volatility in cereal prices: intra- versus inter-annual volatility

Volatility in Cereal Prices:Intra- Versus Inter-annual Volatility

Herve Ott1

(Original submitted December 2012, revision received March 2014, acceptedMay 2014.)

Abstract

Intra-annual (within crop year) price volatility and inter-annual (between cropyears) price volatility are measured for wheat, maize, rice, barley, oats and rye. Aset of explanatory variables is used in a pooled regression to explain variations inthese price volatilities. With low cereal stocks, supply (yield) shocks (defined hereas volatilities, as for the price volatilities) mostly influence inter-annual volatilitywhile other influential factors are the crude oil price and exchange rate. Cerealdemand and interest rate shocks combined with low stocks affect intra-annual vola-tility, while other explanatory factors include exchange rate and crude oil priceshocks. The derivatives market activity appears to have no significant effect oneither intra- or inter-annual volatility. In contrast, large cereal stocks and awell-functioning international cereal market reduce the effects of shocks in theexplanatory variables on both intra- and inter-annual volatilities.

Keywords: Agricultural commodities; cereal price volatility; instrumental variables;pooled GMM estimates; unit root tests.

JEL classifications: C26, C32, Q11.

1. Introduction

Agricultural commodities have exhibited significant boom-bust cycles since the mid-2000s. For instance, the hard red winter wheat (No. 1, Gulf of Mexico) traded atapproximately US$ 195 per metric ton in May 2007. Exactly 1 year later, it rose toUS$ 329 but then fell to US$ 220 by the end of 2008. Again, the wheat price almostdoubled from June 2010 to May 2011 reaching a new record high at US$ 355, fallingto US$ 264 exactly 1 year later. It again reached a new record high in November 2012

1Herve Ott is with the Thuenen Institut, Market Analysis, Brunswick, Germany and is also with

the European Commission, Joint Research Centre, Institute for Prospective TechnologicalStudies, Seville, Spain. E-mail: [email protected] for correspondence. The author would liketo thank first, the Editor, David Harvey, and the reviewers of the JAE who have improved the

writing and the content of the study. Also big thanks are due to Oliver von Ledebur for his com-ments during the 123rd EAAE seminar ‘Price Volatility and Farm Income Stabilisation; Model-ling outcomes and assessing market and policy based responses’ in 2012 in Dublin, Ireland. The

views expressed here are solely those of the author.

Journal of Agricultural Economics, Vol. 65, No. 3, 2014, 557–578doi: 10.1111/1477-9552.12073

� 2014 The Agricultural Economics Society

at US$ 361 and then fell more moderately to US$ 308 in April 2013. Other agricul-tural commodities, such as soybeans, maize and rice, exhibited similar volatility whichcannot be explained simply by seasonality. For wheat, Ott (2014) suggests that a newperiod of high volatility began in 2006 with price data through 2010. More recentprice developments confirm that a new era of high cereal price volatility began aroundthe mid-2000s. This new evidence has triggered renewed interest in an old subject,especially the causes of volatility. Gilbert and Morgan (2010, 2011) show that theextent of price volatility depends upon the following: (i) the variability of productionand/or consumption; (ii) the variability of stock demand; and (iii) the degree of elas-ticity of the supply and the demand. Since the Efficient Market Hypothesis may notgenerally hold, the uncertainty about future supplies and demand, information flowsand their reliability may also have an impact. There is less agreement, however, on thedecisive factors explaining the recent volatility surge.

Climate scientists note that climate change increases the occurrence and the areacovered by extreme weather events such as heat waves, cold winters, rainfall andfloods (see Peterson et al., 2012 for some examples). For instance, Hansen et al.(2012) argues that the strong la Ni~na of 2011 may have contributed to the heatand drought in southern USA and Mexico in 2012. Climate change and associatedincreased variability of agricultural production can be expected to have increasedprice volatility. However, Gilbert and Morgan (2011) downplay the global warmingargument and instead suggest that the negative effects of global warming on agri-cultural yield may be limited to some dry regions such as Australia and Africa bor-dering the Sahara and to some crops. They further argue that global warningcannot explain the volatility increase observed in the entire spectrum of agriculturalcommodities.

The revolution in information technology has radically changed the trading prac-tice of commodities on the spot and even more on the futures market, and thismight have impinged on the price volatility. Futures and options exchanges world-wide have shifted from conventional open-outcry markets to electronic tradingplatforms (e.g. GLOBEX, LIFFE Connect) thanks to progress in information tech-nology. Execution speed and transaction costs are considerably lower in the elec-tronic market (Shah and Brorsen, 2011). In turn, this has increased liquidity byincreasing the volume of trades. Furthermore disintermediation (no need for bro-kers) and globally decentralised markets (Domowitz, 2002) followed. The recentimplementation of automated algorithmic trading and particularly HFT (High Fre-quency Trading) might exacerbate price volatility due to large directional bets: onelarge trade can trigger a domino effect because of HFT aggressive sales to elimi-nate positions. Thus, the new highly liquid and decentralised futures commoditymarket with automated computerised trading orders might be more vulnerable tolarge price swings. Furthermore, since the mid-2000s, substantial volumes of invest-ment funds have flowed into the commodity derivatives market via index-basedswaps, a phenomenon called the financialisation of commodity markets (Gilbert,2010a). Numerous non-academic authors and practitioners (see Urbanchuk, 2011for a review) blame derivatives market activity for amplifying price oscillation onthe physical market. For instance, the FAO (Food and Agriculture Organization)(2010) mentions, “trading in futures markets may have amplified volatility in theshort-run”, while Urbanchuk (2011) blames the non-commercial index traders forexacerbating the volatility. While some of the arguments put forward in this litera-ture appear incoherent (see Irwin et al., 2009 for a critique), one possible coherent


558 Herve Ott

explanation may be through the price-discovery function of the futures market.The derivative market is more liquid than the spot market and so responds morequickly to new information. As index traders seek to take advantage of apparentshort-term trends in prices the derivative market is more subject to speculativebubbles and bursts. Due to uncertainty or chartist traders, the futures prices mayoscillate in a short period of time with higher frequency than normal. These swingswould be mimicked in the physical market, thus mirroring the futures marketexpectations. However, Ott (2014) does not find empirical support for financialisa-tion as a cause of intra-annual volatility.

Another explanation is that cereals have become a source of energy. High crude oilprices and new energy and agricultural policies, e.g. biofuel mandates in the USA andthe EU, have led to the partial integration of agricultural and energy markets. Whencrude oil prices rise above a certain threshold, the demand for biofuel feedstock risesrapidly with crude oil price, and when the prices fall below the threshold, the demandfalls sharply (Meyer and Thompson, 2010). Du et al. (2011) find some evidence of oilprice volatility being ‘imported’ into the maize market. Similarly, Serra and Gil (2013)recently confirmed the transmission of ethanol price volatility in the maize market. Asmaize and wheat are relatively close substitutes for feed and food grain, energy shocksmay simultaneously impact the wheat and maize markets. This can lead to an increasein variances of demand shocks, which, in turn, could increase volatility in agriculturalcommodity prices. However, the discussion based on the literature in Zilberman et al.(2013) provides clear evidence that the impact of biofuel only impacts the agriculturalcommodities when biofuel is competing with food crops for resources, such as landand water. Meyer and Thompson (2010) also argue that the net effect of crude oilprice on the commodity price volatility depends on the market context.

Obviously, common factors are at play as the commodity markets of energy, metaland agriculture simultaneously experience a new regime of high volatility. The rapideconomic growth in the emerging economies, particularly China, feeds rapidly grow-ing consumption of energy, metal and agricultural commodities which generatessimultaneous stress in most commodity markets, as explained in Gilbert and Morgan(2010, 2011). Regarding the grain market, Wright (2011) argues that volatilityincrease is primarily attributable to the historic low levels of world grain stocks(including China) from 2007 onward. Wright (2011) asserts that the strong Chineseconsumption was off-set by the run-down in Chinese stocks until 2007, which conse-quently shielded world markets from the demand pressure. From 2007 on, Chinesegrain stocks have reached a very low level, and the rest of the world has been unableto meet the Chinese demand due to the biofuel mandates in western countries. Whenan adverse weather event harms production, as has been the case since the mid-2000s,price peaks are the logical consequence and prices fall again when the production out-look improves. Wright (2011) also blames non-western governments for exacerbatingthe volatility issue by trying to insulate their local markets following the commodityprice shock with misguided trade measures, i.e. export bans and discontinuance ofimport taxes. The consumption habits of both poor and wealthy households appearto be very inelastic to prices and to the government interventions that have reducedsupply and demand elasticities, the combination of which can explain the volatilityincrease since 2007, according to Wright (2011). Serra and Gil (2013) show that lowstock levels significantly increase volatility.

Other common macro-factors such as the interest rate and the US dollar exchangerate may impinge on the volatility of agricultural commodities. First, Frankel (2006)


Volatility in Cereal Prices 559

argues that the real interest rate influences the incentive to store commodities. Forinstance, monetary contraction (higher real interest rate) implies higher real cost ofstorage and reduces the incentive to hold stocks. Thus, interest rate shocks can propa-gate variability in the demand for stocks. Serra and Gil (2013) find some evidence thatthe interest rate volatility has significantly impacted maize price volatility. Second, thevalue of the US dollar has a real effect on the price of commodities, as explained inGilbert (1989), essentially because most of the international trade in agricultural com-modities is denominated in US dollars, and accordingly, depreciating US dollars willfuel higher international demand and lower the stock-to-use ratio, which under thecondition of low stocks impinge on volatility. In a similar manner, exchange rateuncertainty may impinge on the volume traded. According to the empirical estimatesof Kandilov (2008), exchange rate volatility on trade has disruptive effects on the vol-ume traded in agricultural commodities, which in turn may fuel the price volatility ofthe given commodity.

Serra and Gil (2013) assess the factors that influence the volatility of maize bymeans of a multivariate generalised autoregressive conditional heteroskedastic specifi-cation. Balcombe (2011), on the other hand, uses time series and panel regressions,while Roache (2010) employs the new Spline-GARCH methodology. However, thespecifications of their models may be questioned. First, the autoregressive term maynot be justified in the specification of a model analysing factors driving volatility.Moreover, numerous potential factors are not included in the specification, amongthem the thinness of the trade of the international commodity market, the possibleinfluence of the derivatives market (Balcombe, 2011; Serra and Gil, 2013) or thebehaviour of the exchange rate (Serra and Gil, 2013). Additionally, Roache (2010)does not consider stock levels in his multifactor model, while Ott (2014) attempts tofill the gap by investigating a large set of variables.

This study focuses on the cereal sector as a whole. In contrast, Ott (2014) derivescommodity-specific conclusions, for wheat, maize, rice, soybeans, coffee and cotton.Rather than using time series econometrics, this study employs panel econometrics.The pooled sections include all cereals (wheat, maize, rice, barley, oats and rye).The consequent homogeneity assumption across the cereals allows first, the cerealsector as a whole to be analysed, and second, stable and reliable coefficients to beestimated. In this paper, the extent of volatility increase and the evolution of thedriving factors over time are not investigated. Additionally, the analysis is based onreal data rather than on nominal data. More importantly, however, this studyinforms on the controversies regarding factors driving volatility by proposing tomeasure intra-annual volatility (within crop year) vs. inter-annual volatility(between crop years). Indeed, depending on whether inter- or intra-annual volatilityis investigated as the explained variable, the driving factors may differ. As a result,this study proposes to fill the gap by (i) measuring intra- and inter-annual volatility,and (ii) finding empirical evidence on the quantitative importance of each type ofvolatility.

The remainder of the paper is organised as follows. Section 2 discusses the vari-able-series: measuring volatility, building of the proxies, and the stationary propertiesof the proxies. Section 3 presents the empirical estimation method and an empiricalinterpretation of the results. Finally, section 4 summarises the results and presents theconclusions.


560 Herve Ott

2. Data and Model Specification

2.1. The international crop year

The sources for the data are shown in Table 1. Wheat, maize and rice provide two-thirds of human food consumption according to FAO (2014). Barley, rye and oats arealso included as data are available, and the increased number of observations improvesthe estimates. Oats and rye are hardly major international grains and the internationalrice market is noticeably different from the other cereals. However, in the empiricalestimates the idiosyncrasies of specific markets are controlled for by including cerealmarket conditions (scarcity indicator, stock-to-use ratio, thinness of the internationaltrade, and Herfindahl index), see section 3. The first difficulty with this approach is todefine an international crop year since the production of cereals occurs in both thesouthern and northern hemispheres. The major producers of cereals for the last50 years were identified by calculating their share of production relative to the totalproduction of cereals over the last 50 years. The major producers include the USA, theEU-27 (the 27 Member States of the European Union), the FSU (Former SovietUnion), Canada, Australia, India, China, and for rice also Indonesia, Vietnam andBangladesh. The starting month of the crop year for each country producer is reportedby the USDA FAS (US Department of Agriculture, Foreign Agricultural Service).Each first month of the crop year was assigned its share in world production for the fivemost important producers. To calculate the barycentre, the reference month was April.For instance, a barycentre of 1 (3) refers to May (July) of the international crop year.The summary of the results are given in Table 2, and the barycentres of 3.09, 3.16, 3.06and 2.98 for wheat, barley, oats and rye, respectively, show that the international cropyear, defined as the barycentre, begins in July for these four cereals. For maize, theinternational crop year begins in August and finally for rice the barycentre is closest toDecember. Instead of calculating the barycentre relative to the share of the world pro-duction, it was also calculated with respect to the total exports (results not reported).For all cereals the beginning of the crop year was unchanged except for rice. Thailandwas clearly the dominant exporter in a relatively small international market, whichmoves the barycentre to January. As a result, for rice the crop year January to Decem-ber was used, although this is very similar to a December to November crop year.

2.2. Measurement of volatility

Using a sample of more than 50 years requires that all prices should be deflated to acommon measure. Svedberg and Tilton (2006) and Cuddington (2010) argue thatdeflating with the CPI (consumer price index) leads to biased real commodity prices.Here, monthly cereal prices are deflated by the US PPI (producer price index) as inGilbert and Morgan (2010, 2011) prior to the calculation of the intra-annual andinter-annual volatility. Intra-annual volatility measures the dispersion of cereal priceswithin the crop year. The typical measure is the standard deviation (r) of log changesin monthly prices within the crop year, i.e.:

ry ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

10

X12m¼2

ln Py;m=Py;m�1

� �� ly� �2

vuut ;

where ly = 1/11 ln(Py,12/Py,1) and Py,m is the price in monthm of crop year y. This mea-sure indicates the uncertainty that farmers face in their planting decisions. Typically,



Table

1

Sourceofdata

Series

Description

Sample

Freq.

Unit

Source

Price

bycommodity

Wheat

CBOT1Softredwinter#1,Frontcontract

(W1),USA

1960–2013

Month

US$/m

onth

Open

FinancialData

Project

2

Maize

CBOTCorn

yellow#1,Frontcontract

(ZC),USA

1960–2013

Month

US$/m

onth

Open

FinancialData

Project

Rice

Survey

exportBangkokWhitemilled5%

broken,Thailand

1960–2013

Month

US$/m

onth

WorldBank(Pinksheet)

Barley

WCE3IC

E4Feedwestern

#1(BW),farm

erprice,Canada

1960–2013

Month

US$/m

onth

WorldBank(Pinksheet)

Oats

CBOTOats#1,Frontcontract

(01),USA

1960–2013

Month

US$/m

onth

Open

FinancialData

Project

Rye

Averagefarm

erprice,Germany

1960–2013

Month

€/m

onth

Statistiches

Bundesamt(Pendelliste)

Totalopen

interest(allmaturities)

Wheat

CBOTSoftredwinter#1,USA,1st?

6thexpiration

1960–2013

Month

Contract

Open

FinancialData

Project

Production,Areaharvested,Exports,Endingstock,BeginningStock,

Totaldistribution,Feeddomesticconsumption,ConsumptionFSI

(feed,seed,industrial),Domesticconsumption,foreach

country,

worldandbycommodity

Wheat,maize,rice,barley,oats,ryequantity

inweight

1960–2013

an.crop

1000mt

USDA

FAS5

Commonseries

andmacrovariables

USPPI

Producerprice

index

finished

goods(total)1980=100

1960–2013

Month

Index

USBLS6

USCPI

Consumer

price

index

allurbanconsumersallitem

s1980=100

1960–2013

Month

Index

USBLS

Deflator

USim

plicitprice

deflator:gross

domesticproduct

1980=100

1960–2013

Month

Index

USBEA

7

GDP

Worldgross

domesticproduct,currentprices

1960–2013

An.

calendar

Index

WorldBank

GDPpc

WorldGDPper

capitaGDPdivided

bymid-yearpopulation

1960–2013

An.

calendar

Index

WorldBank

Crudeoil

Brent,Dubai,WestTexasInterm

ediate

(averagespot

price

equallyweight)

1960–2013

Month

US$/bbl

WorldBank(PinkSheet)

Exch.FX

TradeweightedUS$index:broad1980=100

1960–2013

Month

Index

BGFRS8

Interest

1yearUSGovtTreasury

bill:secondary

market

rate

1960–2013

Month

%BGFRS

Notes:

Abbreviationandsource:

1ChicagoBoard

ofTrade;

2http://w

ww.quandl.com;3Winnipeg

CommodityExchange;

4IntercontinentalExchange;

5USDepartmentofAgriculture:ForeignAgriculturalService;

6USDepartmentofLabor:BureauofLaborStatistics;

7USDepartmentofCommerce:

BureauofEconomicAnalysis;

8Board

ofGovernors

oftheFederalReserveSystem

.


562 Herve Ott

Table

2

Cropyearandbarycentrewithweightingsrelativeto

worldproduction(1960–2

012)

star

ng cr

op y

ear

April

May

June

July

Augu

stSe

ptem

ber

Oct

ober

Nov

embe

rDe

cem

ber

Janu

ary

WHE

ATan ihC

a dan aC7 2-UE

A SUai dnI

se irt nu ocFS

Uw

eigh

tba

ryce

ntre

●

MAI

ZEco

untr

ies

Braz

ilUS

A, E

U-27

Chin

aFS

Uw

eigh

tba

ryce

ntre

●

RICE

mant eiV,ani hCai dnI

hs edal gna Bse irt nuo c

Indo

nesi

aw

eigh

tba

ryce

ntre

●

BARL

EYaila rtsu A

adan aC7 2- U E

ASUse irt nu oc

FSU

wei

ght

bary

cent

re●

OAT

Saila rtsuA

adan aC7 2- UE

AS Use i rtnu oc

FSU

wei

ght

bary

cent

re●

RYE

a danaC72-UE

ASUseirtnuoc

Turk

eyFS

Uw

eigh

tba

ryce

ntre

●

7.67

46.3

%

2.983.

06

4.0%

91.9

%1.

8%

%3.3%1. 01

% 1.06%3 .81

%1 .3% 7.7

% 8.9 6% 7.5

3.16

10.9

%

54.1

%

3.09

%5.5%7.64

%1.41%6.5 23.7

%

20.8

%5.

7%

4.17



farmers can substitute different grains relatively easily, especially between cereals. Thehigher the intra-annual volatility, the more difficult the optimal planting choice will be.

Inter-annual volatility, in contrast, measures the dispersion of cereal prices betweencrop years. Following Sarris (2000), the monthly prices were averaged over the respec-tive crop year of each cereal. However, using the same formulae of volatility as forintra-annual volatility would cause problems. First, measuring inter-annual volatilityin a rolling-window framework would break an important assumption of regressioneconometrics: the independence of the observations. Second, calculating a standarddeviation every 5 years (the smallest possible time span) gives only 10 independentobservations for each cereal, for a total of 60 observations which would be restrictivefor reliable multivariate regression estimates, especially where IV (instrument vari-able) techniques are needed to treat endogeneity. Moreover, the explanatory factors(regressors) would also need to be averaged over five crop years. A lot of informationwould have been lost, and comparing the two regressions with different frequencies inthe explanatory factors would have been another challenge. As a consequence, inter-annual volatility is measured as the price deviation (DEV) between crop years, that is,

DEVy ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðPy �MA5yÞ2

q;

where Py is the crop year price and MA5y is the 5 year moving average. By defininginter-annual volatility as the deviation of the price level relative to the 5 year movingaverage generates independent observations and almost as many observations as forintra-annual volatility. The standard deviation of the log change over five crop yearswas also calculated every 5 years and compared to the volatility DEVy every five cropyears, which clearly shows that DEVy tracks relatively faithfully the standard devia-tion of the log change (result available on request). This measure (DEVy) supposesthat agents can correctly forecast the long-term price trend, here assumed as the5 year moving average, but not the deviation around this moving trend. Thus, it indi-cates the risk borne in storage over crop years, for instance, or the risk borne by farm-ers in their long-term investment decisions such as the purchases of machinery andequipment. As both farmers’ and storers’ revenues depend on the crop price level,large deviations around the forecast trend price implies greater uncertainty.

Figure 1 shows the intra- and inter-annual volatility for wheat, maize, rice, barley,oats and rye. The chronology of the inter-annual volatilities of all six cereals appearsto show two distinct periods of high volatility – the beginning of the 1970s and the late2000s. With respect to intra-annual volatility, these two periods are less pronounced,especially for rye where the beginning of the 1970s high volatility period does notappear at all. Wheat, maize and barley experienced, in addition, a period of highintra-annual volatility in the late 1980s, beginning of the 1990s and in the mid-1990s,respectively. Volatility (intra- and inter-) has apparently been high since themid-2000s, especially for rye, though in contrast has been low for rice. Finally, periodsof high intra-annual volatility seem to be longer lasting than periods of high inter--annual volatility. Notice that the indicated years are crop years, meaning that, forexample, 2012 refers to the crop year July 2012 to June 2013 for wheat, barley, oatsand rye, crop year August 2012 to July 2013 for maize, and crop year January 2012 toDecember 2012 for rice in all figures and in the remainder of this paper.

Table 3 summarises the basic statistics of the two volatilities. Inter-annual volatilityis, on average, higher than intra-annual volatility. Rye exhibits the lowest intra-annual volatility among the cereals. Both volatilities are far from normally


564 Herve Ott

distributed, though individually inter-annual volatility of oats nears normal distribu-tion. Intra-annual volatility is moderately right skewed while inter-annual volatility iseven more so, meaning that most observed volatilities are concentrated to the left ofthe mean, with extreme values to the right. Furthermore, both volatilities are lep-tokurtic with observed volatilities concentrated around the mean and displayingthicker tails than normal, reflecting a higher probability for extreme values. As a con-sequence, the regression analysis in the next section is undertaken with logarithmicvolatilities.

2.3. Model and explanatory variable specification

Production shocks are proxied by the volatility of yields, which in turn are typicallyinfluenced by weather shocks. Weather shocks occur within the crop year and in the

Wheat Maize

Rice Barley

Oats Rye

0

0.2

0.4

0.6

0.8

1

1.2

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

scale intra-annual volatility

scale inter-annual volatility

intra-annual inter-annual

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

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1984

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1986

1987

1988

1989

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1991

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1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012


scale inter-annual volatilityintra-annual inter-annual

0

0.5

1

1.5

2

2.5

3

3.5

4

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

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2003

2004

2005

2006

2007

2008

2009

2010

2011

2012




0

0.05

0.1

0.15

0.2

0.25

0.3

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012


scale inter-annual volatilityintra-annual inter-annual

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

1959

1960

1961

1962

1963

1964

1965

1966

1967

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2012




0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

1959

1960

1961

1962

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2012




Figure 1. Intra- and inter-annual volatility from crop year 1959 to 2012



case of cereals should not normally affect the following crop year harvest. For illustra-tion, suppose that in a given crop year t the annual price equals a normal averageprice. In the next crop year t + 1 a negative weather shock (optimal weather condi-tions) will drive the cereal price to a peak (low level). Typically, the inter-annual vola-tility of the cereal price considered will surge. Thus, supply shocks are crucial driversof inter-annual volatility. The dataset includes variations in the area harvested, whichdepends partly on weather shocks and also reflects changes in farmers’ planting deci-sions. As with the yield shock, this variable should drive mostly inter-annual volatilitybecause it is typically confined to the crop year. Overshooting of supply from onecrop-year to the next – proxied by the variability in the area harvested – increases theinter-annual volatility.

In contrast, demand shocks (cereal demand, global commodity demand) and stockdemand shocks (interest rate) propagate into higher volatility within the crop year. Ashort-lived demand shock which ceases before the start of the following crop year canaffect the intra-annual volatility. A longer-term shock which spreads beyond the cropyear is also likely to affect intra-annual volatility but for more than 1 year. Crude oilprice shocks and exchange rate shocks can both affect intra-annual volatility and canbe linked to demand side shocks as well as to supply side shocks. However, mediumand longer-term movements (and not shocks) of crude oil price and exchange ratesmay also have an impact on the oscillation of the average crop year price from onecrop year to the next. Typically the average crop year price is affected by the cost ofenergy (crude oil) because producers are confined in the crop year cycle production(planting, harvesting and selling). By the same token, the international marketingchain of agricultural commodities relies on the crop year. Thus, medium and long-term exchange rate appreciation (depreciation) is integrated into the crop year price,which in turn might affect inter-annual volatility.

Finally, some conditions prevailing in the cereal market might be a catalyst to theseshocks. First, the state of the stock in the cereal market might be the prerequisite con-dition for a shock to materialise into cereal price volatility. In particular, an emptystockpile cannot act as a buffer to supply or demand shocks in the event of a low

Table 3

Characteristics of the intra- and inter-annual volatility

Volatility Cereal Obs. Min Max Mean Std. dev. Skewness Kurtosis

Intra-annual All 323 3.77E-5 0.295 0.042 0.041 1.78 7.92

Wheat 54 7.38E-4 0.150 0.043 0.035 1.10 3.95Maize 54 2.96E-4 0.171 0.048 0.043 1.33 3.92Rice 53 1.72E-4 0.295 0.049 0.048 2.71 14.31

Barley 54 3.77E-5 0.138 0.042 0.038 1.03 3.08Oats 54 3.50E-4 0.178 0.047 0.043 1.30 4.42Rye 54 1.11E-4 0.150 0.023 0.030 2.67 10.31

Inter-annual All 318 9.54E-5 3.606 0.142 0.276 7.78 85.87Wheat 53 0.014 1.017 0.151 0.167 3.07 15.24Maize 53 2.22E-3 0.377 0.097 0.086 1.33 4.20Rice 53 9.54E-4 3.606 0.398 0.576 3.76 19.95

Barley 53 1.85E-3 0.283 0.068 0.064 1.31 4.53Oats 53 2.29E-3 0.174 0.061 0.039 0.52 2.99Rye 53 3.69E-3 0.368 0.078 0.078 1.94 6.74


566 Herve Ott

harvest so the transmission shock to cereal price volatility might be real. In contrast,large stocks allow mitigating supply or demand shocks and so the transmission to vol-atility may not happen. Second, a deep and well functioning international market withlarge trade volumes, smooths spatial re-allocations and can reduce the consequence ofshocks on cereal price volatility, though whether this affects inter- or intra-annual vol-atility is an empirical question. Third, the highly liquid derivative market and thenumerous traders and their High Frequency Trading may cause larger short-termprice swings and so affect intra-annual volatility. However, the derivative market mayalso reduce price swings due to its price discovery function, and so stabilise price overcrop years (inter-annual volatility) or even within crop years (intra-annual volatility).

Shocks are defined here as volatilities (as opposed to movements in the raw vari-able). The volatility of the explanatory variable-series (e.g. yield shock, consumptionshock) was determined by calculating their volatility using the methods explained insection 2.2. When the variable-series were monthly (respectively annual), the intra-annual volatility (respectively inter-annual volatility) was chosen to proxy the shock.The frequency of the raw variable-series is given in Table 1. All other variable-serieswere transformed to crop years by averaging the appropriate 12 monthly data. Thedata on production, consumption, and stocks were already by crop year. GDP, whichin its raw form is by calendar year was unchanged for rice but transformed artificiallyto crop year by summing 7/12 of current calendar year t with 5/12 of the next calendaryear t + 1 for crop year July to June (crop year August to July).

The proxies chosen as the potential explanatory variables are:

1 Yield shock should reflect weather shocks (and thus climate change effects) of eachcereal (wheat, maize, rice, barley, oats, rye). Another proxy, area harvested shocks,is included (historical series for area planted were not available), to reflect changesin producers’ decisions.

2 Demand shocks of the six cereals are proxied by the following: (i) domestic feedconsumption shock; (ii) consumption FSI (feed, seed and industrial) shock; and(iii) domestic consumption (total use) shock.

3 Stock demand shock is supposed to be driven by interest rate shocks. FollowingFrankel (2006), the real interest rate is defined as the 1 year Treasury bill interestrate minus the previous year’s US CPI inflation rate.

4 Shocks occurring in the overall world commodity market due to emerging econo-mies like China and India are proxied by world GDP2 shock. In addition, the influ-ence of neighbouring markets connected to commodities like energy and currencymarkets were also investigated in terms of shocks and price movements, i.e. theprice in its raw form. Thus, the proxies are: crude oil price3 and US effectiveexchange rate expressed in terms of shocks and price movements. Regarding theeffect of bio-fuel mandates, as shown in Meyer and Thompson (2010), the demandfor feedstock to produce ethanol does not drive the volatility of food commodities,but rather the price of crude oil (threshold effect).

5 Finally, following Gilbert (2010b), the influence of the derivatives market is proxiedby open interest of all market participants. Soft red winter wheat and yellow maizeCBOT contracts were considered. The Dow Jones-UBS and the Goldman SachsS&P GSCI commodities indexes do not contain rice, barley or oats because they

2The world GDP was deflated by the US GDP deflator.3The price of crude oil was deflated by the US CPI (consumer price index).



are not liquid enough, and there is no derivative contract for rye. Consequently, aweighted open interest of soft red winter and corn contract was constructed withequal weight for each contract. This 50% weight replicates approximately the rela-tive weight of maize and wheat (including hard red winter) within the S&P GSCIcommodity index.

Shocks might only materialise into higher cereal price volatility in an unfavourablecereal market environment such as: low stocks, low stock-to-use ratio, dysfunctionaland thin international cereal market. Consequently, the proxies above were multipliedby: (i) a scarcity indicator, i.e. the inverse of the ending stock level of each cereal, as inGeman and Ohana (2009), or the stock-to-use ratio to also take into account the even-tual pressure of the demand, (ii) the thinness of the international cereal trade volume,i.e. the inverse of the export share (total export volume divided by world production)which should take into account trade restriction policies (export bans) or structurallythin international markets, (iii) the Herfindahl index, i.e. market concentration indica-tor based on the number of exporting countries.

2.4. Unit root tests

Unit root tests were performed to assess the stationarity of the variables. The Breitungpanel unit root test (Breitung, 2000; Breitung and Das, 2005) was used as it showssubstantially more power than other panel unit root tests. In the null hypothesis allcereals contain a unit root. The test also allows the inclusion of a fixed effect and atime trend. When a deterministic time trend is included, the alternative hypothesissupposes that the series is stationary around a trend. When no time trend is included,the alternative hypothesis supposes that the series is stationary around a mean. Theresults are reported in Table 4, and the p-values show that all variable-series are sta-tionary or trend stationary at the 10% confidence level at least.

3. Empirical Results

The purpose of this analysis is to derive an overall conclusion for the cereal sector andnot for each cereal specifically. Two possible alternatives were considered. The firstwould have been to estimate each cereal separately in a time-series framework (hetero-geneous setting) and then to average the coefficient estimates as suggested in Pesaranand Smith (1995): the so-called mean group estimator (MGE).4 The second was to usepooled estimators (homogeneous setting). However, neglecting slope heterogeneitycan lead to inconsistency as shown in Robertson and Symons (1992). The primarysource of the inconsistency is due to the autoregressive term (lagged dependent vari-able) as shown by Pesaran and Smith (1995). In the static case, assuming a commonslope coefficient does not lead to major inconsistency. This study shows that using anautoregressive model to analyse the factors driving commodity price volatility is notjustified (see Table 5, Wooldridge Wald test). Furthermore, the lack of robustness of

4Credit used to be given to this option at the beginning of the empirical investigation because

the null hypothesis of coefficient equality among cereals was rejected at 5% for both intra andinter regressions; the Roy-Zellener test for poolability as suggested by Baltagi (2001) was per-formed. The heterogeneous setting was eventually abandoned due to the unreliability and insta-

bility of the different coefficient estimates.


568 Herve Ott

the parameter estimates in time series estimators (heterogeneous setting) has beenobserved in numerous applied studies (e.g. Baltagi and Griffin, 1997). Even in adynamic setting the pooled estimators outperform their heterogeneous counterparts(e.g. MGE) in terms of stability, reliability and out-of-sample forecasting propertiesas shown in Baltagi et al. (2000).5 Highly unstable and unreliable cereal coefficientestimates would have been a major drawback as the purpose of the study is to cometo a robust conclusion for the cereal sector as a whole. All these elements support theuse of panel econometrics for this case.

Cereal idiosyncrasies are controlled including the specific market environment,namely: the scarcity indicator, the stock-to-use ratio, the thinness of the international

Table 4

Breitung panel unit root tests

Variable-series Time trend Statistics: k* P-value

Intra-annual volatility (log) No –3.94 0.00

Yes –5.98 0.00Inter-annual volatility (log) No –11.30 0.00

Yes –10.20 0.00

IV1: cereal price: forward minus current No –6.87 0.00Yes –6.77 0.00

IV2: cereal price: forward over current No –9.97 0.00

Yes –8.46 0.00IV3: cereal price: forward over current (log) No –7.17 0.00

Yes –3.11 0.00IV4: level of beginning stock (log) No –1.67 0.04

Yes –1.61 0.05Scarcity indicator 9 yield shock (log) No –1.44 0.07

Yes –1.63 0.05

Scarcity indicator 9 domestic feedconsumption shock (log)

No –1.24 0.10Yes –1.63 0.05

Scarcity indicator 9 real interest rate shock (log) No –4.52 0.00

Yes –4.44 0.00Thin international cereal trade 9 US exchangerate shock (log)

No –5.99 0.00Yes –6.08 0.00

Stock-to-use ratio 9 US exchange rate (growth rate) No –0.61 0.27Yes –1.31 0.09

Scarcity indicator 9 crude oil price shock (log) No –0.62 0.27Yes –1.35 0.09

Thin international cereal trade 9 crude oilprice shock (log)

No –2.69 0.00Yes –3.23 0.00

Thin international cereal trade 9 crude oil

price in level (log)

No –1.34 0.09

Yes –3.14 0.00Wheat and corn open interest volatility (log) No –6.15 0.00

Yes –3.37 0.00

Note: *Lambda robust to cross-sectional correlation.

5Baltagi et al. (2000, p. 125) argue, “even with a relatively long time series, heterogeneous mod-els [. . .] tend to produce implausible estimates with inferior forecasting properties. The efficiency

gains from pooling appear to more than offset the biases” due to neglected heterogeneity.



cereal trade, and the Herfindahl index. For instance, oats, rye and even rice are hardlymajor international grains, though rice is a major cereal but its international market isnoticeably different to the other major cereal markets. However, excluding thembecause their production is low and the volume traded on the international market ismodest does not seem justified. The low export share (thinness of the internationalrice market) should pick up this idiosyncrasy. Other aspects of the idiosyncrasies ofwheat, maize, rice, barley, oats and rye are picked up by the fixed effect and the cer-eal-specific time trend. Finally, intra-annual volatility and inter-annual volatility wereregressed against all potential explanatory variable-series. Initially, the regression wasspecified in its most heterogeneous form (cereal-specific time trend and fixed effects),and the time trend was only removed under the condition that it was statistically insig-nificant at any conventional level and did not alter the coefficient estimates.

The two-step GMM (generalised method of moment) estimator (Hansen, 1982) wasused by pooling the different cereals. The performance of the pooled estimatordepends crucially on the treatment of the endogeneity issue, as argued in Baltagi et al.(2000). Indeed, greater price volatility (i.e. more risk) leads rational agents (e.g. agro-industrialists) to increase stocks to be able to meet their needs without fearing the riskof disruption (convenience yield) (see for instance Pindyck, 2004). Thus, the stock isendogeneous to volatility. The endogeneity of the scarcity indicator (inverse of stocklevel) multiplied by the supply or demand shocks and the volatility of the open interest(derivatives market) was treated by means of IV techniques. The set of IVs chosen is‘basis prices’ (forward price minus current price) in different forms to gain more IVsand so avoid the under-identification problem. The full set of IVs is reported inTable 4 under the heading ‘variable-series’. The quality of the IVs set depends on itsrelevance and validity. First, regarding the relevance of the IVs. The price basis is atrigger for storers to change stock levels. If the price basis is high (normally associatedwith a sharp contango situation), then there is an incentive for hoarder (or specula-tors) to stockpile, and vice versa. Thus the chosen IVs and the level stored (stocks) arecorrelated. Second, valid IVs are orthogonal with the explained variable-series (vola-tility of the cereals prices). Pindyck (2004) shows a relationship between volatility andprice level (when the stocks increase due to increased volatility, the spot priceincreases as well), but not with the price basis. So there is no reason to suppose thatthe price basis will be systematically correlated with price volatility.

Table 5

Inference tests of the two regressions

Regression Test d.&d.f. Stat. H0 hypothesis P-val.

Intra-annual Wooldridge Wald test F(1,5) 0.10 Errors: no serial

correlation

0.77

Kleibergen-Paap rank LM v2(3) 117.6 IV setunder-identified

0.00

Hansen J over-identification v2(2) 0.18 IV set valid(orthogonal errors)

0.91

Inter-annual Wooldridge Wald test F(1,5) 0.06 Errors: no

serial correlation

0.82

Kleibergen-Paap rank LM v2(4) 18.72 IV set under-identified 0.00Hansen J over-identification v2(3) 1.00 IV set valid

(orthogonal errors)0.80


570 Herve Ott

In the intra-annual regression, the IV – non log of forward minus spot price – didnot add any information and was therefore dropped to improve the quality of the IVset. The relevance and validity of the IV set was confirmed. As reported in Table 5,the null hypothesis of under-identification of the Kleibergen and Paap (2006) rankLM test is rejected at 5% for both regressions. The orthogonality of the errors cannotbe rejected at any conventional level according to the Hansen (1982) J over-identifica-tion test. Although the standard errors are consistent, the absence of serial correlationwas checked by means of the Wooldridge (2002) Wald test to show that an autore-gressive term in the specification is not justified, contrary to Balcombe (2011) andSerra and Gil (2013). The post-regression tests reported in Table 5 imply that theregression is well specified, and the case of endogeneity was well addressed, suggestingthat the coefficient estimates are reliable.

The results of the intra-annual and inter-annual volatility specifications arereported in Table 6 and the figures in parentheses are the respective P-values underthe assumption that the coefficient estimate equals zero. The centred6 R2 equals0.21 and 0.25 for the intra-annual and inter-annual volatility, respectively, whichimplies a relatively good degree of explanatory power for a panel with six sections(cereals). The signs of the coefficient estimates are consistent with expectations, withthe exception of the open interest rate (derivative market) volatility, which is nega-tive for intra-annual volatility and positive for inter-annual but statistically insignif-icant at any conventional level in either regression. Shocks in world raw materialdemand, proxied by the volatility of the GDP, are not significant in either regres-sion. Finally, the different factors multiplied by the Herfindahl index are also notsignificant.

The empirical results show clearly that the conditions faced by the cereal marketwhen a shock occurs play a major role. Large stocks (low scarcity indicator) as well asa deep and well-functioning international trade market (large export shares relative toproduction) have a stabilising effect on both within- and between-crop year prices.First, large stocks can absorb both demand and supply shocks, acting as a buffer toweaken price movements. This conforms to the theory of storage pioneered by Work-ing (1949) and applies to both intra- and inter-volatilities. Price dispersions within acrop year are less frequent when stocks are abundant since larger stocks moderatetraders’ expectations of the consequences of news on supply or demand shocks. Fur-thermore, the large grain reserve carried from the current to the new crop yearsmoothes the price and thus lowers the inter-annual volatility. Similarly, as stockssmooth inter-temporal allocations of cereals, so does trade smooth spatial re-alloca-tions. This, in turn, mitigates international price movements and thus volatility, asargued by Jacks et al. (2011). International trade lowers volatility of international cer-eal prices whatever the time horizon (within or between crop years), and thin tradeexchanges render the market vulnerable to any kind of shock. The inverse of theexport share proxies structural thinness of a given cereal market and its possiblechange to a more international and integrated market over time. It also proxies theshort-term disruption of markets due to sporadic policy intervention such as exportbans or other temporary distortive measures that impede the flow of internationaltrade movements. Thus, the significance of some factors when multiplied by theexport share in both regressions (intra- and inter-annual volatility) is not unexpected.

6The centered R2 is relevant as constants (fixed effects) are included in the regression.



Table 6

GMM panel coefficient estimates

INTRA annualvolatility

INTER annualvolatility

Cereal supply shock (volatility)Scarcity indicator 9 yield shock(log)

_(a) 0.19(0.00)(b)

Scarcity indicator 9 areaharvested shock (log)

_ _

Cereal demand shock (volatility)Scarcity indicator 9 domesticfeed consumption shock (log)

0.23 _(0.00)

Scarcity indicator 9 FSI (feed,

seed, industrial) consumption(log)

_ _

Scarcity indicator 9 total

domestic consumption (log)

_ _

Cereal stock demand shock (volatility)

Scarcity indicator 9 realinterest rate shock (log)

0.08 _(0.04)

Macro, petrol and exchange rate shocksScarcity indicator 9 GDPshock (log)

_ _

Scarcity indicator 9 US

exchange rate shock (log)

_ _

Thin international cerealtrade 9 US exchange rate

shock (log)

0.13 _(0.05)

Stock-to-use ratio 9 USexchange rate in growth rate

_ –0.17(0.00)

Scarcity indicator 9 crude oilprice shock (log)

0.22 _(0.00)

Thin international cereal

trade 9 crude oil price shock(log)

_ 0.03

(0.32)

Scarcity indicator 9 crude oilprice in level (log)

_ _

Thin international cerealtrade 9 crude oil price in level(log)

_ 0.26(0.00)

Derivative market information flowWeighted wheat and corn openinterest (volatility) (log)

–0.07 0.30(0.22) (0.32)

Fixed effectsWheat Constant 1.14 –0.59

(0.52) (0.29)Time _ _


572 Herve Ott

The empirical results also show that when the level of cereal stock is low supply(yield) shocks propagate into higher volatility between crop years but have no effectwithin the crop year. This is explained by the fact that weather shocks are confined tothe crop year and only occur once during the crop year. In contrast, the estimatesshow that cereal demand (feed) shocks and cereal stock demand shocks (interest rate)are significant drivers of intra-annual volatility but not for inter-annual volatility.These types of shocks typically affect the monthly price oscillation within the cropyear.

Interestingly, in a cereal market under stress (low stock-to-use ratio) exchange ratemovements influence the inter-annual volatility, while exchange rate shocks amplifiedby dysfunctional international trades (like export bans) affect intra-annual volatility.First, sharp short-term exchange rate fluctuations of the US dollar against other cur-rencies almost instantaneously harm trade flows and drive up volatility within thecrop year but to a lesser extent beyond. Second, as most of the international trade in

Table 6(Continued)

INTRA annualvolatility

INTER annualvolatility

Maize Constant –0.64 –1.03(0.13) (0.00)

Time –0.01 _(0.22)

Rice Constant 0.49 1.08(0.26) (0.01)

Time –0.04 –0.04(0.00) (0.00)

Barley Constant –1.45 –2.20(0.00) (0.00)

Time trend 0.02 0.02

(0.29) (0.09)Oats Constant –1.49 –1.97

(0.00) (0.00)

Time trend _ _

Rye Constant –2.96 –1.55(0.00) (0.00)

Time trend 0.01 –0.02(0.36) (0.19)

Regression statistics

Uncentered R2 0.91 0.86Centered R2 0.21 0.25Root MSE 1.223 1.119

P-value of F test 0.00 0.00Number of observations 312 312

Notes: (a) ‘_’ means not significant at any conventional level and the omission does not alter theother coefficient estimates; (b) in parentheses are the P-values under the assumption that the

true estimated coefficient estimate equals zero.



agricultural commodities is denominated in US dollars, depreciation of the US dollaragainst other currencies reduces import prices and thus fuels demand, which, in turn,reduces the stock-to-use ratio. Exchange rate movements (depreciation/appreciation)exhibit long-term cycles over several years. For instance, the US effective exchangerate had been appreciating from 1973 to 2000 without interruption, and has sincedepreciated to date with an interruption in 2009. This pattern suggests that the USeffective exchange rate cycles are long lasting, and as a consequence, their impact ismainly on inter-annual volatility.

The same holds for the crude oil price shocks, which influence the intra-annualvolatility while crude oil price movements influence the inter-annual volatility. It isacknowledged that crude oil price shocks amid cereal penury (high scarcity indica-tor) are statistically significant in explaining intra-annual volatility. Volatility spill-over from the typically volatile fossil fuel market to the cereal market via theethanol market is realistic as crude oil prices can change quite markedly in 1 month.Although the GDP volatility is not significant, the emerging economies might causelarger crude oil price volatility and so drive up intra-annual volatility when stocksare low. The estimates also show that the movements of the price level of crude oilinfluences inter-annual volatility. The coefficient was significant when multiplyingthe level of crude oil price by the thin international cereal trade at 5% (Table 6), butwas also significant when multiplied by the stock-to-use ratio at 10% (not reported).A strong demand in fossil fuel by the emerging economies can directly drive up thecrude oil price, which in turn will induce an increase in the demand of bioethanoland so of the cereal consumption leading to larger inter-annual volatility. However,crude oil price movements not only induce new demand to produce bioethanol butalso impact the input cost in cereal production. Thus, the increase in cereal demandand the increased production costs tighten the market clearing conditions betweensupply and demand, which, in turn lowers the stock-to-use and increases the scarcityindicator provoking more frequent peaks and, consequently, reinforcing higherinter-annual volatility.

The dispersion importance, i.e. the contribution of each factor to R2 was alsomeasured. As explained in Gr€omping (2006), the factors are typically correlated,and depending on the order of the explanatory factors in the regression, the rela-tive importance of the factors differs. The method of Lindeman et al. (1980) pro-vides meaningful results by averaging over orderings (sequential R2s), and so themeasured relative importance is ordering independent. Furthermore this methodconsiders both the unique contribution of the factor and its contribution whencombined with other factors. The proportionate contribution of each stochasticexplanatory factor is shown in Figure 2 for the intra- and inter-annual volatilityregressions. The sum of the contribution of each explanatory factor equals the R2

of the respective regression.First, Figure 2 shows that the major drivers of inter-annual volatility are supply

(yield) shocks (essentially due to extreme weather events) amplified by low cerealstocks. The other factors, by level of importance, that explain inter-annual volatil-ity are crude oil and exchange rate movements. When the cereal market is understress from a low stock-to-use ratio and is characterised by a thin international cer-eal trade, exchange rate movements and crude oil price movements explain propor-tionally more than half of the R2 among the stochastic factors. Thus, the increasein global international demand of crude oil (including ethanol demand) and thedepreciation of the US exchange rate are quantitatively substantial factors. Second,


574 Herve Ott

intra-annual volatility depends overwhelmingly on crude oil price shocks, while cer-eal demand shocks and real interest rates are quantitatively secondary factors.These shocks propagate into higher intra-annual volatility when the level of stockis too low to act as a buffer in mitigating price swings within crop years. The otherquantitatively secondary factors explaining intra-annual volatility are the exchangerate shocks combined with disruptive trade measures of policy intervention, e.g.export bans or import subsidies. Finally, activity in the derivatives market appearsto have no effect either on price dispersions within a crop year or between cropyears according to these estimates as it is statistically insignificant. Even if it weresignificant, quantitatively it explains a very minor part of R2 in both intra-annualand inter-annual regressions. While the proxy for volatility as weighted open inter-est of soft red winter wheat and corn at the CBOT (Chicago Board of Trade) canbe challenged, substitution of the scalping index7 and the trading volume did notchange the results.

4. Summary and Conclusions

Intra-annual volatility (within crop year) and inter-annual (between crop years) vola-tility are measured and regressed against a set of potential explanatory variables in apanel consisting of data for six cereals: wheat, maize, rice, barley, oats and rye. Usinga crop year sample from 1960 to 2012, GMM estimators are employed and the endo-geneity issue is addressed with a valid and relevant IV set. This panel analysis gives anoverall picture of the cereal sector and does not pretend to generate particular conclu-sions for each specific cereal.

The empirical results show clearly that large cereal stocks act as a buffer againstadverse (demand or supply) shocks and thereby mitigate price dispersion within andbetween crop years. By the same token, a structurally deep and integrated interna-tional cereal market absorbs exchange rate shocks and crude oil price movements andso reduces significant swings in cereal prices within and between crop years. The

Intra-annual volatility Inter-annual volatility

0 2 4 6 8 10 12

Weighted wheat and corn open interest vola lity(*)

Scarcity indicator x Crude oil price shock

Thin interna onal cereal trade x US exchange rate shock

Scarcity indicator x Real interest rate shock

Scarcity indicator x Domes c feed consump on shock

0 1 2 3 4 5 6 7 8

Weighted wheat and corn open interest vola lity(*)

Thin interna onal cereal trade x Crude oil price in level

Thin interna onal cereal trade x Crude oil price shock(*)

Stock to use ra o x US exchange rate

Scarcity indicator x Yield shock

Figure 2. Proportionate relative importance of the stochastic factors (% contribution to R2) ofintra- and inter-annual volatility based on the regressions in Table 6.

Note: *Coefficient estimate (Table 6) not significant at conventional level.

7Scalping index is the trading volume to open interest and proxies the opportunities of profit

making on small price changes.



available historical records from the dataset show that the scarcity ratio reached apeak in 2007 and seems to be a major reason for the volatility increase observed sincethe mid-2000s while the sporadic trade restriction policies (e.g. export bans) amplifiedthe volatility surge within and between the crop years.

The estimates also show that cereal demand shocks and stock demand (interestrate) shocks primarily influence the intra-annual volatility but quantitatively are notthe most important factors. The shocks in the currency market (exchange rate) andparticularly the fossil fuel market shocks translate into intra-annual volatility whenthe cereal market is under stress (low cereal stocks, dysfunctional internationaltrades). In contrast, supply (yield) shocks primarily influence inter-annual volatility.Thus, yield shocks primarily due to extreme weather events propagate into largerprice dispersions between crop years. As the production shocks occur typicallybetween crop years, they logically impact inter-annual volatility. If climate changemeans that the frequency of extreme weather events will increase in the future, then itis to be expected that inter-annual volatility will increase significantly.

Exchange rate shocks and crude oil price shocks propagate into the cereal marketby inflating the price swings within the crop year when the scarcity of the cereal stocklevel is high and disruption of international cereal trades occurs. Historically,exchange rate shocks are a common feature, the last occurring in 2008. Regarding thefossil fuel market, the volatility of crude oil price experienced two peaks in the begin-ning of the 1970s and also in the mid-1980s. The following peaks (e.g. 2008) are muchmore moderate (half of the previous ones) but their occurrences have increased sincethe mid-2000s. These elements help to understand the increase of the intra-annual vol-atility in the cereal sector over the last 10 years.

The empirical results also show that the US exchange rate depreciation and peaksin the level of crude oil price drive up the inter-annual volatility when the cereal mar-ket is under stress (low stock-to-use ratio, thin volume in international cereal trades).Also it is realistic to assume that longer-term growth in crude oil prices will fuel thedemand for ethanol and agricultural commodities. Moreover, the trade weighted USdollar index has continuously depreciated without interruption from 2000 onwards(with the exception of 2008). Thus, over the last 10 years the increase in cerealdemand amplified by a declining US effective exchange rate lowered the stock-to-useratio and propagated into higher between crop year price dispersion in the cerealmarket.

Finally, activity in the derivative market appears to have no effect either on pricedispersions within or between crop years.

To conclude, recent history shows that intra- and inter-annual volatilities haveincreased since the mid-2000s. This is explained by a combination of the strongdemand for cereals due to the biofuel mandate and the world consumption of rawmaterials in the emerging economies as well as the decrease in cereal production (e.g.production of wheat decreased from 1997 to 2006). As a result, the low level of stockcould not act as a buffer against the weather shocks occurring in the mid-2000s. Inter-annual volatility was also affected by the depreciation of the US dollar from 1998 to2008 and the increase in crude oil price from 1998 until today. In contrast, intra-annual volatility was more affected by short-term shocks (crude oil, exchange rate,demand) that occurred especially after the mid-2000s. All these elements imply thatthe current debate on factors causing increased volatility should first focus on the typeof dispersion (within- vs. between-crop years) being considered.


576 Herve Ott

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578 Herve Ott

volatility in cereal prices: intra- versus inter-annual volatility

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