Download a scandal or a scapegoat? - University of Nottingham

 "This Dissertation is presented in part fulfilment of the requirement for the completion of an MSC in Applied Economics in the School of Economics, University of Nottingham. The work is the sole responsibility of the candidate". Speculation and the price of oil: a scandal or a scapegoat? Dissertation supervisor: Professor C W Morgan. Word Count: 14,902 Mark McDonnell Acknowledgements I would like to express my sincere gratitude and special thanks to my supervisor, Professor Wyn Morgan, who has provided guidance, value advice and encouragement during this research study. I would also like to include Dr Tim Lloyd for his support. I would also like to thank my girlfriend Maxine and finally, my parents, for their great and never‐
ending support. 1 Table of Contents ABSTRACT ................................................................................................................................................ 5 INTRODUCTION ....................................................................................................................................... 6 What motive has spurred this study? ................................................................................................. 6 Traditional view on speculators .......................................................................................................... 7 Diagrammatical model of how speculation affects the futures market. ............................................ 8 Dangers of passive speculation ......................................................................................................... 10 Current Policy Stance ........................................................................................................................ 12 Aim of my study ................................................................................................................................ 13 THEORY ................................................................................................................................................. 14 Motivation of index investment ....................................................................................................... 14 Does such trading behaviour cause limits to arbitrage? .................................................................. 14 Does this theory hold in reality? ....................................................................................................... 15 DATA ..................................................................................................................................................... 17 Legacy commitment of traders (COT) reports .................................................................................. 17 Disaggregated COT reports ............................................................................................................... 17 Supplementary COT report (SCOT) ‐ also called commitment of index traders (CIT) ...................... 17 Using implied CIT positions ............................................................................................................... 18 Index Investment Data (IID) .............................................................................................................. 21 LITERATURE REVIEW ............................................................................................................................. 23 Financial flows affect futures prices ................................................................................................. 23 Financial flows don’t affect futures prices ........................................................................................ 26 Other methods for investigating the impact of index speculation ................................................... 28 The main challenges to the financialisation debate ......................................................................... 29 FUNDAMENTALS ................................................................................................................................... 32 The weak dollar ................................................................................................................................. 32 Emerging market growth .................................................................................................................. 32 Supply and the OPEC effect .............................................................................................................. 34 Short run supply and demand elasticity ........................................................................................... 35 Return of the scarcity rent ................................................................................................................ 36 METHODOLOGY .................................................................................................................................... 37 2 Selecting the best measure of index investment .............................................................................. 37 Index flows impact futures price – tests for granger causality ......................................................... 39 Results from Granger Causality tests ................................................................................................ 44 Investigating the price impact when index funds roll over contracts .............................................. 44 Investigating the price impact of index roll – the results ................................................................. 50 CONCLUSION/POLICY RECOMMENDATIONS ........................................................................................ 52 Policy makers barking up the wrong tree ......................................................................................... 52 Role played by fundamentals ........................................................................................................... 52 The change in term structure is causing a dangerous precedent for index funds ........................... 53 Policy recommendations .................................................................................................................. 53 References ............................................................................................................................................ 55 Bibliography .......................................................................................................................................... 59 Appendix ............................................................................................................................................... 60 Selecting the optimal lag length in the short run models ..................................................................... 61 3 Table of figures Figure 1 – A market without speculation with a net long hedging imbalance ....................................... 8 Figure 2 – Impact of long speculator on a market with a net short hedging imbalance ........................ 9 Figure 3 – Flow of investments into commodity futures markets ........................................................ 11 Figure 4 – CFTC estimates of commodity index investment................................................................. 12 Figure 5 – Interaction between different CFTC classifications ............................................................. 18 Figure 7 – Comparison of the GSCI’s value using implied KC Wheat and Feeder Cattle positions ....... 21 Figure 8 – Financial futures market relative to the physical market .................................................... 23 Figure 9 – The degree of disagreement and price level of WTI Crude. ................................................ 24 Figure 10 – Price rises for exchange traded and non‐exchange traded commodities ......................... 29 Figure 11 – inventories in the US and OECD ......................................................................................... 30 Figure 12 – Per capita oil consumption ................................................................................................ 33 Figure 13 – Rate of growth in oil consumption ..................................................................................... 33 Figure 14 – A comparison of metrics against interpolated IID in WTI Crude, Natural Gas and Heating Oil futures. ............................................................................................................................................ 38 Figure 15 – ADF tests on our variables ................................................................................................. 40 Figure 16 – Results from Cointegrating (long run) Regression ............................................................. 41 Figure 17 – Residuals from the cointegrating equation ....................................................................... 42 Figure 18 – Test for granger causality between implied index flows and prices .................................. 43 Figure 19 – Diagnostic statistics from short run model ........................................................................ 43 Figure 20 – Results ................................................................................................................................ 47 Figure 21 – Contracts held by non‐commercial spread traders as %age of open interest ................... 48 Figure 22 – Mean excess returns and Sharpe Ratios ............................................................................ 49 4 ABSTRACT A popular view amongst policymakers is that the rising price of oil is due to increasing participation by financial speculators, and more specifically index speculators. One area of significant controversy is the best measure by which to measure index speculation. By comparing a number of commitment of trader reports to index investment data (IID) we find that the most suitable data for a time series analysis is based on implied index positions derived from feeder cattle futures. After conducting time series causality tests we find that index inflows do not granger cause price changes although the adequacy of the data and econometric technique are questionable. I augment this analysis with a trading simulation where I investigate the price impact of the Goldman Roll by using calendar spread positions. I find partial evidence for this price impact caused by index funds rolling their contracts to avoid taking delivery. 5 INTRODUCTION What motive has spurred this study? We live in interesting times. As the global economy attempts to lift itself from one of the largest recessions since the 1920’s and Britain teeters on the edge of a triple dip recession, consumers look on in dismay as they try to cope with rising oil prices. What is even more concerning is that this is happening when households are facing reductions in real incomes, all when the economy is yet to feel the pain from government cutbacks. Prices seem to have been subject to a bubble that burst in 2008 and have since recovered to their pre bubble peak. This has led a number of academics and policy makers to question whether speculators rather than fundamentals are driving the recent commodity boom. Rising commodity prices do not only affect consumers but they have far reaching consequences for governments and possibly fatal consequences for the world’s poorest. The International Taskforce for Commodities (henceforth ITFC) (2008) have stated that the nominal value of US oil imports has soared recently, which is driving the imbalance in the US current account deficit. The rising price of oil is also contributing to increasing prices in all commodities including food. In the developed world we see such high prices as an inconvenience; however, in the developing world it’s a matter of life and death. Joachin von Braun, director for Germany’s Center for Development Research (2010) took the criticism of speculators one step further in a speech about world hunger: “We have good analysis that speculation played a role in 2007 and 2008…..Speculation did matter and it did amplify, that debate can be put to rest. These spikes are not a nuisance, they kill. They’ve killed thousands of people” (cited in Irwin & Saunders, 2011, p3). This point highlights an important moral issue that stresses the significance of this subject. While a bubble in equities or bonds may lead to someone losing a large sum of money, the vast majority of participants in these markets have the money to lose. This is completely different when we think about a market where we speculate on physical items and not on paper. For this reason it is essential for regulators to ensure that speculation doesn’t have any detrimental impact on the operation of the futures markets. 6 Traditional view on speculators The long held belief is that speculators have a positive effect on price discovery and price insurance; the two essential functions of futures markets. The price insurance function is central to the role of futures markets. Such markets are able to carry out this function due to centralisation and standardisation of markets which makes futures markets extremely fungible. This allows these markets to be highly liquid and makes it cheaper and easier for hedgers to offload price risk on to (less risk averse) speculators, something that is backed up by theory and empirical evidence (Fattouh 2012). The other important function of futures markets is price discovery. Futures markets are able to carry out this function because the markets work as a way of collecting and disseminating relevant information and bringing together people’s expectations about future prices. This can lead to greater allocative efficiency as changes in futures prices will affect the inter‐temporal allocation of the resource, inducing higher welfare for society. Futures markets often react instantly when new information enters the market. Take for example when; Where FtT the futures price at time t for delivery at time T, Pt is the spot price at time t and CtT is the cost of carry between time t to time T. Such an equation would imply that futures prices are ‘rich’ and creates a chance for riskless arbitrage. To earn a risk free profit a merchant can simultaneously sell the futures contract and buy spot. They then hold the physical commodity until maturity and deliver to close out their corresponding short position. The arbitrager is then left to pocket the difference as profit. This example helps show how expectations about futures prices can effect storage and ensure an efficient intertemporal allocation of the commodity. It also teaches us a useful lesson about the channel whereby changes in the futures price can affect the physical market, although this is something I will revisit later. This price discovery function allows firms and consumers to “recognize, plan and finance needed adjustments in supply and demand early on……which in turn can help reduce volatility” (ITFC, 2008, p17). The obvious downside to this price discovery role is that if futures prices are allowed to become overpriced in any way, it can create false signals about its value which can lead to a commodity being overproduced, resulting in inefficient allocations of resources (Irwin & Saunders, 7 2011, p9). This indicates that our investigation into futures markets is twofold. Firstly, we want to investigate whether speculation is driving prices above their fundamental value. Secondly we also want to investigate whether speculators are having a detrimental impact on the efficiency of these markets. Diagrammatical model of how speculation affects the futures market. To be able to understand how speculation affects the futures markets it’s important to introduce the concept of the expected spot price. This is the price that the market thinks will prevail at some point in the future and it represents a distillation of forecasts from participants in the market including commercials, individuals and speculators. ,
exp
∑
The formula above states that the expected future spot price at time t for time t+k is the expectation of spot prices at time t+k. This equates to the summation of expectations from participants in the market of one of several prices Pi and where (ρi) is the probability of each price occurring. Figure 1 – A market without speculation with a net long hedging imbalance Source: Edwards & Ma, 1992. Figure 1 shows how the market would operate without speculation. Exp(Pt+K) is the cash price the market expects will occur at time t+k. SS’ is the supply of contracts short hedgers want to sell. Short hedgers are commercial participants who are naturally long in the market and sell futures to protect 8 themselves against falling prices. If they expect the Ft+k < exp(Pt+k) then they would be reluctant to hedge against a higher price in the future. That is why supply starts low and gradually increases until Ft+k ≥ exp(Pt+k). At this point every short hedger has a hedged position as they expect the price to fall. DD’ is the quantity of contracts long hedgers wish to buy. Long hedgers are short in the spot market and buy futures to protect against rising prices. If Ft+k > exp(Pt+k) then long hedgers are unlikely to hedge as they can buy in the spot market cheaper. As the Ft+k gets nearer to to exp(Pt+k) more long hedgers buy futures until they get to Ft+k ≤ exp(Pt+k). At this point there is the maximum hedging volume as long hedgers expect the price to fall. This situation results in a net short hedging imbalance of ba as there are too many short hedgers in the market. This also creates a risk premium (bc) which indicates that each short hedgers would be willing to sell futures at F1, a price which represents a sizable discount to exp(Pt+k). This represents a significant cost to hedgers and would be typical in a market without speculators as the number of contracts demanded by short hedgers often outweighs the number of contracts demanded by long hedgers. If speculation was introduced into the market it would create huge benefits to the market. For example when Ft+k < exp(Pt+K) a long speculator would buy futures as they are seen to be cheap. The larger the gap between Ft+k and exp(Pt+k) the more opportunity there is for speculators to make money and the more who enter the market. Notice that this action is the exact opposite of the action taken by the short hedger. Figure 2 – Impact of long speculator on a market with a net short hedging imbalance Source: Edwards and Ma, 1992. 9 Demand for long future contracts from speculation when Ft+k < exp(Pt+K) causes the demand for contracts to pivot the demand schedule from DD’ to DD’’. This pushes up the price and the quantity of contracts demanded. This additional demand reduces the risk premium from bc to bd making it cheaper for short hedgers to offload risk. Speculators do this because they believe that, over time, the expected price will prevail which will earn them profits, which compensates them for taking on the risk from the short hedgers. In theory, the more risk‐loving the speculator the more the demand schedule would shift at point b, therefore theoretically we would get to the position where there is no risk premium in the market whatsoever. This favourable view of speculation has also been backed up by empirical evidence. In a series of papers Holbrook Working (1960, 1953) found that the level of speculation wasn’t deemed to be excessive and that speculation would change according to hedging demand. Dangers of passive speculation We have seen that overall the benefits from speculation are clear. Speculators help the market by improving price discovery and by bringing new information to the market. They also lower the cost of hedging for commercial participants and provide liquidity to the market. However, we have seen a rise in another type of speculation over the last decade whose impact is less clear. This type of speculation is commonly known as passive speculation. Unlike typical speculators who trade frequently in search of profit, index speculators are passive and are primarily in search of diversification. Parsons (2010, p92) argues that “such investors take no view of whether the commodity price is too high or too low, but merely seeks to hold exposure brought at the market rate”. The danger is that in such a situation our theoretical model breaks down. Under such circumstances it also seems rational to question Working’s findings. Saunders et al (2008, p14) acknowledge this point by stating; “Unlike traditional speculators in Working’s day – who were regarded as scalpers, day traders, or position traders who were responsible to hedging needs in the market – long only funds appear to be more mechanical and less responsive to hedging demands. Whilst this does not alter the calculation of the speculative index, it does bring into question Working’s maintained assumption about the nature of speculation in today’s markets.” 10 Index traders are like other non‐commercial participants in the sense that they have no interest in taking delivery of the commodity. In order to avoid handling the physical commodity, index funds have to roll over their contracts every month. This process involves selling their contracts that are nearest to maturity and buying longer dated contracts. There are two main commodity index funds: the Goldman Sachs Commodity index (GSCI) and the UBS DJ Commodity index. The Goldman Sachs Commodity index is the larger of the two market leaders and widely considered an industry benchmark. The index is based on a production‐weighted average of the prices of 24 commodity futures markets (Irwin & Saunders, 2011); this production weighting results in a large proportion of the index (c.67%) being weighted towards the energy sector. The UBS DJ index weights are based on a combination of economic significance and market liquidity, resulting in a much smaller energy weighting of c.28% (Irwin & Saunders, 2011). Due to the sheer size of the GSCI and its large weightings in commodities such as WTI and Brent crude, many have argued that its mechanical rolling would put significant upward pressure on futures prices during the rolling period. A number of academics and policy makers have been concerned that this process, often referred to as the ‘Goldman roll’, is pushing up commodity prices. Index investors can either gain exposure to commodities by holding the positions directly, or they can go to a swap dealer (usually an investment bank) for a fee. The swap dealer then nets the long and short positions and then purchases futures contracts in order to offset any exposure it has (Gilbert, 2012). In many cases this transaction means that the swap dealer and not index fund is the party who actually has the long exposure in the futures market. This means that it is extremely difficult to quantify index investment and to regulate the market. Figure 3 – Flow of investments into commodity futures markets Source: Irwin & Saunders, 2011, p5 11 Despite the pitfalls of trying to quantify the scale of index investment it hasn’t stopped people from trying. According to Parsons (2010) press estimates for mid‐2008 estimate that commodity index investment was somewhere in the region of $400 billion. As we know the index weightings attached to WTI and Brent, estimates would suggest that around $130 billion was in crude oil alone. The US regulator, the Commodities and Futures trading commission (CFTC) has also attempted to estimate the size of the inflow, although its estimates have been markedly lower (around $200 billion). Figure 4 – CFTC estimates of commodity index investment Source: Irwin & Saunders, 2010b, p3 Current Policy Stance The hedge fund manager Michael Masters (2008) was one of the first to re‐ignite the speculation debate when testifying before the US Congress and the CFTC that: “Institutional Investors, with nearly $30 trillion in assets under management, have decided en masse to embrace commodities futures as an investible asset class. In the first five years, they have poured hundreds of billions of dollars into the commodity futures markets, a large fraction of which has gone into energy futures…they are having a massive impact on the futures markets which makes the Hunt Brothers pale in comparison” (cited in Irwin and Saunders, 2011, p1‐2). Generally economists have been dismissive about Masters’ comments; however it appears to have had the opposite impact on policymakers. Policymakers in continental Europe have always been sceptical of Anglo‐Saxon capitalism and have viewed unconstrained speculation with an element of mistrust; the idea now seems to have spread to the US Congress. Fattouh et al (2012) believes that speculators are being used by government as a scapegoat for high commodity prices. 12 The recently passed Dodd‐Frank Wall Street Reform and Consumer Protection Act was another opportunity to apply pressure to speculators. An important component of the bill was the proposed ‘Volker Rule’ whereby the deposit taking and speculative elements of a bank must be split into two. The Act also gave the CFTC the broad authority to implement speculative limits on future and swap positions in all non‐exempt physical commodity markets in the US (Irwin and Saunders, 2010b, p2). Aim of my study For years academics have widely accepted that speculators are beneficial to futures market. However, it seems apparent that the old models used to justify this position are outdated. It’s not only the behaviour of index funds that gives me reason to believe that they could be effecting futures markets but also their size. As I mentioned earlier in my introduction it’s important to look at two factors when investigating this issue. Firstly, it’s important to look at whether index inflows have been pushing up prices and secondly, does the behaviour and size of these investors cause the futures markets to operate inefficiently? I firstly use a time series approach to investigate whether index inflows predict higher futures prices. I then look at whether the size of the roll causes a price impact on the deferred contract, which could lead to concerns that futures markets are failing to perform their price discovery and price insurance role. 13 THEORY Motivation of index investment Unlike traditional speculators who are out seeking to capture the risk premium left due to imbalances between long and short hedgers, index speculators are primarily trying to diversify risk. For example Gorton and Rowenhurst (2006) found that commodities have a “statistically insignificant correlation with equities and a low but significantly negative correlation with bond returns” (cited in Gilbert, 2012, p1). This means that a position in a long passive index fund would help to diversify a portfolio of equities. The fact that commodity index investment has been motivated by Markowitzian portfolio diversification has led Stoll and Whaley (2010) to question whether we can even classify index investors as speculators as they don’t seem to be speculating on anything, let alone prices. However, in reality it seems there is definitely some speculative element involved. In a recent study by Barclays, a series of people who were investing in index funds were interviewed about their motives. When they were asked why they invested in commodities “43% said portfolio diversification, 31% said absolute returns, 9% said inflation hedge and 17% said emerging market growth” (in Norrish, 2010, cited in Irwin & Saunders, 2011, p8). Does such trading behaviour cause limits to arbitrage? The passive, long only behaviour of index speculators means we need to reconsider Milton Friedman (1953) and his idea of stabilizing speculation and consider the impact of noise traders (De Long et al, 1990), speculative bubbles and herding. All these theories predict that prices can be pushed away from their fundamental value due to the behaviour of speculators (Brunetti & Buyuksahin, 2009). In Friedman’s (1952) theory of riskless arbitrage, he argues that irrational traders will not be able to impact prices in a competitive marketplace. His theory states that over time they will lose money and get displaced by rational traders who are able to arbitrage a mispricing out of the system without facing any risk. However, De Long et al (1990) shows that noise traders can impact prices as long as they hold a large enough share of assets. Under such a situation De Long et al (1990) argues that it more beneficial to allow the bubble to happen rather than to bet against it. Similarly, Arbreu and Brunnermieir (2002) argue that it would take a number of other actors to bet against a bubble and therefore they face “synchronisation risk”. This risk arises from the difficulty in getting arbitragers to synchronise their capital to attack a mispricing. In addition, the arbitrager would have 14 to maintain the position despite short term losses. The ability to maintain these positions can often be affected by ‘liquidy risk’ as the ability to maintain these positions depends on how long investors are able to accept negative returns before withdrawing their money. These theories would suggest that during a roll period the upward pressure on longer dated futures contracts could result in limits to arbitrage, meaning that prices can diverge from their fundamental value and this can persist for some time. This problem could pose a major threat to the efficiency of futures markets. An important factor affecting limits to arbitrage is the predictability of the trades made by index investors. De Long et al (1990) argue that if position changes are predictable it means that index funds will have no impact on prices as other market participants will be able to trade against them. This would appear to be the case as the GSCI is very open about its rolling procedure. It rolls its contracts over from the 5th to the 9th business day, rolling over 20% of the dollar value each day. Despite the predictability of their trades we also have to consider the interpretation of information by market participants. For example it’s difficult for the arbitrager to distinguish between the impacts of informed trades and uninformed trades. Arbitragers might think that the price of longer dated futures going up is a signal that reflects valuable private information about future price prospects or economic growth (Irwin & Saunders, 2010b, p7). The theory of noise traders also relates to Keynes (1936) theory of a beauty contest. In his theory he discusses a contest in which participants have a series of pictures of women and are asked to choose the most beautiful woman. Keynes argues that under such circumstances it is naïve to pick who you think is the prettiest. A more rational approach would be to pick who you think is the most beautiful in the eyes of the majority. Keynes believed that this approach was also used in the stock market as participants would try and forecast how the beliefs of other would change when new information was released into the market. For example if arbitragers believed that irrational traders would react to higher futures prices by speculating prices would go even higher, it may be rational to allow this to happen and in some cases encourage it. Does this theory hold in reality? Theories of noise traders and heterogeneity of beliefs have also been proved in empirical work. One such piece was by Brunnermeier and Nagel (2004) who looked at the technology bubble at the turn of the millennium. They found that hedge funds, who are often considered to be rational arbitragers, didn’t exert a correcting force on stock prices but actually encouraged them. The only hedge fund that bet against the bubble went bankrupt as investors were not prepared to face paper losses and 15 withdrew their money. This problem is often called the “separation of brains and money” and relates to ’liquidity risk’. The authors also found that the bubble was also characterised by an emphatic media and a large influx of money from people who had never invested before. Nonetheless, it appears that hedge funds were able to correctly forecast how these ‘irrational’ traders would throw money into the overheating stock market in search of large profits. Irwin and Saunders (2010b) ere a note of caution and state that you must be careful using such evidence to imply limits to arbitrage in futures markets. They suggest that you wouldn’t expect limits to feature as much in futures markets due to the fact that the cost of trading is very low, prices are highly transparent, there are no constraints on short selling and there is less basis risk. 16 DATA Legacy commitment of traders (COT) reports To try to keep track of the level of hedging and speculation in commodity futures markets the CFTC have collected the number of contracts held by commercial and non‐commercial participants that are above a reporting level. For some time this has been the basis for any study trying to look at speculation within a particular market. However, there are now fears that the non‐commercial category underrepresents speculation due to the way it treats swap dealers. This is because legacy COT reports classify swap dealers under commercials as some commercials will hedge their exposure via a swap dealer at an investment bank. However, we now know that index investors and other speculators can also gain their exposure to a particular commodity through a swap dealer. The main problem is we don’t know the exact value because it all depends on how the swap dealer nets the two positions. Disaggregated COT reports In response to concerns that the COT reports underrepresented speculation the CFTC released a new set of data that broke down the two parts into subcategories. For example ‘commercials’ were broken down into ‘processors and merchants’ and ‘swap dealers’ and ‘non‐commercials’ were broken down into ‘managed money’ and ‘other reportable.’ While the DCOT reports are useful in the sense that they provide a better idea of the contracts held by different categories, they still don’t resolve the problem. It means now we have an element of index investment in ‘managed money’, ‘other reportable’ and in ‘swap dealers’, although we still don’t know the exact value. Both the COT data and the DCOT are constructed via a survey whereby each participant is asked to ‘a‐priori’ classify themselves under one of the categories. Such a system already has problems of perverse incentives as it is within a speculator’s best interest to be classified as a commercial participant and hence be restricted from any position limits (Ederington and Lee, 2002). Supplementary COT report (SCOT) ­ also called commitment of index traders (CIT) In response to futher criticism of the DCOT reports the CFTC decided to produce a supplementary report based solely on the intention of capturing index investment. As part of this report traders were classified by re‐interviewing traders subject to their ‘ex poste’ trading patterns, meaning there 17 is less classification error. However, this means that as soon as a participant is defined as an index trader, all their investment is classified as an index investor even if this only includes a proportion of their portfolio. The CIT categories are aggregated into 3 classifications: ‘commercials (less index investment)’, ‘non‐commercials (less index investment) and finally ‘index traders’. So as you can see from figure 5 index investment in the CIT reports came from a variety of DCOT categories including ‘swap dealers’, ‘managed money’ and other reportables’. Figure 5 – Interaction between different CFTC classifications Irwin & Saunders, 2011, p52 While the CIT data is explicitly better than COT or DCOT data, it had a pretty large shortcoming in the fact that it over covers 12 agricultural commodities. Like the DCOT reports the CIT reports are only available over a short time period as they started releasing the reports in 2007. Using implied CIT positions To get around the fact that the CFTC only releases CIT data on 12 agricultural commodities Masters (2008) proposed a way whereby they can capture the size of index investment in other markets including oil, subject to a number of assumptions. Masters (2008) make the assumption that the GSCI and DJ‐UBS commodity indexes are the only two index funds. They then point out that, because feeder cattle and KC wheat are unique to the GSCI and soybean oil is unique to the UBS‐DJ index, we can calculate the size of the two indexes. Therefore, we can take the number of contracts, multiply this by the contract value and divide by the weighting in the index to find the size of the two indexes. For example, according to the CFTC’s January 17, 2006 CIT report, index speculator’s had positions i 18 n KC wheat, feeder cattle and soybean oil of 21366 , 5613 and 59264 contracts respectively. Plugging in the weights and contract values from the appropriate sources yields the following calculations shown in figure 6. Figure 6 – Calculating the implied size of index investment. Commodity Number of Contract Weighting Value of Index Composition Value (in $) contracts Calculation/Source From CIT report Price x Size of contracts (eg of commodity tons of grain) in index KC Wheat (GSCI) 21,366 x 18,762.50 ÷ 0.82% $48,887,753,049 Feeder Cattle 5,613 x 56,137.50 ÷ 0.68% $46,338,204,044 59,264 x 12,732.00 ÷ 2.77% $27,240,045,054 (GSCI) Soybean Oil (UBS‐
DJ) Source: adapted from Masters 2008 So the table above suggests that the GSCI had somewhere between $46 and $49 billion invested in it and the UBSDJ had around $27 billion invested in it. We can then divide by the weight attached to WTI crude to get a figure for an idea of WTI crude index investment and finally we divide by the contract value of WTI crude to get the number of contracts. Rightly, questions have been asked about how useful this data is after being aggregated in this way. It’s important to have very accurate data when using this method because the commodity weights are very small and hence there can be a large error in up scaling the data. For example if the weight of KC wheat was 0.01% higher than stated in the index it would lead to a change of $589,009,073 in the implied size of the GSCI. Irwin and Saunders (2011) argue that this could cause problems when up scaling from minor positions such as KC wheat into large positions such as WTI crude oil, which is exactly what I im indenting to do. This is particularly relevant as getting hold of some time series data of index weights is extremely difficult 1 . In the end I was able to get hold of monthly data for 1
I contacted S&P and DJ to try and get some time series data on weights on the GSCI and UBS‐DJ indexes respectively. S&P were initially helpful until they found out I was writing my thesis on index investment in commodity futures markets at which point they have not responded. DJ didn’t respond to my request either. 19 GSCI weights, although this only goes back to the beginning of 2009 (see appendix). Weighting for the DJ‐UBS index were even more difficult to get hold of and in the end I had to assume the weightings have been constant. Another factor is the uncertainty surrounding the role of swap dealers in agricultural futures markets and how well this corresponds to the role of swap dealers in energy commodities such as oil? Evidence suggests that swap dealers in agricultural markets carry out a limited amount of non‐index long or short swap transactions. Irwin and Saunders (2008) calculate that for agricultural commodities 85% of index exposure from COT data comes from the commercial category, or through swap dealers, meaning that swap dealers are a reasonably good proxy for index investment. Irwin and Saunders (2011) argue that this is why the CFTC was only able to produce CIT data for the 12 agricultural commodities. The CFTC (2008, cited in Irwin and Saunders, 2011) contrast this with evidence from energy futures markets who conduct a large amount of non‐index swap transactions on both sides of the market, which means it is unclear how well the net long position of swap dealers proxies as a measurement of index investment. The CFTC (2008) calculates that only 41% of long swap positions in crude oil were linked to index fund positions. Irwin and Saunders (2011) agree that, whilst neither implied CIT positions or swap dealer (from DCOT data) are ideal proxies for oil index investment, they would suggest using DCOT swap dealer positions as they suffer no aggregation problems. A quick way of testing the validity of the implied CIT data is to check to see whether the estimated value for the GSCI is the same when using both KC wheat and from feeder cattle. As you can see from figure 7 the comparisons are far from perfect. 20 Figure 7 – Comparison of the GSCI’s value using implied KC Wheat and Feeder Cattle positions Source: Author using figures from CFTC, 2012, Bloomberg, 2012 and S&P, 2012. The graph is concerning, as at times the difference can be over $100 billion dollars. In theory this could be possible as other less know index funds could be holding KC wheat futures and not holding feeder cattle futures which is causing implied KC wheat futures to overestimate the size of the GSCI. However you wouldn’t expect it to account for all of this error, especially as this undermines the assumption that the GSCI and UBS‐DJ index’s account for all of the market share. Index Investment Data (IID) Concerns about the inadequacy of the data were finally addressed in late 2008 when the CFTC started releasing index investment data for a wider range of commodities including metals, soft commodities and energy commodities including WTI crude oil. In addition, the data deals with problems surrounding swap dealers, as the positions are measured before the swap dealers internally net their positions. However, the IID data still has shortcomings. Firstly, the data was only released at quarterly intervals up until 2010 when data was released monthly; in contrast the COT/DCOT/CIT data is released on a weekly basis. This makes any time series analysis impossible due to the sparsity of the data and because the sample size is not sufficient. Irwin and Saunders (2011) also pick some additional concerns about the data. The three main concerns are that “first, small entities or entities unknown to the CFTC may be omitted from the special call. Second, trading records are not independently examined by the CFTC. And thirdly, “index” activity is not specifically 21 defined, which means there coutld be some inconsistency in the reported data across firms” (Irwin & Saunders, 2011, p15.) What appears to be more of a concern to anyone trying to quantify index investment in oil is that a large proportion of index investment is not even captured by any CFTC report. This is because the other major contracts for crude oil are offered by the Intercontinental Exchange (ICE) which runs a copycat contract pegged to the WTI crude oil contract on the NYMEX exchange. In addition, the ICE exchange also has its own oil futures contract based on Brent crude, a pricier blend of crude oil that is extracted from the North Sea (Parsons, 2010). What’s important to note is that because ICE is based in London it means that it is outside of the jurisdiction of the CFTC and hence not subject to any of its regulations and its data released in its COT/DCOT/CIT or IID reports. The final concern about the data is that any useful report excludes the important period between 2004 and 2006, which, evidence suggests, was the time when the majority of the money flowed into oil futures. According to interviews with market participants the growth of index investment accelerated in the second half of 2004, which was also the point at which the crude oil term structure changed from an inverted market into a contango market (Robe et al, 2008). This is also consistent with findings from studies across commodities in general. For example, Irwin and Saunders (2011) found that the number of CBOT (Chicago Board of Trade) wheat contracts held by index investors increased nearly fourfold from 2004 to 2006. 22 LITERATURE REVIEW Financial flows affect futures price As I mentioned earlier Masters (2008) helped to reignite the debate on the impact of index investors back in 2008. What was central to Master’s argument is that when index investors roll over their contracts it creates ‘buy side’ pressure which pushes up the price of the deferred contract. This point was re‐enforced by Petzel (2009) who stated that “unleveraged futures positions of index funds are effectively synthetic long positions in physical commodities, and hence represent new demand.” (cited in Irwin and Saunders, 2011, p10). Petzel (2009) is arguing that if the demand is large enough relative to the amount of the physical commodity available in the short run it can push the price above its fundamental value. As you can see in figure 8 the size of the futures markets has increased exponentially relative to the physical market. Figure 8 – Financial futures market relative to the physical market Falkowski, 2011, p7 Although this argument may be appealing to certain policy makers, evidence also seems to show that the view is wrong and somewhat misled. For example, Ripple (2008 in Fattouh, et al, 2012) shows that this ratio of the financial market relative to physical market is a completely different story when the number of days to delivery is included. After this is considered, the fraction is only about one half of daily US oil usage which invalidates this argument. Irwin and Saunders (2010b) are highly critical of the idea as they argue that there is potentially no limit to the number of futures contracts that can be created at a given price level. Therefore, the position bought by index funds 23 are no more new demand than any corresponding selling would be new supply. In addition, Irwin and Saunders (2010b) are very sceptical about the parallels that are drawn between the behaviour of index funds and that of the Hunt Brothers when they cornered the silver market. They argue that index investors only purchase futures and at no point do they buy or hoard any of the physical commodities or attempt to manipulate the market in such any way. Therefore it is wrong to draw such a comparison. Perhaps one of the most highly regarded papers purporting the negative impact from index speculation comes from Kenneth Singleton, a highly respected financial econometrician at one of the USA’s Ivy League Universities. His paper led Kemp (2011), a financial journalist at Reuters, to declare that “Stanford University’s Kenneth Singleton has mounted the most wide ranging and influential assault so far on the orthodoxy among academics that speculation does not affect commodity prices” (cited in Fattouh et al, 2012, p10). The foundation of Singleton’s paper is the idea that differences of opinion can create price drift, even when the information is publicly available. When we include all the uncertainties that surround the global oil market, such as the size of oil reserves, technological progress, global inventories and supply disruptions it can exacerbate this problem further. Figure 9 – The degree of disagreement and price level of WTI Crude. Singleton, 2011, p8 Singleton (2011) uses figure 9 to show the strong correlation between disagreement among forecasters and the WTI crude oil price. He argues this co‐movement is consistent with the positive 24 relationship between price drift and differences of opinions that have been documented in other asset markets as documented by Cao and Ou‐Yang (2009) and Banerjee and Kremer (2010). While many others studying the issue have used a simple bivariate approach, Singleton (2011) carries out a linear least squares projection to look at how CIT implied index inflows, arbitrage capital and open interest effect can influence the price of WTI oil futures. He finds a positive relationship between CIT implied index inflows and the price of WTI crude and also detects a large inflow of money flowing into managed money spread positions before rolling periods. Singleton (2011) states that this variable which proxies for arbitrage capital is reflected by the growth in spread trading by hedge funds to try and capture the price impacts of the GSCI rolling over its contracts. The one area where Singleton doesn’t find any significant relationship is between the amount of open interest and the price of WTI crude. This negative result, which is contrary to findings by Hong & Yogo (2010) is pivotal as it was an important component of his argument. The fact that he doesn’t find a relationship means that investors don’t believe that the level of open interest contains private information about the future prospects for the commodity; this finding also dampens fears that participants may face limits to arbitrage due to the difficulty separating noise and useful private information. However, Fattouh (2012) believes this is irrelevant as even if open interest doesn’t have a positive relationship with prices, it not due to the failure of the market but more a failure of how humans process information. Fattouh (2012) also questions whether Singleton’s findings allow us to conclude that index speculation increases prices, due to the fact he has no explanation of how this could impact on the spot price of oil. As I outlined earlier, for this impact to spill over to the spot price at some point it must involve the purchasing and storing of the physical commodity. Another study that looks at the relationship between index investment and futures prices comes from Gilbert (2012). Although Gilbert investigates soft commodities and doesn’t focus on oil it gives an interesting solution to the inadequacies of the econometric techniques being used by the majority of studies. Gilbert argues that the failure to find a relationship using the granger causality approach is due to the fact that markets being investigated are liquid and the econometric technique lacks the statistical power to find a relationship in such markets. Gilbert proposes that by looking at thinner markets results might provide a better indication of whether these flows are affecting the price. Gilbert tests for granger causality in four liquid grains markets (CBOT corn, CBOT soybean, CBOT wheat, KCBT Wheat) and, like Irwin and Saunders (2010b), finds no effect. However, 25 he then conducts the same analysis on four thin markets that are included in the GSCI (soybean oil, feeder cattle, and lean hogs). He finds that CIT inflows granger cause prices changes in soybean oil, live cattle and lean hog’s futures. Gilbert concludes by arguing that for so long academics have looked at financialisation and fundamentals as if they were mutually exclusive; however he argues that they were both a factor in driving up prices of commodities. Financial flows don’t affect futures price The first study to argue that speculation was not excessive or detrimental to the market was from Holbrook Working (1953 & 1960). He studied this by developing an adequacy index which looked at the size of speculation relative to hedging demand. However, the world has moved on since then. I have already discussed the difficulty quantifying index investment, especially when accuracy is essential to the outcome of the study. For that reason the adequacy index seems dated and unsuitable to use, given the data available to us. Currently the main empirical approach is to test for granger causality which has a distinct advantage over Workings adequacy index. The advantage of granger causality tests of over the adequacy index is that when using granger causality tests the data doesn’t have to accurately measure the size of the inflows as long as it accurately measures the timing of the inflows. When you consider that the size of the inflows are likely to be inaccurate due to the role of swap dealers and the fact that people can trade through the copycat ICE contract, it becomes apparent why the granger causality approach is more suitable. A number of studies have been completed using granger causality tests, although there are few that look specifically at oil due to problems with swap dealer classification. Despite this they offer an interesting insight into the literature. One highly influential paper was by Irwin and Saunders (2010b) who looked at the issue on behalf of the OECD. They investigated whether changes in net CIT positions in the 12 agricultural markets had affected price changes. In addition the study deals with the concern that using the front month contract will generate problems because prices are volatile and illiquid in the last few days before contracts close out. In order to get around this Irwin and Saunders create their price data by rolling over contracts on the last day of the penultimate month, meaning they miss the index roll period (the 5th to the 9th business day on month of expiration.) After conducting tests for granger causality they found that position changes granger caused prices changes in corn and cotton futures. However, the directional value was negative in corn and positive in cotton, which is confusing given 26 the context of the debate. They also look at whether changes in DCOT net swap positions granger cause changes in realised volatility where they also included crude oil and natural gas futures. They find that positions changes granger cause realised volatility in the soybean and cocoa futures market; however it tends to decrease volatility rather than to increase it. For some unstated reason Irwin and Saunders don’t test to see whether DCOT swap positions granger cause price changes but they do test to see if it granger causes volatility. They are not able to reject the null of no causality; however they could reject it at significance of 10% (p‐stat of 0.0889). Aulerich et al (2010) carry out another study using granger causality tests, but again the study concentrates on the 12 agricultural commodities included in the CIT reports. However, one benefit of the study is that it includes non‐public data on CIT positions that goes back to 2004. This data was made available following a special call from the US Senate investigation subcommittee, although the CFTC has decided not to make this data public. Like Irwin and Saunders (2010b), Aulerich et al (2010) also controls for illiquidity by using the same roll methodology. Following criticism that the tests may not be able to detect any relationship due to the way CIT changes affect prices over months rather than weeks, Aulerich et al (2010) attempt to deal with this by introducing a ‘fads’ model where CIT positions enter as a moving average calculated over recent observations. Aulerich et al (2010) use two proxies for CIT participation, the first being the change in CIT net CIT contracts and the second being the percentage of open interest held by CIT traders. Their results provided very little evidence that index flows granger cause price changes. Irwin and Saunders (2011), in a paper called ‘testing the master’s hypotheses’, look at the impact of index flow funds using the IID data. As the data is too infrequent to conduct any time series regressions, Irwin and Saunders use a Fama‐Mcbeth cross sectional analysis. With this method they find very little evidence to suggest that index positions affect prices in any of the 19 commodity futures markets including crude oil. They supplement this with a time series regression on oil and natural gas markets using position data from two large exchange traded funds (ETF’s), in which they find no evidence that index flows granger cause price changes. They propose that this metric is suitable as long as investment flows in these funds have been proportional to aggregate investment flows in index funds. However, I am quite sceptical of this assumption as these two ETF are not part 27 of a wider commodity fund and hence its investors would be driven by speculating on price, much like traditional speculators, rather than driven by diversification which seems to be the major driver of GSCI investment. While the evidence from such tests seems to suggest that index flows don’t granger cause price changes, there have been further criticisms of the technique used. For example Rossi (2011, p13) asks whether “the data for this kind of analysis can be called into question due to a high degree of aggregation of contracts and the data’s reliance on weekly reporting. A lack of position matching and granularity at a daily level could render the use of granger causality tests of limited value.” Furthermore, Irwin and Saunders (2011) are ready to admit that the statistical power of granger causality tests can be weak due to the volatility of the dependant variable (the change in the futures price). Other methods for investigating the impact of index speculation Due to mounting criticism of both the time series approach being used and the adequacy of the CFTC data, a number of studies have developed alternative ways to investigate the issue. For example Phillips and Wu (2009) develop a recursive time series test which uses Dicky‐Fuller unit root tests in prices of index funds and other futures against “mildly explosive alternatives” that would classify a bubble (Irwin and Saunders, 2011). They find evidence for bubbles in seven of the nine markets they tested and most importantly they claim that WTI crude oil satisfies the bubble conditions. The authors conclude that global imbalances drove a series of bubbles in a number of markets including the technology stocks and the US housing market, which eventually reached commodities when the US subprime housing market collapsed. However, Gilbert (2010, in Gilbert, 2012) uses the same method and fails to find a bubble in WTI oil or US agricultural futures, except a short bubble in soybeans futures in 2007. Yinqun Mou (2011) finds that the rolling strategy of index funds causes a significant price impact that is caused by selling the front month contract and buying the deferred contract. This price impact which can be captured by entering calendar spread positions can offer significantly higher returns (and higher sharpe ratios) despite the fact that the GSCI publishes their rolling schedule. Under perfect capital markets traders shouldn’t be able to make such returns, suggesting a market suffering from limits to arbitrage. Mou (2011) later demonstrates this by proving that the profits from these calender spreads increases relative to the flows of index investment and decreases 28 relative to arbitrage capital. Mou uses non‐commercial spread positions to proxy for arbitrage capital; however it’s not very clear where the figures for index investment have come from. In a similar study to Mou (2011), Frank and Turbeville (2011) also found that markets were unable to arbitrage out the price impact of index traders. They go on to argue that this impact got higher when the market was under stress. However, it’s not clear whether the increased return from calendar spread positions could be explained by an increase in the risk premia due to risk aversion of intermediaries, as proposed by Etula (2010) or due to limits to arbitrage. Frank and Turbeville also assert that the rolling process could be a determining factor in why the market changed from being in backwardation and switched to contango in about 2004, although it’s very difficult to prove this. The main challenges to the financialisation debate There are three substantial challenges to the theory that index flows have pushed up the physical price of a commodity. Firstly, if we anticipated that index flows were pushing up prices we would expect that the price of a commodity that has a futures market would be going up more quickly than prices of a commodity without a futures market, but this is not the case. Stoll and Whaley (2010) “find that the price of commodities not traded on futures exchanges rose as much or more than the price of exchange‐traded commodities (cited in Fattouh, 2012, p10), this impact can be seen in figure 10. Figure 10 – Price rises for exchange traded and non‐exchange traded commodities Falkowski, 2011, p12 29 Secondly, you would expect that commodities that had the highest concentration of index funds (as measured by CIT positions) to have the largest price rises. However, when Irwin, Saunders and Merrin (2008) looked at the 12 CIT agricultural markets they found that the highest concentration of index investment (in the livestock sector) was associated with the smallest increase in price. Finally, for the price to be transmitted to the spot market it would have to involve someone storing the commodity, which would be apparent in the shape of higher inventories. However, as we can see from figure 11, inventories in the US and across the OECD have remained stable from 2006 to 2008. Figure 11 – inventories in the US and OECD ITFC, 2008, p25 This has developed into a large debate surrounding the role of inventories and whether an increase in inventories is a necessary condition to higher spot prices. Some suggestions to why this has not happened include the fact inventory capacity is too low, as suggested by Echaus (2008) and Singleton (2011) who argues that reserves have increased amongst developing countries, although we are not able to see this because of political discretion in the reporting of their reserves. Parsons (2010) believes that, as long as the long term price of oil is high, it doesn’t make any sense to store any more oil as producers can keep oil underground, a view shared by Cabellero, Farhi and Gourinchas (2008). However, Hamilton (2009) suggests that the only condition that would allow this to happen would be if the short run elasticity of demand for oil was virtually zero and, although oil is a very inelastic good, evidence suggests it isn’t close enough to zero to warrant no increase in inventories. 30 Some other studies have looked at how financialisation might occur by investigating the behaviour of trades. For example Robe et al (2008) find that contracts that had more than a year until maturity previously had very little correlation with near dated futures. However, this has been changing since 2001 and since 2004 the two contracts have become cointegrated. Robe et al (2008) conclude that enhanced linkages between the contracts appear to have increased alongside increased participation from financial traders and commodity swap dealers. Tang and Xiong (2010) use a panel regression approach to find that prices of non‐energy commodities have become increasingly correlated with oil prices and, more specifically, they found that this is more pronounced for commodities that are in the commodity indexes, such as the GSCI. They use this result to warrant their conclusion that index funds have financialised commodity markets. Irwin and Saunders (2011) are slightly sceptical of the findings, arguing that they chose unsuitable controls for the non‐index group. Saunders and Irwin (2011) are critical of using markets such as rough rice, oats, lumber and orange juice as they are very thinly traded. They are even more critical of the control for the livestock market labelling pork belly futures as a market that “for all intents and purposes, a dead market with close to zero open interest” (Irwin and Saunders, 2011, p14). 31 FUNDAMENTALS The weak dollar There are some important macroeconomic factors that have caused commodity prices to rise such as high inflation and low interest rates. During the mid 2000’s we also had a depreciating dollar. As the majority of commodities are prices in dollars, it means that when the dollar is weak oil is priced at a higher price than it would be if the dollar was high. The ITFC (2008) estimate that generally a 10% depreciation of the nominal, trade weighted dollar will increase the dollar price of oil by 10%. As you can see from the ITFC calculation, the value of the dollar undoubtedly played a role, however this seems small in relation to the overall price run‐up. Emerging market growth Many have proposed that the increase in prices can be explained by the rapid growth of emerging economies like India and China who have fast growing economies and also contain around 40% of the world’s population. The potential demand for oil and other energy commodities from these two countries is huge. To put this into perspective consider that, during 2006, China only used 2 barrels of oil per person. In comparison Mexico used 6.6 barrels and the US used 25 barrels. Therefore, in theory Chinese oil consumption could triple yet they would still be using less oil than Mexico is today (Hamilton, 2008). In terms of car ownership there are only 3.3 passenger cars per 100 Chinese residents while there are 77 cars per 100 residents in the US (Hamilton, 2008). Figure 12 illustrates the sheer growth potential that these emerging countries have. 32 Figure 12 – Per capita oil consumption ITFC, 2008, p8 Now China is growing rapidly, China’s growth in oil consumption far outstrips the growth from any other country with India a close third after Saudi Arabia (see figure 13). Although growth in the developed world has been sluggish, overall growth has been strong, especially up to 2008. The ITFC (2008) calculated that if you weight each country by oil consumption, global growth has averaged close to 5% a year since 2004, which the ITFC (2008) claims is the highest rate in two decades. For this reason it’s not surprising that the scale of the increased demand has been unexpected. Kilian and Hicks (2009) use a vector auto‐regression (VAR) framework to study the global oil market and argue that this unexpectedly high growth from emerging Asian economies predicts most in the price increase up to 2008; likewise the sharp drop in price can be explained by the unexpectedly slow growth following the recession. Figure 13 – Rate of growth in oil consumption ITFC, 2008, p9 33 Supply and the OPEC effect Oil is an interesting market as it contains a producer cartel who colludes to try to keep prices high by managing supply. For a long time OPEC have had a difficult time trying to manage production quotas as it is always in a individual country’s best interest to cheat and produce as long as their marginal benefit for production is above the marginal cost of production, despite not being in the best interests of the group as a whole. When different leaders have different discount rates, this can be a difficult task. Hamilton (2008) suggests that things might be beginning to change: as Saudi Arabia accounts for such a large production, it could now be the case that it has monopoly power while the other members operate as a more competitive fringe. This is re‐enforced by the ITFC (2008) who argue that world surplus capacity remains low at around 2% of consumption which puts pressure on prices. The ITFC (2008) also re‐assert Hamilton’s (2008) statement that Saudi Arabia holds pretty much all of the capacity making it easier for OPEC (or Saudi Arabia) to control prices. Now the majority of oil is in the hands of sovereign nations rather than private firms. For example, in 2007 Exxon Mobil, the world’s largest private oil company, only produced 3.1% of the world total supply. The largest 5 private oil companies only managed less than 12% of world production, which is less than the whole production of Saudi Arabia (12.1%). For this reason Hamilton (2008) believes it’s the sovereign countries rather than private companies calling the shots in the global oil market. Sovereign governments also will not produce and invest in the same way as a private firm would. One criticism of OPEC has been that they have been underinvesting in supply for some years now. The ITFC (2008, p3‐4) states that “in the last three years OPEC production growth has slowed to levels well below historical averages, and world surplus capacity has fallen below historical norms.” Moreover, non OPEC countries don’t seem to be able to offset the increase in demand as world consumption growth has been higher than non OPEC production every year since 2003 (ITFC, 2008). One challenge to this argument has been put forward by Echaus (2008) who believes that it is impossible for fundamentals to explain all of the price run‐up. Echaus (2008, p7) believes that fundamentals can’t explain the run‐up because “proven oil reserves have been increasing at about 2.5% per year since 2004, faster than the rate of increase in oil consumption 2 .” One flaw in Echaus’s argument is that proven reserves are completely different to oil production. We know that the potential oil reserves are huge. Conventional oil sources only constitute a small part of overall supply and the unconventional sources such as oil sands can answer the world’s demand problem. 2
Not weighted by oil consumption unlike the calculation from the ITFC (2008). 34 However, such sources, despite being currently profitable, are very difficult to scale up quickly, even in the long run. For example, the Canadian Association of Petroleum Producers (nd, in Hamilton, 2008) estimate that current production is 1.3 mbbl/d 3 and due to a number of constraints they only forecast production rising to 4 mbbl/d by 2020. In addition, there is a distinct possibility that a large proportion of proven oil reserves will not ever be used due to the costs or difficulty of extracting them. Short run supply and demand elasticity The theory of non‐renewable resource extraction (Krautkraemer, 1998) tells us that investment in extractive industries is very capital intensive. Due to the non‐malleability and non‐shift‐ability of this capital, combined with the fact that production of a finite resource will decrease over time, leads to underinvestment. This makes it very difficult to increase supply in the short run in response to an increase in demand. The fact that increasing supply will take a long time due to the long lead times means that oil has an inelastic short run supply curve, as do most energy commodities. This, combined with the low inventory capacity of oil, can mean that supply is not very responsive to price, meaning that the majority of the change has to be transmitted through demand (ITFC, 2008). Echaus (2008, p6) is again sceptical that any price elasticity of demand can justify the 2008 price of oil by stating “short of virtually complete shutdown of Middle East oil production, no plausible price elasticity of demand would justify the quadrupling of oil prices.” Hamilton (2008) is quick to disprove this theory by introducing a simple model of intermediate oil price elasticity of demand to estimate the mark‐up that Saudi Arabia would place on producing one extra barrel of oil making the assumption that every other country produces on a competitive basis and has zero supply elasticity. 1
1
Where P is price of oil, s is the price elasticity of demand for Saudi oil and Ms is the Saudi marginal cost of producing an extra barrel. The price elasticity of global oil ( s) would be calculated by dividing the global demand elasticity ( g) by the Saudi share of the global oil market (Ks). This would mean that the Saudi’s would place a mark‐up of price over marginal production of: 3
Millions barrels a day 35 1
1
Hamilton (2008) the plugs in some estimates for Saudi share of supply and for the price elasticity of demand for oil, Hamilton calculates that every marginal barrel produced by Saudi Arabia would seek to gain a markup if 1.86. 1
1
1
1
0.12
0.26
1.86 As you can see from the model as soon as the global price elasticity of demand approaches ‐0.12 then in theory the short run price could reach infinity. Return of the scarcity rent The idea of the scarcity rent comes from the Harold Hotellings (1931) theory that the price of the good should exceed marginal costs if the resource is scarce, even if it is produced under perfect competition. Hotellings (1931) rule states that in a competitive market the increase in futures price should be equal to the interest rate, which gives the producer a figure of how much oil they should extract and how much they should leave underground. For example if a producer enlarged production this would increase current supply and lower the price. Due to the higher scarcity of the commodity, futures prices would increase. In this case the scarcity rent (λ) would be higher than the rate of interest (r). Other competitors would choose not to follow the producer who increased production as they would rather take advantage of higher prices in the future. Suppose that the producer decides to decrease production so the resource becomes depleted more slowly. Futures prices subsequently fall, meaning that the scarcity rent (λ) is less than the rate of interest (r). At this point competitors would benefit from increasing their production and then using the proceeds to invest in the money market and earning a higher rate of interest than leaving the oil in the ground. What is interesting is that for so long this rule has not held with regard to oil prices, as represented by falling prices throughout the early 90’s due to oversupply in the market. However, Hamilton (2008) argues that now supply is constrained and demand is rising the scarcity rent may be a contributing factor to the price increase and, if not, it’s not far away. 36 METHODOLOGY Selecting the best measure of index investment It’s clear from the literature that a great sticking point is the data and what measurement is the best to proxy for index investment. What is clear is that, despite its limitations, IID data is far superior to DCOT and CIT data, although it does have the obvious downside that it can’t be used in time series analysis. Therefore, to decide on the best data to use for time series analysis I have created time series data for net long positions of index traders from implied positions by estimating the size of the GSCI. I have created two different series for implied figures: one using implied figures from KC wheat, and the other using implied figures from feeder cattle. These two figures are then added to the implied figures from soybean oil to get the total implied index investment. From this figure we can transform this figure back into the number of contracts by dividing by the contract value. I then calculated the net long contracts from DCOT data by using the swap dealer positions as recommended by Irwin and Saunders (2010b). Finally, I got a list of net long contracts from the IID data and then interpolated between the data points to get weekly time series data. As it is important to distinguish between short and long positions, I decided to use net positions as advised by a number of studies. Furthermore, I also decided to focus on the number of net contracts rather than the net dollar value of contracts. This is advised by (DEFRA, nd) who argue that it can be dangerous using a figure that represents the dollar value of contracts because a large proportion of the increase will be down to the increased price itself. Having this endogeniety in the regressor could lead the econometrician into falsely accepting a relationship when in reality it doesn’t exist. While I want to focus on crude oil I have also included a number of other energy commodities including natural gas and heating oil, all of which are traded on the NYMEX. This is because they all have high weightings in the GSCI and the UBS‐DJ and hence one would expect that, if index investment is to blame, one would expect them to behave in a similar way. 37 Figure 14 – A comparison of metrics against interpolated IID in WTI Crude, Natural Gas and Heating Oil futures. Source: Authors Calculation. Data from CFTC, 2012, S&P, 2012 and Bloomberg, 2012. 38 As you can see from figure 14 the data paints a confusing picture. Firstly, it’s interesting to look at net long contracts according to swap positions from DCOT data. This appears to bear no relationship with IID except for the heating oil market. Also they seem to be negative in some circumstances which represent the concerns about how the swap dealers net their short and long positions. Secondly, implied figures from KC Wheat seem to constantly overstate index investment except in natural gas where it appears to be a good fit. Finally, we have the net long contracts based on an implied figure from feeder cattle. This appears to have the best fit relative to IID data and its turning points also appear to match the IID data, although it does seem to exaggerate index investment in WTI crude. The one concern I have about using such a figure is that from a theoretical viewpoint it’s difficult to justify its validity given it should be similar to implied KC wheat figure. While the figures match up to feeder cattle figures in the natural gas market there is are large discrepancies in the WTI crude and heating oil markets. As I mentioned earlier small differences could be down to traders from smaller index funds, although it’s hard to explain differences that we see in the WTI crude and heating oil data. Although none of the data seems particularly ideal I opt for implied index investment based on feeder cattle positions as it appears to bear the best resemblance to IID data. Index flows impact futures price – tests for granger causality To investigate whether index flows have affected prices, I test for granger causality between index flows and prices. This is a standard linear technique that determines whether one variable forecasts another. The basic idea is that if X is said to cause Y then X should precede Y in time. Testing positive for granger causality doesn’t necessarily give concrete evidence that X will cause Y but just provides us with statistical probability that, over time, changes in X should granger cause changes in Y. I obtain futures price data from Bloomberg (2012) getting data for the front month contract and the second contract. In order to control for liquidity concerns I use an approach similar to Robe et al (2008) by rolling over to the next contract not on the last day of the contract but when the open interest in the penultimate contract exceeds that of the nearest contract. This is done as prices can be very volatile towards to end of a contract due to liquidity concerns and due to index funds rolling over their contracts. By employing such a technique I avoid picking any of this up in our pricing data. Prices are then deflated by a CPI price index from the ONS (2012) which is converted into weekly 39 data by interpolating between months. This is important step considering that inflation was high during the sampled period. Firstly, I take natural logs of our data series. This is useful as it helps problems of heteroscedasticity when I run my regression. It also means that, when I conduct our test for granger causality in first differences, my data will represent the rate of change in futures prices and index flows. As we are dealing with non‐stationary data the first thing is to clarify that all of my variables are non‐stationary in levels and stationary in first differences. Conducting ADF test on all our variables confirms that all our variables are integrated of order one. Figure 15 – ADF tests on our variables Variabl
Lag
ADF e s Stat* 0 0.061 0 Variable Lag
ADF Infere
s Stat* nce 0
0.000
I(1)
0.252 0
0.000
I(1)
0 0.429 0
0.000
I(1)
0 0.097 0
0.000
I(1)
0 0.063 0
0.000
I(1)
0 0.081 1
0.000
I(1)
*MacKinnon (1996) one‐sided p‐values. This allows us to check that the futures price and index flows are cointegrated. The theory is that, if the two series are cointegrated, then the residuals from the cointegrating regression should be stationary about mean. This can be tested by using a single equation Engel Granger test which is an ADF test of the residuals from the cointegrating equation. Due to the nonstationarity variables we conduct our test for causality in the short run model. 40 Figure 16 – Results from Cointegrating (long run) Regression Equation Price of WTI Crude
Price of Natural Gas
Price of Heating Oil
Intercept ‐4.951 (1.601)
3.149 (4.633)
‐8.222 (1.178) Net inflows WTI 0.701682 (0.120935)
Net inflows Nat Gas Net inflows Heating Oil
adjR2 0.453 0.138
0.601 N 156 156
156 EG Tau P Stat4
0.057 0.093
0.111 ‐0.136 (0.054)
0.797(0.104) Can’t denote significance as critical values have changed We can see from the regression table that none of the series seem to be cointegrated at a 5% significance level although the equation for WTI crude oil and natural gas appear to be cointegrated at 10% significance, as indicated by the Engle Granger tau statistic with a p value <0.1. After inspecting the residuals from all three equations it appears that they are stationary (see fig 17) about mean, suggesting that the series are cointegrated and that the Engle Granger test could have failed to reject the null, meaning it suffered from a type II error. The question of cointegration can be further investigated by including the lagged residuals into the error correction model, where we can use a standard t test to test their significance. 4
The p value from the Engle‐Granger tau statistic. If p<0.005 the rejects null of cointegration at 5% significance level. 41 Figure 17 – Residuals from the cointegrating equation WTI Crude Natural Gas Heating Oil After testing for cointegration we now decide on the number of lags we wish to include. In order to decide this is have taken a similar approach to Aulerich et al (2010) and Gilbert (2012) who start off with an ADL(4,4) model and tests and then test down to more parsimonious specifications using the Akaike (AIC) and Schwartz (BIC) information criterion. I have tending to opt for the BIC criterion in this instance as it places a greater penalty on over parameterising the model. After considering 16 different types of lag structure the BIC criteria chooses a ADL(1,1) model for oil and natural gas and an ADL(1,2) for heating oil. All three equations are now estimated in first differences with the chosen lag length as to test for short run granger causality between the lagged explanatory and dependant variables and the dependent variable. ∆
∆
∆
42 Where ∆ is the rate of change in the futures price, ∆
is the rate of change in implied net long contracts (based on feeder cattle positions), ε is the residual and m and n are the chosen lags of two variable respectively. If the dependant and independent variables are cointegrated then I also augment this with the lagged residuals from the cointegrating regression and form an error correction model (ECM). Figure 18 – Test for granger causality betw een implied index flows and prices market m,n βj = 0, j
Direction
γ i = βj = 0, i, j NYMEX WTI Crude Oil 1,1 0.5427 ‐ 0.8301 NYMEX Natural Gas 1,1 0.5367 ‐ 0.7272 NYMEX Heating Oil 1,2 0.2282 ‐ 0.3007 The granger causality test is either given by a t (one lag) or F test (1+ lags) for the hypothesis H0: =0 against H1: ≠0, which can be using a Wald test. As you can see in all three examples we are unable to reject the null that index flows don’t granger cause price changes in the short run model. We can also use a similar Wald test to test rationality in the market. As you can see from the table above an F test on both γ and indicates neither lagged variable can predict the change in futures price. This provides evidence for full rationality of the markets, which re‐enforces our result. Figure 19 – Diagnostic statisti cs from short run mo de l Equation Intercept ‐1 0.031 (0.090) ‐1 ‐1 ‐1 ‐1 0.038 (0.062) ‐0.005 (0.110)
‐0.061874 (0.100)
0.054 (0.096) ‐1 0.077 (0.075) ‐2 0.101 (0.060)* Res (‐1) ‐0.057 (0.024)**
‐0.134 (0.044)***
‐0.055 (0.024)** adjR
0.020 0.044
0.063 LM test p stat 0.133 0.117
0.185 BPG test p stat 0.008 0.001
0.108 JB test p stat 0.008 0.000
0.078 N 155 155
156 2 Indicates significance at *10%, **5% and ***1% 43 Results from Granger Causality tests None of the coefficients attached to the short run models are statistically at the 5% significance level except the lagged residuals, which is further evidence that their component variables cointegrate, but more importantly we must consider the diagnostic statistics. The p value of over 5% in the LM test for autocorrelation is encouraging as we can’t reject the null of no autocorrelation. On the other hand the oil and gas ECM’s have a low p value in the Breusch‐Pagan‐Godfrey (BPG) test meaning that we reject the null of homoscedasticity demonstrating that the variance of the coefficients are biased, although this doesn’t appear to be a problem for heating oil. The residuals in the oil and gas equations are normally distributed but we can reject normality in the heating oil equation, which creates a concern about the model. The low adjR2 figure also appears to indicate that all three equations appear to have low explanatory power. In conclusion the tests for granger causality reject the hypothesis that index flows granger cause price changes. However, it’s difficult to distinguish what part is down to the inadequacies of the data and the econometric technique; for that reason it’s very hard to draw many conclusions from this result. Even Irwin and Saunders (2010b) who have conducted this type of test for the majority of their data are also careful when drawing their conclusion. They argue; “There is still a need for further research on the market impact of commodity index funds. The first reason is that direct tests of the relationship between index fund positions and price movement in energy futures have been hampered by the lack of publically‐available data on positions in these markets. The second reason is ongoing concerns about the power of time series statistical tests used in the studies that fail to find evidence of a relationship between index fund positions and movement in commodity markets” (Irwin and Saunders, 2011, 11). Investigating the price impact when index funds roll over contracts For that reason I am going to augment the tests for causality with a trading simulation to test the price impact that could arise with the mechanical rolling of index funds. The main focus of this study is to capture the price impact of when the (GSCI) largest index rolls it contracts over. The reason why I want to concentrate on the GSCI is twofold. Firstly, it has the vast majority of the market share, which Masters (2008) estimated to be around 63% of the index fund market. Also the GSCI has a very large weight in WTI crude (c.37%) and more generally in energy commodities (c.70%). 44 Energy commodities are also quite useful to look at in this simulation as unlike agricultural futures the GSCI rolls their energy contracts every month. The GSCI publicly release information about their rolling dates and currently have a rollover period of five days which starts on the fifth business day and ends on the ninth business day of each month. On each rolling day the GSCI will close out 20% of its dollar value positions in the nearby contract and roll them over into the deferred contract. It depends on the commodity in question but at the end of the roll the nearby futures contracts only have a matter of days until maturity. As these contracts will be illiquid and more volatile as they approach maturity, the best way to test this price impact is to create calender spread positions before the roll in a process known as ‘front running the roll.’ In a technique similar to the one used by Mou (2010) I create five calendars spread positions prior to the roll and unwind each calender spread on each of the five rolling days. The calendar spread position creates a speculative position by shorting the nearby contract and going long on the deferred contract and is defined as follows: ,
,
,
,
,
,
/2
Where rt is the excess return from investing in the commodity, tj is when the commodity position is created and t’ is when the calendar spread position is unwound, T1 is the nearby futures contract and T2 is the deferred futures contract. To get the excess return for the month I take a five day average across all the calendar spread positions that are created. I also make the assumption that the investor has access to a storage technology and therefore is able to store their capital between taking these speculative positions. An area where my analysis is different from Mou (2011) is I split my sample into three sections rather than two. The first sample is from 1982 to 1990 and is chosen to represent a time when the GSCI commodity didn’t exist and hence we would expect not to see any price impact. The second sample is from 1991 5 to 2003 and is chosen to represent a time period where the GSCI existed but where index investment was relatively small. The final sample period is from 2004 to 2012 and represents a time when financial markets are said to have become financialised 6 and where index investment is high relative to the historical norms. Another difference is that I have designed the simulation to take account of noise that happens in the two days before the roll. Frank and Turbeville (2010) argue that prices can be quite volatile two days before the roll period and for that 5
6
The GSCI fund started in 1991 As stated by Domanski and Heath, 2007. 45 reason my calendar spreads start eight days before the roll takes place and finish on the 2nd business day of the month (3 days before the roll starts.) In perfect capital markets an opportunity to make excess profits shouldn’t be possible. This is because arbitragers will take calendar spread positions and arbitrage any price impact from the market. If I am able to make excess returns is suggests that the market is suffering from limits to arbitrage. Under such circumstances arbitragers are not capturing all of the price impact caused by the roll by the index traders. While I can’t be sure of the exact reasons, it links very well to the theory of ‘synchronisation’ and ‘liquidy risk’ which I alluded to earlier in the theory section of the paper. 46 Figure 20 – Results Source: Authors Calculation based on pricing data from Bloomberg, 2012 47 As the monthly data was extremely noisy I converted data into quarterly data by averaging excess returns so as to give a clearer picture of the trends; I then overlayed the amount of open interest over the top to get an idea of how liquid the market is bearing in mind this could be an important factor. As you can see from the data the markets seem to behave differently. Profits from WTI crude oil appear to be relatively low and the market appears to be well arbitraged compared to natural gas and heating oil. Also, there seems to be a sharp increase in returns in 2009 when open interest suddenly falls. It’s interesting to note that this sharp increase in returns comes just after the collapse of Lehman Brothers and when the financial system was plagued by uncertainty and instability. As you can see from figure 21 this came at a time when the number of non‐commercials taking spread positions fell, a metric which is often used to represent arbitragers. Figure 21 – Contracts held by non‐commercial spread traders as %age of open interest Source: Authors Calculations based on CFTC, 2012. This suggests that the number of arbitragers in the market fell following the financial instability. This could be due to a number of factors but the fact that it happened at a time of high instability suggests that contracts held by spreaders fell due to limits to arbitrage, possibly caused by ‘synchronisation risk’ arising from low liquidity in the financial system. The other markets appear to be a lot more volatile, which could be explained by the fact that they are not as liquid as the oil market. Interestingly, excess returns appear to be high for heating oil in the 1980’s which could be explained by the relatively low levels of open interest relative to now. However, there is again a sharp jump in returns during 1990, but it’s not clear whether this was due to the 1990 recession or due to the Iraq war. 48 Another factor that has to be considered is the risk free rate of interest and the variance of the excess returns from the calendar spread position. It’s very probable some of the higher returns in the late 80 and early 90’s for heating oil could be explained by the higher risk free rate of interest and the greater volatility in retuns. We can help include this into our analysis by using Shape ratios for the three different periods to try and compensate for these factors. The sharp ratio helps give a meaningful measure of risk relative to risk free asset such as treasury bills. Sharpe ratios help evaluate how much excess return you are receiving per extra unit of risk you incur from holding a riskier asset. Figure 22 – Mean excess returns and Sharpe Ratios WTI Crude Period Excess Return Period Sharpe Ratio 83 to 90 ‐0.21% 83 to 90 ‐1.05 91 to 03 0.11% 91 to 03 ‐0.28 04 to 12 0.28% 04 to 12 0.14 Period Excess Return Period Sharpe Ratio 83 to 90 ‐ 83 to 90 ‐ 91 to 03 0.95% 91 to 03 0.26 04 to 12 0.51% 04 to 12 0.12 Period Excess Return Period Sharpe Ratio 83 to 90 0.27% 83 to 90 ‐0.20 91 to 03 0.55% 91 to 03 0.09 04 to 12 0.06% 04 to 12 ‐0.17 Natural Gas 7 Heating Oil Source: Authors Calculations and US T‐Bill 3 month data from Federal Reserve, 2012. 7
I couldn’t calculate the excess return for period 83 to 90 as price data only goes back to 1990. 49 Investigating the price impact of index roll – the results As you can see from the WTI crude oil market the mean excess return and the Sharpe ratio have increased over the three periods, suggesting that limits to arbitrage have grown, although a lot of this is down to the sharp increase that we saw during early 2009. This is similar to the result found by Mou (2010) and Frank and Turbeville (2010). However in natural gas and heating oil the excess returns and the Sharpe ratios have fallen, indicating that limits to arbitrage is less of a problem. In the heating oil market a negative Sharpe ratio appears to give the indication that the risk free asset would perform even better even though it carries less risk. The results once again paint a confusing picture as there appears to be an element of limits to arbitrage in the WTI oil market as shown by greater returns in periods where we would expect higher index flows. Alternatively, results from natural gas and heating oil seem to suggest no price impact, as mean excess returns are higher in time periods where there is considerably less index investment. Overall, it seems that all three markets are generally well arbitraged. However excess return appears to rise during times of financial stress, or more specifically when the number of contracts held by spread traders falls. This suggests that arbitragers are unable to arbitrage away the price impact out due to number of reasons. A strong possibility is that liquidity constraints could have led to synchronisation risk as arbitragers fear there is not enough capital to provide a counterbalancing force to the index roll. There is also a worrying trend emerging from the literature. Index funds seem to have picked up on the idea that having a more flexible and dynamic approach to rolling over contracts can save them money, as when arbitragers enter calendar spread positions they usually make profits at the expense of the index investors. Mou (2010) calculated that since 2004 the GSCI investors lost over $2 million relative to a copycat index that rolls its contracts over 10 day earlier. In addition Mou (2010) argues that the new generation of index funds will choose maturities as far as one year ahead and whose roll methodology is dependent on the market structure. For example, if the market is in backwardation then the fund will roll into contracts with close maturities and take advantage of the positive roll yields. However, if the term structure is in contango, then funds will roll into longer dated contracts to reduce the roll cost. The danger of this happening is twofold. Firstly longer dated contracts have a considerably smaller amount of open interest, which is an indication that price impacts will be amplified. Secondly, arbitragers don’t face limits to arbitrage when index funds 50 predictably roll into the highly liquid deferred contract; however, this could change if index funds start rolling, unpredictably into longer dated contracts. Under such circumstances it seems very possible that index funds could force up the shape of the yield curve especially out towards the longer dated contracts. If this was allowed to happen such that the futures prices didn’t reflect future expectations, it could seriously damage that efficiency of the futures markets, meaning that they failed in their two critical roles of price insurance and price discovery. At the same time these results only raise a concern rather than offering concrete evidence. There are still many questions that have been left unanswered. One of the most relevant is that question of why excess returns in all three energy markets didn’t follow a similar pattern. One would expect that the excess return would have been higher in early 2009 for natural gas and heating oil just for the sheer fact that their markets are noticeably thinner than the WTI crude oil market. Also this concern would only explain why the efficiency of futures markets could be damaged and doesn’t explain how spot prices have risen without an increase in inventories. 51 CONCLUSION/POLICY RECOMMENDATIONS Policy makers barking up the wrong tree It seems that for far too long academics and policy makers have been barking up the wrong tree when it comes to investigating the impact of index investors in commodity markets. A prime example is the debate regarding the best metric with which to quantify index speculation. Although I discovered that implied index positions are superior to DCOT swap positions when measuring index positions, I think the whole debate is irrelevant as they are all unsuitable. I am also sceptical about the power of granger causality tests and how useful they really are due to the volatility of the dependant variable. Perhaps this technique may become slightly more relevant when the CFTC is able to offer its IID data on a more frequent basis and when the sample size is sufficiently large to conduct time series tests. I believe a better, more rounded approach to the issue comes from techniques such as those developed by Mou (2010) and Robe et al (2008) who look at the issue of financialisation without having to prove the credibility of the data being used. It seems for far too long this debate has been going nowhere due to that exact reason. I also believe the argument about positions limits to be slightly irrelevant in the markets I investigated. While they may be more suitable for thinner markets to prevent manipulation, I think their use in large liquid markets such as crude oil is missing the point. Role played by fundamentals After looking at the evidence I believe that the major factor driving futures prices has been due to fundamentals and the increasing impact played by the very high short run elasticity of supply and demand, the expectedly high growth of emerging economies and underinvestment from OPEC which may even have caused the scarcity rent to be an additional factor. I believe in the long run pressure on prices will lessen as new production comes online. The fact that the oil price has been high in recent years means that there is considerable new investment in oil at the moment, indicating new supply will increase in the not too distant future. 52 The change in term structure is causing a dangerous precedent for index funds The results from my simulation raise concerns that the rolling process can put significant pressure on prices although it seems that most of the time this pressure is muted by arbitragers. The danger is that, even when the arbitrager knows when the index fund is rolling their contract and when the market is highly liquid, they can fail to arbitrage out this discrepancy due to financial stability. The danger is that now the market is in contango and now many index funds are rolling in an unpredictable way into thinner, longer dated contracts which suggest the price impact is going to be amplified. This could result in a futures market that fails to provide price insurance and price discovery. This could send out false messages to merchants and producers which would result in an inefficient allocation of this resource. The worst case scenario would be the market collapses as hedgers lose confidence in the market. We also have to be careful when interpreting such spread traders as arbitragers in the same way Friedman would interpret an arbitrager. Arbitragers who front roll the GSCI roll do not attack a mispricing resulting from upward pressure on the deferred contract; they actually promote this by going short on the nearby contract and long on the deferred contract. They merely capture the price impact, usually be reducing the price gap between the two contracts which reduces the roll yield of the index fund when the market is in backwardation. Although Randall‐Wray (2008) and Frank and Turbeville (2010) have claimed this forces the market into contango, it’s very difficult to distinguish what part we can attribute to index funds rolling over and what part is down to future expectations of prices, but this seems a good area to research in the future. Policy recommendations We need to consider the damage that could be caused by implementing position limits which would be likely to reduce the size index investment. Irwin and Saunders (2010b) strongly believe that such policies could deprive futures markets of their liquidity and risk absorption capacity when demand on such markets is high. The result of this is that futures markets become less efficient and add costs to those who are looking to transfer risk. This could ultimately end up putting up prices for producers who then pass this onto consumers. Due to the potential downside of position limits on the futures markets, such policies must be based on solid evidence and not just the opinion of a few people in the minority, even if it is convenient for the government. 53 A good place for regulators to start would be to make the data a lot clearer and easier to use. This could be a combined effort to get better, more frequent IID from the CFTC and for the International Energy Association to strive to get better data on investment, inventories and supply and demand. Moreover the CFTC should apply pressure on the FSA 8 to publish data similar to the CFTC IID data, but on the ICE exchange, which would allow better analysis to be conducted. Most importantly, the CFTC needs to keeps its eye on the dynamic rolling mechanism that many new commodity funds are using. The GSCI is yet another fund that has recently latched onto this opportunity and introduced a new GSCI fund with a dynamic roll strategy (S&P, 2012b). The CFTC should require all index funds over a certain size to publically publish their rolling mechanism at a specific time before the roll is executed; including what contracts they are rolling into. Secondly, the CFTC should set a limit on funds rolling into far out contracts that are thinly traded as it could have a significant impact on prices. All these policies combined should mean that any price impact caused by the index funds can be arbitraged away and this can be achieved without reducing the size of index investment. I believe it not the size of index investment that is the problem but rather the way in which they are starting to trade in a contango market that is of greater importance. 8
Financial Services Authority – UK futures regulator. 54 References Abreu, D. and M. Brunnermeier (2002), "Synchronization Risk and Delayed Arbitrage," Journal of Financial Economics. Vol. 66, pp.341‐360. [online]. Accessed from: http://jfe.rochester.edu/02023.pdf on the 30th of July 2012. Bloomberg,. 2012. “Futures prices”. Bloomberg terminal at University of Nottingham. Brunnermeier, M, K & Nagel, S,. 2004, “Hedge Funds and the Technology Bubble”. Journal of Finance, Vol.59, Iss.5 pp.2012‐2040. [online]. Accessed from: http://www.princeton.edu/~markus/research/papers/hedgefunds_bubble.htm on the 25th of March 2012. Brunetti, C & Büyüksahin, B,. 2009. “Is Speculation Destabilizing?” Working paper, 29 Commodity Futures Trading Commission. [online]. Accessed from: http://www.cftc.gov/ucm/groups/public/@swaps/documents/file/plstudy_03_cftc.pdf on the 30th of July 2012. CFTC,. 2012. “Commitment of Traders”. CFTC. [online]. Accessed from: http://www.cftc.gov/marketreports/commitmentsoftraders/index.htm on the 30th of July 2012. DEFRA. Nd. The 2007/08 “Agricultural Price Spikes: Causes and Policy Implications”. HM Government. [online]. Accessed from: http://archive.defra.gov.uk/foodfarm/food/pdf/ag‐
price100105.pdf on the 15th of August 2012. DeLong, J, B, Shleifer, A, Summers, L, H & Waldmann, R, J,. 1990. “Positive Feedback Investment Strategies and Destabilizing Rational Speculation,”Journal of Finance, Vol.45, Iss.2, pp.379‐395. [online]. Accessed from: http://onlinelibrary.wiley.com/doi/10.1111/j.1540‐
6261.1990.tb03695.x/abstract on the 30th of July 2012. Domanski, D & Heath, A,. 2007. “Financial investors and commodity markets”. BIS. [online]. Accessed from: http://www.bis.org/publ/qtrpdf/r_qt0703g.pdf on the 25th of June 2012. Edwards, F.R. and Ma, C.W. (1992) Futures and Options, McGraw‐Hill. Etula, Erkko. 2010. “Broker‐Dealer Risk Appetite and Commodity Returns.” Working paper, Federal Reserve Bank of New York. [online]. Accessed from: http://www.newyorkfed.org/research/staff_reports/sr406.pdf on the 30th of July. Falkowski, M,. 2010. “Financialization of commodities”, Contemporary Economics. Vol. 5, Iss. 4, pp.4‐
17. [online]. Accessed from: http://core.kmi.open.ac.uk/display/5793080 on the 30th of July. Fattouh, B, Kilian, L & Mahadeva, L,. 2012. “The Role of Speculation in Oil Markets: What Have We Learned So Far?” University of Michigan. [online]. Accessed from: http://www‐
personal.umich.edu/~lkilian/milan030612.pdf on the 15th of June 2012. 55 Federal Reserve,. 2010. “Selected Interest Rates”. Federal Reserve. [online]. Accessed from: http://www.federalreserve.gov/releases/h15/data.htm on the 27th of July 2012. Friedman, M,. 1953. Positive Economics. University of Chicago Press: Chicago, pp.157‐203. Gilbert, C, L & Pfuderer, S,. 2012. “Index Funds Do Impact Agricultural Prices”, Working Paper. [online]. Accessed from: http://www.york.ac.uk/res/mmf/documents/SI_5.pdf on the 15th of June 2012. Gorton, G.B., and K.G. Rouwenhorst. 2006. "Facts and Fantasies about Commodity Futures." Financial Analysts Journal, Vol. 62, Iss. 2, pp. 47‐68. [online]. Accessed from: http://www.jstor.org/discover/10.2307/4480744?uid=3738032&uid=2129&uid=2&uid=70&uid=4&si
d=21101214669667 on the 30th of July 2012. Hamilton, J, D,. 2009. “Understanding Crude Oil Prices” Energy Journal, Vol. 30, pp. 179‐206. [online]. Accessed from: http://dss.ucsd.edu/~jhamilto/understand_oil.pdf on the 15th of June 2012. Hong, H., and M. Yogo, 2010, “Commodity Market Interest and Asset Return Predictability," working paper, Princeton University.[online]. Accessed from: http://www4.gsb.columbia.edu/filemgr?file_id=733719 on the 13th of June 2012. Hotelling, H,. 1931. “The Economics of Exhaustible Resources,” Journal of Political Economy, Vol. 39, Iss. 2, pp. 137‐75. [online]. Accessed from: http://www.jstor.org/discover/10.2307/1822328?uid=3738032&uid=2129&uid=2&uid=70&uid=4&si
d=21101214669667 on the 30th of July 2012. Keynes, J, M,. 1936. The General Theory of Employment, Interest and Money. Harcourt Brace and Co: New York Kilian, L & Hicks, B,. 2012. “Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003‐2008?” University of Michigan. [online]. Accessed from: http://www‐
personal.umich.edu/~lkilian/kilianhicks031109.pdf on the 30th of July 2012. Inamura, Y, Tomonori, K, Kimura, T & Takashi, M,. 2011. “Recent Surge in Global Commodity Prices”. Bank of Japan Review. [online]. Accessed from: http://www.boj.or.jp/en/research/wps_rev/rev_2011/data/rev11e02.pdf on the 15th of June 2012. ITFC. 2008. “Interim Report on Crude Oil.” Interagency Taskforce on Commodity Markets. [online]. Accessed from: http://www.cftc.gov/ucm/groups/public/@newsroom/documents/file/itfinterimreportoncrudeoil07
08.pdf on the 15th of June 2012. Irwin, S,H & Saunders, D,R,. 2010. “Index Funds, Financialization, and Commodity Futures Markets”, Applied Economic Perspectives and Policy, Vol. 32. [online]. Accessed from: 56 http://aepp.oxfordjournals.org/content/early/2011/02/02/aepp.ppq032.abstract on the 15th of June 2012. Irwin, S,H & Saunders, D,R,. 2010b. “The Impact of Index and Swap Funds on Commodity Futures Markets”, OECD Food, Agriculture and Fisheries Working Papers No. 27, [online]. Accessed from: http://www.oecd.org/trade/agriculturaltrade/assessingagriculturalmarkets/45534528.pdf on the 30th of April 2012. Irwin, S, H & Sanders, D, R,. 2011. “Testing the Masters Hypothesis in Commodity Futures Markets.” Energy Economics, Vol.34, pp.256‐269. [online]. Accessed from: http://www.sciencedirect.com/science/article/pii/S0140988311002362 on the 15th of June 2012. Krautkraemer, Jeffrey A. 1998. “Nonrenewable Resource Scarcity,” Journal of Economic Literature, Vol. 36, Iss.4, pp. 2065‐2107. [online]. Accessed from: http://www.jstor.org/discover/10.2307/2565047?uid=3738032&uid=2129&uid=2&uid=70&uid=4&si
d=21101214669667 on the 30th of July. Masters, M, W,. 2008. “Testimony before the Committee on Energy and Natural Resources Subcommittee on Energy”. September 16th. [online]. Accessed from: http://www.energy.senate.gov/public/index.cfm/files/serve?File_id=6cbe7e73‐0cb3‐d4e1‐6864‐
2dccc32d9bc2 on the 30th of July 2012. Mou, Y,. 2010. “Limits to Arbitrage and Commodity Index Investment: Front‐Running the Goldman Roll.” Columbia University. [online]. Accessed from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1716841 on the 15th of June 2012. ONS,. 2010. Consumer Price Indices. ONS. [online]. Accessed from: http://www.ons.gov.uk/ons/rel/cpi/consumer‐price‐indices/march‐2012/cpi‐time‐series‐data.html on the 30th of August 2012. Parsons, J, E,. 2010. “Black Gold and Fool’s Gold: Speculation in the Oil Futures Market”. Economia, Vol. 10, No. 2 pp.81‐116. [online]. Accessed from: http://www.mit.edu/~jparsons/publications/Reprint_228_WC.pdf on the 15th of June 2012. Petzel, T. 2009. “Testimony before the CFTC.” U.S. Commodity Futures Trading Commission. CFTC. [online]. Accessed from: http://www.cftc.gov/ucm/groups/public/@newsroom/documents/file/hearing072809_petzel2.pdf on the 30th of July 2012. Reuters. 2011. “Stanford professor cracks orthodoxy on oil prices: Kemp”. Reuters. [online]. Accessed from :http://in.reuters.com/article/2011/06/20/idINIndia‐57804520110620 on the 15th of June 2012. 57 Robe, M, A, Büyükşahin, B & Haigh, M, S,. 2008. “Commodities and Equities: Ever a ‘Market of One’?” Journal of Alternative Investments, Vol, 12, pp.76‐95. [online]. Accessed from: http://www.iijournals.com/doi/abs/10.3905/JAI.2010.12.3.076 on the 15th of June 2012. S&P,. 2012. “Index Research”. [online]. Accessed from: http://www.standardandpoors.com/indices/index‐research/en/us/?type=All&category=Commodity on the 30th of July 2012 S&P,. 2012b. “S&P GSCI Dynamic Roll”. S&P. [online]. Accessed from: http://www.standardandpoors.com/indices/sp‐gsci‐dynamic‐roll/en/us/?indexId=sp‐gsci‐dynamic‐
roll on the 2nd of September 2012. Sanders, D, R, Irwin, S, H & Merrin, R, P,. 2008. “The Adequacy of Speculation in Agricultural Futures Markets: Too Much of a Good Thing?” Department of Agricultural and Consumer Economics, University of Illinois at Urbana‐Champaign. [online]. Accessed from: http://www.farmdoc.uiuc.edu/marketing/morr/morr_archive.html on the 15th of June 2012 Singleton, K,. 2011. “Investor Flows and the 2008 Boom/Bust in Oil Prices”. University of Stanford. [online]. Accessed from: http://www.stanford.edu/~kenneths/OilPub.pdf on the 15th of June 2012. Tang, K, & Xiong, W,. (2011). “Index Investment and Financialization of Commodities.” Working paper. Princeton University. [online]. Accessed from: www.princeton.edu/~wxiong/papers/commodity.pdf on the 15th of June 2012. Wray, L, R,. 2008. “Commodities Market Bubble”. The Levy Economics Institute of Bard College. Iss.96. [online]. Accessed from: http://www.levyinstitute.org/pubs/ppb_96.pdf on the 22nd of May 2012. Working, H,. 1960. “Speculation on Hedging Markets.” Stanford University Food Research Institute Studies. Vol. 1, pp.185‐220. Working, H,. 1953. “Futures Trading and Hedging.” American Economic Review, Vol.43, pp.314‐343. [online]. Accessed from: http://www.jstor.org/discover/10.2307/1811346?uid=3738032&uid=2129&uid=2&uid=70&uid=4&si
d=21101214669667 on the 30th of June 2012. 58 Bibliography Dwyer, A, Hollaway,J & Wright, M,. 2012. “Commodity Market Financialisation: A Closer Look at the Evidence”, Reserve Bank of Australia: March 2012 Bulletin. [online]. Accessed from: http://www.rba.gov.au/publications/bulletin/2012/mar/bu‐0312‐8a.html on the 15th of June 2012. Duffie, D. (2010), "Presidential Address: Asset Price Dynamics with Slow‐Moving Capital," Journal of Finance, Vol. 65, pp.1237‐1267. [online]. Accessed from: http://www.darrellduffie.com/uploads/pubs/DuffieAFAPresidentialAddress2010.pdf on the 30th of July FT. 2011. “Challenge to conventional wisdom on speculators”. FT.com. [online]. Accessed from: http://www.ft.com/cms/s/0/58de284a‐d314‐11e0‐9aae‐00144feab49a.html#axzz1xuaTFTGY on the 15th of June 2012. Krugman, P,. 2008. “Speculation and Signatures”. Princeton University. [online]. Accessed from: http://www.princeton.edu/~pkrugman/Speculation%20and%20Signatures.pdf on the 15th of June 2012. Masters, M.W., and A.K. White. 2008. “The Accidental Hunt Brothers: How Institutional Investors are Driving up Food and Energy Prices.” [online]. Accessed from: http://accidentalhuntbrothers.com/ on the 30th of July 2012. Pirrong, S. C,. 2008. “Stochastic Fundamental Volatility, Speculation, and Commodity Storage.” University of Houston. [online]. Accessed from: http://www.wsuc3m.com/3/Craig_Pirrong_sv_storage.pdf on the 15th of June 2012. Till, H,. 2009. “Has There Been Excessive Speculation in the US Oil Futures Markets?”, EDHEC – Risk Institute. [online]. Accessed from: http://msc‐entrepreneurship.edhec.com/news/edhec‐
news/edhec‐risk‐finds‐no‐excessive‐speculation‐in‐oil‐futures‐markets‐139085.kjsp on the 17th of June 2012. 59 Appendix GSCI weighting for WTI Crude, Natural Gas and Heating Oil Source: S&P, 2012 GSCI for KC Wheat and Feeder Cattle Source: S&P, 2012 60 Futures contract characteristics Commodity Traded on Contract size Found in Index WTI Crude Oil NYMEX 1000 barrels GSCI & UBSDJ Natural Gas NYMEX 10,000 mmBtu 9
GSCI & UBSDJ Heating Oil NYMEX 42,000 gallons GSCI & UBSDJ KC Wheat KBOT 5,000 bushels GSCI Feeder Cattle CME 50,000 pounds GSCI Soybean Oil CME 60,000 pounds UBSDJ UBSDJ Weights – as of 17th of January 2006. Assumed to be constant. Commodity WTI Crude Oil Natural Gas Heating Oil Weight 12.8% 12.3% 3.8% Source: Masters, 2008. Selecting the optimal lag length in the short run models Lag Length WTI Nat Gas AIC Schwartz AIC 4,4 ‐3.157450 ‐2.980735 ‐2.514829 4,3 ‐3.167123 ‐3.010043 ‐2.496098 4,2 ‐3.154339 ‐3.016894 ‐2.454942 4,1 ‐3.163397 ‐3.045587 ‐2.461016 3,4 ‐3.170333 ‐3.013253 ‐2.501828 2,4 ‐3.175179 ‐3.037734 ‐2.484378 1,4 ‐3.187446 ‐3.069636 ‐2.497243 3,3 ‐3.178308 ‐3.040863 ‐2.506370 3,2 ‐3.163910 ‐3.046100 ‐2.462361 3,1 ‐3.172823 ‐3.074648 ‐2.469350 2,3 ‐3.183460 ‐3.065650 ‐2.490434 1,3 ‐3.196042 ‐3.097867 ‐2.503098 2,2 ‐3.176813 ‐3.078638 ‐2.475211 2,1 ‐3.185673 ‐3.107133 ‐2.482002 1,2 ‐3.189275 ‐3.110735 ‐2.488065 1,1 ‐3.198378 ‐3.139473 ‐2.491017 9
Million British thermal units 61 Schwartz ‐2.338114 ‐2.339018 ‐2.317497 ‐2.343206 ‐2.344748 ‐2.346933 ‐2.379433 ‐2.368925 ‐2.344551 ‐2.371175 ‐2.372624 ‐2.404923 ‐2.377036 ‐2.403462 ‐2.409525 ‐2.432112 Soybean Oil 2.77% Heating Oil AIC ‐3.261678 ‐3.267545 ‐3.255446 ‐3.250991 ‐3.260256 ‐3.272649 ‐3.272564 ‐3.246181 ‐3.250292 ‐3.242188 ‐3.257791 ‐3.261802 ‐3.262796 ‐3.254710 ‐3.267958 ‐3.226785 Schwartz ‐3.105275 ‐3.130692 ‐3.138144 ‐3.153239 ‐3.123403 ‐3.155347 ‐3.174813 ‐3.128879 ‐3.152540 ‐3.163986 ‐3.160039 ‐3.183601 ‐3.184595 ‐3.196059 ‐3.209307 ‐3.187684 Price data for time series study Source: Bloomberg, 2012 62 Price data for trading simulation Source: Bloomberg, 2012. 63 Test for Cointegration WTI crude oil
Cointegration Test - Engle-Granger
Date: 09/17/12 Time: 15:05
Equation: COINTEG_WTI
Specification: LPRWTI LNETPWTI C
Cointegrating equation deterministics: C
Null hypothesis: Series are not cointegrated
Automatic lag specification (lag=0 based on Schwarz Info Criterion,
maxlag=13)
Engle-Granger tau-statistic
Engle-Granger z-statistic
Value
-3.320582
-21.20897
Prob.*
0.0571
0.0368
Natural Gas
Cointegration Test - Engle-Granger
Date: 09/17/12 Time: 15:06
Equation: COINTEG_GAS
Specification: LPRGAS LNETPGAS C
Cointegrating equation deterministics: C
Null hypothesis: Series are not cointegrated
Automatic lag specification (lag=0 based on Schwarz Info Criterion,
maxlag=13)
Engle-Granger tau-statistic
Engle-Granger z-statistic
Value
-3.107313
-18.41675
Prob.*
0.0927
0.0678
Heating Oil
Cointegration Test - Engle-Granger
Date: 09/17/12 Time: 15:06
Equation: COINTEG_HEAT
Specification: LPRHEAT LNETPHEAT C
Cointegrating equation deterministics: C
Null hypothesis: Series are not cointegrated
Automatic lag specification (lag=0 based on Schwarz Info Criterion,
maxlag=13)
Engle-Granger tau-statistic
Engle-Granger z-statistic
Value
-3.023903
-18.28442
Prob.*
0.1107
0.0697
64 Error Correction Models
WTI crude oil
Dependent Variable: D(LPRWTI)
Method: Least Squares
Date: 09/17/12 Time: 15:08
Sample: 1/13/2009 12/27/2011
Included observations: 155 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(LPRWTI(-1))
D(LNETPWTI(-1))
RESIDWTI(-1)
0.030563
0.037561
-0.057461
0.089658
0.061569
0.024400
0.340889
0.610063
-2.354918
0.7337
0.5427
0.0198
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.032574
0.019845
0.048426
0.356458
250.8743
1.880585
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
0.004454
0.048914
-3.198378
-3.139473
-3.174453
Natural gas
Dependent Variable: D(LPRGAS)
Method: Least Squares
Date: 09/17/12 Time: 15:09
Sample: 1/13/2009 12/27/2011
Included observations: 155 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(LPRGAS(-1))
D(LNETPGAS(-1))
RESIDGAS(-1)
-0.005217
-0.061874
-0.133702
0.110282
0.099923
0.043903
-0.047302
-0.619216
-3.045371
0.9623
0.5367
0.0027
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.056520
0.044106
0.068974
0.723121
196.0538
1.949304
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
65 -0.004445
0.070547
-2.491017
-2.432112
-2.467091
Heating oil
Dependent Variable: D(LPRHEAT)
Method: Least Squares
Date: 09/17/12 Time: 15:14
Sample: 1/13/2009 12/27/2011
Included observations: 155 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(LPRHEAT(-1))
D(LNETPHEAT(-1))
D(LNETPHEAT(-2))
RESIDHEAT(-1)
0.068383
0.034008
0.100551
-0.053346
0.088505
0.071047
0.060485
0.023984
0.772642
0.478673
1.662404
-2.224213
0.4409
0.6329
0.0985
0.0276
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.062890
0.044272
0.043596
0.286994
267.6727
1.862676
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
test for granger causality – Wald test
WTI crude oil
Wald Test:
Equation: Untitled
Test Statistic
t-statistic
F-statistic
Chi-square
Value
df
Probability
0.610063
0.372177
0.372177
152
(1, 152)
1
0.5427
0.5427
0.5418
Value
df
Probability
-0.619216
0.383428
0.383428
152
(1, 152)
1
0.5367
0.5367
0.5358
Value
df
Probability
1.491917
2.983835
(2, 151)
2
0.2282
0.2249
Natural gas
Wald Test:
Equation: Untitled
Test Statistic
t-statistic
F-statistic
Chi-square
Heating oil
Wald Test:
Equation: Untitled
Test Statistic
F-statistic
Chi-square
66 0.003477
0.044594
-3.402229
-3.323689
-3.370327