SEASONAL PRICE TRANSMISSION IN SOYBEAN

Transcription

1 SEASONAL PRICE TRANSMISSION IN SOYBEAN INTERNATIONAL MARKET: THE CASE OF BRAZIL AND ARGENTINA 1 Eduardo Luz Machado 2 Maro Anono Margardo 3 Resumo: esse rabalho analsou o comporameno sazonal e o relaconameno enre as coações da soja em grão na Bolsa de Chcago (Chcago Board of Trade CBOT), e os preços Cos Insurance and Fregh (CIF) do grão de soja no poro de Roerdam, preços Free on Board (FOB) no Brasl e Argenna. A prncpal hpóese é de que os preços no Brasl e Argenna esão mas dreamene relaconados aos preço em Roerdam do que às coações de Chcago em função do fao de que a Unão Européa é o prncpal desno da soja exporada por esses dos países. Espera-se que o comporameno sazonal dos preços FOB do grão de soja no Brasl e Argenna seja mas semelhane ao comporameno sazonal dos preços CIF em Roerdam do que às coações da Chcago. Palavras-chave: sazonaldade, soja, ssema de ransmssão de preços JEL: C32; F31. 1 A Prelmnary verson of hs paper was presened n he World Food and Agrbusness Congress X, accomplshed n Chcago, n he perod from 25 on June 28, Ph.D. canddae n Economcs a Unversy of São Paulo. E-mal: edumach@usp.br 3 Maser n Economcs a Geúlo Vargas Foundaon, Ph.D. n Appled Economcs a ESALQ/USP, and Scenfc Researcher from Insuo de Economa Agrícola. E-mal: mamargardo@ea.sp.gov.br

2 prce ransmsson n nernaonal soybean marke Inroducon Brazl and Argenne are wo mporan players n soybean nernaonal marke n erms of boh producon and exporaon. The radonal and domnan commercalzaon sysem has been srongly dependen of Chcago Board of Trade (CBOT) and Roerdam Por. Prces are se whn hese ceners, whch domnae he commercalzaon of he produc and nfluence he sraeges of all chan agens. Accordng o ABIOVE 4, Brazl s responsble for some 20 percen of world's producon and he world's second larges exporer. Almos 70% of oal Brazlan and Argenne exporaon of soybean s desned o European Unon. Thus, s expeced ha Brazlan and Argenne prces are more assocaed wh Roerdam han Chcago. The expeced resul s ha he seasonal prce behavor n FOB prces of hese counres wll be more smlar o CIF prces n Roerdam han CBOT quoaon. Thus, here s a prce ransmsson sysem based on hs seasonal behavor. The drecon of causaly has been showed parally n dfferen cases. AGUIAR and BARROS (1991) and NEVES (1993) use Granger causaly es o deermne whch he drecon of Brazlan soybean causaly. The man concluson s ha Brazl doesn se prces n nernaonal marke. PINO and ROCHA (1994) conclude ha Brazlan soybean prce s affeced by CBOT varaons, usng ARIMA's models and Box-Jenkns ransfer funcon beween 1985 and MARGARIDO and SOUSA (1998) show ha CBOT varaons are mmedaely ransmed from Brazlan prces, usng he ARIMA's models developed by HAUGH and BOX (1977) beween 1990 and Ths approach ncorporaes a causaly es n ransfer funcon. The presen paper nends o ncorporae more nformaon abou he drecon of causaly n soybean nernaonal marke. 1. Objecves Ths paper ams o analyze he seasonal behavor and relaonshp among soybean prce n Chcago Board of Trade (CBOT), CIF prces n Roerdam and FOB prces n Brazl and Argenne. In parcular, s expeced ha he amplude of seasonal sandard s 4 Brazlan Assocaon of Vegeable Ol Indusres 93

3 Eduardo Luz Machado e Maro Anono Margardo more accenuaed n USA off-season perod n Brazl and Argenne. On he oher hand, he seasonal sandard n Roerdam mus be less accenuaed han anoher seres, due o he fac ha supply n European Unon s consan durng all year. These resuls are accordng o he expeced for hs marke, due o USA, Brazl and Argenne crops occur n dsnc perods of he year (Table 1). Anoher expeced resul s ha he seasonal prce behavor n FOB prces of Brazl and Argenne wll be more smlar o CIF prces n Roerdam han CBOT quoaon. Thus, here s a prce ransmsson sysem based on hs seasonal behavor. Table 1 Soybean crop and rade me Counry MONTHS J F M A M J J A S O N D EUA BRAZIL ARGENTINE Source: Brazlan Vegeal Ols Indusry Assocaon (ABIOVE) 2. Maerals and mehods 2.1. Maerals The paper focuses on he perod beween January/91 and Sepember/99, conanng 105 observaons. The daa for soybean quoaons were obaned n Chcago Sock Marke (January/91 o Ocober/98) and n Vegeal Ol Indusry Brazlan Assocaon, ABIOVE, (November/98 o Sepember/99). The Brazlan and Argenne Free on Board (FOB) prces and he Roerdam Por Cos Insurance and Fregh (FOB) prces were founded n Olseeds publcaon (several numbers). The seasonal ndex of each seres and he ARIMAs models were obaned from Sascal Analyss Sofware (SAS, verson 6.12), usng he mehodologcal framework developed by he U.S. Bureau of 94

4 prce ransmsson n nernaonal soybean marke he Census and SAS Insue (1993, 1994). For he causaly es was used Economerc Vews (Evews, verson 2.0) 2.2. Mehods ARIMA X-11 mehod The X-11 approach s a mehodologcal framework developed by he U.S. Bureau of he Census and SAS Insue. The mehod s based on decomposon of orgnal seres (O) n four componens: seasonal (S), cycle endency (C), radng-day (D) and resdual (I). The frs componen caches modfcaons ha s repeaed consanly durng a year. The second ncludes long run varaons of endency, busness cycles and oher long run cycle facors. The hrd shows varaons relaed wh he composon of he calendar ("calendar effecs"). The las represens all nformaon no explaned by he oher componens. Thus, he seasonal componen s separaed and he comparson among successve monhly daa s faclaed. There are wo knds of adjusmen models: addcve (1) and mulplcave (2). O = S + C + D + I (1) O = S * C * D * I (2) The X-11 process uses symmerc movng average o esmae all componens. However, hs symmerc weghng can' be appled n he las observaons. The ARIMA X-11 mehod was developed o solve hs problem ha compromsed he model resuls. Accordng o SAS Insue (1993, p.897), hs "mehod adjus a ARIMA model for a orgnal seres, and so uses a forecas model o lenghen he orgnal seres. A las, he lenghen seres wll be modfed n X-11 seasonal adjus". The ARIMA X-11 mehod uses an auomac selecon mehod, whch chooses he bes model among fve predefned Auoregressve Inegraed Movng Average Models (Table 2). The selecon of hese models was based n ess appled on a large number of economcs seres. Dagum (1988) manans ha hese pre-defned models provde good forecass for he majory of economc seres. Box-Jenkns and Rensel (1994) developed he bes sysemac approach o undersand he ARIMA models. 95

5 Eduardo Luz Machado e Maro Anono Margardo Table 2 ARIMA X-11 models ARIMA MODEL Specfcaon Mulplcave Model Addcve Model 1 (0,1,1) (0,1,1) s Wh log ransformaon Whou log ransformaon 2 (0,1,2) (0,1,1) s Wh log ransformaon Whou log ransformaon 3 (2,1,0) (0,1,1) s Wh log ransformaon Whou log ransformaon 4 (0,2,2) (0,1,1) s Wh log ransformaon Whou log ransformaon 5 (2,1,2) (0,1,1) s Whou log ransformaon Whou log ransformaon Source: Adaped from SAS Insue (1993, p. 924) The man dea of Box-Jenkns approach consss n o exrac he predcable movemens from he observed daa, usng prmarly hree lnear flers: he auoregressve, he negraon, and he movng average fler. Equaon (3) represens a general form of he ARIMA models. ~ y θ ( B) Θ( B) = φ ( B) Φ ( B) a (3) y~ = y ; e y~ s he dfferenced varable ( where: µ y ) cenred n relaon of your mean ( µ ), whle he dfferenced varable s represened by: y Y = Y Y ), d D = s Y, where D ( 1 s s Y = Y Y s, whle d s he dfference operaor s he seasonal dfference operaor, so ha Y s he level varable, and B s he j backward shf operaor so ha B y = y j. Fnally, φ ( B) = 1 φ B φ B 2... φ B p 1 2 p s he p order auoregressve q operaor, θ ( B) = 1 θ B θ B 2... θ B 1 2 q s he q order movng average operaor, S S 2S PS Φ ( B ) = 1 Φ 1 B Φ 2 B... Φ p B s he seasonal auoregressve operaor and 96

6 prce ransmsson n nernaonal soybean marke S S 2S QS Θ ( B ) = 1 Θ 1 B Θ 2 B... Θ Q B s he seasonal movng average operaor. Accordng o SAS Insue (1993) here are hree rules o choose he bes predefned model n ARIMA X-11 mehod. The frs rule s he average mean absolue percenage error (MAPE): MAPE = 100 n n = 1 y yˆ y (4) where: y ŷ are he corresponden las hree years values of me seres and are he esmaed value of one sep forward forecas. The MAPE decson rule s ha he seres for he las hree years mus be lesser han 15,0%. The second s he Box-Ljung es, whch s appled on model resdual. Ljung and Box (1978) defne hs es as: 2 r χ m = n ( n + 2 ) (5) k m 2 k k = 1 n where: n s he number of resdual, m=24 for monhly seres and χ 2 r k n k a = 1 = n = 1 a a 2 + k (6) a where s a resdual sequence. Therefore, he Box-Ljung es allows checkng f he ARMA resduals are no auocorrelaed usng a χ2 wh m-p-q degrees of freedom. If he numercal sasc value s greaer han he preseleced crcal value of χ2, so here s resdual auocorrelaon. The hrd rule s a es abou seres dfferencng. 97

7 Eduardo Luz Machado e Maro Anono Margardo Mlls (1990, p ) argued ha he seres over dfferencng ncrease he varance and useless parameers can emerge n he model. Freas e al. (1998) oban, n percenage form, he seasonal coeffcen amplude usng: ( Índce máxmo Índce mínmo) C. A. = X 2 X ( Índce máxmo + Índce mínmo) 100 The seasonal coeffcen amplude perms o verfy wheher a seres s seasonal or no, and wha nensy has Causaly es Gujara (1995) defnes he causaly concep as "f varable x causes varable y, hen changes n x should precede changes n y". A causaly es relavely smple was proposed by Granger (1969). Ths es assumes he nformaon relevan o he predcon of he varables s conaned solely n he me seres daa on hese varables. The es esmaed of wo varables (y e x) s represened by he regresson below: y x = α = β k = 1 k = 1 α β y x + + k = 1 k = 1 β x α y 1 + ε + ε 1 2 (7) where he dsurbances erm are uncorrelaed. One mporan observaon s ha he number lagged erms ncluded n regresson (7) can affec he drecon of causaly, because he Granger es s very sensve o he number of lags used n he analyss. Gujara (1995) dsngushes four possble resuls o he regresson (7): Undreconal causaly from x o y exss f β 0 e α = 0 ; Undreconal causaly from y o x s ndcaed f α 0 e β = 0 ; 98

8 prce ransmsson n nernaonal soybean marke Blaeral causaly s suggesed f β 0 e α 0 ; Independence or absen causaly occurs f β = 0 e α = 0. The es s used o verfy he ndvdual sascal relevance β ' α ' of boh and parameers. The jon sgnfcance of he complee se of varables s esed usng F es. 3. Resuls 3.1. Seasonal ndexes The seasonal ndexes of boh Brazlan and Argenne soybean FOB prces are more assocaed wh seasonal ndex of Roerdam CIF prce han wh Chcago quoaon (CBOT), conform llusraed n Graphc 01. Ths resul confrms a srong dependence of boh Brazlan and Argenne prces on he nernaonal marke. Ths fac does no happen wh EUA soybean complex. Graphc 1 - Comparave seasonal ndces Jan. Mar. Mao Jul. Se. Nov. CH ROT BR ARG Source: Prmary daa from Chcago Board of Trade (CBOT), Brazlan Vegeal Ols Indusry (ABIOVE) and OILSEEDS ( ) 99

9 Eduardo Luz Machado e Maro Anono Margardo The seasonal ndex of CBOT quoaon vared beween 95,88 (Sepember) and 104,31 (May), respecvely he begnnng of crop and he off-season perod n he Norh Hemsphere, wh amplude coeffcen 8,41%. These resuls are conssen because he prces durng he crop are smaller han he off-season prces. In Brazl, he seasonal ndex of FOB prces vared beween 97,42 (February) and 102,46 (Sepember), respecvely he begnnng of crop and he off-season perod n he Souh Hemsphere, wh amplude coeffcen 5,04%. Thus, as n he former case, he ndexes seem o capure he marke condons. Argenna's FOB prces reached mnmum value of 97,49 n February and maxmum value of 105,596 n Sepember, wh amplude coeffcen 7,98%. The seasonal ndex of Roerdam's CIF prces vared from a mnmum of 96,90 n Ocober o a maxmum of 101,763 n December, wh amplude coeffcen 4,89%. The seasonal amplude ndexes resuls show ha he Roerdam prces presen lower amplude varaon. Probably due o he sably of soybean supply o he European Unon along he year. Durng he Souh Hemsphere's off-season perod, Norh Amercan crop supples he EU marke; and, n he Norh Hemsphere's offseason perod, Souh Amercan crop (Brazl and Argenna) supples he EU marke. 3.2.ARIMA Models The resuls of ARIMAs models, whch were auomacally adjused by he X-11 mehod, revealed he predomnance of movng average parameers n hree of he four esmaed models. The only model ha presened major complexy level was he Brazlan FOB prces model, once was necessary he nroducon of a leas wo auoregressve parameers, whch showed hgh sgnfcance n erms of her respecve ess resuls (Table 3). Oher aspec observed s ha, excepon made o he BR varable model, all esmaed models are very analogous, wh predomnance of he movng average parameer of 12 order, n erms of sgnfcance level of es. The presence of hs parameer n all of he four esmaed models apparenly capures he soybean producon cycle unl s arrval n he marke. 100

10 prce ransmsson n nernaonal soybean marke Logarhmc ransformaon was necessary and he respecve Box-Ljung ess were sgnfcan n all of he esmaed models, showng ha he resduals auocorrelaon was surely elmnaed (Table 3) Granger causaly es The causaly ess resuls show ha he null hypohess of Brazlan FOB prces no causng Chcago Board Trade prces has 22,91% probably, whch means ha he probably of rue null hypohess rejecon (comm Error ype I) s 22,91%. Thus, can be saed ha Brazlan soybean FOB prces do no cause he quoaon of soybean n Chcago. Analysng he nverse way, ha s, he null hypohess of soybean quoaons n Chcago no causng Brazlan FOB prces, he probably of rue null hypohess rejecon s only 12,62% (Table 4). Thus, akng as bass o decson makng he commonly adoped sgnfcance level of 10,0%, can be saed ha Chcago Board Trade soybean quoaons do no cause he commody's FOB prces n Brazl and vce-versa, ha s, here s an absence of causaly n boh drecons. Analogous resuls were obaned n he causaly es wh he CBOT quoaons and Argenna's soybean FOB prces, once he probably of rue null hypohess rejecon ha Argenna's FOB prces do no cause he CBOT soybean quoaons (comm Error ype I) s 21,67%; whle n he nverse way he probably of rue null hypohess rejecon ha CBOT quoaons do no cause he Argenna's soybean FOB prces s 14,11% (Table 4). Thus, akng as bass he sgnfcance level of 10,0%, can be saed ha does no exs any causaly beween Argenna and Chcago's soybean prces. 101

11 Eduardo Luz Machado e Maro Anono Margardo Table 3 ARIMAs model s parameers esmaes Sere Parameer esmae es Chcago (CH) Consan = θ 0 θ 1 θ 12-0, (0, ) -0,31788 (0,10135) 0,88063 (0,06761) -0,64-3,14(*) 13,03(*) Model 1 Choce Creron: (0,1,1)(0,1,1)s, log ransformaon Box-Ljung es: 27,77 wh 22 freedom degrees, Probably=0,18 (Probably>0,05) Over dfferencng es: MA parameers sum = 0,88 (mus be < 0,90) MAPE hree las years : 4,21% (mus be < 15,00) Toerdam (ROT) Consan = θ 0 θ 12 φ 1 φ 2-0, (0, ) 0,87302 (0,06336) 0,03901 (0,10678) -0,09682 (0,10844) -0,74 13,78(*) Model 1 Choce Creron: (2,1,0)(0,1,1)s, log ransformaon Box-Ljung es: 11,77 wh 21 freedom degrees, Probably=0,95 (Probably>0,05) Over dfferencng es: MA parameers sum = 0,87 (mus be < 0,90) MAPE hree las years : 3,61% (mus be < 15,00) Brazl (BR) Consan = θ 0 θ 1 θ 2 θ 12 φ 1 φ 2-0,22079 (0,41655) 1,68579 (0,03521) -0,99896 (0,03566) 0,85726 (0,07079) 1,52723 (0,09194) -0,84942 (0,0955) 0,37-0,89-0,53 47,88(*) -28,02(*) 12,11(*) 16,61(*) Model 1 Choce Creron: (2,1,2)(0,1,1)s, log ransformaon Box-Ljung es: 10,56 wh 19 freedom degrees, Probably=0,94 (Probably>0,05) Over dfferencng es: MA parameers sum = 0,86 (mus be < 0,90) MAPE hree las years : 4,30% (mus be < 15,00) -8,89(*) (connua) 102

12 prce ransmsson n nernaonal soybean marke (connuação) Table 3 ARIMAs model s parameers esmaes Sere parameer esmae es Argenne (ARG) Consan = θ 0 θ 1 θ 12-0, (0, ) -0,16546 (0,1047) 0,78325 (0,07204) Model 1 Choce Creron: (0,1,1)(0,1,1)s, log ransformaon Box-Ljung es: 21,43 wh 22 freedom degrees, Probably=0,49 (Probably>0,05) Over dfferencng es: MA parameers sum = 0,78 (mus be < 0,90) MAPE hree las years : 3,86% (mus be < 15,00) -1,03-1,58 10,87 (*) Sgnfcance a 5,0% level Source: Prmary daa from Chcago Board of Trade (CBOT), Brazlan Vegeal Ols Indusry Assocaon (ABIOVE) and OILSEEDS (1991/1999) The analyss of he relaonshp beween Brazlan soybean FOB prces and s respecve Roerdam CIF prces shows ha he null hypohess of ROT no causng BR has probably of 11,79%, ha s, here s only 11,79% chances of rue null hypohess rejecon. The adopon of he null hypohess of BR no causng ROT has a probably of 81,57% of rue null hypohess rejecon (Table 4). Agan, adopng as paern he sgnfcance level of 10,0%, here s absence of causaly from ROT o BR, and from BR o ROT. Meanwhle, f adoped he sgnfcance level of 12,0%, here s causaly from ROT o BR, ha s, soybean prces n Roerdam cause soybean prces n Brasl, whle he nverse does no occur. The analyss of he relaonshp beween ROT and ARG demonsraes ha he rue null hypohess rejecon of ROT no causng ARG has probably of only 0,94% showng he presence of causaly from Roerdam soybean prces on he soybean FOB prces n Argenna. Analysng he nverse way, he probably of ARG no causng ROT s 99,95%, hus, he possbly of rue null hypohess rejecon s 99,95%, ndcang ha Argenna's soybean FOB prces do no nfluence he soybean CIF prces n Roerdam. 103

13 Eduardo Luz Machado e Maro Anono Margardo Table 4 Granger causaly es (*) Null hypohess F es Probably BR no causng CH CH no causng BR ARG no causng CH CH no causng ARG ROT no causng BR BR no causng ROT ROT no causng ARG ARG no causng ROT CH no causng ROT ROT no causng CH 1,3253 1,5686 1,3489 1,5244 1,5955 0,6211 2,5210 0,1492 0,7991 1,2613 0,2292 0,1263 0,2168 0,1412 0,1179 0,8158 0,0095 0,9995 0,6497 0,2656 (*) Monly daa were used, requrng 12 lags each case analysed Source: Prmary daa from Chcago Board of Trade (CBOT), Brazlan Vegeal Ols Indusry Assocaon (ABIOVE) and OILSEEDS ( ) Fnally, he causaly es shows ha neher Chcago cause Roerdam nor Roerdam cause Chcago. Ths resul confrms he assumpon ha soybean prces are se whn hese ceners. 4. Concluson The resuls confrm he exsence of srong dependence of he Brazlan and Argenne FOB prces wh he CIF prces n Roerdam, dfferenly of USA prces, ha are se whn CBOT. Oher mporan resul s ha he amplude of seasonal sandard s more accenuaed n USA off-season perod n Brazl and Argenne. On he oher hand, he seasonal sandard n Roerdam s less accenuaed han anoher seres, due o he fac ha supply n European Unon s consan durng all year. These resuls are accordng o he expeced for hs marke, due o USA, Brazl and an Argenne crop occurs n dsnc perods of he year. Margardo e al. (1999) had obaned smlar resuls o he Granger causaly es ha confrms he dependency of boh Brazlan and Argenne prces o foregn marke. 104

14 prce ransmsson n nernaonal soybean marke Absrac: hs paper ams o analyze he seasonal behavor and he relaonshp among soybean prce n Chcago Board of Trade (CBOT), CIF prces n Roerdam and FOB prces n Brazl and Argenne. The man conjecure s ha Brazlan and Argenne prces are more assocaed wh Roerdam han Chcago, due o he European Unon s he man desnaon of soybean expored by boh counres.the expeced resul s ha he seasonal prce behavor n FOB prces of hese counres wll be more smlar o CIF prces n Roerdam han o CBOT quoaon. Thus, here s a prce ransmsson sysem based on hs seasonal behavor. Key words: seasonal; soybean; prce ransmsson sysem. Bblographc references AGUIAR, D.R.D.; BARROS, G.S.A.C. - "Causaly and asymery n Brazlan soybean and dervaves prces ransmsson n 1980's". In : Esudos Econômcos, São Paulo, Vol.21, n.1, p , jan.-abr./1991 (Poruguese). BOX, G.E.P.; JENKINS, G.M.; REINSEL, G.C. - Tme seres analyss: forecasng and conrol. New Jersey: Prence Hall, 3rd Ed., 1994, 598p. DAGUM, E.B. - The X-11-ARIMA/88 Seasonal Adjusmen Mehod. Oawa: Sascs, Canada, EVIEWS - User Gude - Verson 2.0. Calfórna: QMs, , 372p. FREITAS, S.M.; FERREIRA, C.R.R.P.T.; BARBOSA, M.Z. - "Opporunes and mpedmens n Brazlan dende culure expanson". In : Agrculura em São Paulo, Vol.45,.2, p.1-16, 1998 (Poruguese). GRANGER, C.W.J. - "Invesgang causal relaons by economerc models and cross specral mehods". In : Economerca, Vol.37, p , GUJARATI, D.N. - Basc Economercs. New York: McGraw-Hll, 3rd Ed., 1995, 838p. LJUNG, G.M.; BOX, G.P.E. - "On a measure of lack of f n me seres models". In : Bomerka, Vol.66, p.66-72, MARGARIDO, M.A.; SOUSA, E.L.L. - "Soybean prces formaon n Brazl". In : Agrculura em São Paulo, Vol.45, n.2, p.52-61, 1998 (Poruguese). 105

15 Eduardo Luz Machado e Maro Anono Margardo MARGARIDO, Maro A ; SOUSA Eduardo L. Leão de; BARBOSA, Marsa Zeferno e FREITAS, Slene Mara. Transmssão de preços no mercado nernaconal de grãos de soja: uma aplcação da meodologa de séres emporas. In: CONGRESSO BRASILEIRO DE ECONOMIA E SOCIOLOGIA RURAL, 37, Foz do Iguaçu, 1-5 ago Anas... Brasíla: SOBER, MILLS, T. - Tme seres for economss. New York: Cambrdge Unversy Press, 1990, 247p. NEVES, L.C. - Comercalzaon margns and prces ransmsson elascy n he soybean processng ndusry. Dsseraon, Praccaba: USP/ESALQ, 1993, Dsseração de Mesrado (Poruguese). OILSEEDS - World Marke and Trade. Washngon: USDA, 1991/1999. PINO, F.A.; ROCHA, M.B. - "Soybean prces ransmsson n Brazl". In : Revsa de Economa e Socologa Rural, Vol.32, n.4, p , ou.- dez./1994. SAS INSTITUTE - Forecasng Technques Usng SAS/ETS Sofware : Course Noes. Cary, NC, SAS INSTITUTE - SAS/ETS user s gude, verson 6. Cary, NC, 2 nd Ed., 1993, 1022p. 106