Isopacity+induced!minormetal!market! …uu.diva-portal.org/smash/get/diva2:626368/FULLTEXT01.pdf · 2013-06-07 · F.!Söderqvist! Astudy"of"the"tellurium"market! 7! quantitativeanalysisisthenappliedtoaset!ofarticlespublishedin

Is opacity-‐induced minor metal market volatility a threat to promising green

technologies? A study of the tellurium market

Fredrik Söderqvist Master of Science Thesis Uppsala University Department of Economics Submitted June 7, 2013 Supervisor: Associate Professor Mikael Bask

F. Söderqvist A study of the tellurium market 2


This master thesis was written in the spring of 2013 as part of the Uppsala University Master Programme in Economics. I would like to thank Sander de Leeuw at New Boliden AB for the support, inspiration, and data access he has generously granted me, and my supervisor Mikael Bask for his thoughtful guidance and meticulous supervision of this thesis. For questions, comments or inquiries regarding the content, methods, data or conclusions drawn in this thesis, please contact [email protected]


Abstract Tellurium is one of the rarest metals in the earth’s crust. Increased demand for cadmium telluride photovoltaic cells along with an opaque pricing and quantity-‐reporting system, have recently caused high price volatility and a speculative bubble in the tellurium market, resulting in overstocking and depressed prices. In a longer perspective this may be a threat to cadmium telluride photovoltaics as a power-‐generating technology. This master thesis compares how actors may perceive news innovation in the opaque tellurium market compared to the more transparent molybdenum market. A quantitative analysis of industry news reporting on the two metals, combined with a SVAR impulse response analysis, helps me determine which actors and factors exert most influence on spot market prices. In the opaque tellurium market, relatively unreliable proxies of supply and demand are most frequent in the news reporting while having a big impact on prices, whereas the transparent molybdenum market uses more reliable variables – such as futures prices – and transparent supply information, whilst also relying on a frequent stream of dependable proxies to scope market sentiments. My findings lead me to recommend policy makers to implement measures to increase market transparency, which may be accomplished by extending the data-‐sharing regime of the REACH database to minor metal markets. Attempting to limit speculation in minor metal markets is perhaps too blunt a tool to fix an inherent problem of a free exchange-‐pricing mechanism.

Sammanfattning Tellur är en av de mest sällsynta metallerna på Jorden. Ökad efterfrågan av kadmiumtelluridsolpaneler har nyligen orsakat stor volatilitet på tellurmarknaden. Ett opakt prissättnings-‐och kvantitetsrapporteringssystem har bidragit till att en prisbubbla bildats och spruckit, vilket resulterat i att marknadsaktörer köpt på sig stora lager till höga priser som de sedan inte kunnat sälja vidare. I ett längre perspektiv kan detta innebära begränsningar vid tillverkning av solcellsteknologi baserad på kadmiumtellurid, då ett volatilt pris kan göra nya tellurgruvprojekt alltför riskabla. Denna masteruppsats jämför hur en typisk marknadsaktör kan reagera på prisinnovationer i den opaka tellurmarkanden och den mer transparenta molybdenmarknaden. Metoden består av en kvantitativ analys av facknyheter rörande de två metallerna, varifrån variabler väljs till en SVAR modell med impuls-‐responsanalys. Urvalet av variabler är få och volatila på den opaka tellurmarknaden, medan den mer transparenta molybdenmarknaden har ett större utbud av variabler som kännetecknas av god transparens och relativ förutsägbarhet. Mina slutsatser leder mig till att rekommendera beslutsfattare att vidta åtgärder för att öka tellurmarknadens transparens genom EU-‐samarbetet, förslagsvis genom att göra anonymiserad data från REACH databasen tillgänglig för allmänheten. Samtidigt avråder jag från åtgärder som syftar till att minska spekulation, då implementering av en sådan policy kan bli både dyr och komplicerad. Key words: Tellurium, Minor Metal, Market Volatility, Market Transparency, Molybdenum, Market Efficiency, REACH, SVAR, Quantitative Analysis, London Metal Exchange. JEL codes: G13, G28, Q02, Q31, Q32, Q38, Q55.


Table of Contents

IS OPACITY-‐INDUCED MINOR METAL MARKET VOLATILITY A THREAT TO PROMISING GREEN TECHNOLOGIES? A STUDY OF THE TELLURIUM MARKET ............. 1 ABSTRACT .......................................................................................................................................... 4 SAMMANFATTNING ........................................................................................................................ 4 1. INTRODUCTION ........................................................................................................................... 6 2. BACKGROUND .............................................................................................................................. 8 2.1 TELLURIUM ................................................................................................................................................. 8 2.2 TELLURIUM SUPPLY ................................................................................................................................... 9 2.3 TELLURIUM DEMAND .............................................................................................................................. 11 2.4 THE TELLURIUM MARKETPLACE ........................................................................................................... 12 2.5 THE TELLURIUM MARKET TODAY .......................................................................................................... 13 2.6 MOLYBDENUM -‐ A NOT-‐SO MINOR METAL ........................................................................................... 14 2.7 CRITICAL MINOR METALS ....................................................................................................................... 15 2.8 PREVIOUS STUDIES OF MINOR METAL MARKETS ................................................................................ 15

3. METHOD: DETERMINING THE PRICE MECHANISMS OF TELLURIUM AND MOLYBDENUM ............................................................................................................................... 17 3.1 SVAR AND IMPULSE RESPONSE FUNCTIONS ....................................................................................... 17 3.2 QUANTITATIVE ANALYSIS ....................................................................................................................... 18

4. DATA AND RESULTS ................................................................................................................ 22 4.1 SPOT PRICES AND RETURNS ................................................................................................................... 22 4.2 QUANTITATIVE ANALYSIS FINDINGS ..................................................................................................... 24 4.3 INCORPORATING APPROPRIATE ACTORS AND FACTORS INTO THE SVAR MODEL ....................... 28 4.3.1 The Yu et al (2012) model on applied on tellurium ......................................................... 28 4.3.2 A market-‐ specific tellurium model ......................................................................................... 32 4.3.3 A market-‐specific molybdenum model .................................................................................. 36

4.4 OTHER FINDINGS FROM THE QUANTITATIVE ANALYSIS .................................................................... 40 5. CONCLUSIONS ............................................................................................................................ 44 REFERENCES ................................................................................................................................... 46 APPENDIX ........................................................................................................................................ 50 LIST OF ABBREVIATIONS ................................................................................................................................ 50 VAR AND SVAR FUNCTION DERIVATION ................................................................................................... 51 QUANTITATIVE ANALYSIS CODING EXAMPLE ............................................................................................. 53 COMPLETE STRUCTURAL INNOVATION GRAPHS ........................................................................................ 54


1. Introduction As photovoltaic (PV) technologies recently reached grid parity without government subsidies in several places and thus becoming a cheaper source of power compared to buying electricity from the power grid1, demand for critical materials in PV technologies is expected to increase. One of these critical materials is tellurium (Te), a minor metal2 and one of the rarest metals in the Earth’s crust. Until recently, Te has mainly been used as a machinability-‐ increasing alloying agent in steel manufacturing. The metal’s semi-‐conducting properties – when bound with cadmium to produce Cadmium Telluride (CdTe) – have proven excellent at converting solar radiation into electricity in CdTe PV solar cells. CdTe PV is, as of February 2013, the most efficient technology to harness the power of the sun with regards to costs per watt produced ($/Wp) and conversion efficiency; however, long term CdTe growth may be hemmed by the limited supply of Te and its relative rarity. Despite demand looking positive in the long run, the spot market price for Te tells a conflicting story. Prices have rocketed and fallen in recent years, and thus volatility is very high. To compare the highs and lows; in June 2004 99.99% pure Te cost $31 per kg on the open market, seven years later in June 2011 it cost $430 per kg, and in June 2012 the spot price was only $145 per kg. Like most minor metals, Te is not listed on any commodities bourse, and there exists little reporting of traded quantities. This makes business and long-‐term investment difficult for actors on the market and could threaten future development of CdTe PV production. At a UK House of Commons Science and Technology Committee (2011)-‐ hearing, it was suggested that critical metal market supply-‐information, such as Te supply, should be improved, and measures to limit speculative buying should be considered in order to remedy volatility in minor metal markets. This thesis is an attempt to determine what causes volatility in the Te metal market. The two main research questions are: which factors, actors, and market institutions have the biggest impact on Te prices, and what does this tell us about the overall trading conditions on the market? The results and methodology could lend conclusions valid to other industry-‐critical, opaquely traded minor metals, and add to the discussion as to what can be done to reduce volatility in these markets. This thesis also contributes to the scientific literature concerning Te supply limitations to CdTe PV, which to my knowledge has not focused on the threat to the future supply of Te that high price volatility may pose. In order to determine what makes the Te price fluctuate, a SVAR-‐model with impulse response functions is estimated using the same aggregated macroeconomic variables which Yu et al (2012) used to attempt to determine price fluctuations in the photovoltaic silicon feedstock (PVSF) spot market. PVSF is a highly price volatile, critical material in a rival PV solar cell technology. A

1 REneweconomy article UBS: Boom in unsubsidised solar PV flags energy revolution: http://reneweconomy.com.au/2013/ubs-‐boom-‐in-‐unsubsidised-‐solar-‐pv-‐flags-‐energy-‐revolution-‐60218 (accessed May 21 2013). 2 A metal included in the Minor Metal Trade Association: http://www.mmta.co.uk/history-‐and-‐change (accessed May 21 2013).


quantitative analysis is then applied to a set of articles published in an industry newspaper, the Metal Bulletin, in order to better select variables that are more market-‐specific. In order to benchmark and better relate the Te results, the same method is applied to molybdenum (Mo), which is a minor metal with similar characteristics and applications as Te. The selection of Mo is mainly motivated by its introduction to the London Metal Exchange (LME) in 2010; a market regime transition that introduced futures contracts, and transparent pricing and quantitative reporting mechanisms. My findings indicate that market-‐specific actors, factors and institutions amply describe price fluctuations in both the Te and Mo markets, whereas the aggregate macroeconomic variables presented by Yu et al (2012) do not explain price fluctuations well. The quantitative analysis suggests that there are few variables to choose from in the Te market (mainly market specific stock companies). These variables explain price fluctuations quite well, but are not very transparent. On the Mo market there are plenty of proxies of supply, indices, and futures prices that amply explain variation, whilst exhibiting steady information flows of transparent price and quantity reports. From this I advise that measures are taken in the Te market to introduce some of the institutions that help reduce volatility on the Mo market. I deem that the most critical measure would be to improve quantitative transparency in the market, which could be done within the data-‐sharing regime of the REACH framework. In the second chapter, a background to Te, its supply, demand, marketplace, and market today is given, along with a brief introduction to the Mo market, a definition of minor metals, and a summary of older studies regarding minor metal market information, efficiencies, deficiencies and transparency. In the third chapter, the SVAR model, as presented by Yu et al (2012) is introduced, along with a description of my quantitative analysis. In the fourth chapter, spot prices and returns of Te and Mo are selected. Results from the quantitative analysis are then presented, from which variable selection is made, followed by SVAR and impulse response function results from the Yu et al, Te-‐market specific and Mo-‐ market specific SVAR models. Finally, other findings from the quantitative analysis are presented. In the last chapter I discuss my conclusions and policy recommendations.


2. Background

2.1 Tellurium Te is an element in the same family as oxygen, sulphur, selenium and polonium. Its abundance on Earth, as displayed in Figure 1, shows that it is one of the nine rarest metals, where seven of these are considered “precious” (Green 2010). Te has semi conducting properties, meaning it has the electrical properties of both a conducting metal and an insulator (Nussbaum 1962). Te supply has traditionally been a by-‐product of copper, lead, and zinc processing, but can also be extracted from gold processing (Green 2009, New Boliden 2011) and is mined as a primary metal on two locations in China, and one in Mexico (USGS, 2013a).

Figure 1 Shows that Te (inside the yellow Rarest “metals”-‐cloud) is one of the 9 rarest metals in the Earth’s crust. Its abundance is similar to that of gold (Au) and platinum (Pt). Source: USGS 2002.

In recent years, an increase in demand for Te has taken place due to a change in the primary industrial usages of the metal. The Selenium Tellurium Development Association (STDA), whose members include most of the world’s major producers of Te, estimates that global distribution by consumption is 40% in solar cells, 30% in thermoelectric and photoelectric copying devices, 15% in metallurgy as an alloying metal, 5% in rubber formulation as a vulcanisation-‐ and acceleration in rubber compounding processes, and 10% in other applications such as in blasting caps and ceramic-‐ and glass pigments (STDA, 2012, USGS 2012a). The 40% final consumption in photovoltaic cells is due to a recent demand surge that started around the year 2000, when production of CdTe thin PV solar panels increased as a result of technological advancements and government subsidies of PV (Candelise et al, 2011).


2.2 Tellurium supply Estimating an exact volume of world supply for tellurium is difficult. Many countries and companies do not report their production, while volumes recovered from recycled photoelectric devices is not reported at all (USGS, 2012a). The United States Geological Survey (2013) has chosen to withhold total US-‐output from the public in order to “avoid disclosing company proprietary data”, and due to inaccuracies in the data, have chosen to list world output as N/A since 2006. The British Geological Survey (BGS, 2013) has since 2007 published data on Canadian, American, Peruvian and Japanese-‐produced tonnages of Te, estimating US production at 50 tonnes per year. To add to the inaccuracies, all global production estimates are only based on Te produced from copper anode slimes.3 As Te is not traded on any major bourse, there are no accounting or reporting requirements – such as those associated with the London Metal Exchange (2013) – and thus traded quantities remain unreported. This means that an estimated BGS (2013) “total world production” (approximately 96 tonnes) as reported by Speirs et al (2011), is much lower than real production, as it omits data from Te-‐producing countries such as Australia, Belgium, Chile, China, Colombia, Germany, India, Kazakhstan, Mexico, the Philippines, and Poland (USGS, 2013a). The most thorough estimate of total world production from copper anode slimes is between 450 and 500 tonnes per year was carried out by the UK consultancy firm Oakdene Hollins (2012). When discussing future supply of a metal, so-‐called reserves and reserve bases must be taken into account. Reserves are defined by the USGS as the part of the reserve base, which could be economically extracted or produced at a time of determination. Reserve bases are identified sources of a mineral which meet physical and chemical criteria related to current mining practices, and that may one day be extracted economically (USGS 2012a). Reserves reported by the USGS show only reserves of Te bound to copper ores, and are thus an underestimation with regards to real Te reserves. The Oakdene Hollins report (2012) estimate the copper anode slimes reserves to be close to 24 000 tonnes of Te. Scientific literature concerned with photovoltaic progress has made several attempts to estimate present and future world supply of Te, as CdTe technology will not be a viable power generation technology without a steadily available supply of Te. In a meta-‐study of Te availability, Candelise et al (2011) summarises data from six studies between 1998 and 2009 that estimates future yearly cumulative supply of Te from 128 to 2000 tonnes per year. A common fault in many of these estimates is that they use the above-‐mentioned underestimated USGS data to reach their conclusions. Green (2009) does a further analysis of possible Te that can be extracted from other ores, and so-‐called Bonanza deposits that mines Te as a primary metal. Hourari et al (2013) is the latest attempt, and looks at future supply from a dynamic perspective, which means that it implicitly takes Te prices and future demand of CdTe into account when estimating future supplied quantities of Te in 2050. The supply is made dynamic by taking other possible final usages of Te into account, as well as 3 A product of electrolysis copper refinement, from which impurities such as Te can be extracted.


including Te which could be extracted when recycling spent CdTe PV units. The study concludes that future Te supply available for CdTe PV production is expected to be slightly lower than in previous studies. These global flows and feedback loops may in the end influence both supply and demand. Figure 2 illustrates how loops of Te supply are determinant for the production of CdTe PV.

Figure 2 The CdTe casual loop diagram, which highlights areas where production costs of producing CdTe PV can be reduced. Source: Houari et al (2013).

Figure 3, the dynamic model, visualises where future sources of Te may come from, and where it may end up.

Figure 3 The system dynamics model where annual Te production plays a big role. Source: Houari et al (2013).

A common problem in these studies is that they fail to include the estimated 41 tonne yearly output from Kankbergsgruvan in Västerbotten (Boliden 2011) extracted from gold – a method which until recently has faultily been deemed unprofitable for Te-‐prices under $800/kg (Green 2009). Another, more recent threat to future supplies are new, more efficient copper processing techniques, which are not able to extract Te from the anode slimes, and are expected to decrease world Te output as the use of these techniques increase in application (Oakdene Hollins, 2012).


2.3 Tellurium demand Future demand of Te is dependent on estimates of future technological advancements and production improvement, as well as demand for PV power generation. To measure economic efficiency gains in CdTe technology, an index of USD cost per Watt produced is often used, which enables comparison through time and competing power-‐generating technologies. The latest Cost per Watt produced estimate by the market leader First Solar, is $0.68/Wp per panel, at record breaking 20% solar conversion efficiency, making it the most cost-‐efficient PV technology readily available to the market (First solar 2012). This cost per panel is not the same as cost per PV-‐system or facility, which are generally higher. A working paper by Speirs et al (2011) gives a clear overview of potential future demand of Te in CdTe PV manufacturing. Future demand of Te is dependent on the above-‐mentioned cost of producing electricity. The working paper shows that the limited future supply of Te should not be a threat to CdTe development, as CdTe PV-‐units will in the future require less Te to produce the same amount of energy. Figure 4 illustrates the content of a CdTe PV thin film cell and how much of it is composed of an active CdTe layer. This layer is expected to decrease in the future through technological progress. Woodhouse et al (2012) have calculated that at a CdTe module produced at $0.70/Wp spends $0.15/Wp on the CdTe active layer, and that future material intensity will decrease from 74 tonnes of Te per GW today, to 17 tonnes per GW in 2020.

Figure 4 Illustration of composition of a CdTe thin film solar cell. The thickness of the Active CdTe is an area believed possible to make thinner, which would decrease future demand for Te. Source: Speirs et al (2011).

Speirs et al (2011) continues to conclude that CdTe demand for Te in 2030 ranges from 480 to 1800 tonnes per year, which exceeds current supply of Te, including Te usages for in other applications. This is an indication of future


supply shortages, which will ultimately lead to higher prices. The previously mentioned papers on the possible limitations on CdTe PV posed by supply shortages make some estimates to a maximum price where power generation would still be profitable, such as Candelise et al (2011), who estimate a maximum spot price of $700/kg of 99.99% Te, and Green (2009) at $800/kg. Woodhouse et al (2012) estimate that at current prices, production in 2020 will be constrained at 10 GW of annual production, which may only be remedied with higher prices that make future mining projects more profitable. Thus, Te availability ought not to constrict future production of CdTe PV as long as costs for Te do not exceed a certain threshold, and the active CdTe layers in the panels continue to decrease. This thesis attempts to fill a gap in the scientific literature, namely to provide a more robust study of how price mechanisms can affect future Te price scenarios, which has been requested in most of the key literature used in this thesis (Candelise et al 2011, 2012, Green 2009, 2010, and Speirs et al 2011).

2.4 The tellurium marketplace Te is traded through long-‐term supply contracts and individual trades between large consumers and suppliers. Potential buyers and sellers can list proposed prices on specialist websites, which are then matched. Price quotes usually represent expert estimates of representative prices in trades being executed on a particular day, and not actual traded volumes and prices (Oakdene Hollins 2012). My anonymous source (2013) with good insight in the market adds minor metal conferences and companies’ existing costumer networks as possible forums to meet potential customers. These marketplaces are thus thoroughly opaque to outsiders. The only “open” marketplace I have found is the Chinese trading website Alibaba, where sellers can post advertisements to sell various qualities and quantities of Te.4 Te prices are posted on several trading and market news sites, including the Metal Bulletin, a UK-‐based paper that reports on global non-‐ferrous metals and steel markets (Metal Bulletin 2013a). As tellurium is not traded on any bourse, prices are estimated with the aid of different metal warehouses. Metal Bulletin, which lists many different spot prices of metals and commodities, has done this for many years. The goal is to discover at what level market participants have concluded business, made offers or received bids over a certain time period; usually the period between the last price-‐listing in the paper. After interaction with market actors, Metal Bulletin confirm the transaction with both sides, weigh the price and quantity to other transactions during the time period, and finally post a price listing consisting of a low and high price. They reserve the right to remove any data they consider outliers or discount prices they consider questionable. Metal Bulletin stress that they attempt to engage (and encourage engagement) with all sellers and buyers on the market, irrespective of size, are 4 This market can be accessed by searching for Tellurium on www.alibaba.com or via the link: http://www.alibaba.com/trade/search?fsb=y&IndexArea=product_en&CatId=&SearchText=tellurium.


impartial and independent, and do not have any vested commercial interests in pricing of their listed metals. The smallest traded lots taken into consideration when determining the price of Te is 250kg, which recently changed from 500kg (Metal Bulletin 2013b). Figure 5 illustrates how volatile the spot price of Te is, which is a common trait for many minor metals (Candelise et al 2012).

Figure 5 Te average weekly price from February 6 2006 to February 28 2013. Note: There are no price listings for June 12 and 26, as well as October 2 2009. Source: FOB USA Warehouse (February 4 2006 to June 22 2012) and Metal Bulletin (June 29 2012 to February 22 2013).

2.5 The tellurium market today In a volatile spot market based on estimates of long-‐term contracts, there may be incentives for actors to ride bubbles for short-‐term profits (Harrison et al 1978, Biasis et al 1998). For example, in June 2011 the price of Te peaked at $430/kg, up from $165/kg in 2009. After the 2011-‐peak, spot prices declined steadily for a year and are stabilised at levels just above $100/kg. This is indicative that the two-‐year 160% increase in price bears the markings of a speculative bubble. A similar phenomenon can be observed for the years 2006 to 2008, when prices more than doubled and then dropped to half its peak value. It has been suggested that these bubbles were initiated by speculative buying of Te under the pretext that the limited supply of the metal would be insufficient to meet future demand (USGS, 2013b). This lead to a hoarding of the material in warehouses, bought at inflated prices. Once the market discovered this, the price rapidly fell, and prices are still depressed, as the stocked Te bought during the bubble has yet been depleted (Oakdene Hollins, 2012). The recent change in minimum reported quantities in the Metal Bulletin from 500kg to 250kg might further be interpreted as an indicator that volumes on the market are currently so low, that making statistical samples of market interactions are difficult at these volumes. Recent statements by major actors on the Te market predict that 2013 prices will remain in the $100-‐150/kg range, stressing the market would benefit from


reduced volatility, or else suppliers would find it hard to finance future mining projects of the metal.5 The bubble may be the result of speculative trading on an opaque market that lacks transparent reporting over whom trades what to where, which has resulted in high volatility. In a conference paper, Green (2010) compares Te price fluctuations to those experienced by photovoltaic silicon feedstock (PVSF), which is a material whose price has recently been studied by Yu et al (2011). This observation is discussed further in the method chapter. In order to compare how an opaquely traded minor metal may differ from a transparently traded minor metal, I make an assessment of the market for molybdenum (Mo), which is traded under a more transparent market regime, and is listed on the London Metal Exchange (LME).

2.6 Molybdenum -‐ a not-‐so minor metal It is difficult to justify a comparison of the market of one chemical element to another; should chemical characteristics, chemical family, application, or price be used as a basis for comparison? I have chosen to compare Te to Mo for the following reasons: they are both minor metals of similar atomic number (Mo no. 42 and Te no. 52); they are by-‐products of copper production, and thus their supply relies heavily on the extraction and refinement of copper; and they can both be used as steel alloying agents. Finally, Mo was one of two minor metals introduced to the LME in February 2010, which may help to illustrate how a minor metal is traded under the transparent market conditions which were implemented prior to the LME-‐introduction (Oakdene Hollins, 2012). Mo is a refractory metallic element principally used as an alloying agent in iron, steel, and superalloys to enhance desirable properties such as machinability, toughness, strength and corrosion-‐resistance (USGS, 2012b). These properties, along with it having one of the highest melting points of all the chemical elements, means that Mo has few chemical substitutes. Mo does not exist in nature as a free metal, and is usually found in deposits bound to low-‐grade porphyry-‐molybdenum and copper deposits. The most important ore is molybdenite, and total world supply is roughly composed of half Mo mined as a primary product and half as a by-‐product of copper mining. Final usages of the metal are 24% stainless steel, 16% full alloy steel, 11% tool-‐ and high-‐speed steel, 10% high strength low alloy (HSLA) steel, 9% carbon steel, 6% cast iron, 8% catalysts, 6% metal & alloys, 5% superalloys, and 5% others (Oakdene Hollins, 2012). An interesting development is the relatively small-‐scale application of Mo in CIGS-‐PV6 cells as an electrical conductor, which lends the metal a small application-‐ connection with the Te market. Data of yearly production and usage of Mo is readily available and indicates a market roughly in balance with regards to supply and demand (IMOA, 2011). 5 “Tellurium price seen in $100-‐150/kg range this year – 5N Plus” by Martin Hayes, http://www.fastmarkets.com/minor_metals/5nt1 (accessed on March 26, 2013). 6 Another thin film PV technology.


Mo spot prices are reported using the same sampling procedure as Te (Metal Bulletin, 2013). Recently, an official cash price was also made available via the LME, which differs slightly in sampling procedure, but will not be used in this thesis due to the limited time span of the data. The main difference between the two metals is there exists a futures market for Mo via the London Metal Exchange (LME, 2013), and thus the spot prices can be seen as a reflection of long-‐term contracts traded transparently on a free market. Although only 6 702 tonnes of Mo had been traded on the bourse between its opening and March 2012, which amounts to approximately 1 % of total estimated traded volumes (Oakdene Hollins, 2012), one can argue that the mere existence of a regulated futures market will reduce volatility (Slade, 1988).

2.7 Critical minor metals Apart from being considered minor metals, Mo and Te have both been assessed for their criticality by the European Commission (2010). To qualify as a critical material, a raw material must “face high risks with regard to access to it, i.e. high supply risks or high environmental risks, and be of high economic importance… the likelihood that impediments to access occur is relatively high and impacts for the whole EU economy would be relatively significant.” Many of the materials considered in the report are minor metals. Although this assessment from 2010 did not qualify Mo or Te as critical materials, the 2011 the Commissions Joint Research Centre (JRC, 2011) added Te to the list due to it being a critical material in strategic energy technologies. In January 2013 the US Federal Energy Department (2013) followed suit by adding Te to a research hub of critical materials known as the Critical Materials Institute (CMI). The hub mainly focuses on research that reduces supply risks to the metal, which includes making extraction techniques more efficient and reducing the usage in production and manufacturing.

2.8 Previous studies of minor metal markets Although I have not found any studies on the effects of information transparency on a minor metal market, I have found older papers that are tangent to the subject. The first example is Lee et al (1998), who concludes that increased transparency helps the price discovery process become more efficient, by looking at how the opening of limit order books in the Korean stock exchange in 1992 decreased price volatility and increased liquidity in the stock market. The market efficiency of the London Metal Exchange was widely debated in a series of articles in the late 1980’s and early 1990’s. Slade (1989) looked at how changes in pricing systems changed in the 1980’s. At this time, non-‐ferrous metals such as aluminium and nickel were introduced to the LME, which the author (correctly) assumed would signal an industry-‐wide shift in pricing system from traditional producer-‐pricing mechanisms to competitive exchange pricing. Although the producer pricing system – a price cartel system consisting of major metals suppliers – was less volatile, it had no price mechanism to accommodate shifting consumer demand. This meant there were major profit incentives for producers to shift to a well-‐organised exchange system, although this carried a


cost of increased market volatility. Apart from profits, the exchange system has a significant advantage due to its pricing transparency, which means that the transaction price is always true and uniform to all customers. Well functioning institutional rules, such as contract enforcement, where breaches may lead to (a very public) expulsion from the exchange, is another reason why a system shift took place. Slade’s description of a period of transition between two systems of price setting captures the resistance many had (and still have) to future markets; namely that they are inherently risky and bubble-‐inducing. More recent studies have disproved this, and attribute this superstition to a lack of understanding of how transparent futures market actually work (Irwin et al, 2009). Hallwood (1988) argues that an unregulated exchange market is not as efficient as a regulated one. At the time, copper contracts were traded on the LME, but industry preference meant contracts were often negotiated using LME futures as a benchmark. These prices are by definition less efficient than the LME-‐negotiated contracts, and caused prices that fluctuated more than actual cyclical demand. According to this argument, the low-‐volume Mo market of today will become less volatile if higher volumes are traded over the LME. Eggert (1991) looked at how prices of more commonly traded metals and commodities fluctuate more compared to consumption of the metal, thus pointing out inefficiencies in the market. The debate focused mainly on whether or not the market could be deemed efficient. The final say in the debate was the disproval of efficiency by Sephton and Cochrane (1990). Although debating whether or not a market could be deemed efficient was a frequently debated topic at the time, proving or disproving a specific market’s efficiency may be considered an antiquated discussion today. However, these discussions revolved around a proposed paradigm shift in pricing systems, and need to be read from that perspective. This thesis does not focus on the nature of the Efficient Market Hypothesis per se, but acknowledges that more information and transparency both lead to a more efficient market and reduced price volatility. I conclude that the results from these early studies carry little validity in today’s markets where global news have a much more instantaneous effect of markets, nor does their topic of discussion add much to current academic debate. In the following chapter a method is selected to determine how markets react to availability of information, which may differ depending on the efficiency of the market.


3. Method: Determining the price mechanisms of tellurium and molybdenum

3.1 SVAR and impulse response functions Robust long-‐term prognostics of price scenarios on a volatile market are difficult to design, and as with all forecasts under high volatility, results are often imprecise and should merely be seen as best guesses of future scenarios. Still, if one can better understand what makes the market tick, decisions regarding future investments may become better informed. This is what Yu, Song, and Bao (2012) attempt to do by modelling real price fluctuations of PVSF, which is the primary component of a PV technology rival to CdTe. This is done by studying impulse response functions on number of variables using a Structural Vector Autoregressive Model (SVAR) that includes (p) periods of lag.

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where 𝑧! is a 𝑘×1-‐vector of the 𝑘 variables that are to be studied; 𝛼 is a constant 𝑘×1-‐vector; 𝐴! is the time-‐invariant 𝑘×𝑘-‐ matrix where the main diagonal terms are set to 1. 𝜀! is the 𝑘×1 error term, which satisfies the assumptions E 𝜀! = 0, or every error term has mean zero; E 𝜀!𝜀!′ = Σ, or the contemporaneous matrix of error terms is Σ (a 𝑘×𝑘 positive-‐semidefinite matrix); and E 𝜀!𝜀!!! = 0 , meaning for every non-‐zero 𝑘, there is no correlation across time, or more specifically, no serial correlation in individual terms across time. A SVAR model imposes restrictions on the response of underlying Vector Autoregressive (VAR)-‐variables, meaning one can include assumed inter-‐variable causality, from which impulse response functions can be calculated using OLS estimation. More information on derivation and assumptions of the VAR and SVAR models are found in the Appendix. For 𝑒! = 𝐴!!!𝜀! , we can incorporates the causality assumptions for each model into the 𝐴!!!-‐matrix. The optimal number of lags (p) is then determined using the Akaike Information Criterion (AIC). In the Yu et al model, 𝑧! = (𝑒𝑢𝑟𝑜! ,𝑛𝑎𝑡! , 𝑜𝑖𝑙! ,𝑎𝑔𝑔! , 𝑐𝑜𝑛! , 𝑠𝑝𝑜𝑡!), where the lagged variables 𝑒𝑢𝑟𝑜! represents euro-‐to-‐dollar exchange rate, 𝑛𝑎𝑡! and 𝑜𝑖𝑙! the price of natural gas and oil, 𝑎𝑔𝑔! real economic activity, and 𝑐𝑜𝑛! and 𝑠𝑝𝑜𝑡! represents contract-‐ and spot prices of PVSF, all expressed in logs. I use the same assumptions as Yu, Song, and Bao, which can be read in Section 3.1.2 in their article. These assumptions are translated into the equation below, where the diagonal 𝑎!! = 𝑎!! = ⋯ = 𝑎!! = 1 by construction.


𝑒! ≡

𝑒!!"#

𝑒!!"#

𝑒!!"#

𝑒!!"!

𝑒!!"

=

𝑎!! 0 0 0 00 𝑎!! 𝑎!" 0 0𝑎!" 𝑎!" 𝑎!! 0 0𝑎!" 𝑎!" 𝑎!" 𝑎!! 0𝑎!" 𝑎!" 𝑎!" 𝑎!" 𝑎!!

𝜀!!"#

𝜀!!"#

𝜀!!"#

𝜀!!"!

𝜀!!"

This thesis attempts to develop this model further by placing greater emphasis on variable selection. The above Yu et al (2012)-‐ variables are selected to best capture macroeconomic impacts on the market. As PVSF is a critical component of a rival technology, the Yu et al-‐ variables and restrictions should work just as well for the Te market. However, I believe market specific shocks may better capture fluctuations on a specific market via market spillover effects (Morales, 2008). To do this, inter-‐variable causality in the SVAR-‐model has to be explicitly stated, and then translated into the (𝐴!!!)-‐causality assumption matrix as is done above. The error term matrix (Σ) is estimated separately and indicates if the error term assumptions are fulfilled.7 This thesis only considers short-‐term causality shocks to the Te and Mo prices, which means that Te and Mo spot price will not have an effect on other market variables in the short run.8 From the SVAR model, structural impulse response functions and Cholesky accumulated response functions are then calculated. The structural impulse response function gives an indication of how a response variable reacts to a one standard deviation shock from an impulse variable. The Cholesky function is a measure of how an accumulated one standard deviation shock to an impulse variable affects the mean square error of a response variable, expressed as a fraction of the response variable’s total mean square error. This gives a measure of how much a shock of the impulse variable affects a response variable’s deviation from its mean, or more explicitly: its volatility. This thesis is a continuation of the discussion called for by Yu et al regarding variable selection, as they did not achieve significant results in their paper. In some sense, it is also an attempt to validate the appropriateness of using a SVAR-‐model to assess how different variables impact critical minor materials. Apart from applying the Yu et al macroeconomic variables to the Te spot price, this thesis investigates which variables more specific to the Te and Mo markets are appropriate, which is established using quantitative analysis methodology described in the next section.

3.2 Quantitative analysis Selecting reliable market-‐specific variables presents some difficulties to a layman not familiar with a market. In order to determine which factors and actors may be deemed most important in a market, a content analysis is carried

7 All models and estimations are done using STATA 12. The causality assumptions of the 𝐴!!!-‐matrix is input as the A-‐matrix, and the standard assumptions for the Σ-‐matrix is input as the B-‐matrix. 8 Estimating long-‐run impulse response functions could capture these causalities. I have chosen not to include such estimations in this thesis.


out on a set of articles published in the Metal Bulletin. The coding scheme is designed with reliability, validity, accuracy, and precision in mind, using method established in Neuendorf (2002). The methodological inspiration partly comes from Tetlock (2007), which uses a simple quantitative analysis on a popular Wall Street Journal column to study how the media and stock prices interact, and Tetlock et al (2008), that looks at how linguistic qualities in firm-‐specific news reporting may predict a firm’s accounting earnings and stock returns; or more specifically, how the market has a tendency to underreact to the usage of words that may reveal negative sentiments on returns and earnings. My approach is different to these studies, and focuses more on determining if and how a news innovation is expected to cause a price change, and who is the catalyst of the event. The selection of SVAR-‐model variables takes frequency of actor-‐ and factor-‐mentions, market mechanisms, and other insights from the quantitative analysis into 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 the metals. If possible, effects of actors and factors are quantified using their respective stock prices, and relevant factor indices. The articles are collected from the Metal Bulletin news archive by searching for the terms tellurium –“MB NON-‐FERROUS PRICE CHANGE” and molybdenum –“MB NON-‐FERROUS PRICE CHANGE”. The –“MB NON-‐FERROUS…”-‐term excludes so-‐called price-‐update articles, which are not proper news articles, but listings of daily price changes. All articles from February 20 2010 until February 28 2013 are pasted into word documents and imported into excel-‐spread sheets where the coding scheme is inserted at the top of each sheet. The decoding of the articles is done in six steps. The first step determines whether the news article is price-‐pertinent; or can the described event in the article theoretically change the price of the metal? Examples of non-‐pertinent articles are those that do not directly deal with the supply or demand of Te or Mo, such as those dealing with Te as an impurity in steel scrap. Examples of pertinent topics include business reports of increased production, changes in market conditions, opening of new mines, or reporting on changes in trade barriers. Articles may also be deemed pertinent if the content is deemed relevant to the research question of my thesis. If the article is deemed pertinent, the next step is to determine the general topic of the article, which is best described as a one-‐sentence description of the article’s effect on a metal price. This is done with the purpose of improving referencing ability, so the description does not need to be consistent with how previous topics are coded. Next the coding aims to determine whom the main catalyst of the news event is. It is possible that more than one actor is deemed the catalyst, or that there may be no specific catalyst at all. An almost identical topic or catalyst, which has already been covered in a previous Metal Bulletin article is still coded as pertinent, as reporting intensity may be indicative of perceived event importance.


Market impact is then assessed, which is done from a supply and demand-‐ perspective. The goal is to assess if the article is describing a perceived increase or decrease in supply or demand. If an article reports increased steel bar prices which contains Te, this is assessed as a sign of positive demand. When the article describes a supply shortage, or “tight” supply, this is interpreted as a sign of positive demand, as predicted market supply is surpassed by expected demand. The exception to this is when the article describes a true supply shock event, such as natural disasters. The final coding attempts to anticipate how a positive or negative effect on supply and demand can be translated into a price change. Positive demand means expected higher prices, and thus Possible price impact is coded “+”. Positive supply means expected lower prices, and the article would then be coded as “–“. The opposite is applied for negative supply and demand. Articles covering prices “stabilising” or “adjusting” are seen as price changes too. Previous articles then need to be considered; if the price is adjusting after a positive rally, demand is coded as “–“, and the article is coded as “+”if it concerns a stabilisation after a price drop. The above principles are also applied to the Mo Metal Bulletin articles. As the market for Mo is much larger, and the level of market maturity can be considered higher, some special precautions need to be taken when decoding these articles. Articles covering price changes in products where Mo is a component (such as steel) is not deemed pertinent, unless the article explicitly states that this has in turn affected the Mo price, such as when an article states increased demand for steel. In order to assert the reliability and validity of the quantitative analysis, a random sample of 25 articles for each metal is reread and recoded a few weeks after the initial quantitative analysis. The results of these readings are compared with the results of the original quantitative analysis. If the results differ to a large degree, the quantitative analysis will have to be respecified in order to ascertain replicability and then reapplied to the entire dataset. The robustness of a quantitative analysis can always be questioned for its reliability and replicability. My readings are done with the intent of figuring out what effect a news article may have on a “typical market actor”, and how this theoretical person would assess the market situation. The decoding can thus only be considered a best guess of what a typical trader thinks, and is thus biased by my personal opinions of what constitutes a “typical market actor”. Another limitation of the study is it is only conducted on Metal Bulletin articles. This means that the sample only contains news events deemed most relevant by that particular newspaper’s journalists and editors. A future study could include a quantitative analysis of other journals, papers and websites. The quantitative analysis is used to legitimise variable selection for the SVAR model. Quantifying which actors are mentioned most may give some numerical support for choosing a particular actor. A catalyst actor selected from the quantitative analysis is referred to as a market proxy, meaning the articles it is


mentioned in are pieces of information market actors use to assess the overall picture of the market. This raises a question of causality, as the most mentioned companies may also be reliant on the price of the commodity in question. Frequent mentioning as catalysts in a news article may be seen as indication of perceived market importance, which in turn suggests that the proxy’s causality on the metal price is significant from an innovation perspective. Selecting an appropriate proxy means that I implicitly make the assumption that a company’s stock price (or perceived company value) is a good measure of its profitability. This is a bold assumption, but necessary in order to make quantification possible. The most important element of using this method is that it involves reading and digesting a large sample of market-‐specific news in chronological order, giving the reader an insight into a market they previously did not have. This overall impression may give qualitative insights that further help in variable selection. Once the main actors, factors, or other quantifiable instances have been established through the content analysis, these are fitted into a SVAR(p)-‐model, where 𝑧! is composed of a time series vector of these variables, along with assumed short term causality assumptions translated into their respective 𝐴!-‐matrix.


4. Data and results Here, data covering spot prices and indices from February 1, 2004 to February 28, 2013 are presented. For the quantitative analysis, the shorter timespan of February 20, 2010 to February 28, 2013 is used.

4.1 Spot prices and returns To represent Te spot prices, I have chosen to use a weekly price listing based on Freight On Board (FOB) USA 99.95% Te USD/kg, for which I have available data from February 1, 2004 until to June 22, 2012. After this date I have chosen to use Metal Bulletins Tellurium Metal MB free market minimum 99.9% USD/kg. Metal Bulletin posts a bi-‐weekly high and low price on Wednesdays and Fridays. I calculate the Friday spot price as the average of the average high and low price of the Wednesday and Friday price. A change in price in the USA FOB-‐listing is more gradual than the Metal Bulletin-‐price listing, which is why I have chosen to use this index as much as possible, as it captures small price changes better than the Metal Bulletin listings, while its data is collected in a similar manner as the Metal Bulletin index. The returns of Te are displayed in Figure 6 with its statistical properties in Table 1.

Figure 6: Weekly returns of Te from February 2004 to February 2013.

Table 1 indicates that volatility, expressed as standard deviation, is quite high. The positive skewness shows a condensed distribution of negative returns and a more scattered distribution of positive returns, while the relatively high kurtosis indicates that the tails are quite “fat”, meaning returns are often not distributed close to the mean.

Mean return St Dev Skewness Kurtosis

Te Returns 0,003954205 0,03561644 1,406308088 6,051458108 Table 1: Statistical properties of Te returns.


Determining which Mo spot price to use requires some thought. There is no official LME spot-‐ or cash price available until June 2010, and these are quoted in monthly average prices per metric tonne. In order to capture the price of the metal in earlier time periods, a couple of price indices need to be considered: Molybdenum Fe65 (FeMo65 or FeMo) Cost, Insurance, in Freight (CIF), North Western Europe (NEW) USD/kg, and Molybdenum Mo3 CIF NWE USD/LB, which have been sampled from Thompson Reuters Datastream. The prices of these indices are displayed in Figure 7, including the official LME cash price for Mo, which is normally expressed in USD/metric tonne, but is here converted to USD/kg.

Figure 7: Price development of the three Mo metal cash-‐ and spot prices that are considered to represent Mo spot price.

Both indices are Mo-‐products, but may be used in slightly different applications (Oakdene Hollins, 2012). A comparison of the returns of these two commodities in Figure 8 shows that the returns follow a similar pattern, however Mo3 seems more volatile than FeMo65, which is apparent from the statistical aspects of their returns, presented in Table 2. As both metals display similar means and standard deviations, the skewness and kurtosis shows that the Mo3-‐price has fatter tails, and is thus more volatile. On the consumption side, Molybdenum oxide grades made up approximately 29% of the total world-‐Mo market in 2011, whereas Ferro-‐molybdenum products made up approximately 14% (USGS, 2012b). From a technical perspective, Fe65 contains approximately 60-‐65% Mo, which is similar to the 57,4-‐63% grade of concentrate used by the LME (USGS, 2012b, LME, 2012). From this information I have chosen to use the returns of FeMo65, as this Mo-‐product is most similar to the Mo product traded on the LME.


Figure 8: Returns of FeMo and Mo3 from February 2004 to February 2013.

Mean Std Dev Skewness Kurtosis FeMo65 0,002280685 0,057349343 2,336119248 26,98847513 MoO3 0,002392993 0,059340308 2,819789123 44,43852061

Table 2: Statistical aspects of FeMo65 and Mo3 weekly returns.

As the institutions associated with the Mo market changed drastically with the LME introduction in February 2010, comparing how market volatility differs between these two time periods is important. Table 3 shows that both return means and standard deviations are lower after the LME introduction. The negative skewness after the introduction indicates that positive returns are more likely, and the decreased kurtosis indicates that the tails are less “fat”, and thus the expected returns are much closer to the mean than before. Comparing return data that excludes the financial crisis of 2008 still indicates that the market was less volatile before the LME introduction, lending support to the claim that an LME introduction reduces market volatility.

Mean Std Dev 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 4,5714

MoO3 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

Table 3: Comparison of statistical aspects of the two Mo metals weekly returns, pre-‐ financial crisis (February 26 2004 to October 10 2008), and pre-‐ (February 26 2005 to February 19 2010) and post LME launch (February 19 2010 to February 22 2013).

4.2 Quantitative analysis findings The time period selection for the quantitative analysis of Te and Mo was partially based on the fact that there are few articles that deal directly with Te until shortly before the pre-‐2011 bubble. Also, the period before February 20, 2010,


Mo had not yet been introduced to the LME, and is thus not of interest to this study, as I only wish to study the transparent Mo market. Extending the time period would shed little light on selecting variables for the Te market, yet would require much time downloading and decoding Mo articles. In order to ascertain the reliability and validity of my quantitative analysis, a random selection of 25 Te and 25 Mo news articles were reread and recoded on May 24, 2013 with almost identical results to the original decoding, asserting the replicability of my method. The Te-‐ related articles that exist before February 2010 deal mainly with Te-‐ consumption before the PV market increased demand in the late 00’s. One such article is Who needs Tellurium?9, that deals with the smallness, irrelevance and price opacity of the market. For the selected time period there exists a total of 119 articles, where 81 of them were deemed pertinent; these are displayed over time in Figure 9. Out of these 81 pertinent articles, 61 were deemed to be price innovations that alter either the supply or demand of the market.

Figure 9: Total number and number of pertinent Te articles from February 1 2010 to February 28 2013.

The main catalysts to these events are captured in Table 4, which shows that CdTe PV solar cell producer and market-‐leader First Solar Inc. is mentioned most in price innovation articles. When these are further broken down into demand-‐ and supply shocks, we see that First Solar dominates the demand shocks with regards to number of pertinent articles. Supply shocks seem to be dominated by mining companies, who add or subtract supply to the market. The second most mentioned company is 5N Plus Inc., which is exclusively mentioned in articles that can be read as demand innovations. Considering that 5N Plus refines Te into CdTe10, this suggests that the company is a better indicator of the state of supply

9 Metal Bulletin, July 10 2000. 10 5N Plus corporate website: http://www.5nplus.com/index.php/en/selsComposes.html (accessed on May 3, 2013).


in the market. Vital, which is mentioned four times, is another refiner of Te that could be considered a supply proxy. This will be discussed more in section 4.3.2.

Catalysts Demand shocks Supply shocks FIRST SOLAR 8 FIRST SOLAR 8 BOLIDEN 5 BOLIDEN 5 5N PLUS 5 VITAL 3 5N PLUS 5 MCP 2 NYRSTAR 3 VITAL 4 RETORTE 2 MCP 3 NYRSTAR 3 II-‐VI 2 RETORTE 2

Table 4: Number of pertinent Te articles where the actor or factor was deemed to be the catalyst. Only companies with more than one pertinent, price-‐changing article are presented.

In summary, the Te market goes from low levels of reporting activity in 2010, to a much higher levels in 2011-‐2012. 2013 has so far offered very little reporting, which most likely indicates low activity on the market rather than a loss of journalistic interest. For the Mo market, a total of 1022 articles were studies, where 581 were deemed pertinent. Their distribution over time is displayed in Figure 10.

Figure 10: Total number and number of pertinent Mo articles from February 1 2010 to February 28 2013.

Out of these, 561 were deemed to be price innovations, out of which 328 were deemed to have an identifiable catalyst. In Table 5, actors and factors with more than three articles are split up according to catalysts for demand-‐ or supply shocks.


Catalysts Demand shocks Supply shocks MACRO 41 MACRO 34 FREEPORT-‐MCMORAN 24 SEASON 32 SEASON 31 GENERAL MOLY 17 FREEPORT-‐MCMORAN 24 STEEL 3 THOMPSON CREEK 15 GENERAL MOLY 17 CODELCO 12 THOMPSON CREEK 16 ANTOFAGASTA 10 LME 13 RIO TINTO 9 CODELCO 12 MOLY MINES 6 ANTOFAGASTA 10 TASEKO 6 RIO TINTO 9 MACRO 5 MOLY MINES 6 INMET 5 TASEKO 6 MOLYMET 4 INMET 5 ROCA MINES 4 MOLYMET 5 AVANTI 3 TRADE BARRIERS 5 BHP BILLITON 3 ROCA MINES 4 NORILSK NICKEL 3 AVANTI 3 SOUTHERN COPPER CORP 3 BHP BILLITON 3 TECK 3 MERCATOR 3 TRADE BARRIER 3 NORILSK NICKEL 3 XSTRATA 3 SOUTHERN COPPER CORP 3 STEEL 3 TECK 3 TRADE BARRIER 3 USD 3 XSTRATA 3

Table 5: Number of pertinent Mo articles where the actor or factor was deemed to be the catalyst. Only companies with more than two pertinent, price-‐changing articles are presented in this table

The most common catalyst is the MACRO variable, which is most common in form of demand shocks, and mainly concerns exogenous international price shocks. The second most mentioned is SEASON, which may also be considered a demand shock. It is a variant of the MACRO catalyst, but is used to decode articles covering price changes when production is expected to be low, such as summer vacation periods in the north-‐western hemisphere, or Chinese holidays like the Chinese New Year and the semi-‐annual Golden Week. Out of all the 288 demand shocks, 155 were deemed to affect demand negatively and 133 positively. The supply shock side is dominated by mining companies, and usually involves reporting of possible and real supply changes from these actors. Examples of supply shocks are news reports on mining projects gaining key local government support, strikes at mines or production facilities, or possible mining projects being cancelled. Out of 273 supply shocks 94 were deemed to affect supply negatively and 179 positively. A flaw in my coding scheme is that it only captures a catalyst of an event; it does not say much about the day-‐to-‐day structure of the market. Although mainly macro-‐ and seasonal catalysts seem to affect demand, this tells little of who is demanding Mo. From reading the articles, it is clear that buyers are agents for steel mills around the world, which are captured as a catalyst in three instances, but are in reality major price setting players in the market. Another flaw with my methodology is that it fails to capture the intensity of a specific news article. A


single article about a specific innovation may have had a large impact on the market, but cannot be captured properly by my quantitative analysis. As there may be several articles reporting on the same event, the quantitative analysis proves to be a rather blunt instrument in determining which actors or factors to pick. A typical example of this in the Mo market are supply shocks caused by mining companies’ reporting on new production activities, which are reported on several times as the project progresses from prospecting and feasibility studies, to becoming a fully functioning and producing mine. However, several reports on a single, drawn out events like mining projects may be an indication on the importance journalists place on a particular project, which in turn could affect market sentiments. Sometimes the catalyst is coded as N/A due to there being uncertainty over whom a market “buyer” is. I assume these are mainly consumers of Mo, such as steel mills, or price speculators, such as hedge funds. During the readings I noticed that China and Chinese demand is often mentioned in the articles. Having “China” as a catalyst would be an awkward variable, but in order to get an impression of Chinese frequency in the articles, I searched all the articles for the term “China”, which was mentioned at least once in 311 articles, out of which 188 were deemed pertinent. “China” appeared over 902 times in all the articles, and 485 times in articles deemed pertinent.

4.3 Incorporating appropriate actors and factors into the SVAR model My accumulated qualitative knowledge of both markets, along with the numerical indications given by the quantitative analysis, are now to be quantified and inserted into SVAR models.

4.3.1 The Yu et al (2012) model on applied on tellurium First, a SVAR model for Te is run using the same aggregate structural indicators as Yu, Song, and Bao (2012) to explain price fluctuations. The FOB USA 99.95% Te USD/kg price is used to represent the Te spot price. These – and all the below indicators expressed in USD – are deflated using US CPI collected from the US Bureau of Labor Statistics. I assume US CPI is used throughout the Yu, Song, and Bao-‐ article, as this is not explicitly stated. All data are collected on a monthly basis and prices are adjusted to February 2004-‐levels. This means I use the last available price listing of each indicator each month, which for Te-‐listings means the last Friday of each month. The explaining variables in the SVAR model are: a CPI adjusted euro-‐to-‐dollar exchange rate; CPI adjusted WTI natural gas prices; CPI adjusted Henry Hub crude oil prices; Industrial Price Index (IPI), which is the US IPI, sourced from the Board of Governors of the Federal Reserve System, the Euro-‐area IPI sourced from Eurostat, and Japanese IPI, sourced from the Japanese Ministry of Economy, Trade and Industry and Eurostat, weighted by their regional quarterly GDP from


Eurostat.11 As there is no data on Te contract pricing, this is excluded from the model. All SVAR models use monthly price listings and are displayed in Figure 11.

Figure 11: The LOG-‐expression of CPI adjusted variables as selected used by Yu et al. IPI LOG x 10 has been included to better visualise the industrial production index and is not used in the SVAR model.

Using the Yu et al-‐ variables on a structural response SVAR model for the Te market presented some difficulties. First, as my replication does not have the same number of variables as Yu, Song and Bao, so a new optimal set of lags is recalculated using AIC, which is 2 for this dataset. Second, the model does not achieve convergence. Studying the error terms of the Σ -‐matrix in Table 6, which should be close to 0, there is reason to believe assumption 3 of the SVAR model is not fulfilled for oil or gas prices, which may indicate either cointegration or serial correlation between the variables. Regressing the two variables, then running an Augmented Dick-‐Fuller test for unit roots of the residual error terms rejects the null hypothesis that they are cointegrated, which is also confirmed by a Johansen test for cointegration. A Lagrange multiplier test confirms that there is indeed serial correlation between lagged variables, and thus a major assumption of the SVAR model is unfulfilled. I suspect that Yu et al.’s data also had this problem with serial correlation, which would most likely affect their results.

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 Table 6: The output 𝚺 -‐matrix from the structural impulse response function, may indicate that Gas and Oil prices are biased estimates, as their expected error term is not close to 0.

11 As there are yet any estimates of first quarter 2013 GDP, I have assumed them to be the same as fourth quarter 2012.


Another problem I encountered when replicating the study is I have not been able to recreate the two variables “specific policy shocks” or “other specific demand shocks” used in their Cholesky accumulated response function. It is unclear which variables were used or how the authors quantified different national policies or industry specific shocks. The issue with serial correlation was dealt with by first running the impulse response function without gas, then without oil. No convergence error occurred either time, and the error terms were in both cases unbiased. From this information I choose to drop oil as a variable based on the fact that it is not normally used in power generation. The issue of using policy-‐ and industry specific shocks was simply dealt with by excluding them from the SVAR model. Figure 12 displays the results of the altered version of the Yu et al structural impulse response function model on Te with error terms presented in Table 6. The rightmost column is of most interest, as it displays how impulse variable functions may affect the Te price over 24 time periods. Further, Figure 13 displays a cumulative response to a one standard deviation structural innovation.

EURUSDLOG GASLOG IPILOG TELOG EURUSDLOG 0,01375093

GASLOG 0 0,06825692 IPILOG 0 0 0,0065813

TELOG 0 0 0 0,03663012 Table 7: The 𝚺 -‐matrix of the altered model indicates an unbiased impulse response function.

Figure 12: Responses to structural one S.D. innovation. EURUSDLOG is EUR to USD exchange rate, GASLOG is gas price, IPI is the US, Eurozone, Japan Industrial Price Index, OILLOG are oil prices, and TELOG is the Price of Te. All are expressed as logarithms. The rightmost column captures the effect of an impulse on the Te price.

0

.005

.01

.015

-.010

.01

.02

.03

-.002

0

.002

.004

-.06

-.04

-.02

0

-.005

0

.005

.01

0

.05

.1

-.002

0

.002

.004

-.02

0

.02

.04

-.005

0

.005

-.02

0

.02

.04

0

.005

.01

-.04-.02

0.02.04

-.005

0

.005

-.04-.02

0.02.04

-.004

-.002

0

.002

-.05

0

.05

.1

0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30

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

step

Graphs by irfname, impulse variable, and response variable

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


Figure 13: Cholesky accumulated response to structural one S.D. innovation ± 2 S.E with the same variables as in Figure 12.

Comparing my own findings with Yu et al’s I notice that there is but one significant impulse response functions at any given time period. Looking at Figures 11 and 12 in Yu et al, and Figures 12 and 13 above, we can see that the tails of the impulse response functions are quite fat. The only variable that causes a significant effect on Te prices is the Euro to USD exchange rate, which according Yu et al partially captures the effects of the Euro crisis. In my model, the price of Te is affected negatively by an exchange rate shock. The effect is significant, meaning it is non-‐zero on a 95%-‐level for the first 10 time periods. Further, the Cholesky accumulated response function in Figure 13 of exchange rate shows a large fraction of the Te mean square errors are explained by exchange rate shocks. I believe that the lack of significance in the above and Yu et al’s may be the result of omitted variable bias, or simply using the wrong variables. Although my study uses a slightly different time period and a different – but in many regards similar – spot price, our results are only similar with regards to the lack of significance. The Yu et al selection of variables seems to stem mainly from reasoning around general macroeconomic theory and not from real market observations. I argue that each market has its own, specific pricing mechanisms, and these need to be considered when studying the PVSF and Te markets. The only conclusion I draw from the application of the Yu et al paper on Te is that it shows how little different macroeconomic variables affect the price of a metal. Further, their paper does not shed much light on which policies to pursue – one of the purposes of their paper – nor what actually explains longer-‐term fluctuations in the market.

0

.5

1

1.5

-.1

0

.1

.2

-.05

0

.05

0

.5

1

-.2

0

.2

.4

0

.5

1

-.05

0

.05

.1

-.1-.05

0.05

.1

-.1-.05

0.05

.1

-.20.2.4.6

0

.5

1

1.5

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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.


4.3.2 A market-‐ specific tellurium model I now run a market specific-‐model for Te based on the findings from the quantitative analysis. This is done to illustrate that market-‐specific variables better explain variation than the Yu et al variables. The first variable I select for the model is the Industrial Production Index (IPI). Although I only have one price changing innovation coded as a macroeconomic in the quantitative analysis, it may be important to include some index for worldwide industrial demand as this captures conjectural cycles and shocks. Not including a variable for world demand would be an implicit assumption that the Te market is immune to business cycles, which it most likely is not. To capture aggregate world demand I use an IPI variable slightly different from the Yu et al-‐model. The IPI-‐value used by Yu et al is based on US, Eurozone, and Japanese industrial production indices, weighted by each country’s nominal GDP. It omits China, a major actor in the PV industry, and the world’s second economy in GDP terms. For the Te SVAR model I have thus used an IPI which also includes Chinese IPI, sourced from the OECD. From 2006 there are no January values given; this is dealt with by using February IPI for this time period. Adding China also means that GDP-‐ weighting needs to be recalculated, for which I use different nominal GDP data for all the countries as there seems to exist little detailed Chinese GDP-‐data, except from the International Monetary Fund. They provide the most up-‐to-‐date statistics on GDP, including an estimate for 2013. The difference between the two IPIs is illustrated in Figure 14.

Figure 14: The difference in IPI when the index includes (IPINEW, using yearly GDP weights) and excludes (IPI Yu et al, using quarterly GDP weights) China.

The most mentioned actor in the quantitative analysis is First Solar Inc12. First Solar is a world-‐leading producer of PV, both technologically with regards to generation and cost efficiency, and total solar output measured in megawatts.

12 First Solar website: http://www.firstsolar.com/en/About-‐First-‐Solar (accessed on May 3, 2013).


Their primary generation technology is CdTe PV. First solar was listed on NASDAQ on November 17 2006; meaning prices from February 28 2004 are not available. These are listed as blanks which does not pose any problems for the SVAR-‐model. First Solar is my first example of a market proxy. This includes the assumption that First Solar has causality on the Te price, and the Te price only has causality on First Solar in a longer-‐run perspective. In order to capture supply on the market, I have chosen to include the stock of the Canadian chemical refining company 5N Plus Inc. Other mining companies that produce, or are about to add supply of Te to the market could be used, but Te production usually makes up a small fraction of these businesses, thus making their stock prices poor proxies for Te supply. 5N Plus is a good candidate in this aspect, as it specialises in refining chemicals specific to this market; one of them being production of CdTe.13 My variable includes their stock price from 28 December 2007. Another company I considered as a choice of proxy, mainly due to its frequent mentioning in the Metal Bulletin, is Vital Chemicals. This is not possible as the company is not listed on any bourse or stock exchange. There are other mining companies mentioned as well, such as Boliden AB, but their stock price should be poor indicator, as Te mining makes up a small proportion of their business compared to other minerals. All the time series are treated in a similar manner as in Yu et al. The stock values are price adjusted using the same CPI, which is set to be 1 on February 27, 2004. Each index is then expressed as a 10-‐based logarithm, as can be seen in Figure 15.

Figure 15: The Te-‐model variables. IPI LOG x 10 has been included to better visualise the industrial production index and is not used in the SVAR model.

13 5N Plus website: http://www.5nplus.com/index.php/en/apropos/historique.html (accessed on May 3, 2013).


The underlying assumptions for the Te model are: IPI is assumed not to be affected by either of the two companies, nor the Te spot price. The profitability, and thus the stock of First Solar, is expected to be affected by IPI. Although the long-‐term profitability of the company may be threatened from very high Te prices, this is not true for the short run. 5N Plus, who is a major supplier to the CdTe PV-‐industry, is expected to be affected by IPI, as well as the First Solar stock, as First Solar purchases CdTe from 5N Plus. 5N Plus is not expected to be affected by Te prices in the short run for the same reasons as First Solar. I expect the Te spot price to be affected by all the above variables; IPI should affect the CdTe-‐market as a whole, and thus Te; the First Solar stock acts as a proxy for CdTe industry demand; the 5N Plus as a proxy for industry supply.

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The optimal number of lags for this model is determined to be 2 by the AIC. The structural impulse response-‐ and Cholesky accumulated response functions are presented in Figures 16 and 17.

Figure 16: Te response to structural one S.D. innovation. FIVENLOG is 5N Plus stock, FSLRLOG is the First Solar Inc. stock price, IPILOG is the new Industrial Production Index, including China, and TELOG is the Te price. All variables are expressed as logarithms. The complete structural response-‐ and Cholesky accumulated response diagrams are presented in the appendix.

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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.


Figure 17: Te accumulated response to Cholesky one S.D. innovation. The same variables are used as in Figure 16

A 5N Plus shock affects the Te price positively in the first two time periods on a 95% level, at the 90% in the third period, after which it is zero. The Cholesky response function is not significant at a 90%-‐level. This is evidence that although the 5N Plus stock may cause ripples in the Te price, it does not affect Te volatility. The stock of First Solar does not directly affect the price of Te, but after five time periods we see a positive increase in price, which continues to rise until period 13. This then ebbs off until period 20, when the response function reaches 0. First Solar has a large, significant impact on the Te spot price, which supports the findings of the quantitative analysis that the profitability of this company has a large effect on Te prices. The five period lag of the effect is most likely due to the sluggish response of Te prices to contract changes associated with decreased output of the company. If First Solar experiences problems, they most likely let their contracts run out without renewing them. This means that high-‐priced contracts that have yet to run out are still present in warehouses, bringing up the average price. The First Solar contracts are estimated together with lower priced contracts negotiated more recently, which have prices closer to the “real“ market price. The accumulated Cholesky response function supports the lagged variable statement; only after 19(!) periods do the results become significant at a below-‐90% level. The stock price then explains 25% of the Te mean square errors. This may be due to the long time period the Te price took to adjust to the reduced earnings of First Solar in 2011-‐2012. A structural shock from the IPI variable seems to immediately cause a positive response in the Te market. The Te response variable increases at a 90% significance level until period 5 (at which point the response function is at 95% significance level), and reaches 0 at period 10. This may be indicative that the Te market 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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square errors of Te are affected by a structural innovation shift in IPI, meaning IPI does not increase volatility on the Te market.

4.3.3 A market-‐specific molybdenum model The quantitative analysis indicates that most of the demand for Mo comes from steel mills. Instead of picking individual companies as proxies for demand in the steel market, as I did in the Te example, the most common steel grade prices containing Mo would be a better measure of aggregate Mo demand. In 2011, approximately 39% of world consumption of Mo was in low alloy steels, 15 % in stainless alloy steels, and 8% in superalloys (USGS, 2012b). A different study, Outlook for Molybdenum 2008 (2008) states that the market is composed of 41% of stainless steel and 29% low alloy steels. This either indicates a shift in market structure from stainless to low-‐alloy steels, which is highly plausible as the world output dropped in the wake of the financial crisis. It may also be because the reports use slightly different definitions of what constitutes a stainless steel. Out of all the stainless steel grades, most pre-‐crisis demand was for the Society of Automotive Engineers (SAE) International Standard 300-‐series of steel. The second most popular in this series is SAE Type 316, which contains approximately 2% Mo oxide. Data on Mo-‐alloy usage is difficult to estimate, as only the US gather and publish data on this, but picking Type 316 seems to capture a large portion of Mo consumption, and thus demand. However, as I have not been able to access a price index for SAE 316 steel, the LME offers a steel billet futures index, which includes a wide array of different steel types; two which contain Mo. This index reaches back to April 28 2008, which is a sufficient timespan for the SVAR model. The data is expressed in USD and is deflated using the same CPI-‐index as earlier. From a transparency perspective, an LME Mo future price captures market expectations of future supply and demand well. Future demand may be difficult to anticipate – apart from expected seasonal deviations – whereas future supply scenarios are not. The quantitative analysis indicates that much of the regular reporting in the Metal Bulletin regards future supply scenarios. If an actor on the market can anticipate when new supply is added to the market, he or she can speculate on what the price of Mo will be 3 or 15 months in the future. This means that reporting on new mining projects that add supply to the market may not change the spot price as much as the future prices. Figure 18 illustrates that the 3-‐ and 15-‐month prices fluctuate in similar patterns, but are not identical. The quantitative analysis indicates that most of the future supply-‐ reporting concerns projects that will produce output after a period longer than one year, so the SVAR model uses the LME 15 month future price index.


Figure 18: CPI-‐adjusted, 3-‐ and 15 month LME Mo prices, expressed in logarithmic form.

Similar to Te, the Mo quantitative analysis indicates that some actors on the market are more frequently reported on than others. Picking a particular stock for a variable is difficult, as company profitability, and thus its stock price, may be the result of a large number of variables not relevant to Mo spot prices. However, the quantitative analysis shows that the US company General Moly Inc. is the second most reported company between 2010 and early 2013. This may be because it, like 5N Plus in the Te market, could be considered a good market proxy for the overall supply climate on the Mo market. General Moly is a relatively small mining company engaged in exploration, development and mining of Mo in the United States.14 Compared to many of the other top-‐mentioned companies in the quantitative analysis, it is relatively small, and does not seem to be involved in the mining of other metals, meaning its stock price could be a good indicator for Mo supply. Using this variable again raises the issue of causality; it is very likely the profitability of General Moly, and thus its stock price, is affected by the Mo spot price. However, the heavy reporting on the company indicates that opposite causality is perhaps more true. The Mo price should not have an effect as immediate on General Moly profitability as General Moly-‐innovations have on the Mo price, since the company only reports profitability on a quarterly basis. A possible problem in stock price for General Moly is that the stock lies steadily at 0,1 cents (0,001 USD) until June 9 2004, when it jumps up to 2 cents (0,02 USD) (which can be observed in Figure 19). However, as this price is listed on their official website, I have chosen to use the data as it is. Considering the most reported company, Freeport McMoRan Inc, which is a large mining company with gold, copper and Mo operations, its stock price is coupled with three other metal markets. For this reason I have chosen not to include their stock price as a variable. I have omitted Thompson Creek, Codelco, and Antofagasta for the same reason; they are all major suppliers of Mo, but have a

14 Yahoo Finance fact page for General Moly: http://finance.yahoo.com/q/pr?s=GMO (accessed on May 3, 2013).


much broader metal portfolio than General Moly, and their stock prices should thus be worse at explaining Mo prices. The quantitative analysis points that much of the world demand for Mo seems to be from Chinese steel mills, which is why the new IPI established in the Te section is used. The underlying causality assumptions for the SVAR model are as following: IPI is not thought to be directly affected by any of the explaining variables. Steel price is affected by aggregate international demand from businesses. General Moly is affected by a number of factors, but due to my beliefs of its stock playing a role of a market proxy, it is only affected by global demand and steel demand. The 15-‐month price should be affected by market expectations of future demand and supply of Mo, but not the spot price, which captures the immediate demand for physical quantities of Mo at a given moment. The log of CPI adjusted stock prices are presented in Figure 19.

Figure 19: The CPI adjusted LOG prices of stocks used for the Mo SVAR model. IPI LOG is the new Industrial Production Index, MOXV is the LME 15 month Mo price, FEMOLOG is the FeMo 65 price, STEELLOG is the LME 3 month Steel price, and GENMOLOG is the General Molybdenum Inc. stock price.

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The optimal number of lags is 4 as determined by the AIC. The estimated Σ–matrix indicates no biased estimates, and thus no issues of convergence or serial correlation, which a Lagrange-‐multiplier test confirms at a 92% level. With this I also note that the expected similar movements of the FeMo-‐ and the LME 15


month Mo time series are not statistically identical. The structural response-‐ and accumulated Cholesky responses are presented in Figures 20 and 21.

Figure 20: FeMo65 response to structural one S.D. innovation. FEMOLOG is FeMo65 spot price, GENMOLOG is General Moly stock price, IPILOG is the new Industrial Production Index, including China, MOVXLOG is the LME 15 month Mo futures price, and STEELLOG is the LME 3 month futures Steel price. All variables are expressed as logarithms. The complete structural response-‐ and Cholesky accumulated response diagrams are presented in the appendix.

Figure 21: FeMo65 accumulated response to Cholesky one S.D. innovation. The same variables are used as in Figure 20.

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

MoSVAR, MOXVLOG, FEMOLOG MoSVAR, STEELLOG, FEMOLOG


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

MoSVAR, MOXVLOG, FEMOLOG MoSVAR, STEELLOG, FEMOLOG


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A shock from the General Moly stock price affects the FeMo price positively in an increasing, oscillating manner, and the increase is significant at the 95% level for the first three periods, then 90% significant in the fourth, after which it cannot be statistically different from zero. The Cholesky function indicates that General Moly has a big impact on FeMo volatility. At the 90% level we see that a shock explains 25% of the FeMo mean squared errors in the first seven time periods. A conjectural shock from the industrial production index does not indicate any significant changes in results until the ninth time period, when the response is then slightly negative at a 95% level until period eleven. This is counter-‐intuitive to expectations, yet the Cholesky function does not give indication that Mo mean square errors are affected at significant levels. I interpret this as IPI being a poor indicator of Mo demand, and a better index would perhaps be a more disaggregated index of a relevant sector. As expected, a shock to the LME 15 month Mo index affects the Mo price to a very high degree. The structural impulse response function is affected positively, and results are significant at the 90% and 95% levels intermittently until the 23rd time period. The Cholesky function is also significant at the 90% and 95% levels for all time periods, and explains 77% of the Mo mean square errors. This is not surprising as they are indices of the same metal, but it is a good sign that investors look to this transparent future price mechanism when determining the spot price. The issue of causality is not a problem, as a futures product is fundamentally different from a spot price, despite them being the same commodity; a product now is fundamentally different from a promise of a product in the future. Finally, a shock to the LME 3 month aggregate steel market price affects the Mo price in the second time period positively at the 95% level, then decreases to no effect (with intermittent 90%-‐95% significance) in the 15th time period. The Cholesky function is never significant at the 90% or 95% levels, but comes close between the third and twelfth time periods, when close to 20% of the mean square errors can be explained by the steel price shock at an 88-‐89% level. The price of steel is thus a good indicator of Mo prices as a whole, and could perhaps be the more disaggregated, sector-‐specific index mentioned in the IPI shock analysis above. When comparing the Mo and the Te impulse response functions, the Mo ones seem to have much more of an oscillating character, whereas the Te functions have a smoother character. I suspect this may be caused by the differing number of lags, as determined by the AIC.

4.4 Other findings from the quantitative analysis There are many aspects of the Te and Mo markets that are difficult to quantify and measure. The ones I found most important during the quantitative analysis-‐ readings need to be discussed and compared. The quantitative analyses of the Te and Mo markets reveal several aspects with regards to market transparency. The first and most obvious aspect is the amount


of reporting done on market events. There are 10 times as many articles written about Mo than Te during the sample period, which is natural when comparing the physical sizes of markets. However, the number of Te articles increased as market volumes and interest for the metal increased in 2011. If Te continues to be a highly demanded metal the number of actors interested in reporting should increase, which in turn may lead to more reporting on the subject, and thus more transparency. A second aspect is related to the foci of the news reporting itself. Although the Mo reporting seems quite comprehensive, and the Te reporting increases as demand for the metal increases, the limitations and methodology used by news reporters is highly influential on what is considered an innovation. In a market like Mo, where registered stock companies are bound by accounting rules, quarterly reporting and financial statements, it is cheap, convenient and easy for a news reporter to write a story about these stock companies earnings and production changes. On the Te market, however, there are a few dominant actors, where some are not even publically listed, meaning less easily accessible public information for journalists to access and report on. Further, the quantitative analysis contained many Mo “market reports” in the form of interviews from the LME-‐ring, where the newspaper interviews traders and market insiders about how they perceive market supply and demand. These articles have been considered with caution as there is no way to assert the extent, truth or intent behind these often-‐speculative statements. The importance of such statements are crucial in establishing market perception of supply and demand, and such signalling market mechanisms can be seen as natural aspects of a mature market. These types of speculative statements may be planted with the reporter for signalling purposes, and the importance of the news article may thus be exaggerated, as it merely constitutes an easy story for a journalist eager to fill the daily “news hole”. There are fewer of these types of signalling articles in the Te sample, which can partially be explained by the smaller market size and by the fact that there are few or no traders to interview. The sources to these statements in the Te market are often anonymous warehouse officials, whose business models rely on confidentiality and security, and this is perhaps why these statements are so few and less precise. On May 19 2011, the Metal Bulletin published the article UK government advised to investigate speculation in critical metals, which was included in the Te quantitative analysis but not in the Mo version. Although Te is not mentioned in the article, it was included as it is being traded on an off-‐exchange minor metal market, which has been speculated in heavily by private hedge funds. This article refers to a UK House of Commons Science and Technology Committee (2011) hearing that took place on May 17 2011. Apart from discussing national solutions to possible future supply problems, such as stockpiling as a safeguard against market failures, the hearing focused on the increased importance of metals in financial markets, and how financial markets have had an increased impact on metal prices.


The perception of scarcity of certain minerals and metals may lead to increased speculation and volatility in price and supply. There is a need for accurate and reliable information on scarcity of metals. This concern underlines the recommendation we have made, that the Government should establish and regularly update a shared database to provide such information. We are also concerned by reports of hedge funds buying up significant quantities of strategic metals. We recommend that the Government investigate whether there are increasing levels of speculation in the metals markets and, if there are, their contribution to price volatility and whether markets that allow high levels of speculation, with associated price volatility, are an acceptable way to deliver strategic commodities to end users.

The committee thus recommends that the UK government needs to actively encourage supply reporting and transparency. The second recommendation, that the government should investigate the possibility of limiting speculative trading, is not about improving transparency in the market, but rather an attempt to limit whom gets to trade what in the market. The example presented to the committee to illustrate the negative effects of speculation was recent purchases of copper made by UK hedge funds. Although this may have been briefly disruptive to the copper market, it by no means made it collapse. This may in large part be due to copper market transparency, meaning the purely speculative purchase was easy to detect, and actors in the market could quickly adjust to the new supply and demand situation, albeit at a cost. The above hearings brought another regulation to my attention, which deals in minor metals in another aspect. The much-‐debated EU regulation of hazardous materials handling, REACH (Registration, Evaluation, Authorisation, and Restriction of Chemicals), imposes regulations and restrictions on producers and consumers dealing in materials that are considered toxic.15 REACH requires producers and consumers to register usage of registered materials within an industry-‐ or sectorial-‐ specific Substance Information Exchange Forum (SIEF)16, which is handled by the European Chemicals Agency (ECHA). The purpose of the system is to make information sharing of toxic substances more efficient, harmonise labelling of products with toxic contents, and help make markets more efficient with regards to limiting animal testing. This means that actors in the market can request toxicity testing data from the ECHA for a particular material, eliminating unnecessary testing of toxins on vertebrate animals.17 The REACH regulation has been met with industry criticism for being disruptive of trade, while placing much of the costs of implementation on producers and 15 MMTA REACH information page http://www.mmta.co.uk/reach (accessed on May 6, 2013). 16 EU SIEF information page: http://echa.europa.eu/web/guest/regulations/reach/substance-‐registration/substance-‐information-‐exchange-‐fora (accessed on May 6, 2013). 17 ECHA website section on data sharing: http://echa.europa.eu/regulations/reach/substance-‐registration/data-‐sharing (accessed on May 6, 2013).


consumers. Many producers are sceptical of the data sharing, which they say risk exposing corporate secrets.18 As illustrated in Table 9 many of the minor metals covered by the REACH regulation have also been assessed for criticality, or have been deemed critical by the European Commission. This means that the ECHA should have high-‐resolution data of minor metal production and consumption for many actors trading within, and exporting to the EU.

EU minor metals assessed for their criticality

Requires REACH registration in some form

Antimony Beryllium X Chromium X Cobalt Gallium Germanium Indium Lithium Magnesium X Manganese X Molybdenum X Rare earths X Rhenium X Silica sand X Tantalum Tellurium X Titanium Tungsten X Vanadium X

Table 8: List of minor metals assessed for their criticality by the European Commission (2010) and minor metals and REACH-‐registration status as defined by the MMTA.

As the REACH database is used to share information with the purpose of bringing market efficiency in the form of harm reduction to vertebrate animals, my question is: could it be used to bring greater transparency to minor metal markets, improving efficiency and reducing opacity-‐induced market volatility?

18 EU chemicals bill under fire from US-‐led coalition, EU observer website: http://euobserver.com/economic/21813 (accessed on May 6, 2013).


5. Conclusions The application of my methodology to the Te and Mo markets illustrates that market-‐specific variables can amply explain variation in a minor metal market. The types of variables used in the Mo market are more transparent and carry a better degree of explanation compared to the Te market. The Mo future-‐ and steel price, along with a long list of possible market proxy-‐companies, help Mo actors assess the market on day-‐to-‐day basis, which is a clear indication that Mo is traded on a transparent and relatively efficient market. The Te market is wholly different, with no transparent indicators, few actors to use as market-‐ proxies, and little reporting on supplied quantities. These opaque market conditions, coupled with the increased demand for Te, are the major causes of the high Te price volatility, which poses a threat to the future supply of this critical material. Although previous opacity may have been caused by the low news reporting on Te, the increased number of articles in 2011 may be an indication that increased journalism attention to the market does not improve transparency to a degree where speculation becomes less harmful. The quantitative analysis highlights the important role journalists play in a free market as deliverers of price innovations. The Metal Bulletin reporting seems be rather western-‐centric in its reporting, attributing European and American supply and demand changes to specific companies, whereas Chinese counterparts are often only addressed as just Chinese. This makes my study slightly “western-‐biased” from a market perspective. I have chosen not to address the biases, discourses, and language specific restrictions of journalists, but this could be done in future studies. In order to address the problems caused by opacity-‐driven market volatility, policy makers need to consider different remedies. The introduction of a futures price, such as has been done for the PVSF described in Yu et al (2011), does not solve the problem of high volatility. Although the Mo spot price has managed to achieve reduced volatility by being introduced to the LME, this can mostly be attributed to the new transparent pricing and quantity-‐reporting regimes associated with LME introduction. I believe that if there existed a transparent system of quantitative reporting for the Te market, the speculative bubble of 2011 may not have been so severe as producers, consumers and speculators in the market would have noticed that the artificially high prices of the metal were the result of speculative buying, based on over-‐optimistic demand estimates and supply limitation, and not physical consumer demand. The UK committee hearings recommended that the government should set up a database to provide better information on market available quantities of metals. I suggest they do this on the EU-‐level, which means extending the limited data-‐sharing regime that already exists within the EU REACH scheme. This database ought to contain high-‐resolution data of all toxic minor metals traded within the EU. If this data is made available in a responsible and easily accessible way, it may be a cheap remedy to reduce opacity-‐induced volatility in small minor metal markets. It is unlikely that any of these low-‐volume metals will be introduced to


the LME or a bourse similar to it, as this seems to require large markets in order to reach profitability. Although price speculation in all commodities markets may cause efficiency losses, I do not see any efficient means of limiting such behaviour. This would involve identifying buyer intent at each transaction, as well as creating a system of enforcing rules to limit this behaviour. Speculation has long been seen as the major disadvantage – albeit a natural part – of a free exchange system. This thesis is an attempt to illustrate that a free exchange system can only be efficient if the market is transparent. If the Te market remains volatile, fewer suppliers will invest in mining projects, as the market price poses a potential risk to their operations. Further research is needed to better understand commodity market volatility and what possible roles market actors and policy makers ought to play in order to reduce unnecessary risks to companies dependent on minor, critical materials.


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Appendix

List of abbreviations Au – Gold BGS – British Geological Survey CdTe – Cadmium Telluride CIF – Cost, Insurance, in Freight CPI – Consumer Price Index ECHA -‐ European Chemicals Agency FeMo65 and FeMo – Ferro Molybdenum (65) FOB – Freight On Board IPI – Industrial Production Index LME – London Metal Exchange Mo – Molybdenum NWE – North Western Europe Pt – Platinum PV – Photovoltaic(s) PVSF – Photovoltaic Silicon Feedstock REACH -‐ Registration, Evaluation, Authorisation, and Restriction of Chemicals SIEF -‐ Substance Information Exchange Forum STDA – Selenium Tellurium Development Association SVAR(p) – Structural vector autoregression model with p lags. Te -‐ Tellurium USGS – United States Geological Survey


VAR and SVAR function derivation19 A VAR model of p lags can be expressed as:

𝑧! = 𝛼 + 𝐵!𝑧!!!

!

!!!

+ 𝜀!

where 𝑧! is the 𝑘×1-‐vector of the 𝑘 variables that are to be studied; 𝛼 is a constant 𝑘×1 -‐vector; 𝐵! is the time-‐invariant 𝑘×𝑘 -‐ matrix where the main diagonal terms are set to 1; and 𝜀! is the 𝑘×1 error term, which satisfies the assumptions:

1. E 𝜀! = 0, or every error term has mean zero; 2. E 𝜀!𝜀!′ = Σ, or the contemporaneous matrix of error terms is Σ (a 𝑘×𝑘

positive-‐semidefinite matrix) 3. E(𝜀!𝜀!!!), meaning for every non-‐zero 𝑘, there is no correlation across

time, or more specifically no serial correlation in individual terms across time.

A SVAR model of p lags is similar, but imposes a set of initial intra-‐variable causality assumptions at time 0 (𝐴! on the left hand side), using the same error-‐ term assumptions as above, and is expressed as:

𝐴!𝑧! = 𝛼! + 𝐴!𝑧!!!

!

!!!

+ 𝜀!

For simplicity, assume a 2 structural SVAR model with 1 lag. This is expressed as:

1 𝐴!;!,!𝐴!;!,! 1

𝑧!,!𝑧!,! =

𝛼!,!𝛼!,! +

𝐴!;!,! 𝐴!;!,!𝐴!;!,! 𝐴!;!,!

𝑧!,!!!𝑧!,!!! +

𝜀!,!𝜀!,!

where

Σ = E 𝜀!𝜀!! = 𝜎!! 00 𝜎!!

Writing out the equation explicitly and moving 𝑧!,! to the right hand side:

𝑧!,! = 𝛼!;! − 𝐴!;!,!𝑧!,! + 𝐴!;!,!𝑧!,!!! + 𝐴!;!,!𝑧!,!!! + 𝜀!,! we see that variable 𝑧!,! can have an effect on variable 𝑧!,! if 𝐴!;!,! is non-‐zero. This differs from the VAR, where 𝑧!,! can only affect 𝑧!,! in periods 𝑡 + 1 and

19 Derivations and formulas are sourced from the Journal of Statistical Software paper VAR, SVAR, and SVEC models http://www.jstatsoft.org/v27/i04/paper, a Boston College lecture note on VAR, SVAR and impulse response functions: https://www2.bc.edu/~iacoviel/teach/0809/EC751_files/var.pdf, and Wikipedia http://en.wikipedia.org/wiki/Vector_autoregression (both accessed on May 20 2013).


forward, but not directly in time t. Attempting to estimate OLS estimations of 𝑧!,! will at this point prove futile, as it will yield inconsistent results. However, expressing the SVAR-‐function in reduced form enables us to solve for 𝑧! . First, pre-‐multiply the SVAR within the inverse of 𝐴!,

𝑧! = 𝐴!!!𝛼! + 𝐴!!!𝐴!𝑧!!! + 𝐴!!!𝐴!𝑧!!! +⋯+ 𝐴!!!𝐴!𝑧!!! + 𝐴!!!𝜀! then denoting

𝐴!!!𝛼! = 𝛼, 𝐴!!!𝐴!𝑧!!! = 𝐴! for 𝑖 = 1,… ,𝑝, and 𝐴!!!𝜀! = 𝑒! the SVAR can be expressed in reduced form as

𝑧! = 𝛼 + 𝐴!

!

!!!

+ 𝑒!

meaning all causality assumptions can be inserted into 𝐴!!! of the 𝐴!!!𝜀! = 𝑒!-‐ matrix, as is done in the methodology. To preform a structural impulse-‐ or Cholesky accumulated response function, a one standard deviation shock besets the model at time 0, meaning for

𝑒! = 𝐴!!!𝜀! the initial 𝜀! is set to

𝜀! =

𝜀!,! = 1𝜀!,! = 0

⋮𝜀!,! = 0

meaning the variable vector at time t may can be expressed as

𝑧! =

𝑧!,!𝑧!,!⋮𝑧!,!

= 𝐴!!!𝜀! =

1 𝑎!,! … 𝑎!,!𝑎!,! 1 … 𝑎!,!⋮ ⋮ ⋱ ⋮𝑎!,! 𝑎!,! … 1

𝜀!,!𝜀!,!⋮𝜀!,!

where 𝑎!,! in the 𝐴!!!-‐matrix contains causality assumptions for time 0. The impulse response function for periods 𝑠 (with 𝑠 > 0) can thus be expressed as

𝑧! = 𝐴!!!𝐴!𝑧!!! From this, an OLS estimation is computed for each reduced-‐form equation. This can be calculated using most quality econometric or statistical computer software. However, the algorithms involved in solving these the response functions are quite complicated and will not be presented here.


Quantitative analysis coding example

Figure 22: Screenshot of a typical, pertinent Te article with filled-‐in coding scheme on top.

Figure 23: Screenshot of a typical, non-‐pertinent Mo article, with filled-‐in coding scheme on top.


Complete structural innovation graphs

Figure 24: The complete structural response function of the Te model.

Figure 25: The complete accumulated Cholesky response function of the Te model.

-.05

0

.05

-.1

-.05

0

.05

-.0020

.002

.004

.006

-.02

0

.02

.04

-.020

.02

.04

.06

-.05

0

.05

.1

-.004-.002

0.002.004

-.02

0

.02

.04

-.04-.02

0.02.04

-.1

-.05

0

.05

-.005

0

.005

.01

-.02

0

.02

.04

-.05

0

.05

-.1

-.05

0

.05

-.002

0

.002

.004

-.05

0

.05

0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30

TeSVAR, FIVENLOG, FIVENLOG TeSVAR, FIVENLOG, FSLRLOG TeSVAR, FIVENLOG, IPILOG TeSVAR, FIVENLOG, TELOG

TeSVAR, FSLRLOG, FIVENLOG TeSVAR, FSLRLOG, FSLRLOG TeSVAR, FSLRLOG, IPILOG TeSVAR, FSLRLOG, TELOG

TeSVAR, IPILOG, FIVENLOG TeSVAR, IPILOG, FSLRLOG TeSVAR, IPILOG, IPILOG TeSVAR, IPILOG, TELOG

TeSVAR, TELOG, FIVENLOG TeSVAR, TELOG, FSLRLOG TeSVAR, TELOG, IPILOG TeSVAR, TELOG, TELOG


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0

.5

1

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0

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0

.5

1

1.5

-.05

0

.05

.1

-.20.2.4.6

-.05

0

.05

.1

-.05

0

.05

.1

0

.5

1

-.2

0

.2

.4

0

.2

.4

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-.2

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TeSVAR, FIVENLOG, FIVENLOG TeSVAR, FIVENLOG, FSLRLOG TeSVAR, FIVENLOG, IPILOG TeSVAR, FIVENLOG, TELOG

TeSVAR, FSLRLOG, FIVENLOG TeSVAR, FSLRLOG, FSLRLOG TeSVAR, FSLRLOG, IPILOG TeSVAR, FSLRLOG, TELOG

TeSVAR, IPILOG, FIVENLOG TeSVAR, IPILOG, FSLRLOG TeSVAR, IPILOG, IPILOG TeSVAR, IPILOG, TELOG

TeSVAR, TELOG, FIVENLOG TeSVAR, TELOG, FSLRLOG TeSVAR, TELOG, IPILOG TeSVAR, TELOG, TELOG


step




Figure 26: The complete structural response function of the Mo model.

Figure 27: The complete accumulated Cholesky response function of the Mo model.

-.01

0

.01

.02

-.02

0

.02

.04

-.001

0

.001

-.01

0

.01

.02

-.02

0

.02

.04

-.02

0

.02

.04

-.04-.02

0.02.04

-.002

0

.002

.004

-.02

0

.02

.04

-.05

0

.05

.1

-.03-.02-.01

0.01

-.04

-.02

0

.02

-.002

0

.002

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-.04

-.02

0

.02

-.1

-.05

0

.05

-.020

.02

.04

.06

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0.02.04

-.002-.001

0.001.002

-.020

.02

.04

.06

0

.05

.1

.15

-.020

.02

.04

.06

-.05

0

.05

-.002

-.001

0

.001

-.05

0

.05

.1

-.050

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.15

0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30

MoSVAR, FEMOLOG, FEMOLOG MoSVAR, FEMOLOG, GENMOLOG MoSVAR, FEMOLOG, IPILOG MoSVAR, FEMOLOG, MOXVLOG MoSVAR, FEMOLOG, STEELLOG

MoSVAR, GENMOLOG, FEMOLOG MoSVAR, GENMOLOG, GENMOLOG MoSVAR, GENMOLOG, IPILOG MoSVAR, GENMOLOG, MOXVLOG MoSVAR, GENMOLOG, STEELLOG

MoSVAR, IPILOG, FEMOLOG MoSVAR, IPILOG, GENMOLOG MoSVAR, IPILOG, IPILOG MoSVAR, IPILOG, MOXVLOG MoSVAR, IPILOG, STEELLOG

MoSVAR, MOXVLOG, FEMOLOG MoSVAR, MOXVLOG, GENMOLOG MoSVAR, MOXVLOG, IPILOG MoSVAR, MOXVLOG, MOXVLOG MoSVAR, MOXVLOG, STEELLOG

MoSVAR, STEELLOG, FEMOLOG MoSVAR, STEELLOG, GENMOLOG MoSVAR, STEELLOG, IPILOG MoSVAR, STEELLOG, MOXVLOG MoSVAR, STEELLOG, STEELLOG


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-.050

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0

.5

1

0

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MoSVAR, FEMOLOG, FEMOLOG MoSVAR, FEMOLOG, GENMOLOG MoSVAR, FEMOLOG, IPILOG MoSVAR, FEMOLOG, MOXVLOG MoSVAR, FEMOLOG, STEELLOG

MoSVAR, GENMOLOG, FEMOLOG MoSVAR, GENMOLOG, GENMOLOG MoSVAR, GENMOLOG, IPILOG MoSVAR, GENMOLOG, MOXVLOG MoSVAR, GENMOLOG, STEELLOG

MoSVAR, IPILOG, FEMOLOG MoSVAR, IPILOG, GENMOLOG MoSVAR, IPILOG, IPILOG MoSVAR, IPILOG, MOXVLOG MoSVAR, IPILOG, STEELLOG

MoSVAR, MOXVLOG, FEMOLOG MoSVAR, MOXVLOG, GENMOLOG MoSVAR, MOXVLOG, IPILOG MoSVAR, MOXVLOG, MOXVLOG MoSVAR, MOXVLOG, STEELLOG

MoSVAR, STEELLOG, FEMOLOG MoSVAR, STEELLOG, GENMOLOG MoSVAR, STEELLOG, IPILOG MoSVAR, STEELLOG, MOXVLOG MoSVAR, STEELLOG, STEELLOG


step

