Long-Run and Short-Run Co-Movements between Oil and Agricultural Futures Prices

Transcription

1 Long-Run and Shor-Run Co-Movemens beween Oil and Agriulural Fuures Pries By Rober J. Myers, Sanley R. Johnson, Mihael Helmar and Harry Baumes July, 2015 Absra: The relaionship beween oil pries and he pries of agriulural feedsoks for biofuel has reeived onsiderable aenion in he reen lieraure. Here we exend he reen ommon rend-ommon yle analysis of Myers e al. 2014), whih invesigaed long-run and shorrun o-movemens beween fuel and agriulural spo pries, o he ase of fuures pries. I is ofen argued ha he speulaive naure of fuures rading leads o exess o-movemen beween differen fuures prie series. Our resuls do no suppor his hypohesis and show here is even less o-movemen beween fuures pries for oil and agriulural biofuel feedsoks han here is beween spo pries for hese same ommodiies. This suggess variaions in agriulural fuures pries are dominaed by faors no relaed o hanges in oil pries, suh as agriulural supply response and he non-biofuel demand for feedsoks. Respeively, Universiy Disinguished Professor, Mihigan Sae Universiy, Eas Lansing MI 48824; Board Chair, Naional Cener for Food and Agriulural Poliy, Washingon, DC 20036; Researh Sienis, Universiy of Nevada, Reno, Reno Nevada 89557; and Direor, Offie of Energy Poliy and New Uses/Offie of he Chief Eonomis/USDA. Washingon DC Researh suppored by USDA Cooperaive Agreemen.

2 Long-Run and Shor-Run Co-Movemens beween Oil and Agriulural Fuures Pries Inroduion The relaionship beween pries for fuels rude-oil, gasoline, and ehanol) and agriulural feedsoks for biofuel pariularly orn and soybeans) has beome an imporan eonomi and poliy issue. There is now onsiderable evidene ha he growh in biofuel produion has inreased he demand for, and prie of, agriulural feedsoks bu he evidene on he exen o whih hanges in fuel pries ge ransmied o agriulural feedsok pries is more mixed Zilberman e al., 2013). This is imporan beause if here is srong prie ransmission hen higher lower) fuel pries an be expeed o lead o higher lower) food pries, hanging he inenives for agriulural produion and puing pressure on he food seuriy of low inome households Runge and Senauer, 2007; Mihell, 2008). However, if variaions in fuel pries do no ransmi readily o agriulural pries hen many of hese onerns are misplaed and oil prie shoks an be expeed o have muh smaller and more shor-lived effes on agriulural and food pries. A reen sudy by Myers e al. 2014) invesigaes long-run and shor-run o-movemen beween spo pries for rude oil, gasoline, and ehanol and spo pries of orn and soybeans. Resuls sugges ha spo fuel pries ransmi o spo agriulural feedsok pries in he shor run, bu ha he relaionship dissipaes in he long run. In pariular, long-run equilibrium spo fuel and spo agriulural pries were found o be driven by separae sohasi rends and herefore meander away from one anoher over long ime horizons. Furhermore, shoks o long-run equilibrium spo fuel pries only explain a relaively small porion of he foreas error variane in long-run equilibrium spo agriulural pries. These resuls sugges ha while spo fuel and spo agriulural pries o-move o some exen over inermediae ime horizons, in he long run 1

3 spo agriulural pries are deermined primarily by agriulural supply ondiions e.g., produiviy growh, areage expansion e.) and he non-biofuel demand for agriulural feedsoks e.g., he derived demand for livesok feed as inomes grow), wih fuel pries playing a relaively minor role in long-run agriulural spo prie deerminaion. An ineresing addiional quesion is wheher similar kinds of relaionships exis beween fuures pries for fuels and fuures pries for biofuel feedsoks. Fuures pries differ from spo pries beause hey refle he aggregaed expeaions of marke pariipans regarding he fuure value of he underlying ommodiy a he mauriy dae speified on he fuures onra. A fuures onra is herefore a differen asse han he underlying physial ommodiy, even when qualiy speifiaions and delivery loaion are he same. Furhermore, pariipaion in fuures rading does no require produion, onsumpion, or ownership of he physial ommodiy and so opens up addiional opporuniies for low-os speulaive rading aiviy. Some have argued ha his addiional speulaive aiviy an have an imporan influene on fuures prie deerminaion, leading o fuures pries ha o-move in exess of o-movemen in he spo pries of he underlying physial ommodiies e.g., Juvenal and Perella, 2011). This paper builds on Myers e al. 2014) by invesigaing long-run and shor-run omovemens beween fuures pries for rude oil and fuures pries for biofuel feedsoks, speifially orn and soybeans. We do no inlude gasoline pries in he analysis beause Myers e al. 2014) show ha gasoline and rude oil pries o-move srongly in boh he shor-run and long-run. We also do no inlude ehanol fuures pries in he analysis beause ehanol fuures only began rading in 2005, whih would limi he lengh of ime series daa available for analysis. Finally, Myers e al. 2014) inlude exhange raes in heir spo prie analysis o aoun for he possibiliy ha exhange raes provide an imporan link beween energy and agriulural pries, boh of whih are raded goods. In he fuures prie analysis repored here, 2

4 however, we do no inlude exhange raes beause Myers e al. 2014) found ha inluding he exhange rae expliily did no have a signifian influene on inferenes regarding prie ransmission beween fuel and biofuel feedsok pries. We also invesigaed inluding oher poenial biofuel feedsok pries e.g., whea, soybean oil, oher oil seeds, e.) in he fuures prie analysis bu found resuls followed an almos idenial paern o hose using orn and soybean pries only see Myers e. al, 2012 for deails). For all of hese reasons we fous here on o-movemens beween jus hree fuures pries rude oil, orn, and soybeans). I is imporan o invesigae fuures prie as well as spo prie o-movemen beween fuel and agriulural biofuel feedsoks beause of he poenially differen marke pariipans and prie relaionships ha an our in fuures versus spo ommodiy markes. The onribuion of he urren paper is ha i invesigaes long-run and shor-run relaionships beween fuures pries for oil and biofuel feedsok pries, and ompares hese resuls o hose from he previous spo prie analysis. Resuls lead o some new insighs ino he influene of fuures marke rading on ommodiy prie o-movemens, and on he relaionship beween oil and agriulural ommodiy pries. Empirial Approah To haraerize he relaionships among a se of n differen fuel and agriulural fuures pries, le p be an nx1) veor of he logarihms of eah ommodiy fuures prie. 1 Beause eah fuures onra has a fixed mauriy dae, a ime series of fuures pries will have jumps in ime o mauriy as he series swihes from one fuures onra o he nex as he mauriy dae 1 Log ransformaions are ommonly used in ommodiy prie modeling beause hey are onsisen wih he saisial properies of mos prie daa and failiae inerpreaion of oeffiiens in erms of proporional relaionships beween pries. 3

5 for he firs onra expires. This feaure will be aken ino aoun in he empirial analysis whih follows. We follow he empirial approah in Myers e al. 2014). The firs sep is o deompose eah log) prie ino a permanen omponen τ and a ransiory or ylial omponen ha: 1) p = τ + η. s The permanen omponen is defined as = p + lim [ pˆ + k E p )] kh sep ahead bes linear unbiased foreas of s k = 1 η suh τ where p k is he ˆ + p ondiional on informaion available a ime. By definiion, τ is a veor of pries expeed in he very long-run i.e., as he foreas horizon goes o infiniy) ondiional on informaion available a ime bu adjused bak o ime by subraing any known rend or drif. Fuures pries should be nonsaionary based on noarbirage argumens and here is onsiderable exising evidene o suppor his hypohesis. In his ase i an be shown ha τ follows a pure random walk possibly wih drif) and is herefore iself nonsaionary see Beveridge and Nelson, 1981 and Sok and Wason, 1988). Beause τ is he urren value of he prie ha is onsisen wih expeed full adjusmen over an infinie ime-horizon we all i he long-run equilibrium value of he series. By definiion, saionary and represens ransiory deviaions around long-run equilibrium values. η is hen Beause he omponens of τ are nonsaionary hey may also be oinegraed. Suppose all pries are nonsaionary and here are r < n oinegraion relaionships among he n pries in he sysem. Then τ an be expressed in erms of a smaller number k = n r of ommon rends so ha he long-run equilibrium pries an be wrien as = Aτ where τ ~ τ~ is a k x 1) 4

6 veor of ommon rends random walks) and A is an n x k) loading marix ha has full olumn rank Heq, Palm and Urbain, 2000). Similarly, Vahid and Engle 1993) have shown ha he ylial par of he series may also have ommon omponens. In pariular, assuming all pries are nonsaionary hen p is said o be o-dependen wih ommon serial orrelaion feaures hereafer jus o-dependen ) if here are < n linear ombinaions of p ha are innovaions wih respe o informaion available a ime 1 i.e., linear ombinaions of ha are no serially orrelaed). These linear ombinaions are alled o-feaure veors and hey imply ha he ylial omponen p an be wrien as η ~ = Bη where η ~ is an l x 1) veor of ommon ylial omponens wih l = n, and B is an n x l) loading marix for he ommon yles ha has full olumn rank see Vahid and Engle, 1993). Allowing for boh oinegraion and o-dependeny he deomposiion 1) an be expressed: 2) p = Aτ ~ + B ~ η. If here is no oinegraion or o-dependeny hen τ~ and ~ η will be of full dimension n. However, when oinegraion and/or o-dependeny exis imposing he ommon rend and ommon yle resriions in 2) will help idenify he naure of long-run and shor-run relaionships beween he pries in he sysem. Measures of Long-Run and Shor-Run Co-Movemen Myers e al. 2014) sugges measuring he exen of long-run o-movemen beween wo pries p i and p j by he size of he orrelaion oeffiien beween he innovaions in heir 5

7 permanen omponen, Corr τ, τ ). If he long-run equilibrium values of he pries move i j losely ogeher his orrelaion will be lose o one, while if movemens in he long-run equilibrium pries are ompleely unrelaed his orrelaion will be zero. The orrelaion beween he innovaions in he permanen omponens herefore has a naural inerpreaion as a measure of long-run o-movemen beween pries. The number of ommon rends k in τ~ influenes long-run o-movemen beween pries. For example, suppose ha p i and p j are boh driven by a single ommon rend i.e., hey are oinegraed). Then he equilibrium values of boh pries would move ogeher perfely and Corr τ, τ ) = 1. In his ase he long-run equilibrium pries mainain a fixed relaionship i j wih one anoher and here is perfe long-run o-movemen beween he pries. Alernaively, if he wo pries are no driven by a ommon rend i.e., are no oinegraed) hen Corr τ, τ ) < 1and he wo pries will be unrelaed in he very long-run infinie horizon i j foreass of any linear ombinaion of he pries will have infinie variane). In he limiing ase when he permanen omponens of he wo pries are separae unorrelaed random walks hen he long-run equilibrium values of he pries move ompleely independenly and Corr τ, τ ) = 0. In his ase here is no long-run o-movemen in he pries. Inermediae i j ouomes 0 Corr τ, τ ) < 1 indiae long-run equilibrium values of pries move ogeher < i j over inermediae ime inervals beause innovaions in heir permanen omponen are orrelaed, even hough hese pries would evenually meander apar and beome unrelaed in he long-run. In his ase here is inermediae-run o-movemen and values of Corr τ, τ ) i j lose o one indiae sronger inermediae-run o-movemen while values lose o zero indiae weaker inermediae run o-movemen. 6

8 Myers e al. 2014) sugges measuring shor-run o-movemen beween any wo pries p i and p by he unondiional orrelaion beween heir ransiory omponens, Corr η, η ). j i j Beause he ransiory omponens are saionary his unondiional orrelaion is well-defined and provides a onvenien summary measure of he exen o whih pries o-move in he shor run as hey onverge bak o long-run equilibrium values. Jus as he number of ommon rends k influene he exen of o-movemen beween long-run equilibrium pries, he number of ommon yles l in ~ η influenes he exen of o-movemen beween he ransiory omponens. For example, suppose ha pi and p j are boh driven by a single ommon yle i.e., hey are o-dependen). Then heir ransiory omponens would move ogeher perfely, and Corr η, η ) = 1. In his ase here is perfe shor-run o-movemen beween he pries. i j Alernaively, if he wo pries are no o-dependen hen eah prie is driven by separae ylial omponens, and Corr η, η ) < 1. In he limiing ase of no relaionship beween he i j ransiory omponens hen Corr η, η ) = 0 and here is no shor-run o-movemen beween i j he series. In he inermediae ase of 0 Corr η, η ) < 1 values lose o one indiae sronger < i j o-movemen in adjusmens owards long-run equilibrium while values lose o zero indiae weaker shor-run o-movemen. Togeher, he esimaed orrelaion maries of τ and η provide deailed informaion on he ways in whih he pries are relaed o one anoher over differen foreas horizons. 7

9 Esimaion and Tesing Esimaion and esing for nonsaionariy and oinegraion are now sandard and will no be deailed here. 3 Esimaion and esing for o-dependene is oulined in Vahid and Engle 1993) and heir proedures are adaped o he urren ase of fuel and agriulural feedsok pries in Myers e al. 2014). We do no deail he o-dependeny esing proedures again here bu noe ha esimaion and esing is ondiional on oinegraion resriions and so based on a veor error orreion VEC) represenaion: 3) p = μ + αz + i p q 1 i + ε i= 1 where z = β' p is he r x 1) veor of equilibrium errors from he r oinegraing relaionships and β onains he oinegraing veors. Tesing for o-dependene is based on he anonial orrelaions beween and w = z, p,..., p } as oulined in Myers e al. 2014). If p { 1 1 q o-dependene is found he o-feaure veors form a n x ) marix β ~ suh ha eah elemen of ~ β p is an innovaion wih respe o he informaion se w. One he number of ofeaure veors has been esablished via esing we esimae β ~ by imposing he o-dependeny resriions on he VEC model 3) and esimaing he resuling pseudo-sruural form using maximum likelihood. The appropriae pseudo-sruural form is derived by firs noing ha β ~ is no unique and so an be normalized o: ~ I 4) β = ~ β n) ~ for a se of unknown parameers β. Then he o-dependeny resriions imply up o a onsan erm) he following resriions on he VEC model 3): 3 More deails are available in Hamilon 1994) and many oher eonomeri exs. 8

10 9 5) q i i i n r n n ε p 0 z α 0 p I 0 β I + + = = 1 ) 1 ) ) ~ ' where he differeniaes parameers from heir unresried ounerpars, α is r n ), and he i are n n ). Equaion 5) an hen be esimaed using maximum likelihood and imposes he following resriions on he VEC model 3): 6) = ) 1 ) ~ ' α 0 I 0 β I α r n n and = ) 1 ) ~ ' i n n n i 0 I 0 β I for i = 1, 2, q. These resriions will be useful for ompuing he permanen-ransiory deomposiion under o-dependeny resriions. Compuing he Deomposiion One he parameers of he model have been esimaed, eiher wih or wihou oinegraion and o-dependeny resriions as indiaed by es resuls, deomposing eah prie ino permanen and ransiory omponens is sraighforward as shown in Myers e al. 2014). In pariular, he deomposiion for he VEC represenaion 3) an be ompued as: 7a) n L p αβ P I τ ) ] ' 1) )[ 1 = 7b) n L Pp p Ψ αβ P I η + = ) '] 1) )[ 1 where q q n L L L I =... ) 1, q n I =... 1) 1, ) = q q L L L Ψ Ψ Ψ Ψ wih + = = q j i i j 1 Ψ, and where ' } '] 1) '[ { '] 1) [ β α αβ β α αβ P =. These formulas already impose oinegraion resriions direly, so deomposiions ompued using 7) will saisfy all appropriae oinegraion resriions. If here is no oinegraion r = 0)

11 hese formulas are sill appliable if we se αβ ' = 0 and P = 0. If shor-run o-dependeny behavior is found hen he deomposiion 7) is sill appliable, exep ha values for α and he i mus saisfy he resriions in 6) wih ~ β, α, and form 5). Imposing hese resriions ensures ha he ransiory omponen 7b) exhibis all of he behavior implied by he o-dependeny resriions. i esimaed using he pseudo-sruural η ompued from Evaluaing Poenial Nonlineariies and Regime Shifs As argued by Myers e al. 2014), i is possible ha o-movemen in ommodiy pries involves nonlineariies ha are no well apured by he linear oinegraion and o-dependeny models disussed so far. For example i ould be ha he prie relaionships experiened a sruural hange due o he growh of biofuels, or ha when produion of biofuel reahes erain hreshold levels hen he naure of he prie relaionships hange. One way o model suh nonlineariies is o allow model parameers o hange over differen ranges of values for underlying hreshold variables, suh as biofuel produion levels, ommodiy sok levels, and ime. To allow for suh regime hanges, suppose he esimaion equaions for he mulivariae prie model ake he general form p = f w ; θ) + ε where w is as defined previously and θ an assoiaed parameer veor. Then we an define a muliple hreshold model as: 8) p = f w ; θ j ) + ε x R j δ) where j indexes a se of muliple regimes defined by values of he exogenous hreshold variable veor x lying in a se of noninerseing and exhausive ses R j δ) defined by he parameer veor δ. Afer idenifying and esimaing he separae models for eah regime, he oinegraion, o-dependeny and permanen-ransiory deomposiion analyses an hen be 10

12 applied regime by regime o isolae he exen of long-run and shor-run o-movemen in differen regimes see Myers and Jayne, 2012). Given he well-known diffiulies of esing formally for hreshold effes see Davies, 1987; Hansen, 1996; and Balagas and Hol, 2008) we follow Myers e al. 2014) and use he Gonzalo and Piarakis 2002) BIC-like rierion funion: 2 ln T ) = T T md δ T T 10) Q m) max [ L δ) L ] T o evaluae he exisene of hreshold nonlineariies. Here L T is he log-likelihood value for he T single regime no hreshold) model, L δ) is he full sample log-likelihood value for he muliregime model wih hresholds δ, d is he number of parameers o be esimaed in he single regime model, and m is he number of hreshold parameers. The rierion is based on a likelihood raio saisi bu imposes a penaly for over-parameerizaion ha is similar o he BIC rierion for evaluaing lag lengh. Threshold and regime seleion is hen based on: 11) mˆ = arg max Q m) 0 m M T where M is he maximum number of hresholds o be onsidered. Gonzalo and Piarakis provide simulaion evidene o sugges his rierion performs well in seleing he appropriae number of hresholds and regimes. Daa and Preliminary Analysis The appliaion uses end-of-monh urren and one-monh lags of he pries for he neares mauring fuures onra for CBOT orn, soybeans, and Wes Texas Inermediae WTI) rude oil hereafer oil ) raded on NYMEX. The one-period lags are used o onsru a series of fuures prie differenes ha always have he same mauriy dae i.e., fuures prie hanges 11

13 are always ompued using he same underlying fuures onra, no from he differene in pries beween wo onras wih differen mauriy daes). The sample period is January 1990 hrough Augus Table 1 shows he nearby onra expiraion monhs for eah monh of he year. In he orn fuures marke here are five onras for delivery in Marh, May, July, Sepember, and Deember. For soybeans here are seven onras for delivery in January, Marh, May, July, Augus, Sepember, and November. There are oil fuures onras for every monh so he nearby fuures prie daa are always aken from a onra expiring in he subsequen monh. Using his approah reorded prie hanges are always hanges in fuures pries for onras for he same mauriy dae, whih avoids inroduing spurious variaion in fuures prie hange daa ha are due only o hanges in ime o mauriy. To give some iniial insighs ino he daa we plo nearby fuures pries for oil, orn, and soybeans over he sample period, eah normalized o a value of one in January 1990 see figure 1). As an be immediaely observed, nearby fuures pries for orn and soybeans move losely ogeher over he enire sample period. Nearby oil fuures pries appear o o-move wih he agriulural fuures during some periods, bu also go hrough periods where here is lile observed o-movemen. This suggess a deailed invesigaion ino he exen and naure of omovemen beween hese series should provide some ineresing insighs. Model Esimaion and Tesing Resuls Beause of he onsiderable exising evidene ha ommodiy fuures pries are nonsaionary, and beause saionary fuures pries would imply he exisene of sysemai profiable fuures rading sraegies, we do no repor deailed ess for nonsaionariy. However, 12

14 augmened Dikey-Fuller and Phillips-Perron ess srongly suppor nonsaionariy of all hree nearby fuures prie series. Given nonsaionariy i is imporan o es for oinegraion beause his will impose resriions on he permanen-emporary deomposiion. We underook boh Engle-Granger and Johansen rae ess for oinegraion. The resuls are repored in Table 2 and show srong evidene of a single oinegraion relaionship beween nearby orn and soybean fuures pries, bu no oinegraion beween he nearby fuures prie for oil and eiher of he agriulural fuures pries. These findings are similar o hose found for spo oil and agriulural pries in Myers e al. 2014). We also need o esimae he oinegraing veor. Resuls from boh leas squares regression, and Johansen s VEC maximum likelihood mehod for esimaing he oinegraing veor, are repored in able 3. Boh esimaes sugges srongly ha he oinegraing veor is 1, -1), wih he Johansen proedure whih provides onsisen sandard errors) showing a very igh onfidene inerval around his value. Remembering ha he pries are in logarihms, his shows ha nearby orn and soybean fuures pries remain proporional o one anoher in he long-run. This resul is onsisen wih eonomi inuiion and we impose he long-run proporionaliy oinegraion) onsrain from here on. The nex sep is o es for o-dependeny. The anonial orrelaion saisis for esing a leas one and hen a leas wo o-dependeny relaionships, along wih heir assoiaed p- values, are shown in able 4. The resul suggess wo o-dependeny relaionships whih implies one ommon ransiory omponen is driving all of he prie series. Esimaing he resuling pseudo-sruural form wih one oinegraion and wo odependeny resriions imposed revealed ha he agriulural prie differenes ould be exluded from he oil o-dependeny relaionship likelihood raio p-value = 0.985). This 13

15 suggess ha hanges in nearby fuures pries for oil are already innovaions wih respe o he informaion se w = z, p,..., p }. In oher words, oil fuures pries follow a pure { 1 1 q random walk wih all prie movemen due he permanen omponen he series has no ransiory omponen). This is exaly wha we would expe for fuures pries generaed from a wellfunioning fuures marke beause i means fuures prie hanges are unprediable based on pas informaion, wihou even shor-run emporary prediable yles. On he oher hand, orn and soybean fuures are found o have small ransiory omponens ha sugges a leas some prediabiliy of shor-run ylial prie movemens. However, he majoriy of he prie variaion sill omes from he unprediable) permanen omponen of hese series so prediabiliy of prie hanges remains low and shor-run. Full resuls for he resried pseudo sruural form esimaed via maximum likelihood are provided in able 5. The firs wo equaions for oil and orn are he wo o-dependeny relaionships, showing ha oil prie hanges are pure innovaions and a linear ombinaion of ~ orn and soybean fuures prie hanges, given by 1, β ) = 1, ) are also innovaions wih respe o he defined informaion se. The hird equaion for soybeans) is an errororreion equaion using he differene in log orn and soybean fuures pries as he lagged equilibrium error erm i.e. imposing long-run proporionaliy beween orn and soybean fuures pries as he oinegraion resriion). This speifiaion is suppored by previous esing resuls bu we noe ha he resuls sugges very lile abiliy o foreas fuures prie hanges see he very low 2 R in he soybean equaion). The pseudo-sruural form esimaes are of lile ineres by hemselves and are only shown for ompleeness and o provide addiional insigh ino he sruure of he empirial model. The main use of he esimaes in he urren appliaion omes in operaionalizing he permanen-ransiory deomposiion. SOY 14

16 Deomposiion and Co-Movemen Resuls The pseudo-sruural form esimaes are used deompose he hree nearby fuures prie series ino permanen long-run equilibrium) and ransiory shor-run saionary) omponens using he deomposiion defined in equaion 7). Tes resuls showed ha he oil fuures pries have no ransiory omponen all prie movemens represen permanen hanges). However, he orn and soybean fuures proess have boh permanen and ransiory omponens. The orrelaion marix for hanges in he permanen omponens of eah series is shown in able 6. The orn and soybean fuures pries are oinegraed and herefore driven by he same ommon rend. Therefore, as expeed, he permanen omponens of he orn and soybean fuures are perfely orrelaed, showing perfe long-run o-movemen. The oil fuures are driven by a separae rend no oinegraed wih orn and soybean fuures) bu i is sill possible ha he wo rends are orrelaed, showing ha he oil and agriulural fuures pries move ogeher over inermediae ime horizons. However, able 6 shows ha he orrelaion oeffiien beween innovaions in he wo permanen omponens is only 0.01, indiaing very weak long-run omovemen beween oil fuures pries and eiher orn or soybean fuures pries. Following Myers e al. 2014), we invesigae his issue furher by ompuing he horizon independen) foreas error variane deomposiion from a bivariae model of long-run equilibrium oil and orn pries. Resuls show ha less han 1% of he variaion in he innovaions in he permanen omponen of orn fuures pries is aouned for by variaion in he innovaions in he permanen omponen of oil fuures pries. This resul oninues o hold if we use orn fuures prie hanges hemselves, insead of hanges in he permanen omponen of he orn fuures hanges, o do he deomposiion. The finding of very weak o-movemen beween long-run equilibrium oil and agriulural fuures pries is herefore robus o model speifiaion. 15

17 Turning o shor-run o-movemens, able 7 shows he esimaed orrelaion marix of ransiory deviaions around long-run equilibrium values for he hree series. The oil fuures pries have no ransiory omponen and he orn and soybean fuures pries are driven by he same ommon yle. Therefore, able 7 shows no resuls for oil and ha he ransiory omponens of he orn and soybean fuures pries are perfely orrelaed as expeed). The resuls show perfe shor-run o-movemen beween orn and soybean fuures pries, bu no shor-run o-movemen beween oil fuures and fuures pries for he agriulural ommodiies. I migh seem odd ha he orn and soybean series o-move perfely in boh he shor run and he long run, bu he pries hemselves are no perfely orrelaed hough hey are learly highly orrelaed). This happens beause, alhough he wo pries are driven by he same ommon rend and same ylial omponen, he pries hemselves are differen linear ombinaions of hese wo omponens, and herefore do no need o be perfely orrelaed. Overall he evidene suppors srong o-movemen beween orn and soybean fuures pries in boh he long run and he shor run. On he oher hand, oil fuures only o-move very weakly wih he permanen omponen of orn and soybean fuures pries and have no ransiory omponen. Therefore here is very lile o-movemen beween oil fuures pries and eiher of he agriulural fuures pries in eiher he long run or he shor run. These resuls sugges ha fluuaions in agriulural fuures pries are dominaed by faors relaed o agriulural supply and he non-energy demand for biofuel feedsoks and are lile influened by oil fuures prie movemens in eiher he long run or he shor run. The resuls are similar o, bu even sronger han, he o-movemen resuls found for spo oil, orn, and soybean pries in Myers e al. 2014). For spo pries, he permanen omponens of oil and orn/soybean pries are more orrelaed 0.49 versus 0.01 found here for fuures pries) and he foreas error variane deomposiion of he innovaions showed a larger proporion of he 16

18 variane in he permanen omponen of spo orn prie is due o he permanen omponen of spo oil pries 24% versus less han 1% found here for fuures pries). Evidenly, he naure of fuures prie deerminaion leads o even less o-movemen beween oil and agriulural pries over any lengh of run han is found in spo pries. The impliaion is ha here is nohing abou he speulaive naure of fuures markes, or he ease of pariipaion in fuures rading for hose no involved in produion or rade of he physial ommodiies, ha leads o more o-movemen beween oil and agriulural pries. In fa, he reverse is rue wih even less o-movemen han in he ase of spo pries. Nonlineariies and Regime Shifs We evaluaed he possibiliy of nonlineariies using ime as he hreshold variable and he Gonzalo-Piarakis rierion funion approah desribed above. There is some evidene of a regime shif in he model parameers i.e., sruural hange) in Marh 2003 GP rierion = 0.125). However, he GP rierion is only slighly greaer han zero, indiaing he evidene of sruural hange is weak. Furhermore, underaking separae model esimaion, deomposiion and o-movemen analysis for he wo regimes before and afer he sruural break leads o he same onlusions as he full sample analysis srong long-run and shor-run o-movemen beween orn and soybean fuures pries bu very weak o-movemen beween oil fuures and eiher of he agriulural pries. Therefore we only repor omplee resuls for he full sample analysis. Conlusions Common rend-ommon yle deomposiions were used o evaluae long-run and shorrun o-movemen among fuures pries for oil, orn, and soybeans. Coinegraion ess suppored 17

19 he hypohesis ha orn and soybean fuures pries are oinegraed and herefore driven by a single ommon rend, bu oil fuures pries are no oinegraed wih he agriulural pries and follow heir own long-run rend. Co-dependeny ess suggesed ha oil fuures prie hanges are unorrelaed wih pas fuures prie informaion and so have no ransiory omponen. However, orn and soybean fuures pries were found o have small ransiory omponens ha are driven by he same ommon yle. The pseudo-sruural form was esimaed under hese oinegraion and o-dependeny resriions and resuls used o deompose eah of he series ino permanen and ransiory omponens. Correlaion analysis of he wo omponens aross differen ommodiies revealed ha orn and soybean fuures pries o-move srongly in boh he long run and he shor run. However, oil fuures pries have only very weak o-movemen wih he agriulural fuures pries over any ime horizon. These resuls are similar o bu sronger han he resuls found for oil, orn, and soybean spo pries in Myers e al. 2014). Evidenly here is even less o-movemen beween fuures pries for oil and agriulural feedsoks han here is beween spo pries for he same ommodiies. This is an imporan resul beause i is ofen argued ha he speulaive naure of fuures rading an generae prie movemens ha go beyond hose ha an be suppored based on supply and demand fundamenals, and in his sense may lead o exess o-movemen beween fuures prie series. Our resuls are no onsisen wih his hypohesis. Insead, our resuls indiae ha variaion in agriulural fuures pries are dominaed by faors no relaed o hanges in oil pries, faors suh as agriulural supply response and he non-biofuel demand for feedsoks. One impliaion of hese resuls is ha in he long run we an expe oil and agriulural fuures pries o meander apar and be deermined by largely separae eonomi fundamenals. This suggess ha, aking a long-run view, onerns abou ommodiy fuures 18

20 speulaion and higher oil fuures pries leading o food shorages and agriulural ommodiy prie booms may have been over-emphasized. Our resuls sugges ha, in he long run, orn and soybean pries will be driven more by faors suh as produiviy growh, areage response, and he non-ehanol demand for biofuel feedsoks, raher han by hanges in oil pries. This does no imply ha inreased ehanol produion has no had an influene on orn pries. Bu i does imply ha hanges in oil fuures pries do no readily ransmi o agriulural fuures pries in eiher he shor run or he long run. Our findings are also imporan beause hey sugges ha fuures rading has no adverse effes in erms of generaing more o-movemen in oil and agriulural fuures pries han an be suppored by o-movemen in he underlying spo pries. 19

21 Referenes Balagas, J.V., and M.T. Hol 2008). The Commodiy Terms of Trade, Uni Roos, and Nonlinear Alernaives: A Smooh Transiion Approah. Amerian Journal of Agriulural Eonomis 91: Beveridge, S. and C.R. Nelson 1981). A New Approah o Deomposiion of Eonomi Time Series ino Permanen and Transiory Componens wih Pariular Aenion o Measuremen of he Business Cyle. Journal of Moneary Eonomis 7: Davies, R.B. 1987). Hypohesis Tesing when a Nuisane Parameer is Presen only Under he Alernaive. Biomerika 74: Gonzalo, J., and J.-Y. Piarakis 2002). Esimaion and Model Seleion Based Inferene in Single and Muliple Threshold Models. Journal of Eonomeris 110: Hamilon, J.D. 1994). Time Series Analysis. Prineon Universiy Press, Prineon. Hansen, B.E. 1996). Inferene When a Nuisane Parameer is No Idenified Under he Null Hypohesis. Eonomeria 64: Heq, A., F.C. Palm, and J-P Urbain 2000). Permanen-Transiory Deomposiion in VAR Models wih Coinegraion and Common Cyles. Oxford Bullein of Eonomis and Saisis 624): Juvenal, L. and I. Perella 2011). Speulaion in he Oil Marke. Teh. rep., Researh Division, Federal Reserve Bank of S. Louis, S. Louis, MO. Mihell, D. 2008). A Noe on Rising Food Pries. Poliy Researh Working Paper 4682, The World Bank, Washingon, DC. Myers, R.J., and T.S. Jayne 2012). Prie Transmission under Muliple Regimes and Thresholds wih an Appliaion o Maize Markes in Souhern Afria. Amerian Journal of Agriulural Eonomis 941): Myers, R.J., S.R. Johnson, M. Helmar, and H. Baumes 2012) Cohereny of Agriulural Feedsok and Peroleum Pries: An Analysis of Monhly Pries, January 1989 hrough November 2010 Tehnial Repor o USDA OCE hp:// Myers, R.J., S.R. Johnson, M. Helmar and H. Baumes 2014). Long-Run and Shor-Run Co- Movemens in Energy Pries and he Pries of Agriulural Feedsoks for Biofuel. Amerian Journal of Agriulural Eonomis 964):

22 Runge, C.F. and B. Senauer 2007). How Biofuels Could Sarve he Poor. Foreign Affairs May/June. Sok, J.H. and M.W. Wason 1988). Tesing for Common Trends. Journal of he Amerian Saisial Assoiaion 83: Vahid, F. and R.F. Engle 1993). Common Trends and Common Cyles. Journal of Applied Eonomeris 8: Zilberman, D., G. Hohman, D. Rajagopal, S. Sexon, and G. Timilsina 2013). The Impa of Biofuels on Commodiy Food Pries: Assessmen of Findings. Amerian Journal of Agriulural Eonomis 952):

23 Normalized Prie m1 1995m1 2000m1 2005m1 2010m1 Dae Crude Oil Corn Soybeans Figure 1. Normalized Monhly Oil, Corn, and Soybean Fuures log) Pries Table 1. Nearby Fuures Conra by Las Day of Monh Aual Monh Nearby Conra Monh Corn Soybeans WTI Crude Oil January Marh Year Marh Year February Year February Marh Year Marh Year Marh Year Marh May Year May Year April Year April May Year May Year May Year May July Year July Year June Year June July Year July Year July Year July Sepember Year Augus Year Augus Year Augus Sepember Year Sepember Year Sepember Year Sepember Deember Year November Year Oober Year Oober Deember Year November Year November Year November Deember Year January Year +1 Deember Year Deember Marh Year +1 January Year +1 January Year +1 Soure: Barhar.om 22

24 Table 2. Coinegraion Tes Resuls Coinegraing Relaionship Engle Granger Saisi 5% Criial Value Maximum No. of Coinegraing Relaionships Trae Saisi 5% Criial Value Corn-Oil Soybeans-Oil Corn-Soybeans Corn-Soybeans-Oil Noes: All variables are in logarihms. Engle-Granger ess he null of no oinegraion. Trae saisis based on appropriae dimensional VEC esimaions wih wo lagged differenes inluded in eah model as suggesed by lag seleion rieria). indiaes he number of oinegraing veors suppored by he Trae saisi. Table 3. Esimaes of he Coinegraing Veor Mehod Crude Oil Prie Corn Prie Soybean Prie Consan OLS Johansen VEC ) Noes: All variables are in logarihms. No sandard errors are shown for he OLS resul beause hese are known o be inonsisen. Number in parenheses for Johansen s VEC proedure is a onsisen sandard error. Also using Johansen s VEC proedure a likelihood raio es fails o reje exluding oil prie from he oinegraing veor p-value = 0.903). 23

25 Table 4. Co-dependeny Tes Resuls Co-dependeny Relaionship No. of Co-dependeny Relaionships Canonial Correlaion Saisi p-value Oil-Corn-Soybeans > > Noes: All variables are in logarihms and o-dependeny resriions are on he firs differenes of he variables. Resuls sugges 2 o-dependeny relaionships. 24

26 Table 5. Pseudo-Sruural Form Esimaes Parameer Crude Oil Prie Eqn. Corn Prie Eqn. Soybean Prie Eqn. Consan ) 0.004) 0.031) ~ β SOY 0.285) α ) , OIL 0.046) , CORN 0.080) , SOY 0.087) , OIL 0.046) , CORN 0.079) , SOY 0.085) 2 R Noes: All variables are in logarihms and he dependen variables are firs differenes. α is he resried speed of adjusmen parameer on lagged equilibrium errors from he oinegraion relaionship. The are parameers on he jh lagged firs differene of he log rude oil prie j,oil in he relevan equaion and so on for oher ommodiies). Table 6. Correlaion Marix of Innovaions in Long-Run Equilibrium Componens Oil Corn Soybeans Oil 1.00 Corn Soybeans

27 Table 7. Correlaion Marix of Transiory Componens Oil Corn Soybeans Oil - Corn Soybeans