how much wheat of each class is planted and 

weather-related shocks that determine the protein content of harvested grain. When the intrinsic level of protein 

adequately meets current demand, the premium for higher protein specifications, like the MGEX hard red spring 

wheat futures, will be low. Recent empirical studies (Goodwin and Smith (2009) and Wilson and Miljkovic 

(2013) consider the relationship between the supply of wheat quality, as measured by protein level, and prices in 

the three markets. These studies find strong relationships between available protein and prices for higher protein 

wheat classes like HRS. Prices for CBOT wheat do not reflect the availability of quality as at MGEX and KCBT, 

to the point where CBOT essentially reflects the value of feed wheat. 

The presence of three wheat futures 

contracts conflicts with the demand 

from wheat futures traders for market 

liquidity, especially among speculators, as noted generally by Silber 

(1981) and for wheat specifically by 

Gray (1961). Futures trading volume 

in wheat grew rapidly in the 2000s, 

especially in the CBOT market, 

where trading volume is an order of 

magnitude greater than at KCBT or 

MGEX. In 2004, the volume of trade 

at CBOT was approximately 2.7 

times larger than that at KCBT and 

5.7 times larger than that at MGEX 

(see box figure). In 2011, these 

amounts increased to 3.7 and 13.4, 

respectively. It is unclear whether this 

reflects the consolidation of trading 

on a single exchange, as predicted by 

Silber (1981), as trading volume has 

grown for all wheat futures.

Box figure 3a

Trading volume for U.S. wheat futures contracts

Note: KCBT = Kansas City Board of Trade. MGEX = Minneapolis Grain Exchange. 

CBOT = Chicago Board of Trade. 

Source: USDA, Economic Research Service using data from Commodity Futures 

Trading Commission (2012), “Disaggregated Commitment of Traders Report.”

Contracts traded each month, millions

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Jan.-04 May-05 Sep.-06 Feb.-08 Jun.-09 Nov.-10 Mar.-12

KCBT MGEX CBOT 
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indexes to their investment portfolios (Stoll and Whaley, 2010). Most traders following index-trading 

strategies tend to take only long positions (i.e., positions that make money if prices rise). Because 

they want exposure to commodity returns, they do not take short positions (i.e., positions that make 

money if prices drop.) Hereafter, any firms that follow index-tracking trading strategies are referred 

to as commodity index traders, or CITs. 

Criticism of financial speculation and CITs generally focuses on the size of the futures positions held 

by CITs and the invariance of CIT trading strategies to available information about expected future 

prices, leading some observers to refer to these traders as “massive passives.” Market participants, 

such as hedge fund manager Michael Masters (2011) and investor George Soros (2008), and some 

governmental and nongovernmental organizations, such as United Nations Conference on Trade 

and Development (UNCTAD) (2011), publicly allege that the large presence of CITs in commodity 

markets, trading in large volume but uninformed about commodity supply-and-demand fundamentals, causes prices to conform to the actions of these firms. CITs are hypothesized to encourage 

“herding behavior” (UNCTAD, 2011), leading prices to overshoot fundamentally justified levels 

because traders buy or sell to follow trends. A range of policy responses have been suggested as a 

way to curb financial speculation and control commodity price levels and volatility, including speculative position limits (Masters, 2011), virtual grain reserves (Von Braun and Torero, 2009), transaction taxes, and proprietary trading bans (UNCTAD, 2011).

Some econometric studies (e.g., Singleton, 2011) find that empirical evidence of correlation between 

the futures market positions held by CITs and commodity futures prices is suggestive of a causal 

effect running from speculative trading activity to observed prices. Irwin and Sanders (2011) and 

Fattouh et al. (2012) provide extensive criticism of these studies. A considerable body of evidence 

(e.g., Stoll and Whaley, 2010; Buyuksahin and Harris, 2011; Irwin and Sanders, 2011) suggests that 

CIT futures market positions are not associated with price levels or price changes of the underlying 

futures. (For a review of publicly available data on CIT futures market positions in wheat, see box 

“The Prevalence of Index Trading in U.S. Wheat Futures.”)

Tang and Xiong (2012) suggest an alternative mechanism by which financial speculation and 

the presence of CITs may affect agricultural commodity prices: through comovement with other 

commodity prices. The same study finds correlation between many commodity prices and the price 

of crude oil—the most widely traded commodity futures contract—which rose over the period 

in which CIT trading became prevalent, and this effect was stronger for commodities included in 

major indexes than for nonindex commodities. Tang and Xiong test the linkage between returns for 

many nonenergy commodities and crude oil and conclude that this comovement among prices is 

driven by the inclusion of commodities into major indexes such as the GSCI and the DJ-UBSCI. The 

“index inclusion” impact of CITs alleged by Tang and Xiong (2012) follows a similar effect found in 

equity markets by Barberis et al. (2005). That study shows that inclusion in a major index leads to 

comovement among the prices of the index components. This impact of speculation is independent 

of directional position taking by traditional arbitraging speculators that was identified earlier as a 

precautionary demand shock.

Concerns about commodity price comovement are not new and are not uniquely related to CITs. 

Comovement has been a source of concern at least since Pindyck and Rotemberg (1990) found 

“excessive” correlation among prices for commodities with unrelated fundamental supply and 

demand drivers, even after controlling for macroeconomic factors that cause common movement 

among prices. Pindyck and Rotemberg suggested that a herd mentality caused this comovement 

because “traders are alternatively bullish or bearish on all commodities for no plausible reason,” a 
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The Prevalence of Index Trading in U.S. Wheat Futures Market

Much of the recent focus on speculation in agricultural futures markets has been in terms of 

who is buying and selling futures contracts. The trading volume in U.S. wheat futures markets 

rose between 2001 and 2011. How much of this increase in trading can be attributed to financial 

speculators in general and commodity index traders (CITs) in particular?

The Commitment of Traders reports disseminated by the Commodity Futures Trading 

Commission (CFTC) provide a weekly snapshot of the identity of futures traders. Historically, 

these reports presented the number of contracts held by traders in three groups that roughly 

corresponded with large hedgers, large speculators, and small traders. In 2009, the CFTC began 

releasing position data for disaggregated groups of traders, including CITs. Historical data on 

the positions held by CITs are publicly available for a set of agricultural futures, including 

two of the three wheat futures markets from January 2006 to present. CIT positions are not 

available for Minnesota Grain Exchange (MGEX) wheat futures because of the lack of index 

trading in that market. Unlike Chicago Board of Trade (CBOT) and Kansas City Board of 

Trade (KCBT) wheat, MGEX wheat is not included in the major indexes tracked by CITs, 

such as the Goldman Sachs Commodity Index and the Dow Jones-UBS Commodity Index. 

Unfortunately, the CFTC does not provide publicly available records of CIT positions prior to 

2006. Sanders and Irwin (2011) use nonpublic CFTC data to show that index trading came to 

Box figure 4a

Commodity index trader share of total open interest in CBOT wheat futures market

Note: CBOT = Chicago Board of Trade. 

Source: USDA, Economic Research Service using data from Commodity Futures Trading Commission (2012), 

“Index Investment Data.”

Percent

0

10

20

30

40

50

1/1/06 1/1/07 1/1/08 12/31/08 12/31/09 12/31/10 12/31/11

Long percent of open interest

Short percent of open interest
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its current prominence between 2004 and 2006. Similarly, Tang and Xiong (2012) propose that 

CIT-driven comovement began to take effect in 2004.

The box figures present the proportion of outstanding futures positions held by CITs in CBOT 

and KCBT wheat markets since 2006. Most CIT positions are long, since major commodity 

indexes tracked by investors represent the returns to holding long positions in the nearby 

contract. Since 2006, the proportion of long open interest held by CITs has been relatively 

constant. CITs hold 35 to 50 percent of CBOT long open interest and 15 to 35 percent of KCBT 

open interest. These proportions illustrate why the wheat futures markets provide suitable data 

to study the impact of CITs. To the extent that CITs cause comovement-driven price effects 

along the lines suggested by Tang and Xiong (2012), those effects are expected to be stronger 

in markets where CITs account for a greater share of trading. In this case, they should be 

negligible for MGEX wheat, greater for KCBT wheat, and greatest for CBOT wheat.

Note: KCBT = Kansas City Board of Trade.

Source: USDA, Economic Research Service using data from Commodity Futures Trading Commission (2012), 

“Index Investment Data.” 

0

5

10

15

20

25

30

35

40

1/1/06 1/1/07 1/1/08 12/31/08 12/31/09 12/31/10 12/31/11

Box figure 4b

Commodity index trader share of total open interest in KCBT wheat futures market

Percent

Short percent of open interest

Long percent of open interest
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rationale similar to the one employed to criticize CITs. While a number of studies (e.g., Deb et al., 

1996) question the validity of the statistical methods employed by Pindyck and Rotemberg (1990), 

Ai et al. (2006) show that, to the extent that agricultural commodity prices move together, this 

comovement can be explained by common tendencies in supply-and-demand factors. Ai et al. use 

quarterly price and inventory data from the United States to demonstrate that prices of agricultural 

commodities did not exhibit excess comovement, but they did not consider the potential for comovement with other widely traded nonagricultural commodities.

 To separate precautionary demand from comovement related to CITs, one must recognize that financialization, or an increase in the influence of financial markets and institutions, affected numerous 

commodities at the same time. If it affected wheat prices, then it would also have affected other 

commodities, such as crude oil. Therefore, this study measures the extent to which wheat prices 

move together with these external commodities. Essentially, this is a test for “excessive comovement,” or comovement that cannot be explained by fluctuations in supply drivers such as weather, 

demand drivers related to economic activity, or expectations about future price movement.
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Modeling Wheat Price Dynamics

At least two issues make it difficult to use economic modeling to disentangle the many influences 

on wheat prices. First, one must decide which of the many relevant influences are important enough 

to be explicitly identified in the model. Second, one must avoid confounding various explanations 

for observed variation in wheat prices. At the center of this exercise is an implicit thought experiment in which one of the four unique factors identified earlier affects the wheat price, but the other 

factors are held fixed. The magnitude of the observed price change in this case can be attributed to 

the varying factor. In reality, each of these factors affects wheat prices simultaneously, so an econometric model must distinguish price variation attributable to each factor.

As part of the modeling effort, this study identified four key drivers of wheat futures prices in each 

futures market: real economic activity, comovement, precautionary demand, and net supply (see 

fig. 3). Each of these drivers encapsulates numerous economic phenomena, as discussed earlier. 

Structural vector autoregression, or SVAR, is used to identify the influence of each driver on 

observed wheat futures prices, with the econometric estimation procedure run separately for each 

futures market and results compared across markets.

Figure 3

Visual representation of an econometric model relating structural shocks to variables

Source: USDA, Economic Research Service. 

Net supply

Government

intervention

Idiosyncratic

demand

Weather

Precautionary demand

Convenience

yield

Storage

costs

Price

expectations

Comovement

Speculation

unrelated

to E(P)

“Financialization”

Price of 

unrelated

commodity

Real economic activity

Exchange

rates

Interest

rates

Economic

growth

Nearby price Futures market spread Price in external market Economic activity index

Unanticipated

structural

shocks

Observedvariables

Economic

phenomena
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SVAR begins with a simple system of linear equations to represent the observed value of each variable included in the model as a function of past of all of the variables in the system, plus some unanticipated variation. For example, the price of wheat in a given futures market in period t is presented 

as:

(1) t pt p t = + +...+ + +...+ + 1 1 2 2 11 2 2 1 − − −− t t t t p b p b p by by dx e

where y represents other variables relevant to the price of wheat; x represents time-of-year indicators, long-term trends, and other predictable measures affecting the price of wheat; and e is residual 

variation. This residual variation, et, represents new information about the left-hand-side variable 

entering the market in each period. 

The SVAR model isolates the residual variation for each variable in the model and attributes some 

portion of it to each of the four economic factors mentioned earlier. The unique variation in each 

variable attributed to a specific economic factor is called a structural shock. The shocks represent 

unanticipated variation in the system of variables so they have an average value of zero. Negative 

shocks are associated with below-average conditions for that factor, and positive shocks are associated with above-average levels. By construction, the structural shocks are uncorrelated with each 

other, allowing one to examine the relationship between each of the structural shocks and observed 

commodity prices holding all other structural shocks fixed. 

The residual variation in the SVAR model, et, may be thought of as the difference between observed 

prices at a given period t and the best forecast of the period t price based on all past information. 

This difference can then be represented as a weighted sum of the structural shocks. For example, the 

forecast error for the price of wheat is the difference between the observed price, pt, and a forecast 

price, pforecast, based on available information up to and including period t – 1. The forecast error 

can be written as:

(2) =− = + + + 1 2 34

forecast REA CM PD NS

t t t t t t e p p du du du du , 

where the d coefficients are the weights to be identified for each of the structural shocks, u. The 

REA, CM, PD, and NS superscripts for u denote structural shocks related to real economic activity, 

comovement, precautionary demand, and net supply respectively. In figure 3, the arrows linking the 

structural shocks to the variable “nearby price” represent each right-hand-side term in equation 2, 

namely, the contemporaneous impact of each structural shock on the nearby wheat futures price. 

Model Variables

Since there are four structural factors to identify, the model contains four variables, four equations 

similar to 1 to be estimated, and four structural shocks. To begin, the four variables to include in 

the model are selected, which determines the structural interpretation given to each of the four 

shocks. This study chooses to include three variables in addition to the wheat futures price in each 

of the three futures markets, which are the main variables of interest. The three additional variables 

identify the real economic activity, comovement, and precautionary demand shocks; the remaining 

residual variation represents the net supply shock.

Given the importance of demand growth in emerging economies to stimulate wheat consumption, the economic activity measure must be a global measure. To represent the global demand 

for commodities, the analysis uses an index of real economic activity developed in Kilian (2009). 
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This index aggregates ocean freight rates based on an empirically validated relationship between 

freight rates and economic activity. Because freight rates rise and fall in response to fluctuations in 

economic activity in any region of the world, this measure is not biased by changes in the direction 

or composition of trade. This measure was used successfully in Kilian (2009), Kilian and Murphy 

(2013), and Carter et al. (2012), among others.

One can approximate commodity price movements that may be associated with speculation-induced 

comovement using the prices of major commodities themselves. If the implications of the financialization hypothesis are correct, it should follow that nonagricultural commodity prices have driven 

wheat price changes. Following the findings of Tang and Xiong (2012), wheat prices should have 

become increasingly related to crude oil prices since 2004, so the model accounts for the price of 

nearby West Texas Intermediate crude oil futures. As a test of robustness, the analysis is repeated 

using the value of the GSCI, introduced earlier, as an alternate measure of external market price 

movement. Recall that the GSCI represents a basket of commodity prices with heavy weight placed 

on energy commodities tracked by many market participants following commodity index trading 

strategies.

The incentive to hold inventories is measured using the spread (or the difference) between the fifth 

deferred futures contract price and the nearby futures prices based on the Working curve relationship between the distant-to-nearby price spread and inventory levels. This spread represents the 

incentive to hold inventory between the current period and some distant period in which supply 

conditions may be resolved as newly produced grain enters the market. In periods when available 

supply is tight, nearby spreads may be positive to ration available supplies through the remainder 

of the crop year, even though inter-crop-year spreads are negative. In this way, one may capture 

a precautionary demand shock related to expectations about future crop year supply-and-demand 

conditions.

Physical wheat inventory data would be an alternative to the spread variable in the model if the data 

existed. However, quantity measures are either unavailable at the frequency required by the model 

or cover only a limited set of locations, such as the warehouses licensed to receive grain delivered 

against the various wheat futures contracts. Available quantity data are not the global measure of 

wheat scarcity that is required.

Garcia et al. (2012) show that the spread underestimated the true price of storage at various points 

for CBOT and KCBT wheat at various points between 2006 and 2010 due to the maximum storage 

rate imposed by the exchanges. This problem was particularly acute in the period when prices were 

low between the 2008 and 2010 peaks, especially for CBOT wheat. However, since the measurement error only occurred in periods where spreads reached the full carrying charge between periods 

implied by the exchange-determined storage rate, the spread is still indicative of expectations about 

supply-and-demand conditions in the upcoming crop year relative to the current crop year.

The nearby futures prices for each wheat futures contract are included as the final variable in the 

model, which decomposes the changes observed in wheat price to three structural shocks associated 

with the other variables. Any changes in wheat price unrelated to the first three factors represent net 

supply shocks. Thus, this fourth structural shock, which is interpreted as being due to current wheatspecific supply-and-demand factors, encompasses residual variation in price.
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Identification

To identify the impact of the structural shocks on the price of wheat, one must be able to calculate values for the coefficients d1-d4 in equation 2. Because there are four coefficients for each of 

the four equations in the SVAR model, each parameter cannot be identified separately. As with all 

SVAR models, additional assumptions are requited about the economic process that determines the 

observed values of the variables in the model. This section briefly explains the intuition behind the 

identification strategy. For a more complete mathematical representation, see the appendix to this 

report and Janzen et al. (2013). 

Identifying the source of price variation in each wheat futures market, particularly speculative 

shocks, requires different assumptions than other applications of SVAR to agricultural markets. 

Agricultural economists have mainly applied SVAR methods to questions of market integration. In 

the case of wheat, SVAR studies examine linkages between prices for one commodity in different 

locations (e.g., Goodwin and Schroeder, 1991; Mohanty et al. 1999; McKenzie, 2005), prices for 

different degrees of product processing (Babula et al., 2004) for wheat and wheat products, and 

prices across commodities (Power and Vedenov, 2009) for a set of agricultural and energy commodities during the 2008 price boom and bust. In general, these applications sought to examine relationships between prices across markets. This analysis considers the effect of various shifters on a 

single market in a manner grounded in an understanding of commodity price formation based on the 

competitive storage model.

The analysis employs two types of assumptions to identify the structural shocks in the model: 

ordering assumptions and volatility assumptions. Ordering assumptions have been widely used in 

the literature reviewed earlier but require stronger assumptions about the relationship between the 

structural shocks and the variables. Ordering essentially assumes that certain structural shocks have 

no contemporaneous impact on some of the model variables. It assumes that there is an order to the 

shocks such that some shocks take precedence over others in determining the observed variables.

The ordering assumption is justified when identifying the weights for some of the structural shocks 

but not others. Take for instance the measure of real economic activity. Supply-and-demand shocks 

specific to the wheat market are unlikely to affect the macroeconomy immediately, so it is safe to 

assume that the weights on the comovement and wheat-specific shocks in the equation that represents real economic activity are zero. This assumption does not preclude any feedback from the 

wheat market or other specific commodity markets to real economic activity, only that such feedback 

occurs with at least a 1-month delay. Kilian (2009) notes that the sluggish response of economic 

activity to market-specific shocks is consistent with past behavior.

In figure 3, the arrows represent the contemporaneous impact of shocks on variables. The ordering 

assumption is reflected in the absence of arrows from the precautionary demand and net supply 

shocks to the external market price and from these wheat-specific shocks and from the comovement 

shock to the economic activity index. In terms of equation 2, the ordering assumption sets some of 

the coefficients on the right-hand side equal to zero. This assumption does not imply that wheatspecific shocks do not affect general economic activity or the prices of nonagricultural commodities, 

only that these impacts occur with a minimum on period lag. 

A similar assumption is made about the relationship between prices in nonagricultural markets, such 

as crude oil, and the wheat-specific shocks. It is assumed that wheat markets may respond contemporaneously to shocks generated in external markets, but external market prices do not respond 
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immediately to wheat market shocks. This assumption is intended to provide the most robust test of 

the comovement hypothesis generated by Tang and Xiong (2012). If under this assumption wheatspecific shocks are found to cause more observed variation in wheat prices than do external markets, 

one can be confident that the converse outcome was given the best chance to reveal itself in the data. 

The ordering assumption cannot be used to disentangle wheat market shocks. Contemporaneous 

observed changes in prices and spreads could be the result of precautionary demand shocks or 

net supply shocks. Precautionary demand shocks do not take precedence over net supply shocks, 

or vice-versa. One would expect a positive precautionary demand shock to have a positive impact 

on price and a positive impact on the term spread. A net supply shock should also have a positive 

price impact but a negative impact on spreads. Importantly, net supply and precautionary demand 

shocks may affect prices and spreads simultaneously, so assigning priority to one or the other is not 

realistic.

The analysis uses information about the volatility of the variables in the model to separately identify the wheat-market-specific structural shocks due to precautionary demand and net supply. As 

discussed earlier, inventories must be non-negative, and the non-negativity constraint implies that 

prices will be relatively more volatile when inventories are low. Prices and spreads can therefore 

be thought of as following two regimes: one volatile and one tranquil. The presence of volatile and 

tranquil price regimes allows one to use an “identification through heteroskedasticity” approach due 

to Rigobon (2003) to allow net supply shocks and precautionary demand shocks to affect prices and 

spreads simultaneously. Incorporating additional information about volatility regimes overcomes the 

simultaneity problem. For more information on the econometric methodology, see the appendix to 

this report and Janzen et al. (2013). 
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Data and Results

The reduced-form vector autoregression (VAR) system of regression equations as in (1) is estimated using monthly data spanning January 1991 to December 2011. Separate VAR models are 

estimated for each wheat futures contract. Monthly indicator and linear trend variables in each 

equation control for seasonality and long-term trends, such as the longrun decline in inflationadjusted commodity prices. Practical considerations, including the availability of data, determine 

the frequency of observations used for estimation. The analysis uses monthly data to examine price 

behavior at higher frequency than in other studies of comovement for agricultural commodities that 

identified the impact of speculation (Ai et al., 2006). While even higher frequency data may also 

yield interesting conclusions, monthly frequency should adequately capture comovement driven by 

index trading and financial speculation.

The length of the time-series is also dictated by modeling necessity and practical constraints. The 

analysis uses the results to examine the 2008 and 2011 price boom and bust periods, but data in the 

model extend back to 1991. A longer time series enables more precise estimation of the regression 

parameters. Because the econometric identification requires the presence of volatile and tranquil 

periods for wheat prices, the inclusion of additional “price spike” periods allows for a more accurate 

portrayal of how each of the structural shocks drive price volatility. The results become more robust 

than if the model had only used the 2008 and 2011 spikes to estimate price response to structural 

shocks. Prior to the early 1990s, wheat futures contracts for distant-month delivery were traded 

erratically, if at all, on the smaller KCBT and MGEX exchanges. The spread measure in the analysis 

relies on an indicator for the intertemporal price relationship between wheat for nearby delivery and 

wheat for more distant delivery, so the sample is limited to the period when this measure is available 

for each market.

Impulse Response Functions

Figure 4 plots the time path for the response of each variable in the model to the economic activity, 

external market, speculative demand, and net supply shocks. These graphs illustrate, based on the 

average response observed in the data, the response of each variable in the model to a hypothetical 

shock equal to one standard deviation from the assumed zero mean. Note that the normalization 

used to identify the model implies that each of the shocks causes an increase in the price of wheat. 

In particular, positive net supply shocks are defined to represent a supply disruption.

The impulse response functions serve two purposes. First, they act as a check on the validity of 

the assumptions about the shocks. The signed impact of the observed structural shocks should be 

consistent with expectations; otherwise, the structural shocks captured in the model may not capture 

the impacts suggested earlier. Second, the impulse response functions for the price of wheat can be 

compared to ascertain the magnitude and duration of the influence of each structural shock.

Figure 4 shows impulse responses for the model where the CBOT wheat price is the fourth variable. 

(Results for KCBT and MGEX wheat prices are similar.) Each row of graphs shows the response of 

one variable to the four structural shocks. The dashed lines measure the precision of the estimated 

response and constitute 90 percent confidence bands. Focusing on the bottom-right corner of figure 

4, one can check the validity of the identification scheme used to identify the endogenous net supply 

and precautionary demand shocks in the wheat market. Price responds positively to precautionary 

demand shocks and net supply shocks as expected, based on the model assumptions. As shown in 
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Figure 4

Impulse response functions from structural vector autoregression model of CBOT wheat prices 

using crude oil as external market

Note: CBOT = Chicago Board of Trade. 

Source: USDA, Economic Research Service.

Response of economic activity index

Response of crude oil price

Estimated impulse response

90-percent confidence level

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Response of wheat spread

Response of wheat price

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the third graph in the third row, if a precautionary demand shock increases price, the spread also 

increases. Similarly, a net supply shock (equivalent to a supply disruption) raises wheat prices and 

has a negative influence on the spread to draw supplies in storage to the market (see the third graph 

in the fourth row).

As shown in the figure, external forces have a relatively small impact on wheat prices, relative to 

unexpected shocks specific to the wheat market. Real economic activity and external market shocks 

are small but long lived. Real economic activity shocks have minimal initial impact but cause 

moderate effects on wheat prices after approximately 4 months. However, neither type of shock is 

statistically significant on average.

 The insignificance of crude oil market shocks suggests that broad-based commodity market speculation has not affected wheat prices and that returns from wheat markets are not strongly associated with those from crude oil. Figure 5 presents a similar analysis, except that the GSCI is used 

instead of crude oil as a composite measure of commodity price movement. Recall that Tang and 

Xiong (2012) suggest that commodities included in indexes should be more strongly correlated. The 

impulse response functions suggest that the association between wheat price changes and movement 

in the GSCI is relatively small.

Inventory demand and net supply shocks specific to the wheat market have positive and significant initial impacts on prices. The precautionary demand shock displays some evidence of overshooting—prices increase quickly in the months following the shock and then drop sharply toward 

zero. After 9 or 10 months, the response is indistinguishable from zero. In contrast, net supply 

shocks have a significant and long-lasting impact on prices.

Historical Decomposition

Because the forecast error in each variable each month can be expressed by the model as the 

weighted sum of the structural shocks realized in that month, one can plot the cumulative contribution of each structural shock to the price of wheat across time. As shown in figures 6-8, the historical decomposition is constructed such that the sum of the four series equals the realized price net 

of trend in any period. These figures further support the narrative presented in the impulse response 

analysis. Considerable variation in wheat prices is due to the two wheat-market-specific shocks. 

Longer, smaller swings in price are attributed to the shocks to real economic activity and external 

markets. This suggests that these factors can contribute to periods of high and volatile prices, but 

their effects are likely to be small.

Unlike the impulse responses, historical decomposition enables consideration of specific episodes in 

the observed time series of wheat prices. The impulse responses present an average response, which 

may overlook the contribution of the structural shocks to a specific price change. If the financialization hypothesis is correct, the small measured impact of external market shocks discussed earlier 

may be due to a lack of external market effects prior to the rise of CIT activity since 2004. These 

external market shocks are found to be relatively small throughout the period covered in the sample, 

whether crude oil or the GSCI represents the external market (see figs. 9-11). The two largest 

comovement shocks in the sample occur in 2005 and 2011. Though these peaks roughly correspond 

with increases in CIT long open interest, as discussed earlier, they do not correspond to price spikes.

The results of the decomposition analysis differ from analyses of crude oil prices by Kilian (2009) 

and Kilian and Murphy (2013) that use similar methods. These studies find that fluctuations in real 
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Note: CBOT = Chicago Board of Trade. GSCI = Goldman Sachs Commodity Index.

Source: USDA, Economic Research Service.

Figure 5

Impulse response functions from structural vector autoregression model of CBOT wheat prices 

using GSCI as external market

Response of economic activity index

Response of crude oil price

Estimated impulse response

90-percent confidence level

Response of wheat spread

Response of wheat price

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Note: CBOT = Chicago Board of Trade. 

Source: USDA, Economic Research Service.

Figure 6

Historical decomposition of CBOT wheat futures prices with crude oil as external market

Logarithm of the real price of wheat, deviation from trend

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5/7/90 5/6/92 5/6/94 5/5/96 5/5/98 5/4/00 5/4/02 5/3/04 5/3/06 5/2/08 5/2/10

Real economic activity

Comovement

Precautionary demand

Net supply

Note: KCBT = Kansas City Board of Trade.

Source: USDA, Economic Research Service.

Figure 7

Historical decomposition of KCBT wheat futures prices with crude oil as external market

Logarithm of the real price of wheat, deviation from trend

-0.6

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0

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5/7/90 5/6/92 5/6/94 5/5/96 5/5/98 5/4/00 5/4/02 5/3/04 5/3/06 5/2/08 5/2/10

Real economic activity

Comovement

Precautionary demand

Net supply
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-0.6

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5/7/90 5/6/92 5/6/94 5/5/96 5/5/98 5/4/00 5/4/02 5/3/04 5/3/06 5/2/08 5/2/10

Note: MGEX = Minneapolis Grain Exchange.

Source: USDA, Economic Research Service.

Figure 8

Historical decomposition of MGEX wheat futures prices with crude oil as external market

Logarithm of the real price of wheat, deviation from trend

Real economic activity

Comovement

Precautionary demand

Net supply

-0.6

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0

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5/7/90 5/6/92 5/6/94 5/5/96 5/5/98 5/4/00 5/4/02 5/3/04 5/3/06 5/2/08 5/2/10

Note: CBOT = Chicago Board of Trade. GSCI = Goldman Sachs Commodity Index.

Source: USDA, Economic Research Service.

Figure 9

Historical decomposition of CBOT wheat futures prices with GSCI as external market

Logarithm of the real price of wheat, deviation from trend

Real economic activity

Comovement

Precautionary demand

Net supply
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-0.4

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5/7/90 5/6/92 5/6/94 5/5/96 5/5/98 5/4/00 5/4/02 5/3/04 5/3/06 5/2/08 5/2/10

Note: KCBT = Kansas City Board of Trade. GSCI = Goldman Sachs Commodity Index.

Source: USDA, Economic Research Service.

Figure 10

Historical decomposition of KCBT wheat futures prices with GSCI as external market

Logarithm of the real price of wheat, deviation from trend

Real economic activity

Comovement

Precautionary demand

Net supply

Note: MGEX = Minneapolis Grain Exchange. GSCI = Goldman Sachs Commodity Index.

Source: USDA, Economic Research Service.

Figure 11

Historical decomposition of MGEX wheat futures prices with GSCI as external market

-0.4

-0.2

0

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1.0

Logarithm of the real price of wheat, deviation from trend

Real economic activity

Comovement

Precautionary demand

Net supply

5/7/90 5/6/92 5/6/94 5/5/96 5/5/98 5/4/00 5/4/02 5/3/04 5/3/06 5/2/08 5/2/10
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economic activity related to the macroeconomic business cycle were the largest and most persistent 

driver of crude oil prices, particularly during period of rising prices that ended in 2008. Similarly, 

Carter et al. (2012) find that between its 2003 low and 2008 high, real economic activity caused corn 

prices to increase by approximately 50 percent. The results of this analysis for wheat price spikes 

suggest that real economic activity does not similarly impact the wheat market. Between 2003 

and 2008, real economic activity raised wheat prices in the three wheat futures markets by 20-30 

percent. Though not a small amount overall, it is small relative to the net-supply-generated portion of 

the price spike.

Net supply shocks are the visibly dominant series in figures 6-8. Three major peaks in the net supply 

series consistent across the three wheat markets are roughly centered on the years 1996, 2003, and 

2008. Each of these major positive net supply shocks is associated with U.S. and world production 

declines (see box figure on page 5). Net supply shocks remain the primary driver of large changes in 

price when the GSCI is the external market variable in the model, as shown in figures 9-11.

Net supply and precautionary demand shocks follow similar time paths across each of the three 

markets. The net supply shocks in MGEX wheat in 2007 and 2008 are a notable exception; MGEX 

net supply shocks were substantially greater than those in the CBOT and KCBT markets, consistent 

with observed production numbers for spring wheat relative to winter wheat. Whereas U.S. winter 

wheat production rebounded for the 2007-08 marketing year, U.S. spring wheat was below average 

for a second consecutive year. J.P. Morgan (2010) suggests that a “stock out” of high quality wheat 

pushed MGEX prices far above CBOT and KCBT. 

As mentioned earlier in this report, three recent specific shocks to wheat market fundamentals 

included the Australian drought, the Russian export ban, and the global financial crisis. While other 

factors may have affected wheat prices over the period of analysis, these three are representative of 

the types of structural shocks measured in the model. Accordingly, this analysis considers the estimated structural shocks when these events occurred. 

The drought and export ban should reflect wheat-specific shocks that may be captured in the model 

as net supply or precautionary demand shocks (see next section for a discussion of the impact of the 

Russian export ban). The Australian drought shock is consistent with the results observed in the historical decompositions. Figures 6-8 show that net supply shocks increased dramatically in the second half 

of 2007. The net supply shock “peak” in these figures occurs in September 2007. Much of the subsequent increase in wheat prices is due to precautionary demand shocks. The net supply shock coincides 

with information flows about supply shortfalls that would have come during the growing season for 

Australian winter wheat from June through December (USDA, Office of the Chief Economist, 2011). 

The global financial crisis should represent a contraction in real economic activity. Examining the 

series of real economic activity shocks shows that lower economic activity shocks did not hit wheat 

prices until January 2009, slightly later than the first news of the financial crisis. 

Counterfactual Analysis of the Wheat Prices From 2006 to 2011

Following the general results about the origins of price levels and price changes in the three U.S. 

wheat markets, this analysis uses counterfactual scenarios to examine the period of relatively high 

wheat prices between 2006 and 2011. What would have happened to these prices in the absence 

of any one of the effects identified earlier? For example, how would the time series of observed 

wheat prices have differed if the external market shocks did not affect wheat prices? To answer such 
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questions, this study considers the percentage difference between the observed and counterfactual 

scenarios.5

Figures 12-14 and 15-17 show the observed series of prices since 2000 and four counterfactual 

scenarios over the period January 2006 to December 2011 using both crude oil and GSCI to measure 

external market prices. In this period, one would expect the influence of comovement shocks to 

matter more if the financialization hypothesis is correct. Based on this simulation, prices are found 

to be little changed in the absence of external market shocks whether crude oil prices or the GSCI is 

used to measure the comovement effects.

During 2006-2008, comovement shocks are nearly imperceptible. At the peak of the price spike in 

February 2008, wheat prices would have been only 1 percent lower in the absence of comovement 

shocks attributable to financial speculators like CITs. Subsequently, comovement shocks increase in 

magnitude, but the overall effect is still small: comovement shocks related to crude oil prices raised 

wheat prices only 5-8 percent at the peak of their impact, depending on the market. In absolute 

terms, the maximum wheat price impact of comovement shocks at any point in the counterfactual 

analysis is approximately $0.67 per bushel. Relative to the price volatility observed over this period, 

such an effect is small: CBOT and KCBT monthly average prices rose by approximately $8 per 

bushel between 2005 lows and 2008 highs and by nearly $5 per bushel between 2009 lows and 2011 

highs. MGEX prices rose by even more between 2005 and 2008. The comovement shock has similar 

impacts across markets, but the effect is strongest and hits earliest in the MGEX wheat market (see 

figs. 14 and 17). This runs counter to the hypothesis that comovement shocks should be greater in 

CBOT wheat, where the presence of CITs as a percentage of open interest is greatest.

Figures 12-14 and 15-17 show that the price peak in early 2008, when the MGEX price rose far 

above the CBOT and KCBT prices, was driven by combined net supply and precautionary demand 

shocks. In February 2008, in the absence of net supply shocks, prices would have been 53 percent 

lower for CBOT, 40 percent lower for KCBT, and 62 percent lower for MGEX. At the same time, in 

the absence of precautionary demand shocks, prices would have been 11 percent lower for CBOT, 20 

percent lower for KCBT, and 36 percent lower for MGEX. 

Figures 12-17 reveal that the wheat-specific shocks related to precautionary demand and net supply 

differ in impact between the 2006-08 and 2009-11 periods. Net supply shocks caused much of the 

increase in prices for all wheat that occurred in 2006 and 2007. Much of the subsequent increase in 

price was driven by precautionary demand shocks. That the impact of precautionary demand was

greatest at the price peak may be evidence of anticipatory demand by firms who held inventory. A 

dramatic rise in price to historically high levels may have induced fears of further price increases, 

causing firms to add to inventories. 

Elevated wheat prices in 2010-11 did not signal a repeat of the events of 2007-08. Through 2010, 

concurrent shocks to net supply and precautionary demand drove prices higher in the CBOT and 

KCBT wheat markets. Figures 12 and 13 show that precautionary demand and net supply made 

similar contributions to higher prices in 2010-11. That precautionary demand played a more significant role in the initial increase in prices in 2010 than in 2007 is consistent with anticipation of 

the Russian export ban that went into effect in August 2010. The complete elimination of Russian 

exports would lead to higher future wheat exports from the United States and other countries; an 

5We measure this difference as the log difference between prices with and without each of the shocks. These log differences approximate a percentage change, so we use percent to refer to these log differences.
31

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Note: CBOT = Chicago Board of Trade. 

Source: USDA, Economic Research Service.

Figure 12

Counterfactual simulation of CBOT wheat futures prices using crude oil as external market

Logarithm of the real price of wheat, deviation from trend

Observed

No real economic activity

No comovement

No precautionary demand

No net supply

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

7/24/98 12/6/99 4/19/01 9/1/02 1/14/04 5/28/05 10/10/06 2/22/08 7/6/09 11/18/10 4/1/12 8/14/13

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

7/24/98 12/6/99 4/19/01 9/1/02 1/14/04 5/28/05 10/10/06 2/22/08 7/6/09 11/18/10 4/1/12 8/14/13

Note: KCBT = Kansas City Board of Trade. 

Source: USDA, Economic Research Service.

Figure 13

Counterfactual simulation of KCBT wheat futures prices using crude oil as external market

Logarithm of the real price of wheat, deviation from trend

Observed

No real economic activity

No comovement

No precautionary demand

No net supply
32

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement, ERR-165

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-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

1.2

7/24/98 12/6/99 4/19/01 9/1/02 1/14/04 5/28/05 10/10/06 2/22/08 7/6/09 11/18/10 4/1/12 8/14/13

Note: MGEX = Minneapolis Grain Exchange.

Source: USDA, Economic Research Service.

Figure 14

Counterfactual simulation of MGEX wheat futures prices using crude oil as external market

Logarithm of the real price of wheat, deviation from trend

Observed

No real economic activity

No comovement

No precautionary demand

No net supply

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

7/24/98 12/6/99 4/19/01 9/1/02 1/14/04 5/28/05 10/10/06 2/22/08 7/6/09 11/18/10 4/1/12 8/14/13

Note: CBOT = Chicago Board of Trade. GSCI = Goldman Sachs Commodity Index.

Source: USDA, Economic Research Service.

Figure 15

Counterfactual simulation of CBOT wheat futures prices using GSCI as external market

Logarithm of the real price of wheat, deviation from trend

Observed

No real economic activity

No comovement

No precautionary demand

No net supply
33

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement, ERR-165

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-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

7/24/98 12/6/99 4/19/01 9/1/02 1/14/04 5/28/05 10/10/06 2/22/08 7/6/09 11/18/10 4/1/12 8/14/13

Note: KCBT = Kansas City Board of Trade. GSC I= Goldman Sachs Commodity Index.

Source: USDA, Economic Research Service.

Figure 16

Counterfactual simulation of KCBT wheat futures prices using crude oil as external market

Logarithm of the real price of wheat, deviation from trend

Observed

No real economic activity

No comovement

No precautionary demand

No net supply

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

1.2

7/24/98 12/6/99 4/19/01 9/1/02 1/14/04 5/28/05 10/10/06 2/22/08 7/6/09 11/18/10 4/1/12 8/14/13

Note: MGEX = Minneapolis Grain Exchange. GSCI = Goldman Sachs Commodity Index.

Source: USDA, Economic Research Service.

Figure 17

Counterfactual simulation of MGEX wheat futures prices using GSCI as external market

Logarithm of the real price of wheat, deviation from trend

Observed

No real economic activity

No comovement

No precautionary demand

No net supply
34

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increasing impact from precautionary demand beginning in spring 2010 captures these anticipated 

future export sales. 

Wheat futures basis convergence may account for the greater impact of precautionary demand 

shocks in 2010 and 2011. Garcia et al. (2012) find that the futures spread underestimated the true 

price of storage in 2009 and early 2010, especially for CBOT. This means that, as inventories accumulated and prices declined during this period, the futures term spread (the measured price of 

storage) did not increase as much as the true price of physical storage. Because it looked like the 

price of storage was not increasing as much as it ordinarily would from a pure increase in net supply, 

the model may have attributed part of the decline in prices to a decline in precautionary demand 

and, therefore, it would inaccurately attribute part of the increase in prices in 2010 to a subsequent increase in precautionary demand. Thus, the partial attribution of the 2010 price increase to 

increasing precautionary demand may be overstated. More of the observed effect may be due to net 

supply shocks.
35

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Conclusions

U.S. wheat futures markets have been criticized for a perceived inability to reflect underlying supplyand-demand conditions for wheat. Speculation, particularly by commodity index traders and other 

financial speculators, is often cast as the culprit for poor price discovery in wheat futures markets. 

This analysis uses a structural vector autoregression model to measure the relationship between 

U.S. wheat futures prices and four unique structural explanations: real economic activity, comovement induced by speculative trading, precautionary demand, and shocks to current net supply. The 

comovement-driven portion of wheat prices represents the impact of speculation that is unrelated to 

expected future prices. Such speculation is attributed to commodity index traders who are accused 

of causing recent periods of elevated prices in agricultural markets.

Wheat price spikes are fundamentally driven and strongly associated with shocks to current supply. 

Agricultural production remains susceptible to weather-related risk and other factors that cause 

unexpected variation in available supply. Additionally, supply shocks are compounded by expectations about future shocks; wheat price spikes are a combination of shocks to current supplyand-demand fundamentals and expectations about future prices as represented by the demand for 

inventories.

The analysis finds little evidence of a comovement-driven price effect associated with commodity 

index traders. The model is designed to capture comovement with nonagricultural markets as 

suggested by Tang and Xiong (2012). The analysis even allows comovement shocks to take precedence over wheat-specific shocks and finds negligible comovement effects at the frequency of the 

data. The absence of a comovement effect suggests that wheat futures markets have performed 

efficiently in the sense that wheat futures prices have reflected fundamental factors. Consequently, 

restrictions on commodity index trading are not likely to prevent future price spikes.

Unlike studies of other commodity markets using a similar approach, this study finds that broad 

trends in global commodity demand related to real economic activity matter less to wheat price 

determination than do its own supply-and-demand factors. This may be due to the nature of wheat as 

a staple cereal grain. While global wheat production and consumption is growing, per capita demand 

growth is decelerating even in the developing world (Alexandratos and Bruinsma, 2012). Emerging 

economies may not be as hungry for wheat as they are for industrial commodities.
36

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References

Adjemian, M.K., and A.D. Smith. 2012. “Using USDA Forecasts To Estimate the Price Flexibility 

of Demand for Agricultural Commodities,” American Journal of Agricultural Economics 94: 

978-995. 

Adjemian, M.K., P. Garcia, S.H. Irwin, and A.D. Smith. 2013. Non-convergence in Domestic 

Commodity Futures Markets: Causes, Consequences, and Remedies, Economic Information 

Bulletin No. 115, U.S. Department of Agriculture, Economic Research Service, August, www.ers.

usda.gov/publications/eib-economic-information-bulletin/eib115.aspx#.UxjX14XDt28

Ai, C., A. Chatrath, and F. Song. 2006. “On the Comovement of Commodity Prices,” American

Journal of Agricultural Economics 88:574-588.

Alexandratos, N., and J. Bruinsma. 2012. World Agriculture Towards 2015/2030: The 2012 

Revision, ESA Working Paper 12-03, Food and Agriculture Organization of the United Nations, 

June. 

Antle, J.M., and V.H. Smith. 1999. “An Overview of World Wheat Markets,” in J.M. Antle and V.H. 

Smith (eds.), The Economics of World Wheat Markets, CABI Publishing, pp. 1-20.

Antle, J.M., D. Hayes, S. Mohanty, P. Vavra, and V.H. Smith. 1999. “Long-Term Supply and Demand 

Trends: Whither the Real Price of Wheat,” in J.M. Antle and V.H. Smith (eds.), The Economics 

of World Wheat Markets, CABI Publishing, pp. 39-76.

Babcock, B.A. 2007. “Money for Nothing: Acreage and Price Impacts of U.S. Commodity Policy for 

Corn, Soybeans, Wheat, Cotton, and Rice,” in B.L. Gardner and D.A. Sumner (eds.), The 2007 

Farm Bill and Beyond, Washington, DC: AEI Press, pp. 5-20.

Babula, R.A., D.A. Bessler, and W.S. Payne. 2004. “Dynamic Relationships Among U.S. WheatRelated Markets: Applying Directed Acyclic Graphs to a Time Series Model,” Journal of 

Agricultural and Applied Economics 36:1-22.

Barberis, N., A. Shleifer, and J. Wurgler. 2005. “Comovement,” Journal of Financial Economics

75:283-317.

Buyuksahin, B., and J.H. Harris. 2011. “Do Speculators Drive Crude Oil Futures Prices?” The 

Energy Journal 32:167-202.

Carter, C.A. 2001. “Current and Future Trends in the Global Wheat Market,” in J. Ekboir (ed.), 

World Wheat Overview and Outlook 2000-2001: Developing No-Till Packages for Small-Scale 

Farmers, CIMMYT, pp. 45-51.

Carter, C.A., and C.L. Revoredo-Giha. 2007. “The Working Curve and Commodity Storage Under

Backwardation,” American Journal of Agricultural Economics 89:864-872.

Carter, C.A., G.C. Rausser, and A. Smith. 2011. “Commodity Booms and Busts,” Annual Review of 

Resource Economics 3:87-118.
37

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement, ERR-165

Economic Research Service/USDA

Carter, C.A., G.C. Rausser, and A.D. Smith. 2012. “The Effect of Ethanol Production on Corn 

Prices,” Working Paper, University of California, Davis.

Chambers, W. 2004. Forecasting Feed Grain Prices in a Changing Environment, Outlook Report

No. FDS-04F-01, USDA, Economic Research Service.

Chicago Mercantile Exchange. 2012. “Global Wheat Futures Market Share,” Working Paper.

Curtis, B.C. 2002. “Wheat in the World,” in B.C. Curtis, S. Rajaram, and H. Gomez-Macpherson 

(eds.), Bread Wheat: Improvement and Production, Rome: Food and Agriculture Organization of 

the United Nations.

Deb, P., P.K. Trivedi, and P. Varangis. 1996. “The Excess Co-movement of Commodity Prices 

Reconsidered,” Journal of Applied Econometrics 11:275-291.

Fattouh, B., L. Kilian, and L. Mahadeva. 2012. “The Role of Speculation in Oil Markets: What

Have We Learned So Far?” CEPR Discussion Paper No. DP8916, Center for Economic Policy 

Research. 

Flood, C. 2008. “Australia Wheat Hit by Drought,” Financial Times, June 17.

Frankel, J.A. 1986. “Expectations and Commodity Price Dynamics: The Overshooting Model,”

American Journal of Agricultural Economics 68:344-348.

Garcia, P., S.H. Irwin, and A.D. Smith. 2012. “Futures Market Failure?” Working Paper, University 

of California, Davis.

Goncalves, S., and L. Kilian. 2004. “Bootstrapping Autoregressions With Conditional 

Heteroskedasticity of Unknown Form,” Journal of Econometrics 123: 89-120.

Goodwin, B.K., and T.C. Schroeder. 1991. “Price Dynamics in International Wheat Markets,”

Canadian Journal of Agricultural Economics 39:237-254.

Goodwin, B.K., and V.H. Smith. 2009. “Harvest-Time Protein Shocks and Price Adjustment in U.S. 

Wheat Markets,” Journal of Agricultural and Resource Economics 34:237-255.

Gorton, G., and K.G. Rouwenhorst. 2004. “Facts and Fantasies About Commodity Futures,” 

Working Paper No. 10595, National Bureau of Economic Research, June.

Gray, R.W. 1961. The Relationship Among Three Futures Markets: An Example of the Importance of 

Speculation, Food Research Institute Studies 2:21-32.

Hurt, C., and M. Boehlje. 2008. “Financial Crisis Engulfs Agriculture,” Working Paper, Purdue

University, October 16.

Irwin, S.H., and D.R. Sanders. 2011. “Index Funds, Financialization, and Commodity Futures

Markets,” Applied Economic Perspectives and Policy 34:1–31.

Janzen, J.P., A.D. Smith, and C.A. Carter. 2013. “Commodity Price Comovement and Financial 

Speculation: The Case of Cotton,” Working Paper, University of California, Davis.
38

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement, ERR-165

Economic Research Service/USDA

J.P. Morgan. 2010. “The Wheat Price Spike of 2007/08: A Case Study of How Markets Resolved a 

Stock-Out in Physical Inventory,” in Global Commodities Research: Commodity Markets Outlook 

and Strategy, New York: J.P. Morgan, 5-9.

Kilian, L. 2009. “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in 

the Crude Oil Market,” American Economic Review 99:1053-69.

Kilian, L., and D. Murphy. 2013. “The Role of Inventories and Speculative Trading in the Global 

Market for Crude Oil,” Journal of Applied Econometrics, article first published online: 10 APR 

2013DOI: 10.1002/jae.2322

Kramer, A.E. 2010. “Russia, Crippled by Drought, Bans Grain Exports,” New York Times, August 6, 

pp. A1.

Liefert, W., O. Liefert, G. Vocke, and E. Allen. 2010. “Former Soviet Union Region To Play Larger

Role in Meeting World Wheat Needs,” Amber Waves, June, pp. 12-20, www.ers.usda.gov/amberwaves/2010-june/former-soviet-union-region-to-play-larger-role-in-meeting-world-wheat-needs.

aspx#.UxjbsIXDt28

Masters, M.W. 2011. “Testimony of Michael W. Masters Before the U.S. Senate,” in B. Lilliston and 

A. Ranallo (eds.), Excessive Speculation in Agriculture Commodities: Selected Writings from 

2008-2011, Minneapolis, MN: Institute for Agriculture and Trade Policy, 89-94.

McKenzie, A.M. 2005. “The Effects of Barge Shocks on Soybean Basis Levels in Arkansas: A 

Study of Market Integration,” Agribusiness: An International Journal 21(1): 37-52. 

Mohanty, S., W.H. Meyers, and D.B. Smith. 1999. “A Reexamination of Price Dynamics in the

International Wheat Market,” Canadian Journal of Agricultural Economics 47:21-29.

Pindyck, R.S., and J.J. Rotemberg. 1990. “The Excess Co-Movement of Commodity Prices,” 

Economic Journal 100:1173-1189.

Power, G., and D.V. Vedenov. 2009. “The Price Shock Transmission During the 2007-2008 

Commodity Bull Cycle: A Structural Vector Auto-Regression Approach to the “Chicken-or-Egg” 

Problem,” Selected paper, Agricultural and Applied Economics Association Annual Meeting.

Rigobon, R. 2003. “Identification Through Heteroskedasticity,” The Review of Economics and

Statistics 85:777-792.

Sanders, D.R., and S.H. Irwin. 2011. “New Evidence on the Impact of Index Funds in U.S.

Grain Futures Markets,” Canadian Journal of Agricultural Economics/Revue canadienne 

d’agroeconomie 59:519-532.

Senate Permanent Subcommittee on Investigations. 2009. “Excessive Speculation in the Wheat

Market,” Staff report, U.S. Senate.

Silber, W.L. 1981. “Innovation, Competition, and New Contract Design in Futures Markets,” Journal 

of Futures Markets 1:123-155.

Singleton, K.J. 2011. “Investor Flows and the 2008 Boom/Bust in Oil Prices,” Working Paper, 

Stanford University.
39

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement, ERR-165

Economic Research Service/USDA

Soros, G. 2008. “The Perilous Price of Oil,” New York Review of Books 55: 36.

Stiegert, K., and B. Balzer. 2001. “Evaluating the U.S. Wheat Protein Complex,” Working Paper No. 

FSWP2001-5, Food System Research Group, University of Wisconsin.

Stoll, H.R., and R.E. Whaley. 2010. “Commodity Index Investing and Commodity Futures Prices,”

Journal of Applied Finance, pp. 1-68.

Tang, K., and W. Xiong. 2012. “Index Investment and the Financialization of Commodities,” 

Financial Analysts Journal 68:54-74.

Taylor, R.D., and W.W. Koo. 2011. 2011 Outlook of the U.S. and World Wheat Industries, 2011-2020, 

Agribusiness and Applied Economics Report No. 680, Center for Agricultural Policy and Trade 

Studies, North Dakota State University.

Timmer, C.P. 2009. “Did Speculation Affect World Rice Prices?” Working Paper No. 09-07, 

Agricultural and Development Economics Division of the Food and Agriculture Organization of 

the United Nations (FAO - ESA).

Trostle, R. 2008. Global Agricultural Supply and Demand: Factors Contributing to the Recent 

Increase in Food Commodity Prices, Outlook Report No. WRS-0801, U.S. Department of 

Agriculture, Economic Research Service, July, www.ers.usda.gov/publications/wrs-internationalagriculture-and-trade-outlook/wrs-0801.aspx#.UxjdE4XDt28

UNCTAD. 2011. “Price Formation in Financialized Commodity Markets: The Role of Information,” 

Working Paper, United Nations Conference on Trade and Development.

U.S. Department of Agriculture, Economic Research Service. 2011. Wheat Outlook. Outlook Report 

No. WHS-11.

U.S. Department of Agriculture, Foreign Agricultural Service. 2011. “Production, Supply, and 

Distribution Online.” Online database, U.S. Department of Agriculture, www.fas.usda.gov/

psdonline/.

U.S. Department of Agriculture, Office of the Chief Economist. 2011. “Major World Crop Areas and 

Climatic Profiles,” Online database.

Von Braun, J., and M. Torero. 2009. Implementing Physical and Virtual Food Reserves to Protect 

the Poor and Prevent Market Failure, IFPRI Policy Brief No. 10, International Food Policy 

Research Institute.

Wegren, S.K. 2011. “Food Security and Russia’s 2010 Drought,” Eurasian Geography and 

Economics 52:140-156.

Williams J.C., and B.D. Wright. 1991. Storage and Commodity Markets. New York: Cambridge 

University Press.

Wilson, W.W., and D. Miljkovic. 2013. “Dynamic Inter-Relationships in Hard Wheat Basis Markets,” 

Canadian Journal of Agricultural Economics 61:395-416.
40

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement, ERR-165

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Working H. 1933. Price Relations Between July and September Wheat Futures at Chicago Since 

1885, Wheat Studies of the Food Research Institute 9: 187-238.

Working, H. 1970 “Economic Functions of Futures Markets,” in H.H. Bakken, Futures Trading in 

Livestock: Origins and Concepts, Chicago: Chicago Mercantile Exchange.

Wright, B.D. 2011. “The Economics of Grain Price Volatility,” Applied Economic Perspectives and 

Policy 33:32-8.
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Appendix: Model Specification, Identification, and 

Estimation

We use a structural vector autoregression (SVAR) model of the wheat market that captures 

commodity price formation as understood in the competitive storage model (Williams and Wright, 

1991) and the impact of financial speculation as put forward in the work of Tang and Xiong (2012). 

SVAR models the contemporaneous interrelationships between all variables included in the model. 

Our data vector yt includes four variables: (i) real economic activity, rea; (ii) the real price of the 

external market commodity, ext; (iii) the spread between distant and nearby futures prices for wheat, 

spr; and (iv) the real price of nearby wheat futures, pwt. Price variables are expressed as logarithms. 

We plot each of these variables net of seasonality and trend, including price and spread measures, 

for each wheat market in figure A1. 

Specification and Identification

The SVAR model is a system of equations relating current levels of each variable to past observations of all variables up to some finite lag p, a vector of exogenous variables xt

, and the structural 

shocks, ut, from all equations in the model. The model is: 

(A1) ( ) ( ) 2 ≡ − − −...− = + 01 2

p A L y A A L A L A L y Cx u t p t tt

where A and C represent the coefficients to be estimated and L represents the lag operator. The 

parameters and the structural shocks from this model cannot be directly estimated, so the SVAR 

model is related to a reduced-form vector autoregression model that can be estimated by ordinary 

least squares. The reduced-form model is: 

(A2) ( ) ( ) 2 ≡ − − −...− = + 1 2

p B L y I B L B L B L y Dx e t p t tt

where B and D represent the coefficients in the reduced-form model, I is the identity matrix, and et

are the reduced-form residuals. From this representation, the estimable reduced-form residuals are 

related to the structural residuals, 1

0

− e Au t t = . Therefore, identifying the structural residuals requires 

the identification of the matrix A0. 

We can expand the equation relating reduced-form and structural residuals to describe our identification strategy. This condition, applying our identification assumptions for the matrix A0, is: 

(A3)

This condition shows clearly that the reduced-form residuals are a weighted sum of the structural 

shocks. We note our interpretation of the structural shocks as being driven by real economic activity, 

comovement with external markets, precautionary demand, and net supply with the superscripts 

REA, CM, PD, and NS. 

et

rea

et

ext

et

spr

et

pw

































=

1 0 0 0

A21 1 0 0

A31 A32 1 A34

A41 A42 A43 1





























−1

ut

REA

ut

CM

ut

PD

ut

NS
































42

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Figure A1

Wheat market variables

Note: GSCI = Goldman Sachs Commodity Index. CBOT = Chicago Board of Trade. KCBT = Kansas City Board of Trade. 

MGEX = Minneapolis Grain Exchange. 

Source: USDA, Economic Research Service using data from Kilian (2009) and Commodity Research Bureau (2011). 

Response of economic activity index

-80

-60

-40

-20

0

20

40

60

January-1991January-1992January-1993January-1994January-1995January-1996January-1997January-1998January-1999January-2000January-2001January-2002January-2003January-2004January-2005January-2006January-2007January-2008January-2009January-2010January-2011

Real economic activity

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

January-1991

February-1992

March-1993

April-1994

May-1995

June-1996

July-1997

August-1998

September-1999

October-2000

November-2001December-2002

January-2004

February-2005

March-2006

April-2007

May-2008

June-2009

July-2010

August-2011

Crude oil price GSCI index value

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

January-1991

February-1992

March-1993

April-1994

May-1995

June-1996

July-1997

August-1998

September-1999

October-2000

November-2001December-2002

January-2004

February-2005

March-2006

April-2007

May-2008

June-2009

July-2010

August-2011

CBOT spread CBOT price

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

January-1991

February-1992

March-1993

April-1994

May-1995

June-1996

July-1997

August-1998

September-1999

October-2000

November-2001December-2002

January-2004

February-2005

March-2006

April-2007

May-2008

June-2009

July-2010

August-2011

KCBT spread KCBT price

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

1.2

January-1991

February-1992

March-1993

April-1994

May-1995

June-1996

July-1997

August-1998

September-1999

October-2000

November-2001December-2002

January-2004

February-2005

March-2006

April-2007

May-2008

June-2009

July-2010

August-2011

MGEX spread MGEX price
43

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We use a combination of three methods to identify A0. First, we normalize A0 so that we consider 

only the relative magnitude of the structural shocks (not the absolute level) by setting the diagonal 

terms in A0 to one. Second, we restrict the contemporaneous impact of the structural shocks in the 

first two equations in the SVAR by setting the above-diagonal terms in the first two equations equal 

to zero. The section titled “Identification” justifies the recursive ordering assumption. 

We cannot assume that either the A43 or A34 parameters in A0 equal zero. To identify both of these 

parameters, we employ the Identification through Heteroskedasticity technique described in Rigobon 

(2003). Underlying this technique is an assumption that the structural shocks exhibit heteroskedasticity. The competitive storage model of Williams and Wright (1991) supports this assumption since 

structural shocks in their model have a larger price impact when inventories are scarce relative to 

when they are plentiful. We consider heteroskedasticity through the second moments of the condition relating the reduced-form and structural shocks. Moving the A0 term to the left-hand side of 

equation A1, this condition is:

(A4) ( 0 ) = ≡= ( ) 0 0 Ω Σ '

t t var A e var u A A

Assuming the existence of volatile periods when inventories are scarce and a tranquil regime when 

inventories are plentiful generates additional equations that can be used to identify the parameters 

of A0. Equation A4 can be written as 0 0 Ω Σ = r' r A A , where the superscript r denotes the variancecovariance matrix calculated only using observations from one regime. So long as these variancecovariance matrices differ between the two regimes, there are twice as many equations to identify 

the model parameters. For more information about the procedure, see Rigobon (2003) and Janzen et 

al. (2013). 

Estimation Procedure

To estimate the structural shocks associated with real economic activity, comovement, precautionary 

demand, and net supply, we first estimate the reduced-form VAR model using ordinary least squares. 

We choose the number of lags in this model based on Akaike and Schwarz-Bayesian Information 

Criteria. These measures find that three lags are optimal. We include seasonal (monthly) indicators 

and a linear trend as exogenous variables, xt, in these regressions. 

To employ the Identification through Heteroskedasticity technique, we divide our time series into 

volatile and tranquil periods. Based on the competitive storage model, we select as volatile periods 

crop years where projected inventories for that crop year were low relative to consumption. Volatile 

periods in our model are those where USDA World Agricultural Supply and Demand (WASDE) 

projected stocks-to-use are below 20 percent for at least 3 months. We extract the estimated reducedform residuals, partition them by regime, and calculate residual variance-covariance matrices for 

each regime, Ωr. Using constrained optimization methods, we minimize the distance function 

0 0 Ω Σ− r' r A A defined by the conditions that relate reduced-form and structural variances to determine the parameters in A0 where Σr

 represents the structural shock variance from each regime r. In 

identifying the simultaneous impacts of precautionary demand and net supply shocks on prices and 

spreads, we restrict the magnitude of the weighting parameters so that the spread must be at least as 

responsive to precautionary demand shocks as it is to net supply shocks. The solution allows us to 

calculate values for the structural shocks in each period covered by our sample. 
44

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement, ERR-165

Economic Research Service/USDA

We present the relationship between observed wheat prices and the structural shocks using impulse 

response functions, historical decompositions, and counterfactual scenarios. The impulse responses 

represent the time path of the response of each variable in the model to a one-time, one-standard 

deviation structural shock, where the standard deviation of the structural shocks is calculated across 

all time periods in the sample. We generate confidence intervals about these impulse responses using 

1,000 iterations of the wild bootstrap technique developed by Goncalves and Kilian (2004). For 

the historical decompositions and counterfactual scenarios, we rely upon the interpretation of the 

reduced-form residual as a forecast error equal to a weighted sum of the structural shocks. We plot 

the weighted structural shocks representative of the contribution of each shock to the unexpected 

variation in each observed wheat price series. The sum of the four series in the historical decomposition is the observed wheat price in our dataset. For the counterfactual analysis, we take the historical 

decomposition and set one of the structural shocks equal to zero. Since the wheat price in our model 

is the logarithm of the real wheat price, the difference between the series shown in the counterfactual analysis is a log-difference approximating a percentage difference between wheat prices 

including the influence of a given structural shock and without that shock.