a study of tellurium market finlanda iun.2013

7/27/2019 A Study of Tellurium Market Finlanda Iun.2013

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Isopacity-inducedminormetalmarket

volatilityathreattopromisinggreentechnologies?

Astudyofthetelluriummarket

FredrikSderqvist

MasterofScienceThesisUppsalaUniversityDepartmentofEconomicsSubmittedJune7,2013Supervisor:AssociateProfessorMikaelBask


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F.Sderqvist Astudyofthetelluriummarket 2


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This master thesis was written in the spring of 2013 as part of the UppsalaUniversityMasterProgrammeinEconomics.IwouldliketothankSanderdeLeeuw

atNewBolidenABforthesupport,inspiration,anddataaccesshehasgenerouslygranted me, and my supervisor Mikael Bask for his thoughtful guidance andmeticuloussupervisionofthisthesis.

For questions, comments or inquiries regarding the content, methods, data orconclusionsdrawninthisthesis,[email protected]


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AbstractTelluriumisoneoftherarestmetalsintheearthscrust.Increaseddemandforcadmiumtelluridephotovoltaiccellsalongwithanopaquepricingandquantity-reporting system, have recently caused high price volatility and a speculativebubbleinthetelluriummarket,resultinginoverstockinganddepressedprices.

Inalongerperspectivethismaybeathreattocadmiumtelluridephotovoltaicsasapower-generatingtechnology.Thismasterthesiscompareshowactorsmayperceivenewsinnovationintheopaquetelluriummarketcomparedtothemoretransparent molybdenum market. A quantitative analysis of industry newsreportingonthetwometals,combinedwithaSVARimpulseresponseanalysis,helps me determine which actors and factors exert most influence on spotmarket prices. Inthe opaquetelluriummarket, relativelyunreliableproxies ofsupplyanddemandaremostfrequentinthenewsreportingwhilehavingabigimpact on prices, whereas the transparent molybdenum market uses morereliablevariablessuchasfuturesprices andtransparentsupplyinformation,whilstalsorelyingonafrequentstreamofdependableproxiestoscopemarket

sentiments. My findings leadme to recommend policy makers to implementmeasures to increase market transparency, which may be accomplished byextending the data-sharing regime of the REACH database to minor metalmarkets.Attemptingtolimitspeculationinminormetalmarketsisperhapstoobluntatooltofixaninherentproblemofafreeexchange-pricingmechanism.

SammanfattningTellur r en av demest sllsynta metallerna p Jorden. kad efterfrgan avkadmiumtelluridsolpaneler har nyligen orsakat stor volatilitet p

tellurmarknaden.Ettopaktprissttnings-ochkvantitetsrapporteringssystemharbidragit till att en prisbubbla bildats och spruckit, vilket resulterat i attmarknadsaktrer kpt p sig stora lager till hga priser som de sedan intekunnatsljavidare.Iettlngreperspektivkandettainnebrabegrnsningarvidtillverkningav solcellsteknologibaseradpkadmiumtellurid,dettvolatiltpriskangranyatellurgruvprojektalltfrriskabla.Dennamasteruppsatsjmfrhuren typisk marknadsaktr kan reagera p prisinnovationer i den opakatellurmarkanden och den mer transparenta molybdenmarknaden. Metodenbestr av en kvantitativ analys av facknyheter rrande de tv metallerna,varifrnvariablervljstillenSVARmodellmedimpuls-responsanalys.Urvaletavvariablerrfochvolatilapdenopakatellurmarknaden,medandenmer

transparenta molybdenmarknaden har ett strre utbud av variabler somknnetecknas av god transparens och relativ frutsgbarhet. Mina slutsatserleder mig till att rekommendera beslutsfattare att vidta tgrder fr att katellurmarknadenstransparensgenomEU-samarbetet,frslagsvisgenomattgraanonymiseraddatafrnREACHdatabasentillgngligfrallmnheten.Samtidigtavrder jag frn tgrder som syftar till att minska spekulation, dimplementeringavensdanpolicykanblibdedyrochkomplicerad.Keywords:Tellurium,MinorMetal,MarketVolatility,MarketTransparency,Molybdenum,MarketEfficiency,REACH,SVAR,QuantitativeAnalysis,London

MetalExchange.JELcodes:G13,G28,Q02,Q31,Q32,Q38,Q55.


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TableofContents

ISOPACITY-INDUCEDMINORMETALMARKETVOLATILITYATHREATTO

PROMISINGGREENTECHNOLOGIES?ASTUDYOFTHETELLURIUMMARKET..... ........1

ABSTRACT................. .................. .................. ................. .................. .................. .................. ..............4

SAMMANFATTNING.............. .................. .................. ................. .................. .................. ................. 41.INTRODUCTION................. .................. .................. ................. .................. .................. ................. 6

2.BACKGROUND............................. .................. ................. .................. .................. .................. ........82.1TELLURIUM.................................................................................................................................................8 2.2TELLURIUMSUPPLY...................................................................................................................................92.3TELLURIUMDEMAND..............................................................................................................................112.4THETELLURIUMMARKETPLACE ...........................................................................................................122.5THETELLURIUMMARKETTODAY ..........................................................................................................132.6MOLYBDENUM-ANOT-SOMINORMETAL.......................................................... .................................142.7CRITICALMINORMETALS .......................................................................................................................152.8PREVIOUSSTUDIESOFMINORMETALMARKETS ................................................................................15

3.METHOD:DETERMININGTHEPRICEMECHANISMSOFTELLURIUMAND

MOLYBDENUM...............................................................................................................................17 3.1SVARANDIMPULSERESPONSEFUNCTIONS .......................................................................................173.2QUANTITATIVEANALYSIS.......................................................................................................................18

4.DATAANDRESULTS................................................................................................................22 4.1SPOTPRICESANDRETURNS...................................................................................................................224.2QUANTITATIVEANALYSISFINDINGS .......................................................... ...........................................244.3INCORPORATINGAPPROPRIATEACTORSANDFACTORSINTOTHE SVARMODEL.......................28

4.3.1TheYuetal(2012)modelonappliedontellurium.........................................................28 4.3.2Amarket-specifictelluriummodel.........................................................................................32

4.3.3Amarket-specificmolybdenummodel..................................................................................364.4OTHERFINDINGSFROMTHEQUANTITATIVEANALYSIS ........................................................ ............40

5.CONCLUSIONS............................................................................................................................44

REFERENCES................ .................. .................. ................. .................. .................. .................. ........46

APPENDIX.................. .................. .................. ................. .................. .................. .................. ...........50LISTOFABBREVIATIONS................................................................................................................................50VARANDSVARFUNCTIONDERIVATION ...................................................................................................51QUANTITATIVEANALYSISCODINGEXAMPLE .............................................................................................53COMPLETESTRUCTURALINNOVATIONGRAPHS ........................................................................................54


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1.IntroductionAs photovoltaic (PV) technologies recently reached grid parity withoutgovernmentsubsidiesinseveralplacesandthusbecomingacheapersourceofpowercomparedtobuyingelectricityfromthepowergrid1,demandforcritical

materials in PV technologies is expected to increase. One of these criticalmaterialsistellurium(Te),aminormetal2andoneof therarestmetals in theEarths crust. Until recently, Te has mainly been used as a machinability-increasing alloying agent in steelmanufacturing. The metals semi-conductingpropertieswhenboundwithcadmiumtoproduceCadmiumTelluride(CdTe)haveprovenexcellentatconvertingsolarradiationintoelectricityinCdTePVsolar cells. CdTePV is, as of February 2013, themost efficient technology toharnessthepowerofthesunwithregardstocostsperwattproduced($/Wp)andconversionefficiency;however,longtermCdTegrowthmaybehemmedbythelimitedsupplyofTeanditsrelativerarity.Despitedemandlookingpositiveinthelongrun,thespotmarketpriceforTetellsaconflictingstory.Priceshave

rocketedandfalleninrecentyears,andthusvolatilityisveryhigh.Tocomparethehighsandlows;inJune200499.99%pureTecost$31perkgontheopenmarket,sevenyearslaterinJune2011itcost$430perkg,andinJune2012thespotpricewasonly$145perkg.Likemostminormetals,Teisnotlistedonanycommoditiesbourse,andthereexistslittlereportingoftradedquantities.Thismakesbusinessandlong-terminvestmentdifficultforactorsonthemarketandcould threaten future development of CdTe PV production. At a UK House ofCommonsScienceandTechnologyCommittee(2011)-hearing,itwassuggestedthat critical metal market supply-information, such as Te supply, should beimproved, and measures to limit speculative buying should be considered in

ordertoremedyvolatilityinminormetalmarkets.This thesis is an attempt to determine what causes volatility in the Te metalmarket.Thetwomainresearchquestionsare:whichfactors,actors,andmarketinstitutionshavethebiggestimpactonTeprices,andwhatdoesthistellusabouttheoveralltradingconditionsonthemarket?Theresultsandmethodologycouldlendconclusionsvalidtootherindustry-critical,opaquelytradedminormetals,andaddtothediscussionastowhatcanbedonetoreducevolatilityinthesemarkets.This thesis also contributes to the scientific literature concerning TesupplylimitationstoCdTePV,whichtomyknowledgehasnotfocusedonthethreattothefuturesupplyofTethathighpricevolatilitymaypose.

In order to determine whatmakes the Te price fluctuate, a SVAR-modelwithimpulse response functions is estimated using the same aggregatedmacroeconomicvariableswhichYuetal(2012)usedtoattempttodeterminepricefluctuationsinthephotovoltaicsiliconfeedstock(PVSF)spotmarket.PVSFisahighlypricevolatile,criticalmaterialinarivalPVsolarcelltechnology.A

1REneweconomyarticleUBS:BoominunsubsidisedsolarPVflagsenergyrevolution:http://reneweconomy.com.au/2013/ubs-boom-in-unsubsidised-solar-pv-flags-energy-revolution-60218(accessedMay212013).2AmetalincludedintheMinorMetalTradeAssociation:http://www.mmta.co.uk/history-and-change(accessedMay212013).


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quantitativeanalysisisthenappliedtoasetofarticlespublishedinanindustrynewspaper,theMetalBulletin,inordertobetterselectvariablesthataremoremarket-specific.InordertobenchmarkandbetterrelatetheTeresults,thesamemethod is applied to molybdenum (Mo),which is aminormetalwith similarcharacteristicsandapplicationsasTe.TheselectionofMo ismainlymotivatedby its introduction to the London Metal Exchange (LME) in 2010; a marketregimetransitionthatintroducedfuturescontracts,andtransparentpricingandquantitativereportingmechanisms.Myfindingsindicatethatmarket-specificactors,factorsandinstitutionsamplydescribe price fluctuations in both the Te and Mo markets, whereas theaggregatemacroeconomicvariablespresentedbyYuetal(2012)donotexplainprice fluctuations well. The quantitative analysis suggests that there are fewvariables to choose from in the Te market (mainly market specific stockcompanies). These variables explain price fluctuations quite well, but are notvery transparent. On the Mo market there are plenty of proxies of supply,

indices,andfuturespricesthatamplyexplainvariation,whilstexhibitingsteadyinformationflowsoftransparentpriceandquantityreports.FromthisIadvisethatmeasuresaretakenintheTemarketto introducesomeoftheinstitutionsthat help reduce volatility on the Mo market. I deem that the most criticalmeasurewouldbe to improvequantitativetransparency in themarket,whichcouldbedonewithinthedata-sharingregimeoftheREACHframework.Inthesecondchapter,abackgroundtoTe,itssupply,demand,marketplace,andmarket today is given, along with a brief introduction to the Mo market, adefinition of minor metals, and a summary of older studies regarding minormetal market information, efficiencies, deficiencies and transparency. In thethirdchapter,theSVARmodel,aspresentedbyYuetal(2012)isintroduced,alongwithadescriptionofmyquantitativeanalysis.Inthefourthchapter,spotprices and returns of Te and Mo are selected. Results from the quantitativeanalysisarethenpresented,fromwhichvariableselectionismade,followedbySVARandimpulseresponsefunctionresultsfromtheYuetal,Te-marketspecificand Mo- market specific SVAR models. Finally, other findings from thequantitativeanalysisarepresented.InthelastchapterIdiscussmyconclusionsandpolicyrecommendations.


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2.Background

2.1Tellurium

Teisanelementinthesamefamilyasoxygen,sulphur,seleniumandpolonium.

ItsabundanceonEarth,asdisplayedinFigure1,showsthatitisoneoftheninerarestmetals,wheresevenoftheseareconsideredprecious(Green2010).Tehassemiconductingproperties,meaningithastheelectricalpropertiesofbothaconductingmetalandaninsulator(Nussbaum1962).Tesupplyhastraditionallybeenaby-productofcopper,lead,andzincprocessing,butcanalsobeextractedfromgoldprocessing(Green2009,NewBoliden2011)andisminedasaprimarymetalontwolocationsinChina,andoneinMexico(USGS,2013a).

Figure1ShowsthatTe(insidetheyellowRarestmetals-cloud)isoneofthe9rarestmetalsinthe

Earthscrust.Itsabundanceissimilartothatofgold(Au)andplatinum(Pt).Source:USGS2002.

Inrecentyears,anincreaseindemandforTehastakenplaceduetoachangeintheprimaryindustrialusagesofthemetal.TheSeleniumTelluriumDevelopmentAssociation (STDA), whose members include most of the worlds majorproducers ofTe, estimates that global distribution by consumption is 40% in

solar cells, 30% in thermoelectric and photoelectric copying devices, 15% inmetallurgy asanalloyingmetal, 5% inrubber formulationasa vulcanisation-and acceleration in rubber compounding processes, and 10% in otherapplications such as in blasting caps andceramic- and glass pigments (STDA,2012,USGS2012a).The40%finalconsumptioninphotovoltaiccellsisduetoarecentdemandsurgethatstartedaroundtheyear2000,whenproductionofCdTethinPVsolarpanelsincreasedasaresultoftechnologicaladvancementsandgovernmentsubsidiesofPV(Candeliseetal,2011).


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2.2Telluriumsupply

Estimating an exact volume of world supply for tellurium is difficult. Manycountries and companies do not report their production, while volumesrecovered from recycled photoelectric devices is not reported at all (USGS,

2012a).TheUnitedStatesGeologicalSurvey(2013)haschosentowithholdtotalUS-output from thepublic in order to avoid disclosing company proprietarydata,andduetoinaccuraciesinthedata,havechosentolistworldoutputasN/A since 2006. The British Geological Survey (BGS, 2013) has since 2007published data on Canadian, American, Peruvian and Japanese-producedtonnagesofTe,estimatingUSproductionat50tonnesperyear.Toaddtotheinaccuracies,allglobalproductionestimatesareonlybasedonTeproducedfromcopper anode slimes.3As Te is not traded on anymajor bourse, there are noaccounting or reporting requirements such as those associated with theLondonMetalExchange(2013)andthustradedquantitiesremainunreported.This means that an estimated BGS (2013) total world production

(approximately96tonnes)asreportedbySpeirsetal(2011),ismuchlowerthanrealproduction,asitomitsdatafromTe-producingcountriessuchasAustralia,Belgium, Chile, China, Colombia, Germany, India, Kazakhstan, Mexico, thePhilippines, and Poland (USGS, 2013a). The most thorough estimate of totalworldproductionfromcopperanodeslimesisbetween450and500tonnesperyearwascarriedoutbytheUKconsultancyfirmOakdeneHollins(2012).Whendiscussingfuturesupplyofametal,so-calledreservesandreservebasesmustbetakenintoaccount.ReservesaredefinedbytheUSGSasthepartofthereservebase,whichcouldbeeconomicallyextractedorproducedatatimeofdetermination. Reserve bases are identified sources of a mineral whichmeetphysicalandchemicalcriteriarelatedtocurrentminingpractices,andthatmayonedaybeextractedeconomically(USGS2012a).ReservesreportedbytheUSGSshowonlyreservesofTeboundtocopperores,andarethusanunderestimationwithregardstorealTereserves.TheOakdeneHollinsreport(2012)estimatethecopperanodeslimesreservestobecloseto24000tonnesofTe.Scientific literature concerned with photovoltaic progress has made severalattemptstoestimatepresentandfutureworldsupplyofTe,asCdTetechnologywill not be a viablepower generation technologywithout a steadily availablesupply of Te. In a meta-study of Te availability, Candelise et al (2011)

summarisesdatafromsixstudiesbetween1998and2009thatestimatesfutureyearlycumulativesupplyofTefrom128to2000tonnesperyear.Acommonfault in many of these estimates is that they use the above-mentionedunderestimated USGS data to reach their conclusions. Green (2009) does afurther analysisof possible Te that canbe extracted from other ores, and so-calledBonanzadepositsthatminesTeasaprimarymetal.Hourarietal(2013)isthelatestattempt,andlooksatfuturesupplyfromadynamicperspective,whichmeansthatitimplicitlytakesTepricesandfuturedemandofCdTeintoaccountwhenestimatingfuturesuppliedquantitiesofTein2050.Thesupplyismadedynamic by taking other possible final usages of Te into account, as well as3Aproductofelectrolysiscopperrefinement,fromwhichimpuritiessuchasTecanbeextracted.


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includingTewhichcouldbeextractedwhenrecyclingspentCdTePVunits.Thestudy concludes that future Te supply available for CdTe PV production isexpectedtobeslightlylowerthaninpreviousstudies.Theseglobalflowsandfeedback loops may in the end influence both supply and demand. Figure 2illustrateshowloopsofTesupplyaredeterminantfortheproductionofCdTePV.

Figure2TheCdTecasualloopdiagram,whichhighlightsareaswhereproductioncostsofproducing

CdTePVcanbereduced.Source:Houarietal(2013).

Figure3,thedynamicmodel,visualiseswherefuturesourcesofTemaycomefrom,andwhereitmayendup.

Figure3ThesystemdynamicsmodelwhereannualTeproductionplaysabigrole.Source:Houariet

al(2013).

Acommonprobleminthesestudiesisthattheyfailtoincludetheestimated41tonne yearly output from Kankbergsgruvan in Vsterbotten (Boliden 2011)extractedfromgoldamethodwhichuntilrecentlyhasfaultilybeendeemedunprofitable for Te-pricesunder$800/kg (Green2009).Another,more recentthreattofuturesuppliesarenew,moreefficientcopperprocessingtechniques,which are not able to extract Te from the anode slimes, and are expected to

decreaseworldTeoutputastheuseofthesetechniquesincreaseinapplication(OakdeneHollins,2012).


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2.3Telluriumdemand

Future demand of Te is dependent on estimates of future technologicaladvancementsandproductionimprovement,aswellasdemandforPVpowergeneration.TomeasureeconomicefficiencygainsinCdTetechnology,anindex

ofUSDcostperWattproducedisoftenused,whichenablescomparisonthroughtime and competing power-generating technologies. The latest Cost per WattproducedestimatebythemarketleaderFirstSolar,is$0.68/Wpperpanel,atrecord breaking 20% solar conversion efficiency, making it the most cost-efficientPVtechnologyreadily availabletothemarket (First solar2012).Thiscost per panel is not the same as cost per PV-system or facility, which aregenerallyhigher.AworkingpaperbySpeirsetal(2011)givesaclearoverviewofpotentialfuturedemandofTeinCdTePVmanufacturing.FuturedemandofTeisdependentonthe above-mentioned cost of producing electricity. Theworking paper shows

thatthelimitedfuturesupplyofTeshouldnotbeathreattoCdTedevelopment,asCdTePV-unitswillinthefuturerequirelessTetoproducethesameamountofenergy.Figure4illustratesthecontentofaCdTePVthinfilmcellandhowmuchofitiscomposedofanactiveCdTelayer.Thislayerisexpectedtodecreaseinthefuturethroughtechnologicalprogress.Woodhouseetal(2012)havecalculatedthat at a CdTemodule produced at $0.70/Wp spends $0.15/Wp on the CdTeactivelayer,andthatfuturematerialintensitywilldecreasefrom74tonnesofTeperGWtoday,to17tonnesperGWin2020.

Figure4IllustrationofcompositionofaCdTethinfilmsolarcell.ThethicknessoftheActiveCdTeis

anarea believedpossible tomake thinner,whichwoulddecrease futuredemand forTe. Source:

Speirsetal(2011).

Speirs et al (2011) continues to conclude that CdTe demand for Te in 2030rangesfrom480to1800 tonnesperyear,whichexceedscurrentsupplyofTe,including Te usages for in other applications. This is an indication of future


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supply shortages, which will ultimately lead to higher prices. The previouslymentioned papers on the possible limitations on CdTe PV posed by supplyshortagesmake someestimates toamaximumpricewherepower generationwould still be profitable, such as Candelise et al (2011), who estimate amaximumspotpriceof$700/kgof99.99%Te,andGreen(2009)at$800/kg.Woodhouseetal(2012)estimatethatatcurrentprices,productionin2020willbeconstrainedat10GWofannualproduction,whichmayonlyberemediedwithhigherpricesthatmakefutureminingprojectsmoreprofitable.Thus,TeavailabilityoughtnottoconstrictfutureproductionofCdTePVaslongascosts forTedonotexceedacertainthreshold,andtheactiveCdTelayersinthepanelscontinuetodecrease.Thisthesisattemptstofillagapinthescientificliterature,namelytoprovideamorerobuststudyofhowpricemechanismscanaffect futureTeprice scenarios,which has been requested inmost of the keyliteratureusedinthisthesis(Candeliseetal2011,2012,Green2009,2010,andSpeirsetal2011).

2.4Thetelluriummarketplace

Teistradedthroughlong-termsupplycontractsandindividualtradesbetweenlarge consumersand suppliers. Potential buyers and sellers can list proposedprices on specialist websites, which are then matched. Price quotes usuallyrepresentexpertestimatesofrepresentativepricesintradesbeingexecutedonaparticular day, and not actual traded volumes and prices (Oakdene Hollins2012).Myanonymoussource(2013)withgoodinsightinthemarketaddsminormetal conferences and companies existing costumer networks as possibleforums tomeet potential customers. Thesemarketplaces are thus thoroughly

opaquetooutsiders.TheonlyopenmarketplaceIhavefoundistheChinesetradingwebsite Alibaba,where sellers canpost advertisements tosell variousqualitiesandquantitiesofTe.4Te pricesare postedon several trading andmarket news sites, including theMetalBulletin,aUK-basedpaperthatreportsonglobalnon-ferrousmetalsandsteelmarkets(MetalBulletin2013a).Astelluriumisnottradedonanybourse,pricesareestimatedwiththeaidofdifferentmetalwarehouses.MetalBulletin,whichlistsmanydifferentspotpricesofmetalsandcommodities,hasdonethisformanyyears.Thegoalistodiscoveratwhatlevelmarketparticipantshave

concluded business, made offers or received bids over a certain time period;usually theperiodbetweenthe lastprice-listing inthepaper.Afterinteractionwithmarketactors,MetalBulletinconfirmthetransactionwithbothsides,weighthepriceandquantitytoothertransactionsduringthetimeperiod,andfinallypostapricelistingconsistingofa lowandhighprice.Theyreservetherighttoremove any data they consider outliers or discount prices they considerquestionable.MetalBulletinstressthattheyattempttoengage(andencourageengagement)withallsellersandbuyersonthemarket,irrespectiveofsize,are

4ThismarketcanbeaccessedbysearchingforTelluriumonwww.alibaba.comorviathelink:http://www.alibaba.com/trade/search?fsb=y&IndexArea=product_en&CatId=&SearchText=tellurium.


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impartialandindependent,anddonothaveanyvestedcommercialinterestsinpricingoftheirlistedmetals.ThesmallesttradedlotstakenintoconsiderationwhendeterminingthepriceofTeis250kg,whichrecentlychangedfrom500kg(MetalBulletin2013b).Figure5illustrateshowvolatilethespotpriceofTeis,whichisacommontraitformanyminormetals(Candeliseetal2012).

Figure5TeaverageweeklypricefromFebruary62006toFebruary282013.Note:Thereareno

pricelistingsforJune12and26,aswellasOctober22009.Source:FOBUSAWarehouse(February4

2006toJune222012)andMetalBulletin(June292012toFebruary222013).

2.5Thetelluriummarkettoday

Inavolatilespotmarketbasedonestimatesoflong-termcontracts,theremaybeincentivesforactorstoridebubblesforshort-termprofits(Harrisonetal1978,Biasisetal1998).Forexample,inJune2011thepriceofTepeakedat$430/kg,upfrom$165/kgin2009.Afterthe2011-peak,spotpricesdeclinedsteadilyforayearandarestabilisedatlevelsjustabove$100/kg.Thisisindicativethatthetwo-year160%increaseinpricebearsthemarkingsofaspeculativebubble.Asimilarphenomenoncanbeobservedfor theyears2006to2008,whenpricesmorethandoubledandthendroppedtohalfitspeakvalue.Ithasbeensuggested

thatthesebubbleswereinitiatedbyspeculativebuyingofTeunderthepretextthatthelimitedsupplyofthemetalwouldbeinsufficienttomeetfuturedemand(USGS,2013b).Thisleadtoahoardingofthematerialinwarehouses,boughtatinflatedprices.Oncethemarketdiscoveredthis,thepricerapidlyfell,andpricesare still depressed, as the stocked Te bought during the bubble has yet beendepleted (Oakdene Hollins, 2012). The recent change in minimum reportedquantities in the Metal Bulletin from 500kg to 250kg might further beinterpretedasanindicatorthatvolumesonthemarketarecurrentlysolow,thatmakingstatisticalsamplesofmarketinteractionsaredifficultatthesevolumes.RecentstatementsbymajoractorsontheTemarketpredictthat2013priceswillremain in the $100-150/kg range, stressing the market would benefit from


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reducedvolatility,orelsesupplierswouldfindithardtofinancefutureminingprojectsofthemetal.5Thebubblemaybetheresultofspeculativetradingonanopaquemarketthatlacks transparent reporting over whom trades what to where, which hasresulted in high volatility. In a conference paper, Green (2010) compares Tepricefluctuationstothoseexperiencedbyphotovoltaicsiliconfeedstock(PVSF),whichisamaterialwhosepricehasrecentlybeenstudiedbyYuetal(2011).Thisobservationisdiscussedfurtherinthemethodchapter.In order to compare how an opaquely tradedminormetal may differ from atransparently traded minor metal, I make an assessment of the market formolybdenum (Mo),which is tradedunderamore transparentmarket regime,andislistedontheLondonMetalExchange(LME).

2.6Molybdenum-anot-sominormetal

Itisdifficulttojustifyacomparisonofthemarketofonechemicalelementtoanother;shouldchemicalcharacteristics,chemicalfamily,application,orpricebeused as a basis for comparison? I have chosen to compare Te to Mo for thefollowingreasons:theyarebothminormetalsofsimilaratomicnumber(Mono.42 andTeno. 52); they areby-products of copper production, and thus theirsupplyreliesheavilyontheextractionandrefinementof copper;andtheycanbothbeusedas steelalloyingagents.Finally,Mowasoneof twominormetalsintroduced to the LME in February 2010, whichmay help to illustrate how aminor metal is traded under the transparent market conditions which wereimplementedpriortotheLME-introduction(OakdeneHollins,2012).

Moisarefractorymetallicelementprincipallyusedasanalloyingagentiniron,steel, and superalloys to enhance desirable properties such as machinability,toughness,strengthandcorrosion-resistance (USGS, 2012b). Theseproperties,along with it having one of the highest melting points of all the chemicalelements, means that Mo has few chemical substitutes. Mo does not exist innature as a free metal, and is usually found in deposits bound to low-gradeporphyry-molybdenum and copper deposits. The most important ore ismolybdenite,andtotalworldsupplyisroughlycomposedofhalfMominedasaprimaryproductandhalfasaby-productofcoppermining.Finalusagesofthe

metal are 24% stainless steel, 16% full alloy steel, 11% tool- and high-speedsteel,10%highstrengthlowalloy(HSLA)steel,9%carbonsteel,6%castiron,8% catalysts, 6% metal & alloys, 5% superalloys, and 5% others (OakdeneHollins, 2012). An interesting development is the relatively small-scaleapplicationofMoinCIGS-PV6cellsasanelectrical conductor,which lends themetal a small application- connection with the Te market. Data of yearlyproductionandusageofMoisreadilyavailableandindicatesamarketroughlyinbalancewithregardstosupplyanddemand(IMOA,2011).

5Telluriumpriceseenin$100-150/kgrangethisyear5NPlusbyMartinHayes,http://www.fastmarkets.com/minor_metals/5nt1(accessedonMarch26,2013).6AnotherthinfilmPVtechnology.


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Mo spotpricesarereportedusing the same samplingprocedureasTe (MetalBulletin,2013).Recently,anofficialcashpricewasalsomadeavailablevia theLME,whichdiffersslightlyinsamplingprocedure,butwillnotbeusedinthisthesisduetothelimitedtimespanofthedata.Themaindifferencebetweenthetwo metals is there exists a futures market for Mo via the London MetalExchange(LME,2013),andthusthespotpricescanbeseenasareflectionoflong-termcontractstradedtransparentlyonafreemarket.Althoughonly6702tonnesofMohad been tradedon the bourse between its opening andMarch2012,whichamounts toapproximately1%oftotalestimatedtradedvolumes(OakdeneHollins,2012),onecanarguethatthemereexistenceofaregulatedfuturesmarketwillreducevolatility(Slade,1988).

2.7Criticalminormetals

Apartfrombeingconsideredminormetals,MoandTehavebothbeenassessedfortheircriticalitybytheEuropeanCommission(2010).Toqualifyasacritical

material,arawmaterialmustfacehighriskswithregardtoaccesstoit,i.e.highsupplyrisksorhighenvironmentalrisks,andbeofhigheconomicimportancethelikelihoodthatimpedimentstoaccessoccurisrelativelyhighandimpactsforthe

whole EU economy would be relatively significant. Many of the materialsconsideredinthereportareminormetals.Althoughthisassessmentfrom2010didnotqualifyMoorTeascriticalmaterials,the2011theCommissionsJointResearchCentre(JRC,2011)addedTetothelistduetoitbeingacriticalmaterialinstrategicenergytechnologies.In January 2013 the US Federal Energy Department (2013) followed suit byaddingTetoaresearchhubofcriticalmaterialsknownastheCriticalMaterials

Institute(CMI).Thehubmainlyfocusesonresearchthatreducessupplyriskstothe metal, which includes making extraction techniques more efficient andreducingtheusageinproductionandmanufacturing.

2.8Previousstudiesofminormetalmarkets

AlthoughIhavenotfoundanystudiesontheeffectsofinformationtransparencyon aminormetalmarket, I have found older papers that are tangent to thesubject. The first example is Lee et al (1998), who concludes that increasedtransparency helps the price discovery process become more efficient, bylookingathowtheopeningoflimitorderbooksintheKoreanstockexchangein1992decreasedpricevolatilityandincreasedliquidityinthestockmarket.ThemarketefficiencyoftheLondonMetalExchangewaswidelydebatedinaseriesofarticlesinthelate1980sandearly1990s.Slade(1989)lookedathowchanges in pricing systems changed in the 1980s. At this time, non-ferrousmetals such asaluminium and nickel were introduced to the LME, which theauthor(correctly)assumedwouldsignalanindustry-wideshiftinpricingsystemfromtraditionalproducer-pricingmechanismstocompetitiveexchangepricing.Althoughtheproducerpricingsystemapricecartelsystemconsistingofmajormetalssupplierswaslessvolatile,ithadnopricemechanismtoaccommodate

shifting consumerdemand. Thismeant thereweremajor profit incentives forproducerstoshifttoawell-organisedexchangesystem,althoughthiscarrieda


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costofincreasedmarketvolatility.Apartfromprofits,theexchangesystemhasasignificant advantage due to its pricing transparency, which means that thetransactionpriceisalwaystrueanduniformtoallcustomers.Wellfunctioninginstitutionalrules,suchascontractenforcement,wherebreachesmayleadto(averypublic)expulsionfromtheexchange,isanotherreasonwhyasystemshifttookplace.Sladesdescriptionofaperiodoftransitionbetweentwosystemsofpricesettingcapturestheresistancemanyhad(andstillhave)tofuturemarkets;namelythattheyareinherentlyriskyandbubble-inducing.Morerecentstudieshavedisprovedthis,andattributethissuperstitiontoalackofunderstandingofhowtransparentfuturesmarketactuallywork(Irwinetal,2009).Hallwood(1988)arguesthatanunregulatedexchangemarketisnotasefficientasaregulatedone.Atthetime,coppercontractsweretradedontheLME,butindustrypreferencemeantcontractswereoftennegotiatedusingLMEfuturesasa benchmark. These prices are by definition less efficient than the LME-negotiatedcontracts,andcausedpricesthatfluctuatedmorethanactualcyclical

demand.Accordingtothisargument,thelow-volumeMomarketoftodaywillbecome lessvolatileifhighervolumesare tradedovertheLME.Eggert (1991)looked at how prices of more commonly traded metals and commoditiesfluctuate more compared to consumption of the metal, thus pointing outinefficienciesinthemarket.Thedebatefocusedmainlyonwhetherornotthemarketcouldbedeemedefficient.ThefinalsayinthedebatewasthedisprovalofefficiencybySephtonandCochrane(1990).Althoughdebatingwhetherornotamarketcouldbedeemedefficientwasa frequentlydebatedtopicat thetime,proving or disproving a specific markets efficiency may be considered anantiquated discussion today. However, these discussions revolved around aproposed paradigm shift in pricing systems, and need to be read from thatperspective.ThisthesisdoesnotfocusonthenatureoftheEfficientMarketHypothesisperse,butacknowledgesthatmoreinformationandtransparencybothleadtoamoreefficientmarket and reducedprice volatility. I conclude that the results fromthese early studies carry little validity in todays markets where global newshave a much more instantaneous effect of markets, nor does their topic ofdiscussion add much to current academic debate. In the following chapter amethodisselectedtodeterminehowmarketsreacttoavailabilityofinformation,whichmaydifferdependingontheefficiencyofthemarket.


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3. Method: Determining the price mechanisms of tellurium

andmolybdenum

3.1SVARandimpulseresponsefunctions

Robustlong-termprognosticsofpricescenariosonavolatilemarketaredifficultto design, and as with all forecasts under high volatility, results are oftenimpreciseandshouldmerelybeseenasbestguessesoffuturescenarios.Still,ifone can better understand what makes the market tick, decisions regardingfutureinvestmentsmaybecomebetterinformed.ThisiswhatYu,Song,andBao(2012)attempttodobymodellingrealpricefluctuationsofPVSF,whichistheprimarycomponentofaPVtechnologyrivaltoCdTe.Thisisdonebystudyingimpulse response functions on number of variables using a Structural VectorAutoregressiveModel(SVAR)thatincludes(p)periodsoflag.

!! = ! + !!!!

!

!!!

+ !

where!isa1-vectorofthevariablesthataretobestudied;isaconstant1-vector;!isthetime-invariant-matrixwherethemaindiagonaltermsaresetto1.!isthe1errorterm,whichsatisfiestheassumptionsE ! = 0,oreveryerrortermhasmeanzero;E !! = ,orthecontemporaneousmatrixof error terms is (a positive-semidefinite matrix); andE !!!! = 0 ,meaning for every non-zero, there is no correlation across time, or morespecifically,noserialcorrelationinindividualtermsacrosstime.

A SVAR model imposes restrictions on the response of underlying VectorAutoregressive (VAR)-variables, meaning one can include assumed inter-variable causality, from which impulse response functions can be calculatedusingOLS estimation.More informationonderivation andassumptions of theVARandSVARmodelsarefoundintheAppendix.For! = !

!!! ,we can incorporates thecausality assumptions for eachmodel

intothe!!!-matrix.Theoptimalnumberoflags(p)isthendeterminedusingthe

AkaikeInformationCriterion(AIC).IntheYuetalmodel,! = (!,!, !,!, !, !),wherethelaggedvariables!represents euro-to-dollar exchange rate,!and!the priceofnaturalgasandoil,!realeconomicactivity,and!and!representscontract- and spot prices of PVSF, all expressed in logs. I use the sameassumptions asYu, Song, andBao, whichcanbe read in Section3.1.2intheirarticle. These assumptions are translated into the equation below, where thediagonal!! = !! = = !! = 1byconstruction.


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!

!

!"#

!

!"#

!!"#

!

!"!

!

!"

=

!! 0 0 0 0

0 !! !" 0 0

!" !" !! 0 0

!" !" !" !! 0

!" !" !" !" !!

!

!"#

!

!"#

!!"#

!

!"!

!

!"

Thisthesisattemptstodevelopthismodelfurtherbyplacinggreateremphasisonvariable selection.TheaboveYuetal (2012)-variablesareselectedtobestcapturemacroeconomicimpactsonthemarket.AsPVSFisacriticalcomponentofarivaltechnology,theYuetal-variablesandrestrictionsshouldworkjustaswell for the Temarket.However, I believemarket specific shocksmay bettercapturefluctuationsonaspecificmarketviamarketspillovereffects(Morales,2008).Todothis,inter-variablecausalityintheSVAR-modelhastobeexplicitlystated,andthentranslatedintothe(!

!!)-causalityassumptionmatrixasisdoneabove. The error termmatrix () is estimated separately and indicates if the

error term assumptions are fulfilled.7This thesis only considers short-termcausalityshockstotheTeandMoprices,whichmeansthatTeandMospotpricewillnothaveaneffectonothermarketvariablesintheshortrun.8From the SVAR model, structural impulse response functions and Choleskyaccumulated response functions are then calculated. The structural impulseresponsefunctiongivesanindicationofhowaresponsevariablereactstoaonestandarddeviationshockfromanimpulsevariable.TheCholeskyfunctionis ameasure of how an accumulatedone standard deviation shock to an impulsevariable affects themeansquare error of a response variable, expressed asafractionoftheresponsevariablestotalmeansquareerror.Thisgivesameasure

of how much a shock of the impulse variable affects a response variablesdeviationfromitsmean,ormoreexplicitly:itsvolatility.ThisthesisisacontinuationofthediscussioncalledforbyYuetalregardingvariableselection, as theydidnot achievesignificant results intheirpaper. Insomesense,itisalsoanattempttovalidatetheappropriatenessofusingaSVAR-model toassesshowdifferent variables impactcriticalminormaterials. ApartfromapplyingtheYuetalmacroeconomicvariablestotheTespotprice,thisthesisinvestigateswhichvariablesmorespecifictotheTeandMomarketsareappropriate, which is established using quantitative analysis methodology

describedinthenextsection.3.2Quantitativeanalysis

Selecting reliable market-specific variables presents some difficulties to alayman not familiar with a market. In order to determine which factors andactorsmaybedeemedmostimportantinamarket,acontentanalysisiscarried

7AllmodelsandestimationsaredoneusingSTATA12.Thecausalityassumptionsofthe!

!!-matrixisinputastheA-matrix,andthestandardassumptionsforthe-matrixisinputastheB-matrix.8Estimatinglong-runimpulseresponsefunctionscouldcapturethesecausalities.Ihavechosennottoincludesuchestimationsinthisthesis.


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out on a set of articlespublished in theMetal Bulletin. The coding scheme isdesignedwithreliability,validity,accuracy,andprecisioninmind,usingmethodestablishedinNeuendorf(2002).ThemethodologicalinspirationpartlycomesfromTetlock(2007),whichusesasimplequantitativeanalysisonapopularWallStreet Journal column to study how the media and stock prices interact, andTetlocketal(2008),thatlooksathowlinguisticqualitiesinfirm-specificnewsreportingmaypredict a firmsaccountingearningsandstockreturns;ormorespecifically,howthemarkethasatendencytounderreacttotheusageofwordsthatmay reveal negative sentiments on returns andearnings. Myapproach isdifferent tothesestudies,and focusesmoreondetermining ifandhowanewsinnovation is expectedto cause aprice change, andwho is the catalystof theevent.The selection of SVAR-model variables takes frequency of actor- and factor-mentions,marketmechanisms,andotherinsightsfromthequantitativeanalysisinto account. Actors and factors can either have an effect on supply, such as

stocks of mining companies, or demand, such as stocks of consumers of themetals. If possible, effects of actors and factors are quantified using theirrespectivestockprices,andrelevantfactorindices.ThearticlesarecollectedfromtheMetalBulletinnewsarchivebysearchingforthetermstelluriumMBNON-FERROUSPRICECHANGEandmolybdenumMBNON-FERROUSPRICECHANGE. The MBNON-FERROUS-term excludes so-called price-updatearticles,whichare not propernews articles, but listingsofdailypricechanges.AllarticlesfromFebruary202010untilFebruary282013arepasted intoworddocumentsand imported intoexcel-spreadsheetswherethecodingschemeisinsertedatthetopofeachsheet.The decoding of the articles is done in six steps. The first step determineswhether the news article isprice-pertinent; or can thedescribedevent in thearticle theoretically change the price of themetal? Examples ofnon-pertinentarticlesarethosethatdonotdirectlydealwiththesupplyordemandofTeorMo, suchas those dealing with Te as an impurity in steel scrap. Examples ofpertinent topics include business reports of increased production, changes inmarket conditions, opening of new mines, or reporting on changes in tradebarriers.Articlesmayalsobedeemedpertinentifthecontentisdeemedrelevanttotheresearchquestionofmythesis.

Ifthearticleisdeemedpertinent,thenextstepistodeterminethegeneraltopicof the article, which is best described as a one-sentence description of thearticles effect on a metal price. This is done with the purpose of improvingreferencingability,sothedescriptiondoesnotneedtobeconsistentwithhowprevioustopicsarecoded.Nextthecodingaimstodeterminewhomthemaincatalystofthenewseventis.Itispossiblethatmorethanoneactorisdeemedthecatalyst,orthattheremaybe no specific catalyst at all. An almost identical topic or catalyst, which hasalready been covered in a previous Metal Bulletin article is still coded as

pertinent, as reporting intensity may be indicative of perceived eventimportance.


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Market impact is then assessed, which is done from a supply and demand-perspective.Thegoalistoassessifthearticleisdescribingaperceivedincreaseordecreaseinsupplyordemand.IfanarticlereportsincreasedsteelbarpriceswhichcontainsTe,thisisassessedasasignofpositivedemand.Whenthearticledescribesa supply shortage, or tight supply, this is interpreted as a sign ofpositivedemand,aspredictedmarketsupplyissurpassedbyexpecteddemand.Theexceptiontothisiswhenthearticledescribesatruesupplyshockevent,suchasnaturaldisasters.The final coding attempts to anticipate how a positive or negative effect onsupply and demand can be translated into a price change. Positive demandmeans expected higher prices, and thus Possible price impact is coded +.Positive supply means expected lower prices, and the article would then becodedas.Theoppositeis applied fornegativesupplyanddemand.Articlescovering prices stabilising or adjusting are seen as price changes too.

Previous articles then need to be considered; if the price is adjusting after apositiverally,demandiscodedas,andthearticleiscodedas+ifitconcernsastabilisationafterapricedrop.TheaboveprinciplesarealsoappliedtotheMoMetalBulletinarticles.AsthemarketforMoismuchlarger,andthelevelofmarketmaturitycanbeconsideredhigher,somespecialprecautionsneedtobetakenwhendecodingthesearticles.ArticlescoveringpricechangesinproductswhereMois acomponent (suchassteel)isnotdeemedpertinent,unlessthearticleexplicitlystatesthatthishasinturnaffectedtheMoprice,suchaswhenanarticlestatesincreaseddemandforsteel.In order to assert the reliability and validity of the quantitative analysis, arandomsampleof25articlesforeachmetalisrereadandrecodedafewweeksaftertheinitialquantitativeanalysis.Theresultsofthesereadingsarecomparedwiththeresultsoftheoriginalquantitativeanalysis.Iftheresultsdiffertoalargedegree,thequantitativeanalysiswillhavetoberespecifiedinordertoascertainreplicabilityandthenreappliedtotheentiredataset.The robustness of a quantitative analysis can always be questioned for itsreliabilityandreplicability.Myreadingsaredonewiththeintentoffiguringout

whateffectanewsarticlemayhaveonatypicalmarketactor,andhowthistheoretical person would assess themarket situation. The decoding can thusonlybeconsideredabestguessofwhatatypicaltraderthinks,andisthusbiasedbymypersonalopinionsofwhat constitutesa typicalmarket actor. Anotherlimitation of the study is it is only conductedon Metal Bulletin articles. Thismeansthatthesampleonlycontainsnewseventsdeemedmostrelevantbythatparticular newspapers journalists andeditors. A future study could includeaquantitativeanalysisofotherjournals,papersandwebsites.The quantitative analysis is used to legitimisevariable selection for the SVARmodel.Quantifyingwhichactorsarementionedmostmaygivesomenumerical

support for choosing a particular actor. A catalyst actor selected from thequantitativeanalysisisreferredtoasamarketproxy,meaningthearticlesitis


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mentionedinarepiecesofinformationmarketactorsuse toassess theoverallpictureofthemarket.Thisraisesaquestionofcausality,asthemostmentionedcompanies may also be reliant on the price of the commodity in question.Frequentmentioningascatalystsinanewsarticlemaybeseenasindicationofperceivedmarketimportance,whichinturnsuggeststhattheproxyscausalityonthemetalpriceissignificantfromaninnovationperspective.SelectinganappropriateproxymeansthatIimplicitlymaketheassumptionthatacompanysstockprice(orperceivedcompanyvalue)isagoodmeasureofitsprofitability. This is a bold assumption, but necessary in order to makequantificationpossible.Themostimportantelementofusingthismethodisthatitinvolvesreadinganddigestingalargesampleofmarket-specificnewsinchronologicalorder,givingthereaderaninsightintoamarkettheypreviouslydidnothave.Thisoverallimpressionmaygivequalitativeinsightsthatfurtherhelpinvariableselection.

Once the main actors, factors, or other quantifiable instances have beenestablishedthroughthecontentanalysis,thesearefittedintoaSVAR(p)-model,where! is composed of a time series vector of these variables, along withassumed short term causalityassumptions translated into their respective!-matrix.


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4.DataandresultsHere,datacoveringspotpricesandindicesfromFebruary1,2004toFebruary28, 2013 arepresented. For the quantitative analysis, the shorter timespanofFebruary20,2010toFebruary28,2013isused.

4.1Spotpricesandreturns

TorepresentTespotprices,IhavechosentouseaweeklypricelistingbasedonFreightOnBoard(FOB)USA99.95%TeUSD/kg,forwhichIhaveavailabledatafromFebruary1,2004untiltoJune22,2012.AfterthisdateIhavechosentouseMetalBulletinsTelluriumMetalMBfreemarketminimum99.9%USD/kg.MetalBulletin posts a bi-weekly high and low price on Wednesdays and Fridays. IcalculatetheFridayspotpriceastheaverageoftheaveragehighandlowpriceoftheWednesday and Friday price.A change in price in the USA FOB-listing ismoregradualthantheMetalBulletin-pricelisting,whichiswhyIhavechosento

usethisindexasmuchaspossible,asitcapturessmallpricechangesbetterthantheMetalBulletinlistings,whileitsdataiscollectedinasimilarmannerastheMetal Bulletin index. The returns of Te are displayed in Figure 6 with itsstatisticalpropertiesinTable1.

Figure6:WeeklyreturnsofTefromFebruary2004toFebruary2013.

Table1 indicatesthatvolatility,expressed asstandarddeviation, isquitehigh.Thepositiveskewnessshowsacondenseddistributionofnegativereturnsandamorescattereddistributionofpositivereturns,whiletherelativelyhighkurtosisindicatesthatthetailsarequitefat,meaningreturnsareoftennotdistributedclosetothemean.

Meanreturn StDev Skewness Kurtosis

TeReturns 0,003954205 0,03561644 1,406308088 6,051458108

Table1:StatisticalpropertiesofTereturns.


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DeterminingwhichMo spot price to use requires some thought. There is noofficialLMEspot-orcashpriceavailableuntilJune2010,andthesearequotedinmonthly averagepricespermetric tonne. Inorder tocapture the priceof themetalinearliertimeperiods,acoupleofpriceindicesneedtobeconsidered:MolybdenumFe65 (FeMo65 orFeMo)Cost, Insurance, in Freight (CIF), NorthWesternEurope(NEW)USD/kg,andMolybdenumMo3CIFNWEUSD/LB,whichhave been sampled from Thompson Reuters Datastream. The prices of theseindicesaredisplayedinFigure7,includingtheofficialLMEcashpriceforMo,which is normally expressed in USD/metric tonne, but is here converted toUSD/kg.

Figure7:PricedevelopmentofthethreeMometalcash-andspotpricesthatareconsideredto

representMospotprice.

BothindicesareMo-products,butmaybeusedinslightlydifferentapplications(OakdeneHollins,2012).AcomparisonofthereturnsofthesetwocommoditiesinFigure8showsthatthereturnsfollowasimilarpattern,howeverMo3seemsmorevolatilethanFeMo65,whichisapparentfromthestatisticalaspectsoftheirreturns,presentedinTable2.Asbothmetalsdisplaysimilarmeansandstandard

deviations,theskewnessandkurtosisshowsthattheMo3-pricehasfattertails,and isthusmorevolatile.Onthe consumptionside,Molybdenumoxidegradesmadeup approximately 29% of the total world-Momarket in 2011,whereasFerro-molybdenumproductsmadeupapproximately14%(USGS,2012b).Froma technical perspective, Fe65 contains approximately 60-65% Mo, which issimilar to the 57,4-63%gradeof concentrateused by the LME (USGS, 2012b,LME,2012).FromthisinformationIhavechosentousethereturnsofFeMo65,asthisMo-productismostsimilartotheMoproducttradedontheLME.


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Figure8:ReturnsofFeMoandMo3fromFebruary2004toFebruary2013.

Mean StdDev Skewness Kurtosis

FeMo65 0,002280685 0,057349343 2,336119248 26,98847513

MoO3 0,002392993 0,059340308 2,819789123 44,43852061

Table2:StatisticalaspectsofFeMo65andMo3weeklyreturns.

AstheinstitutionsassociatedwiththeMomarketchangeddrasticallywiththeLME introduction in February 2010, comparing howmarket volatility differsbetweenthesetwotimeperiodsisimportant.Table3showsthatbothreturnmeans and standard deviations are lower after the LME introduction. Thenegativeskewnessaftertheintroductionindicatesthatpositivereturnsaremorelikely,andthedecreasedkurtosisindicatesthatthetailsarelessfat,andthustheexpectedreturnsaremuchclosertothemeanthanbefore.Comparingreturndatathatexcludesthefinancialcrisisof2008stillindicatesthatthemarketwaslessvolatilebeforetheLMEintroduction,lendingsupporttotheclaimthatanLMEintroductionreducesmarketvolatility.

Mean StdDev Skewness Kurtosis

FeMo65 Pre-LME 0,0042 0,0683 2,0119 19,2141

Pre-Crisis 0,0072 0,0652 2,0124 25,4012

LME -0,0015 0,0231 -0,2852 4,5714MoO3 Pre-LME 0,0044 0,0696 2,6530 34,4931

Pre-Crisis 0,0073 0,0640 5,0656 50,3788

LME -0,0016 0,0299 -2,1049 9,6714

Table3:ComparisonofstatisticalaspectsofthetwoMometalsweeklyreturns,pre-financialcrisis

(February262004toOctober102008),andpre-(February262005toFebruary192010)andpost

LMElaunch(February192010toFebruary222013).

4.2Quantitativeanalysisfindings

ThetimeperiodselectionforthequantitativeanalysisofTeandMowaspartiallybased on the fact that there are few articles that deal directly with Te untilshortlybeforethepre-2011bubble.Also,theperiodbeforeFebruary20,2010,


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MohadnotyetbeenintroducedtotheLME,andisthusnotofinteresttothisstudy, as I onlywish tostudy the transparentMomarket. Extending the timeperiodwouldshedlittlelightonselectingvariablesfortheTemarket,yetwouldrequiremuchtimedownloadinganddecodingMoarticles.Inordertoascertainthereliabilityandvalidityofmyquantitativeanalysis,arandomselectionof25Te and 25Monewsarticleswere reread and recoded onMay 24, 2013withalmostidenticalresultstotheoriginaldecoding,assertingthereplicabilityofmymethod.The Te- related articles that exist before February2010 dealmainlywith Te-consumptionbeforethePVmarketincreaseddemandinthelate00s.OnesucharticleisWhoneedsTellurium?9, thatdealswith the smallness, irrelevanceandpriceopacityofthemarket.For theselectedtimeperiodthereexistsa totalof119articles,where81ofthemweredeemedpertinent;thesearedisplayedovertimeinFigure9.Outofthese81pertinentarticles,61weredeemedtobepriceinnovationsthataltereitherthesupplyordemandofthemarket.

Figure9:TotalnumberandnumberofpertinentTearticlesfromFebruary12010toFebruary28

2013.

ThemaincatalyststotheseeventsarecapturedinTable4,whichshowsthatCdTePVsolarcellproducerandmarket-leader FirstSolarInc.ismentionedmost

inpriceinnovationarticles.Whenthesearefurtherbrokendownintodemand-andsupplyshocks,weseethat FirstSolardominatesthedemandshockswithregardstonumberofpertinentarticles.Supplyshocksseemtobedominatedbyminingcompanies,whoaddorsubtractsupplytothemarket.Thesecondmostmentioned company is5NPlusInc.,which is exclusivelymentioned inarticlesthatcanbereadasdemandinnovations.Consideringthat 5NPlusrefinesTeintoCdTe10,thissuggeststhatthecompanyisabetterindicatorofthestateofsupply

9MetalBulletin,July102000.105NPluscorporatewebsite:http://www.5nplus.com/index.php/en/selsComposes.html(accessedonMay3,2013).


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inthemarket.Vital,whichismentionedfourtimes,isanotherrefinerofTethatcouldbeconsideredasupplyproxy.Thiswillbediscussedmoreinsection4.3.2.

Catalysts Demandshocks Supplyshocks

FIRSTSOLAR 8 FIRSTSOLAR 8 BOLIDEN 5

BOLIDEN 5 5NPLUS 5 VITAL 3

5NPLUS 5 MCP 2 NYRSTAR 3VITAL 4 RETORTE 2

MCP 3

NYRSTAR 3

II-VI 2

RETORTE 2

Table4:NumberofpertinentTearticleswheretheactororfactorwasdeemedtobethecatalyst.

Onlycompanieswithmorethanonepertinent,price-changingarticlearepresented.

Insummary,theTemarketgoesfromlowlevelsofreportingactivityin2010,toamuchhigherlevelsin2011-2012.2013hassofarofferedverylittlereporting,whichmost likely indicates low activity on the market rather than a loss ofjournalisticinterest.For the Mo market, a total of 1022 articles were studies, where 581 weredeemedpertinent.TheirdistributionovertimeisdisplayedinFigure10.

Figure10:TotalnumberandnumberofpertinentMoarticlesfromFebruary12010toFebruary28

2013.

Outofthese,561weredeemedtobeprice innovations,outofwhich328weredeemedtohaveanidentifiablecatalyst.InTable5,actorsandfactorswithmorethan three articles are split up according to catalysts for demand- or supplyshocks.


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Catalysts Demandshocks Supplyshocks

MACRO 41 MACRO 34 FREEPORT-MCMORAN 24

SEASON 32 SEASON 31 GENERALMOLY 17

FREEPORT-MCMORAN 24 STEEL 3 THOMPSONCREEK 15

GENERALMOLY 17 CODELCO 12

THOMPSONCREEK 16 ANTOFAGASTA 10

LME 13 RIOTINTO 9

CODELCO 12 MOLYMINES 6

ANTOFAGASTA 10 TASEKO 6

RIOTINTO 9 MACRO 5

MOLYMINES 6 INMET 5

TASEKO 6 MOLYMET 4

INMET 5 ROCAMINES 4

MOLYMET 5 AVANTI 3

TRADEBARRIERS 5 BHPBILLITON 3

ROCAMINES 4 NORILSKNICKEL 3

AVANTI 3 SOUTHERNCOPPERCORP 3

BHPBILLITON 3 TECK 3

MERCATOR 3 TRADEBARRIER 3

NORILSKNICKEL 3 XSTRATA 3SOUTHERNCOPPERCORP 3

STEEL 3

TECK 3

TRADEBARRIER 3

USD 3

XSTRATA 3

Table5:NumberofpertinentMoarticleswheretheactororfactorwasdeemedtobethecatalyst.

Onlycompanieswithmorethantwopertinent,price-changingarticlesarepresentedinthistable

Themost common catalyst is theMACRO variable,which ismost common inform of demand shocks, and mainly concerns exogenous international price

shocks.ThesecondmostmentionedisSEASON,whichmayalsobeconsideredademand shock. It is a variant of the MACRO catalyst, but is used to decodearticlescoveringpricechangeswhenproductionisexpectedtobelow,suchassummervacationperiodsinthenorth-westernhemisphere,orChineseholidaysliketheChineseNewYearandthesemi-annualGoldenWeek.Outofallthe288demand shocks, 155 were deemed to affect demand negatively and 133positively.Thesupplyshocksideisdominatedbyminingcompanies,andusuallyinvolvesreporting of possible and real supply changes from these actors. Examples of

supplyshocksarenewsreportsonminingprojectsgainingkeylocalgovernmentsupport, strikes atmines or production facilities, or possiblemining projectsbeing cancelled. Out of 273 supply shocks 94 were deemed to affect supplynegativelyand179positively.Aflawinmycodingschemeisthatitonlycapturesacatalystofanevent;itdoesnot say much about the day-to-day structure of themarket. Althoughmainlymacro-andseasonalcatalystsseemtoaffectdemand,thistellslittleofwhoisdemandingMo.Fromreadingthearticles, itis clearthatbuyersareagentsforsteelmillsaroundtheworld,whicharecapturedasacatalystinthreeinstances,butareinrealitymajorpricesettingplayersinthemarket.Anotherflawwithmy

methodologyis thatit failstocapturetheintensityofa specificnewsarticle.A


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single article about a specific innovationmayhave had a large impact on themarket,butcannotbecapturedproperlybymyquantitativeanalysis.Astheremaybeseveralarticlesreportingonthesameevent,thequantitativeanalysisprovestobearatherbluntinstrumentindeterminingwhichactorsorfactors to pick.A typical example of this in theMomarket are supply shockscausedbyminingcompaniesreportingonnewproductionactivities,whicharereported on several times as the project progresses from prospecting andfeasibilitystudies,tobecomingafullyfunctioningandproducingmine.However,several reports on a single, drawn out events like mining projectsmaybeanindicationontheimportancejournalistsplaceonaparticularproject,whichinturncouldaffectmarketsentiments.SometimesthecatalystiscodedasN/Aduetotherebeinguncertaintyoverwhomamarketbuyeris.IassumethesearemainlyconsumersofMo,suchassteelmills,orpricespeculators,suchashedgefunds.

DuringthereadingsInoticedthatChinaandChinesedemandisoftenmentionedinthearticles.HavingChinaasacatalystwouldbeanawkwardvariable,butinordertogetanimpressionofChinesefrequencyinthearticles,IsearchedallthearticlesforthetermChina,whichwasmentionedatleastoncein311articles,outofwhich188weredeemedpertinent.Chinaappearedover902timesinallthearticles,and485timesinarticlesdeemedpertinent.

4.3IncorporatingappropriateactorsandfactorsintotheSVARmodel

My accumulated qualitative knowledge of both markets, along with thenumerical indications given by the quantitative analysis, are now to be

quantifiedandinsertedintoSVARmodels.

4.3.1TheYuetal(2012)modelonappliedontellurium

First,aSVARmodelforTeisrunusingthesameaggregatestructuralindicatorsasYu,Song,andBao(2012)toexplainpricefluctuations.TheFOBUSA99.95%TeUSD/kgpriceisusedtorepresenttheTespotprice.TheseandallthebelowindicatorsexpressedinUSDaredeflatedusingUSCPIcollectedfromtheUSBureauofLaborStatistics.IassumeUSCPIisusedthroughouttheYu,Song,andBao-article,asthisisnotexplicitlystated.AlldataarecollectedonamonthlybasisandpricesareadjustedtoFebruary2004-levels.ThismeansIusethelastavailablepricelistingofeachindicatoreachmonth,whichforTe-listingsmeansthelastFridayofeachmonth.The explaining variablesintheSVARmodelare: a CPI adjusted euro-to-dollarexchange rate; CPI adjusted WTI natural gas prices; CPI adjustedHenry Hubcrudeoilprices; IndustrialPriceIndex(IPI),which istheUSIPI,sourcedfromtheBoardofGovernorsoftheFederalReserveSystem,theEuro-areaIPIsourcedfromEurostat,andJapaneseIPI,sourcedfromtheJapaneseMinistryofEconomy,TradeandIndustryandEurostat,weightedbytheirregionalquarterlyGDPfrom


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Eurostat.11AsthereisnodataonTecontractpricing,thisisexcludedfromthemodel.AllSVARmodelsusemonthlypricelistingsandaredisplayedin Figure11.

Figure11:TheLOG-expressionofCPIadjustedvariablesasselectedusedbyYuetal.IPILOGx10

hasbeenincludedtobettervisualisetheindustrialproductionindexandisnotusedintheSVAR

model.

Using theYu et al- variables on a structural responseSVARmodel for the Temarket presentedsome difficulties. First, asmyreplication does not have thesamenumberofvariablesasYu,SongandBao,soanewoptimalsetoflagsisrecalculatedusingAIC,whichis2forthisdataset.Second,themodeldoesnotachieveconvergence.Studyingtheerrortermsofthe-matrixinTable6,whichshouldbecloseto0,thereisreasontobelieveassumption3oftheSVARmodelisnotfulfilledforoilorgasprices,whichmayindicateeithercointegrationorserialcorrelationbetweenthevariables.Regressingthetwovariables,thenrunninganAugmentedDick-Fullertestforunitrootsoftheresidualerrortermsrejectsthenullhypothesisthattheyarecointegrated,whichisalsoconfirmedbyaJohansentest for cointegration. ALagrangemultiplier testconfirmsthat there is indeedserialcorrelationbetweenlaggedvariables,andthusamajorassumptionoftheSVARmodelisunfulfilled.IsuspectthatYuetal.sdataalsohadthisproblemwithserialcorrelation,whichwouldmostlikelyaffecttheirresults.

EURUSDLOG GASLOG OILLOG IPILOG TELOG

EURUSDLOG 0,01284311 GASLOG 0 3122,4177

OILLOG 0 0 126,22352 IPILOG 0 0 0 0,00475299

TELOG 0 0 0 0 0,03325936

Table6:Theoutput-matrixfromthestructuralimpulseresponsefunction,mayindicatethatGas

andOilpricesarebiasedestimates,astheirexpectederrortermisnotcloseto0.

11Asthereareyetanyestimatesoffirstquarter2013GDP,Ihaveassumedthemtobethesameasfourthquarter2012.


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AnotherproblemIencounteredwhenreplicatingthestudyisIhavenotbeenable to recreate the two variables specific policy shocks or other specificdemand shocks used in their Cholesky accumulated response function. It isunclear which variables were used or how the authors quantified differentnationalpoliciesorindustryspecificshocks.The issuewith serial correlation was dealt with by first running the impulseresponsefunctionwithoutgas,thenwithoutoil.Noconvergenceerroroccurredeither time, and the error terms were in both cases unbiased. From thisinformation I choose to drop oil asa variable based on the fact that it is notnormally used in power generation. The issue of using policy- and industryspecificshockswassimplydealtwithbyexcludingthemfromtheSVARmodel.Figure 12displays the results of the altered versionof theYu et al structuralimpulseresponsefunctionmodelonTewitherrortermspresentedinTable6.The rightmost column isofmost interest, as itdisplays howimpulse variable

functions may affect the Te price over 24 time periods. Further, Figure 13displays a cumulative response to a one standard deviation structuralinnovation.

EURUSDLOG GASLOG IPILOG TELOG

EURUSDLOG 0,01375093 GASLOG 0 0,06825692

IPILOG 0 0 0,0065813TELOG 0 0 0 0,03663012

Table7:The-matrixofthealteredmodelindicatesanunbiasedimpulseresponsefunction.

Figure12:ResponsestostructuraloneS.D.innovation.EURUSDLOGisEURtoUSDexchangerate,

GASLOGisgasprice,IPIistheUS,Eurozone,JapanIndustrialPriceIndex,OILLOGareoilprices,and

TELOGisthePriceofTe.Allareexpressedaslogarithms.TherightmostcolumncapturestheeffectofanimpulseontheTeprice.

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Te3, EURUSDLOG, EURUSDLOG Te3, EURUSDLOG, GASLOG Te3, EURUSDLOG, IPILOG Te3, EURUSDLOG, TELOG

Te3, GASLOG, EURUSDLOG Te3, GASLOG, GASLOG Te3, GASLOG, IPILOG Te3, GASLOG, TELOG

Te3, IPILOG, EURUSDLOG Te3, IPILOG, GASLOG Te3, IPILOG, IPILOG Te3, IPILOG, TELOG

Te3, TELOG, EURUSDLOG Te3, TELOG, GASLOG Te3, TELOG, IPILOG Te3, TELOG, TELOG

95% CI structural irf

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Graphs by irfname, impulse variable, and response variable

Response to structural one S.D. innovation 2 S.E.


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Figure13:CholeskyaccumulatedresponsetostructuraloneS.D.innovation2S.Ewiththesame

variablesasinFigure12.

Comparing my own findings with Yu et als I notice that there is but onesignificant impulse response functions at any given time period. Looking atFigures11and12inYuetal,andFigures12and13above,wecanseethatthe

tails of the impulse response functions are quite fat. The only variable thatcausesasignificanteffectonTepricesistheEurotoUSDexchangerate,whichaccordingYuetalpartiallycapturestheeffectsoftheEurocrisis.Inmymodel,theprice of Te is affectednegatively by an exchange rate shock. The effect issignificant,meaning itisnon-zeroona 95%-level for the first10 timeperiods.Further,theCholeskyaccumulatedresponsefunctioninFigure13ofexchangerate shows a large fraction of the Te mean square errors are explained byexchangerateshocks.IbelievethatthelackofsignificanceintheaboveandYuetalsmaybetheresultofomittedvariablebias,orsimplyusingthewrongvariables.Althoughmystudy

usesaslightlydifferenttimeperiodandadifferentbutinmanyregardssimilarspotprice,ourresultsareonlysimilarwithregardstothelackofsignificance.TheYuetalselectionofvariablesseemstostemmainlyfromreasoningaroundgeneralmacroeconomictheoryandnotfromrealmarketobservations.Iarguethateachmarkethasitsown,specificpricingmechanisms,andtheseneedtobeconsideredwhenstudyingthePVSFandTemarkets.TheonlyconclusionIdrawfrom the application of the Yu et al paper on Te is that it shows how littledifferent macroeconomic variables affect the price of a metal. Further, theirpaper does not shed much light on which policies to pursue one of thepurposesoftheirpapernorwhatactuallyexplainslonger-termfluctuationsin

themarket.

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Te3, EURUSDLOG, EURUSDLOG Te3, EURUSDLOG, GASLOG Te3, EURUSDLOG, IPILOG Te3, EURUSDLOG, TELOG

Te3, GASLOG, EURUSDLOG Te3, GASLOG, GASLOG Te3, GASLOG, IPILOG Te3, GASLOG, TELOG

Te3, IPILOG, EURUSDLOG Te3, IPILOG, GASLOG Te3, IPILOG, IPILOG Te3, IPILOG, TELOG

Te3, TELOG, EURUSDLOG Te3, TELOG, GASLOG Te3, TELOG, IPILOG Te3, TELOG, TELOG

95% CI fraction of mse due to impulse

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Accumulated response to Cholesky one S.D. innovation 2 S.E.


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4.3.2Amarket-specifictelluriummodel

I now run a market specific-model for Te based on the findings from thequantitative analysis. This is done to illustrate that market-specific variablesbetterexplainvariationthantheYuetalvariables.ThefirstvariableIselectforthemodelistheIndustrialProductionIndex(IPI).AlthoughIonlyhaveonepricechanginginnovationcodedasamacroeconomicin the quantitative analysis, it may be important to include some index forworldwideindustrialdemandasthiscapturesconjecturalcyclesandshocks.NotincludingavariableforworlddemandwouldbeanimplicitassumptionthattheTemarketisimmunetobusinesscycles,whichitmostlikelyisnot.TocaptureaggregateworlddemandIuseanIPIvariableslightlydifferentfromtheYuetal-model.TheIPI-valueusedbyYuetalisbasedonUS,Eurozone,andJapaneseindustrialproductionindices,weightedbyeachcountrysnominalGDP.ItomitsChina,amajoractorinthePVindustry,andtheworldssecondeconomyinGDP

terms. For the Te SVAR model I have thus used an IPI which also includesChinese IPI, sourced from the OECD. From 2006 there are no January valuesgiven;thisisdealtwithbyusingFebruaryIPIforthistimeperiod.AddingChinaalso means that GDP- weighting needs to be recalculated, for which I usedifferent nominal GDP data for all the countries as there seems to exist littledetailedChineseGDP-data,exceptfromtheInternationalMonetaryFund.Theyprovidethemostup-to-datestatisticsonGDP,includinganestimatefor2013.ThedifferencebetweenthetwoIPIsisillustratedinFigure14.

Figure14:ThedifferenceinIPIwhentheindexincludes(IPINEW,usingyearlyGDPweights)and

excludes(IPIYuetal,usingquarterlyGDPweights)China.

ThemostmentionedactorinthequantitativeanalysisisFirstSolarInc12.FirstSolar is aworld-leading producer of PV, both technologically with regards togeneration and cost efficiency, andtotal solaroutputmeasured inmegawatts.

12FirstSolarwebsite:http://www.firstsolar.com/en/About-First-Solar(accessedonMay3,2013).


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Their primary generation technology is CdTe PV. First solar was listed onNASDAQonNovember172006;meaningpricesfromFebruary282004arenotavailable.ThesearelistedasblankswhichdoesnotposeanyproblemsfortheSVAR-model.FirstSolarismyfirstexampleofamarketproxy.ThisincludestheassumptionthatFirstSolarhascausalityontheTeprice,andtheTepriceonlyhascausalityonFirstSolarinalonger-runperspective.Inordertocapturesupplyonthemarket,Ihavechosentoincludethestockofthe Canadian chemical refining company5NPlusInc.Othermining companiesthatproduce,orareabouttoaddsupplyofTetothemarketcouldbeused,butTeproductionusuallymakesupasmallfractionofthesebusinesses,thusmakingtheirstockpricespoorproxiesforTesupply.5NPlusisagoodcandidateinthisaspect,asitspecialisesinrefiningchemicalsspecifictothismarket;oneofthembeing production of CdTe.13My variable includes their stock price from 28December2007.

Another companyIconsideredasa choiceofproxy,mainlyduetoits frequentmentioningintheMetalBulletin,isVitalChemicals.Thisisnotpossibleasthecompanyisnotlistedonanybourseorstockexchange.Thereareotherminingcompaniesmentionedaswell,suchasBolidenAB,buttheirstockpriceshouldbepoor indicator, as Te mining makes up a small proportion of their businesscomparedtootherminerals.AllthetimeseriesaretreatedinasimilarmannerasinYuetal.ThestockvaluesarepriceadjustedusingthesameCPI,whichissettobe1onFebruary27,2004.Each indexisthenexpressedasa 10-based logarithm,ascanbeseeninFigure15.

Figure15:TheTe-modelvariables.IPILOGx10hasbeenincludedtobettervisualisetheindustrial

productionindexandisnotusedintheSVARmodel.

135NPluswebsite:http://www.5nplus.com/index.php/en/apropos/historique.html(accessedonMay3,2013).


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The underlying assumptions for the Te model are: IPI is assumed not to beaffectedbyeitherofthetwocompanies,northeTespotprice.Theprofitability,andthusthestockofFirstSolar,isexpectedtobeaffectedbyIPI.Althoughthelong-term profitability of the company may be threatened from very high Teprices,thisisnottruefortheshortrun. 5NPlus,whoisamajorsuppliertotheCdTePV-industry, is expected to be affectedby IPI, aswell as theFirst Solarstock,asFirstSolarpurchasesCdTefrom5NPlus.5NPlus isnotexpectedtobeaffectedbyTepricesintheshortrunforthesamereasonsas FirstSolar.IexpecttheTespotpricetobeaffectedbyalltheabovevariables;IPIshouldaffecttheCdTe-marketasawhole,andthusTe;the FirstSolarstockactsasaproxyforCdTeindustrydemand;the5NPlusasaproxyforindustrysupply.

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Theoptimalnumberoflagsforthismodelisdeterminedtobe2bytheAIC.Thestructuralimpulseresponse-andCholeskyaccumulatedresponsefunctionsarepresentedinFigures16and17.

Figure16:TeresponsetostructuraloneS.D.innovation.FIVENLOGis5NPlusstock,FSLRLOGisthe

First Solar Inc. stock price, IPILOG is the new Industrial Production Index, including China, and

TELOGistheTeprice.Allvariablesareexpressedaslogarithms.Thecompletestructuralresponse-

andCholeskyaccumulatedresponsediagramsarepresentedintheappendix.

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TeSVAR, FIVENLOG, TELOG TeSVAR, FSLRLOG, TELOG

TeSVAR, IPILOG, TELOG TeSVAR, TELOG, TELOG


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Response to structural on S.D. innovation 2 S.E.


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Figure17:TeaccumulatedresponsetoCholeskyoneS.D.innovation.Thesamevariablesareusedas

inFigure16

A5NPlusshockaffectstheTepricepositivelyinthefirsttwotimeperiodsona95%level,atthe90%inthethirdperiod,afterwhichitiszero.TheCholeskyresponsefunctionisnotsignificantata90%-level.Thisisevidencethatalthoughthe5NPlusstockmaycauseripplesintheTeprice,itdoesnotaffectTevolatility.ThestockofFirstSolardoesnotdirectlyaffectthepriceofTe,butafterfivetimeperiodsweseeapositiveincreaseinprice,whichcontinuestoriseuntilperiod

13. This then ebbs off untilperiod20,when the response functionreaches0.FirstSolarhasalarge,significantimpactontheTespotprice,whichsupportsthefindingsofthequantitativeanalysisthattheprofitabilityofthiscompanyhasalargeeffectonTeprices.Thefiveperiodlagoftheeffectismostlikelyduetothesluggish response ofTe prices to contract changes associatedwith decreasedoutputofthecompany.If FirstSolarexperiencesproblems,theymostlikelylettheir contracts run out without renewing them. This means that high-pricedcontractsthathaveyettorunoutarestillpresentinwarehouses,bringinguptheaverageprice.TheFirstSolarcontractsareestimatedtogetherwithlowerpricedcontractsnegotiatedmorerecently,whichhavepricesclosertotherealmarket

price.TheaccumulatedCholeskyresponsefunctionsupportsthelaggedvariablestatement;onlyafter19(!)periodsdotheresultsbecomesignificantatabelow-90%level.Thestockpricethenexplains25%oftheTemeansquareerrors.ThismaybeduetothelongtimeperiodtheTepricetooktoadjusttothereducedearningsofFirstSolarin2011-2012.Astructuralshockfromthe IPIvariableseemstoimmediatelycauseapositiveresponse in the Te market. The Te response variable increases at a 90%significanceleveluntilperiod5(atwhichpointtheresponsefunctionisat95%significancelevel),andreaches0atperiod10.ThismaybeindicativethattheTemarket may be affected by short-term reactions in the global conjuncture.

Studying the accumulated Cholesky response, we do not see that the mean

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TeSVAR, FIVENLOG, TELOG TeSVAR, FSLRLOG, TELOG

TeSVAR, IPILOG, TELOG TeSVAR, TELOG, TELOG


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squareerrorsofTeareaffectedbyastructuralinnovationshiftinIPI,meaningIPIdoesnotincreasevolatilityontheTemarket.

4.3.3Amarket-specificmolybdenummodel

ThequantitativeanalysisindicatesthatmostofthedemandforMocomesfrom

steelmills.Insteadofpickingindividualcompaniesasproxiesfordemandinthesteelmarket,as Idid in theTe example, themost commonsteel grade pricescontaining Mo would be a better measure of aggregateModemand. In 2011,approximately39%ofworldconsumptionofMowasinlowalloysteels,15%instainless alloysteels, and8% insuperalloys (USGS, 2012b).Adifferent study,Outlook forMolybdenum 2008 (2008) states that themarket is composed of41%ofstainlesssteeland29%lowalloysteels.Thiseitherindicatesashiftinmarketstructurefromstainlesstolow-alloysteels,whichishighlyplausibleastheworld output dropped in thewake of the financial crisis. It may also bebecause the reports use slightly different definitions of what constitutes a

stainless steel.Outofallthestainlesssteelgrades,mostpre-crisisdemandwasfortheSocietyofAutomotiveEngineers(SAE)InternationalStandard300-seriesofsteel.ThesecondmostpopularinthisseriesisSAEType316,whichcontainsapproximately2%Mooxide.DataonMo-alloyusageis difficult toestimate, asonly the US gather and publish data on this, but picking Type 316 seems tocapturealargeportionofMoconsumption,andthusdemand.However,asIhavenotbeenabletoaccessapriceindexforSAE316steel,theLMEoffersasteelbillet futures index,which includes awide array of different steel types; twowhichcontainMo.ThisindexreachesbacktoApril282008,whichisasufficienttimespanfortheSVARmodel.ThedataisexpressedinUSDandisdeflatedusingthesameCPI-indexasearlier.

From a transparency perspective, an LME Mo future price captures marketexpectationsoffuturesupplyanddemandwell.Futuredemandmaybedifficulttoanticipateapartfromexpectedseasonaldeviationswhereasfuturesupplyscenariosarenot.ThequantitativeanalysisindicatesthatmuchoftheregularreportingintheMetalBulletinregardsfuturesupplyscenarios.Ifanactoronthemarketcananticipatewhennewsupplyisaddedtothemarket,heorshecanspeculateonwhatthepriceofMowillbe3or15monthsinthefuture.Thismeansthatreportingonnewminingprojectsthataddsupplytothemarketmaynotchangethespotpriceasmuchasthefutureprices.Figure18illustratesthat

the3-and15-monthpricesfluctuateinsimilarpatterns,butarenotidentical.The quantitative analysis indicates that most of the future supply- reportingconcernsprojectsthatwillproduceoutputafteraperiodlongerthanoneyear,sotheSVARmodelusestheLME15monthfuturepriceindex.


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Figure18:CPI-adjusted,3-and15monthLMEMoprices,expressedinlogarithmicform.

Similar to Te, the Mo quantitative analysis indicates that some actors on themarketaremorefrequentlyreportedonthanothers.Pickingaparticularstockforavariableisdifficult,ascompanyprofitability,andthusitsstockprice,maybe the result of a large number of variables not relevant to Mo spot prices.However,thequantitativeanalysisshowsthattheUScompanyGeneralMolyInc.isthesecondmostreportedcompanybetween2010andearly2013.Thismaybebecause it, like5NPlus in the Temarket, could be considered a goodmarketproxy for the overall supply climate on the Mo market. General Moly is arelatively small mining company engaged in exploration, development and

mining of Mo in the United States.14 Compared to many of the other top-mentionedcompaniesinthequantitativeanalysis,itisrelativelysmall,anddoesnotseemtobeinvolvedintheminingofothermetals,meaningitsstockpricecouldbeagoodindicatorforMosupply.Usingthisvariableagainraisestheissueofcausality;itisverylikelytheprofitabilityof GeneralMoly,andthusitsstockprice, is affected by the Mo spot price. However, the heavy reporting on thecompanyindicatesthatoppositecausalityisperhapsmoretrue.TheMopriceshouldnothaveaneffectasimmediateon GeneralMolyprofitabilityasGeneralMoly-innovations have on the Mo price, since the company only reportsprofitabilityonaquarterlybasis.Apossibleprobleminstockpricefor GeneralMoly is that thestock lies steadilyat 0,1cents (0,001USD) until June 92004,whenitjumpsupto2cents(0,02USD)(whichcanbeobservedinFigure19).However,asthispriceislistedontheirofficialwebsite,Ihavechosentousethedataasitis.Consideringthemostreportedcompany, FreeportMcMoRanInc,whichisalargeminingcompanywithgold,copperandMooperations,itsstockpriceiscoupledwith three othermetal markets. For this reason I have chosen not to includetheir stock price as a variable. I have omitted Thompson Creek, Codelco, andAntofagasta forthesamereason;theyareallmajorsuppliersofMo,buthavea

14YahooFinancefactpageforGeneralMoly:http://finance.yahoo.com/q/pr?s=GMO(accessedonMay3,2013).


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muchbroadermetalportfoliothan GeneralMoly,andtheirstockpricesshouldthusbeworseatexplainingMoprices.ThequantitativeanalysispointsthatmuchoftheworlddemandforMoseemstobe from Chinese steelmills, which is why the new IPI established in the Tesectionisused.TheunderlyingcausalityassumptionsfortheSVARmodelareasfollowing:IPIisnotthoughttobedirectlyaffectedbyanyoftheexplainingvariables.Steelpriceisaffectedbyaggregateinternationaldemandfrombusinesses. GeneralMolyisaffectedbyanumberoffactors,butduetomybeliefsofitsstockplayingaroleofamarketproxy,itisonlyaffectedbyglobaldemandandsteeldemand.The15-monthprice shouldbeaffected bymarket expectationsof future demandandsupplyofMo,butnotthespotprice,whichcapturestheimmediatedemandforphysicalquantitiesofMoatagivenmoment.ThelogofCPIadjustedstockpricesarepresentedinFigure19.

Figure19:TheCPIadjustedLOGpricesofstocksusedfortheMoSVARmodel.IPILOGisthenew

IndustrialProductionIndex,MOXVistheLME15monthMoprice,FEMOLOGistheFeMo65price,

STEELLOGistheLME3monthSteelprice,andGENMOLOGistheGeneralMolybdenumInc.stock

price.

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The optimal number of lags is 4 as determined by the AIC. The estimatedmatrixindicatesnobiasedestimates,andthusnoissuesofconvergenceorserialcorrelation,whichaLagrange-multipliertestconfirmsata92%level.WiththisI

also note that the expectedsimilar movements of the FeMo- and the LME15


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monthMotimeseriesarenotstatisticallyidentical.Thestructuralresponse-andaccumulatedCholeskyresponsesarepresentedinFigures20and21.

Figure 20: FeMo65 response to structural one S.D. innovation. FEMOLOG is FeMo65 spot price,

GENMOLOG isGeneralMoly stockprice, IPILOGis thenewIndustrial Production Index, including

China,MOVXLOGistheLME15monthMofuturesprice,andSTEELLOGistheLME3monthfuturesSteel price. All variables are expressed as logarithms. The complete structural response- and

Choleskyaccumulatedresponsediagramsarepresentedintheappendix.

Figure21:FeMo65accumulatedresponsetoCholeskyoneS.D.innovation.Thesamevariablesare

usedasinFigure20.

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MoSVAR, FEMOLOG, FEMOLOG MoSVAR, GENMOLOG, FEMOLOG MoSVAR, IPILOG, FEMOLOG

MoSVAR, MOXVLOG, FEMOLOG MoSVAR, STEELLOG, FEMOLOG


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Response to structural one S.D. innovation 2 S.E.

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AshockfromtheGeneralMolystockpriceaffectstheFeMopricepositivelyinanincreasing,oscillatingmanner,andtheincreaseissignificantatthe95%levelforthefirstthreeperiods,then90%significantinthefourth,afterwhichitcannotbestatisticallydifferentfromzero.TheCholeskyfunctionindicatesthatGeneralMolyhasabigimpactonFeMovolatility.Atthe90%levelweseethatashockexplains25%oftheFeMomeansquarederrorsinthefirstseventimeperiods.Aconjecturalshockfromtheindustrialproductionindexdoesnotindicateanysignificantchangesin resultsuntiltheninthtimeperiod,when theresponse isthenslightlynegativeata95%leveluntilperiodeleven.Thisiscounter-intuitivetoexpectations,yettheCholeskyfunctiondoesnotgiveindicationthatMomeansquareerrorsareaffectedatsignificantlevels.IinterpretthisasIPIbeingapoorindicator of Mo demand, and a better index would perhaps be a moredisaggregatedindexofarelevantsector.Asexpected,ashocktotheLME15monthMoindexaffectstheMopricetoavery

highdegree.Thestructuralimpulseresponsefunctionisaffectedpositively,andresultsaresignificantatthe90%and95%levelsintermittentlyuntilthe23 rdtimeperiod.TheCholeskyfunctionisalsosignificantatthe90%and95%levelsforalltimeperiods,andexplains77%oftheMomeansquareerrors.Thisisnotsurprising as they are indices of the same metal, but it is a good sign thatinvestorslooktothistransparentfuturepricemechanismwhendeterminingthespot price. The issue of causality is not a problem, as a futures product isfundamentally different from a spot price, despite them being the samecommodity; a product now is fundamentally different from a promise of aproductinthefuture.Finally,ashocktotheLME3monthaggregatesteelmarketpriceaffectstheMopriceinthesecondtimeperiodpositivelyatthe95%level,thendecreasestonoeffect (with intermittent 90%-95% significance) in the 15th time period. TheCholeskyfunctionisneversignificantatthe90%or95%levels,butcomesclosebetween the third and twelfth time periods,when close to 20% of themeansquareerrorscanbeexplainedbythesteelpriceshockatan88-89%level.ThepriceofsteelisthusagoodindicatorofMopricesasawhole,andcouldperhapsbe the more disaggregated, sector-specific index mentioned in the IPI shockanalysisabove

a study of tellurium market finlanda iun.2013

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