the pricing effect of large speculators and price run-ups in the new nymex crude oil futures market
TRANSCRIPT
i
The Pricing Effect of Large Speculators and Price Run-Ups in the
New York Mercantile Exchange (NYMEX) Crude Oil Futures Market
Andrew Paul Acosta
Table of Contents
Chapter 1: Introduction to the Study .............................................................................................................................................1
Introduction ...............................................................................................................................................................................1
Problem Statement ....................................................................................................................................................................2
Operational Definitions of Futures Contracts ...........................................................................................................................3
Variables and Conjectured Relationship ..................................................................................................................................5
Nature of Study .........................................................................................................................................................................6
Research Questions and Hypotheses ........................................................................................................................................6
Theoretical Base .......................................................................................................................................................................7
Significance of the Study ..........................................................................................................................................................8
Chapter 2: Literature Review ......................................................................................................................................................10
Introduction .............................................................................................................................................................................10
Regulatory Effect of Pricing ...................................................................................................................................................11
Open Interest and Volume ......................................................................................................................................................13
Market Manipulation ..............................................................................................................................................................14
Granger Causality ...................................................................................................................................................................21
VAR ........................................................................................................................................................................................24
Chapter 3 .....................................................................................................................................................................................25
Methodology ...........................................................................................................................................................................25
Research Questions, Testable Hypotheses, Approaches ........................................................................................................25
Econometric Methods Used for Testing and Approaches ......................................................................................................27
ii
Periods for Testing and Data Frequency ................................................................................................................................28
Data/Variables Used for Testing ............................................................................................................................................28
Research Design and Approach ..............................................................................................................................................28
Setting and Sample .................................................................................................................................................................29
Instrumentation and Materials ................................................................................................................................................30
Data Collection and Analysis .................................................................................................................................................30
Measures taken for protection of participants rights ..............................................................................................................30
References ...................................................................................................................................................................................32
1
Chapter 1: Introduction to the Study
Introduction
Over the last several years, there has been much discussion whether large firms that trade crude oil futures are
responsible for moving the price upwards (Davidson, 2008; Engdahl, 2008; Kaufmann & Ullman, 2009; Smith, 2009).
During the summer of 2008, for example, the delivery price for crude oil rose to nearly $145, and suddenly dropped to $90
within two months (Federal Reserve, 2012). This rapid price change has prompted the United States Senate, notably Senator
Bernie Sanders of Vermont, to propose legislation to stop "excessive speculation" in petroleum markets (Sanders, 2011a),
which would indicate that trading firms are manipulating the price for their own benefit (Sanders, 2011b). Through this
proposed research, I intend to discuss and explore market manipulation using an experimental quantitative time series
analysis method and to find which crude oil market variables forecast when market manipulation is occurring.
When researchers watch something, such as oil prices, change over time, economists call those observations a time
series. A time series usually has occurrences at regular intervals (Chatfield, 2003). Time series data exhibit qualities that
help the researcher discover patterns within the underlying phenomenon, in this case, crude oil futures prices.
Detecting crude oil futures manipulation is not as simple as merely observing the time series data for changes in
market price. There may be factors hidden within the price itself that a quantitative analyst must extract before making any
judgment about price manipulation. Prices, especially energy prices such as crude oil, tend to be seasonal. The market supply
and demand will change at certain times of the year, sometimes in predictable patterns. Prices may exhibit a long-term trend
either up or down; possible reasons could include changes in supply and demand, costs of oil drilling changing over time, or
substitutes for crude oil. It is important to identify any local trend and separate it from the price time series because short-
term changes will become evident (Chatfield, 2003, p.15).
After extracting seasonality and trend, the remaining component is noise because it tends to be random. This could
be day-to-day price fluctuation, unmeasured events that have a very short effect, or it could be showing price manipulation. It
would be nearly impossible to determine what is happening in the noise time series by observing only the price time series
data without regard for other events or possibly related time series. Studying multiple time series that are concurrent with the
price time series may shed some light onto the events that are occurring in periods suspected of price manipulation.
Additionally, comparing time series patterns when it is widely known the price is not manipulated could inform the
2
hypothesis of a potentially-manipulated market. The same comparison could be done with a time series where it is widely
known that the market was manipulated.
Previous studies of crude oil market manipulation has been inconclusive or lacked an instrument to measure the
existence of manipulation (Engdahl, 2008; Kaufmann, & Ullman, 2009). Similar market manipulation studies exist in other
markets such as gold (Bhar & Hamori, S. 2004). This paper will address the lack of a crude oil market manipulation
instrument by providing a time series model. Efficient markets are vital to the world economy and ensuring their existence
has a positive effect.
Problem Statement
Previous studies suggest that the activity of market speculators has driven up the price of crude oil (Davidson, 2008;
Engdahl, 2008; Kaufmann & Ullman, 2009; Smith, 2009), and that the price is volatile given other market forces. There is a
movement within the US Senate to "halt excessive oil speculation" (Sanders, 2011a; Sanders 2011b). Additionally, it is
unclear the extent to which crude oil trading contributes to price run-ups (Krugman 2008a; Krugman 2008b; Krugman
2008c). Studies such as Just & Just (2008) examine potential market manipulation. The research is a quantitative time series
case study of the speculative activity of trading in crude oil futures markets, and with null hypotheses that relate trading
activity to crude oil futures price run-ups.
Operational Definitions of Futures Contracts
Futures contract: A futures contract is a standardized agreement created by a regulated futures exchange for a
physical commodity, such as corn or crude oil, a deliverable service, such as electricity, or many other financial products.
Many futures contracts require actual delivery of a commodity, while others only require that the monetary value of the
contract be delivered. Traders in these markets either buy or sell a contract at a specific price in the hopes of either making
money from the change in price, or protecting their profit when delivering, or accepting for delivery, a commodity. When a
trader buys a futures contract, that trader has taken a "long position" in the market in the hopes of selling when the price
increases. By contrast, when a trader sells a futures contract, that trader has taken a "short position" in the market in the hopes
of buying back the contract when the price decreases.
3
Hedger: When looking at firms that trade futures contracts, there are two types. The first type of firm, a hedger uses
the commodity represented by the futures contract. Hedgers may also deliver the commodity as opposed to accepting for
delivery. Both types of hedgers achieve price certainty by trading in the futures market.
Speculators: The other type of firm that trades futures contracts are speculators, which do not use the commodity in
their business, and therefore are not interested in delivering or accepting for delivery the commodity. The speculator is only
interested in making a monetary profit from the change in prices. Traders, whether they are hedgers or speculators, are only
trading with the exchange, not with each other, although there is an actual futures contract trader selling contracts to another
trader within a trading pit at the exchange. Futures exchanges are established to be the counterparty of every trade.
When a trader takes a long position and it is matched by a short position, an open contract is created. The sum of all
open contracts is called the open interest. There will be a long open interest and a short open interest, which respectively
measure how many contracts are expecting delivery and promising delivery at contract expiration. If the firm that began with
a long position sells a contract, and a matching trade buys a contract, then the contract is no longer open (source, publication
date). The open interest decreases by one contract.
The government regulator of futures contract trading in the United States is the Commodities Futures Trading
Commission (CFTC, 2012). A hedger is a commercial firm, and a speculator is a noncommercial firm. At the end of every
trading day, the futures exchanges are required to report the open interest of firms. The reporting was formerly classified only
by type of firm, commercial and noncommercial. Thus, the CFTC is aggregating four numbers: (a) commercial long open
interest, (b) noncommercial long open interest, (c) commercial short open interest, and (d) noncommercial short open
interest. The CFTC reports these numbers each week for every type of futures contract traded in a futures exchange in the
United States. Interested parties may, for example, discover if commercial firms (hedgers) are increasing their long open
interest during a certain time period. Market fundamentals can be detected by the change in open interest. One question that
could be posed is, “Are noncommercial firms net long or net short?” meaning is the sum of all noncommercial long open
contracts greater or less than all noncommercial short open contracts? This indicates a bias, or what a firm expects in the
future regarding commodity prices based upon supply and demand factors. Changes in open interest and ratios of open
interest imply an opinion or a consensus of trading firms.
The previous version of the COT report separated reportable traders only into “commercial” and “noncommercial”
categories. However, the noncommercial designation did not yield detailed enough information about the nature of the trades.
4
The newer version, the “Disaggregated Commitments of Traders” report increases transparency from the legacy COT reports
by separating traders into four categories of traders: (a) Producer/Merchant/Processor/User, (b) Swap Dealers, (c) Managed
Money, and (d) Other Reportables. Hedgers are in category (a), and speculators are in categories (b) and (c). The newer
classification method presents more details about the speculative market, yet continues to report long and short open interest
positions. The proposed research will analyze both types of reports, as well as other market data.
Crude oil is generally traded at commodity exchanges in contracts that specify the quality of the commodity, the size
of the contract, and when and where it is delivered. The crude oil futures market today consists of two major exchanges: the
New York Mercantile Exchange (NYMEX) and the London-based InterContinental Exchange (ICE). NYMEX crude oil
futures are based on a grade of crude oil called West Texas Intermediate (WTI) because of its geographical location and
relative sulfur content. ICE crude oil futures are based on a grade of crude oil called Brent North Sea because of its location
in and near the North Sea in Europe. Commercial traders consist of oil exploration firms on the long side, and refining firms
on the short side. Non-commercial firms trading crude oil are generally investment banks and hedge funds.
Variables and Conjectured Relationship
The research will study the relationship between crude oil futures open interest and price movement. Additionally, it
will study the relationship between US dollar strength and crude oil price movement. It is conjectured that an increase in long
speculative open interest is causally linked to an increase in prices, which is followed by a rapid decrease in long speculative
open interest, forcing prices back down. This activity is assumed to be market manipulation if no other supply/demand
factors are influencing the market during that time.
A second relationship to be studied is between long speculative open interest and price volatility, the variance of a
time series of prices within a specific window. It is conjectured that rapidly increasing the number of long futures positions
will increase price volatility, and thus create more price uncertainty in the short run. This, again, is an indicator of market
manipulation.
The relationships will be measured using the Granger causality test (Granger, 1969), which compares two different
bivariate time series to create a Wald statistic of the significance of causality of time series TSa to TSb with lag n. If the
relationship is significant, then TSa "Granger-causes" TSb. The research will also examine Granger causality tests with
different lag periods.
5
Nature of Study
The study is intended to create a reliable system with which to measure and forecast market manipulation in crude
oil markets. Tests will be conducted to determine if rapid increases in long open interest by speculators causes the price of
crude oil to increase within a specific period of time. To do so, the study will measure the vector autoregressive behavior
(VAR) of the time series consisting of noncommercial long open interest, and the time series consisting of closing prices of
crude oil futures. Other pairs or combinations of time series will also be considered. Chapter 3 will describe in more detail
the methodology of comparing VAR and Granger causality test statistics to create models to forecast market manipulation.
Research Questions and Hypotheses
Research Question 1: Are large speculators causing the price of crude oil to rise?
H01: An increase in large speculator long open interest does not Granger-cause an increase in crude oil price.
HA1: An increases in large speculator long open interest Granger-causes an increase in crude oil price.
Research Question 2: Are increases in crude oil prices caused by dollar weakness?
H02: A decrease in dollar strength does not have a significant correlation or a Granger causality to an increase in
crude oil price.
HA2: A decrease in dollar strength has a significant correlation or a Granger causality to an increase in crude oil
price.
Research Question 3: Are increases in crude oil prices caused by precious commodity price changes?
H03: An increase in gold or silver prices does not have a significant correlation or a Granger causality to increases in
crude oil price.
HA3: An increase in gold or silver prices has a significant correlation or a Granger causality to increases in crude oil
price.
6
Theoretical Base
The theoretical base for this quantitative time series research is that large speculators will purchase large quantities
of futures contracts to create the appearance of demand that does not exist without their participation in the market. This
rapid buying, in turn, raises the market price because it implies a change in the supply/demand intersection. When there is no
supply shock, that is, a sudden change in supply, and it is accompanied by rapid increases in long open interest and rising
prices, the theory of manipulation holds true.
The purchasing of a large quantity of contracts will be evident by observing the weekly change in open interest as
reported by the CFTC. Changes in weekly prices can also be observed. It has yet to be verified of the causal relationship
between vector autoregressive changes in open interest/price time series and market manipulation. Assumptions that the
crude oil market is being manipulated because prices were rising during a certain period ignores supply and demand
dynamics which will be obtained from OPEC (OPEC, 2012) and OECD (OECD, 2012), respectively, and through the Energy
Information Agency (EIA, 2012).
Significance of the Study
The crude oil futures markets seem to be manipulated by large speculators (Sanders, 2011a, 2011b), but there are
economists who disagree (Krugman, 2008a, 2008b). There is a gap in the literature regarding a quantitative method to
forecast price manipulation in oil markets. What methods do exist are inconsistent or incomplete. Additionally, there is no
quantitative time series method amenable to simulations which could assist in the discovery of other indicators of market
manipulation.
A professional application of this research would be twofold: (a) Trading firms and market analysts could have a
tool that forecasts crude oil price run-ups with respect to supply/demand shocks, and (b) commodities regulators such as the
CFTC could have an objective measure to enforce speculative limits.
The study has a positive social change aspect in that it aids in identifying excessive speculation that creates
artificially high prices. With such identification, regulators or other interested parties could seek ways to stabilize the price
run-up. Making prices more stable benefits the markets and consumers of oil and its products. The global economy becomes
more stable when the price of oil is stable because oil prices are a major factor in production and consumption worldwide.
7
Chapter 2: Literature Review
Introduction
The purpose of this study is to create a method to measure the existence and timing of price manipulation of crude
oil futures by large speculative traders. This literature review will examine different methods of forecasting causality in an
econometric time series. Much of the econometric time series analysis literature examines market forces and their relation to
prices. A smaller amount of literature studies market manipulation. The purpose of using a causality forecasting method is to
measure the strength of a relationship between events in two or more time series.
An important time series method in multivariate time series analysis is vector autoregression (Chatfield, 2003). This
method employs many equations, rather a single one to study trends that are composed of several different time series. Each
time series is presented with its own equation, yet all are assumed to be occurring simultaneously to the others. The date or
time of the observations can be adjusted a specific amount from another time series in order to compare the effect of one
series against another. This adjustment is called a lag. When one time series affects the outcome of another time series, their
relationship becomes important and may indicate a correlation worthy of study. Lagged time series may be used to forecast
future trends, or to explain a relationship in two different independent variables. For example, when lagged values of
explanatory variables are included, they may be called leading indicators" (Chatfield, 2003, p. 87). Creating a leading
indicator helps describe the dynamic behavior and interaction of multiple observation in one time series with respect to
another.
Financial time series data contain observations of the past, whether it is a price, the volume of contracts bought and
sold, or how many contracts are currently open. The rate at which observations differ from previous observations may offer
insight into the activity being studied. Additionally, when two time series are occurring together, and one begins to exhibit
volatility or trending behavior just before a second time series begins to express a similar change in volatility or trend, the
relationship of the two time series is worthy of study.
Regulatory Effect of Pricing
Looking at what drives crude oil prices, there are several issues to consider. Examining the relationship of market
variables to price may provide insight into what factors are likely to be driving changes in price. An important factor, the
8
regulatory environment of a market, might have an effect on prices. Charles and Darne (2009) observed that pricing
efficiency of crude oil prices were not improved by deregulation. They tested weak-form efficient market hypothesis for both
West Texas Intermediate (WTI) crude oil and Brent North Sea crude for the period of 1982 – 2004. Their non-parametric
variance ratio tests on both time series led them to conclude that Brent was weak-form efficient for the entire period, and
WTI was weak-form efficient during the period between 1994 and 2008. The purpose for choosing trades from 1982 – 2004,
was to examine if deregulating oil markets would lead to more efficient markets. However, both WTI and Brent had returns
that were less predictable than of previous periods. With that evidence, the authors concluded that deregulation did not
improve market efficiency. The study failed to examine if changes in oil-related legislation or exogenous factors, such as
geopolitical risk, may have contributed to a less predictable time series of crude oil prices.
By contrast, Mead (1979) counted regulation as one of many ways to improve market efficiency through trading
controls and taxation consistent with current energy policy. Markets can pursue one of two paths: (1) an unregulated free
market, or (2) regulated consistent with energy conservation standards. Regulations on markets and resource use are designed
to enforce energy conservation because, externalities, the unintended consequence of the use or production of a resource
(such as pollution) can occur without cost to the producer. The study credits responsible energy use and price stability to
government regulations. However, market-stabilizing the method was, it only examined energy production and use, and gave
little attention to oil trading activity, which would rely on multiple lagged time series such as price, volume, open interest,
and relative value of the US dollar.
Medlock and Jaffe (2009) uses the perspective the main crude oil futures regulator, the Commodity Futures Trading
Commission (CFTC) to examine crude oil prices changes, particularly the price run-up that sent WTI crude oil prices to $147
per barrel in July of 2008. At the time, politicians and OPEC ministers were blaming price manipulation by non-commercial
traders, those firms that do not use the crude oil, but instead trade to earn a capital gain from differences in buying and selling
price. The authors caution the reader that the CFTC should examine its policies closer because its regulatory powers are
adversely affecting futures markets. They use as evidence, the growing number of non-commercial oil traders since the
passage of the Commodity Futures Modernization Act of 2000, which during the eight year preceding the price run-up in
2008, did not exhibit any evidence of price manipulation.
Additionally, spread trading, the practice of buying a futures contract that expires in a particular month and selling
of another futures contract that expires in a different month, was the fastest growing type of trade during the period 2000 -
9
2008. A trader hopes that during the term of a spread trade, at its first expiration month, the difference in bought and the sold
contract increases enough to make a profit. The “bought” contract is sold, and the “sold” contract is bought back. If spread
traders wanted to manipulate the oil market, they would have to push prices up for one month, and down for another at the
same time. The authors argue that such manipulation would be a virtually impossible task. Regulators generally do not accuse
traders of manipulation when prices go down sharply, only when they rise. Yet, during the period when the CFTC was
investigating market manipulation, a price neutral strategy, spread trading, increased in popularity. In 2008, the CFTC
Chairman Walter L. Lukken told the US Congress that the agency did not find any direct evidence that speculation was
moving prices upward. Their finding was based upon using a GARCH analysis to test for extreme changes in price volatility.
Increased volatility is not an exclusive test of manipulation. It is possible that prices could be manipulated in a low-volatility
environment if speculators were buying increasing quantities of the contracts, thus increasing the open interest, and making it
appear that demand for crude oil is increasing.
Open Interest and Volume
Looking at crude oil price changes from a different perspective, Fagan and Gencay (2008) studied market liquidity
and liquidity-induced price adjustments in crude oil. Noting that large speculators and hedgers provide enough counterparties
to each other, the larger the markets, the more liquidity is provided for trade. That is, being able to buy or sell quickly with a
predictable price where the price to buy and to sell are very close becomes easier when there is a large number of traders
which provide the opportunity to sell to a buyer, and to buy from the seller. The authors reached an important conclusion that
when speculators and hedgers reach extreme positions, price shocks tend to occur. For instance, when hedgers have very
large long positions while speculators have very large short positions, there may be a temporary loss of trading liquidity, and
prices may move rapidly. Therefore, a correlation between the open interest of large traders and the resulting price seems to
exist.
Futures pricing and its relationship to open interest was analyzed in a CFTC working paper for the Office of the
Chief Economist (Haigh, Hranaiov, & Overdahl, 2005). The paper examined the proportion of open interest held by each
class of trader, hedger, speculator, including managed money traders (MMT), which are large speculators. The study intended
to measure if MMTs destabilize the markets which lead to higher trading costs. The research used a Directed Acyclic Graph
(DAG) to detect causality patterns among classes of traders to discover who, if anyone is moving prices by their trading
activity. A DAG is graphic representation of changes using edges and vertices to illustrate changes within sequences. The
10
authors concluded that MMT traders trade less actively compared to others since they do not usually change their positions as
often as traders in the large hedging categories. The DAG analysis suggested that MMT participants are providing liquidity to
large hedging traders. This benefits the market in general because it narrows the spread between the price to buy and to sell,
the “bid offer spread.”
Moving beyond liquidity and price correlations, Mobert (2007) examined crude oil price volatility using an
econometric model with a large set of covariates in order to seek out the origins of the volatility. Using quarterly price data
from 1986 – 2000, the study examined a large number of oil price factors such as drilling capacity, refining, and
transportation capacity. It was vital to study different grades of oil worldwide and how they contributed to exchange-traded
crude oil prices. One of the most important factors discovered was the Organization of the Petroleum Exporting Countries
(OPEC) pricing corridor which required OPEC members to increase production if prices went above $28 per barrel, and to
decrease production if prices went below $22 per barrel. This is price manipulation of a different sort because physical supply
of oil is controlled in order to achieve a price target. The study concluded that OPEC exerted market power until 2005 when
WTI crude oil prices went above $28, and continued to rise well outside of the price corridor. It was at that point OPEC no
longer needed to support oil prices as they were rising quickly enough without modifying supply by limiting production.
Jabir, 2001 notes that US oil demand and the ability for the US Congress to use its Strategic Petroleum Reserve
could play a large role in oil prices that could easily outweigh the market influence that OPEC has. As US oil demand
increases, American refiners will seek out non-OPEC producers which is expected to drive prices downward. It is for this
reason that Jabir (2001) suggests that OPEC is losing market power, and it will cease to control prices in the long-term future.
Additionally, Jones (1990) extends the idea of OPEC losing market power to other producers worldwide because non-OPEC
members make no agreement to share the oil market.
Kaufmann, Dees, Karadeloglou, and M. Sanchez (2004), and in a later study, Mobert (2009) questioned whether
speculators drive crude oil prices, and concluded that they are not driving prices, but they are spreading information through
their trading activity that may be affecting prices, an important distinction. When various large speculators hold different
opinions, often expressed by the size of their open interest, long or short, that dispersion tends to drive crude oil prices and
price volatility. The study concludes that speculative trading does not drive prices, but instead, attributes the movement to the
dispersion in beliefs by traders. This is important because it suggests that price movement from trading activity is
unintentional, and does not support the claim that prices are being manipulated.
11
Market Manipulation
The academic literature contains several papers regarding market manipulation, that is, the actions of traders to alter a market
price through trading activity. Much of the literature focused on insider trader in equities, however, there are notable studies
of market manipulation in commodities futures, and specifically, in crude oil futures trading.
Ripple (2006) compared energy futures trading to physical commodity usage in order to examine whether
manipulation is occurring. The study began by pointing out that the number of futures contracts traded will often exceed the
deliverable supply by a large multiple. This fact exists because a contract is opened, either through a long or short position,
and then closed. A long position is sold to close; a short position is bought to close. The conclusion of the study is that the
number of times a contract is bought and sold is an indication of its market liquidity, and not of any speculator market
manipulation. It is not unusual to observe that more contracts are traded than actually exist for delivery because most of the
opening trades are offset by a matching closing trade. This is not an indication of manipulation, but rather, evidence of an
actively-traded contract.
Finding the origin of oil market manipulation is difficult because there are many inputs that determine price. Just
and Just (2008) suggested that the non-commercial trading firms did not cause a price run-up, but rather, an off-shore
exporter, such as OPEC, was responsible. Just and Just observed that oil prices were stable from 1948 to 1973 with low
variance. However, the ratio of non-commercial open interest to total open interest has been rising, leading them to conclude
that prices have risen as a result of speculation, yet they made no attempt to explain if the concurrent rise in price and open
interest ratio was the result of normal market functioning. The increasing ratio of non-commercial traders may likely be the
result of hedge fund activity or other types of large traders that use crude oil futures as a financial investment, and not as a
trade for delivery of the product. The authors examined separation result, the splitting of production decisions from its
participation in the futures market (p. 7). This allows a commercial firm to decide to either produce oil, which may lower or
stabilize the price, or withhold oil and take speculative long positions and profit from the oil scarcity. Petroleum producing
firms, classified by the CFTC as a commercial firm, may have trading activity that is speculative with the intent of profiting
from changes in price for contracts they do not expect to deliver. Since a commercial firm can produce oil, allowing it to
enter the market, or trade based upon the scarcity of not producing it, it has strategies unavailable to non-commercial firms.
Through the separation result, "manipulative buying causes anticipated future demand to increase following the separation
12
result whereby the futures price drives planning to meet future demand" (p.12). Anticipated future demand may increase in
the short-term, but it is questionable whether actual demand will increase. In fact, it would seem to decrease or remain
because the commercial firm exists primarily to produce oil, not to trade in its futures contracts, and will eventually return to
its intended revenue of selling oil for physical delivery. Regulators attempted to eliminate the separation result by prohibiting
non-commercial traders from the crude oil market. There was a time where only firms taking delivery of oil were allowed to
trade in the crude oil futures market, but after the Commodity Futures Modernization Act of 2000 (CFMA, 2000), a revision
to the Commodity Exchange Act of 1936 (CEA) (CFTC, 1936), anybody with enough margin capital was allowed to trade.
The regulations prevented non-commercial speculation, but failed to address the speculative trading of oil producers
themselves. Many of these firms had a proprietary trading desk, designed to take profits from oil contracts in ways that banks
and hedge funds could not.
Smith (2009) observes that the percentage of oil produced by the eight largest oil firms reduced from 89% in 1969 to
12% in 2009. This is due to factors such as nationalizing of oil production and worldwide regulatory changes. The effect is
that oil prices are more likely to be set by government cartels than by oil firms. Because of this fact, the author attributes
price volatility mainly to cartels, such as OPEC, which is able to limit production, and thus, manipulate the price of crude oil.
With a larger share of oil being produced by cartels, there is an opportunity for them to withhold production in order to
increase its price or to release additional supply to lower its price. Smith asserts, “In fact, there is no evidence of price fixing
on the part of anyone else, which includes both speculators and the “super-major” oil companies” (p. 28).
It is important to understand that in addition to commercial traders being hedgers, and non-commercial traders being
speculators, both trader classifications are capable of trades common to the other. Commercial traders have engaged in
speculative trades, as in the proprietary trading desks at oil-producing firms. Non-commercial traders have engaged in trades
that hedge other speculative trades the firm had made. This may occur if the trading firm has clients that are buying contracts,
and clients that are selling contracts. The trading firm could have an equal position of long and short contracts in different
accounts, resulting in the firm taking no benefit from price changes. Therefore, counting only the open interest of firms does
not indicate much about the firm's level of speculation.
Baffes and Haniotis (2010) looked at the 2006/08 commodity price boom and offered some analysis. Between the
years 2003 and 2008, energy and metals prices more than tripled, while food and precious metals prices doubled. Ultimately,
in July 2008, crude oil prices were up 94% within a year. Baffes and Haniotis (2010) wrote that price run-ups like this
13
occurred in the past during times of rapid economic growth and expansion. Three examples of these times are during the
post-World War II period, during the Korean War, and during the early 1970s. The 1970s expansion was mainly due to
inflation, which makes it different from the two earlier periods, and also different than the 2003-2008 expansion. The growth
of the economy only partially explains the price increases. During 2007 world oil production increased from 85.8 million
barrels per day (mb/d) to 86.8 mb/d, yet at the same time consumption fell from 86.5 mb/d to 86.3 mb/d. In this case, prices
should have fallen, but instead rose from $90/barrel in December 2007 to $132/barrel in June 2008 (p.5). Based on these
numbers alone, it fails to explain the rapid price increase. Without further study, it appears easy to blame speculation as the
cause of the price run-up, and in fact, that occurred historically in numerous occasions.
In a similar case of clamping down on trading activity, Baffes and Kaltsas (2004) discussed that several cotton
futures exchanges were closed by government regulators in the early 20th century in attempts to curb speculative trading.
Baffes and Haniotis (2010) also pointed out a confusing situation of spare capacity, which is. OPEC spare capacity
in 2009 was 6.3 mb/d when petroleum prices averaged $62/barrel, although similar capacity levels during the early 2000s
were associated with much lower prices, $20/barrel. This fact runs contrary to the idea that inventory or production is a main
driver of price, but without additional analysis, seems to indicate another factor that is increasing oil prices. During the late
2000s, investment portfolios were rebalancing by shifting funds out of US dollar-denominated holdings and into commodities
so that the portfolio could have less asset correlation, doing so added large inflows into the commodity markets, especially
crude oil futures. Based on this investment rebalancing, the authors dismissed the theory that futures trading itself led to an
oil price bubble, and offer alternative explanations such as the increased production of biofuels, which led to corn prices
rising. They observed a correlation between corn and crude oil prices, but are uncertain which price tends to drive increases
in the other. Turning corn into fuel, ironically requires great amounts of oil to fuel the process, and increased biofuel
production greatly increased crude oil demand. Chakraborty and Yilmaz (2004) wrote about insider trading and price
manipulation. Prices may reveal private information that traders have about a commodity. Traders with inside information
about a commodity may trade less aggressively and attempt to manipulate the market by trading in the opposite direction of
the trend, taking short-term losses. This behavior increases the noise in the trading time series. They argued that when there is
uncertainty about insider information in a market, informed traders will manipulate prices during longer trading periods.
However, it is difficult to obtain insider information about an entire market, such as crude oil. There are too many producers
14
and world events that could affect the variability of prices, and it is unlikely that crude oil prices can be manipulated by
trading on inside information.
Chatterjea, Cherian, and Jarrow (1993) reviewed the market manipulation literature. They defined manipulation to
be the process of trading the firm’s shares so that the share price is influenced to benefit the trader. They describe three types
of manipulation: (1) action-based, which is, actions that change the perceived value of the assets, (2) information-based,
manipulation by publishing information or spreading false rumors, and (3) trade-based, manipulation based on size of orders
when buying or selling. Their analysis is exclusively on stock price manipulation, but some concepts are applicable to
commodities trading. Traders are unable to change the value of crude oil, to move a supply or demand curve, on a
macroeconomic level. Information-based manipulation may be illegal in some cases, but also many traders often do not
possess non-public information about oil production, although some oil producers also have traders that, in addition to
hedging production costs, also engage in proprietary speculative trading. The most likely type of manipulation listed by
Chatterjea et al. is trade-based. A very large trading order could be perceived as a trader having inside information, when in
fact, no such information exists.
Jarrow (1992) wrote about market manipulation in equities markets as a driver of upward moving price activity. The
paper defines manipulation as a way of generating positive real wealth without incurring risk. In effect, the strategy is to buy
a security in such large quantity within a very short period of time with the intention that the trading activity signals to other
traders the popularity of the security. This tends to raise the equity price in the short term as other traders join the buying
frenzy. The strategy requires selling back the equities at a slower pace. Selling too rapidly would cause the equity price to
decrease to levels at or below the initial buying price when the manipulation strategy began.
For this trading strategy to work, the firm must have a large capital reserve to buy the securities. Additionally, the
strategy is very short-term, usually executed and completed within several minutes. Jarrow (1992) also observes that
increased buying volume as a way to generate higher prices is less effective over longer periods of time. Crude oil futures
contracts are unlike equities because futures contracts expire and require delivery of cash or a physical product at expiration.
This is a key difference and may cast doubt on the effectiveness of long-term market manipulation using a price-volume
trading strategy.
Kaufmann and Ullman (2009) wrote about oil price speculation and causal relationships of trader manipulation. The
authors observe a weak relationship between spot and futures prices, yet discover a causal relationship between West Texas
15
Intermediate and European North Sea crude oil, and the price movement first appears with Dubai-Fateh crude. While
speculative trading is thought to have contributed to the price-up, market fundamentals were already indicating a long term
upward drift.
Smith (2009) attempts to explain world oil prices and volatility first through a historical survey of world events and
price changes, and then attempts to answer why did the price of crude oil spike in the summer of 2008, and what to extent did
the actions of speculators have in its rise? Observing the high volatility of oil prices, the author understands the typical
macroeconomic factors, inelasticity of demand and supply, the substantial lead times required to efficiently alter the stock of
fuel-consuming equipment, and the changing productive capacity of oil fields. They also dismiss the price effect of OPEC
upon the price run-up of 2008. Their conclusions regarding oil speculation are twofold: (1) speculation is not manipulation,
and volatile markets like crude oil benefit from the price discovery mechanism that speculators bring, and (2) speculation is
not the same as price fixing, which only a cartel, such as OPEC, is capable of doing. Furthermore, because speculators cannot
divert large oil supplies from the market, nor can they restrict production, their price-taking behavior is incapable of
manipulating the market.
Pierce Jr. (2003) writes about market flaws and energy market manipulation, particularly in the electricity markets.
In regards to market manipulation, they disregard fraud and cartels withholding capacity (or inventory in crude oil terms),
and discuss ways to discourage market manipulation. The article is not based upon economic theory, but rather, matters of
regulatory law. The authors make recommendations how to make natural gas and electricity markets unprofitable without
evidence that speculation implies market manipulation. The study assumed manipulation was already occurring in crude oil
markets, then proceeded to suggest methods to limit energy price manipulation.
Baumeister and Peersman (2009) examined a volatility model of the crude oil market with respect to oil production.
They observed that as oil price volatility increased, oil production volatility decreased. This contrary effect is explained as the
elasticity of price for oil supply and demand have reduced, and that minor fluctuations in oil market supply or demand will
result in major price reactions, yet minor quantity changes. Additionally, they observed the variance of price shocks that
move supply/demand curves have lessened over time, resulting in fewer oil price fluctuations.
Granger Causality
To be able to answer the question of whether speculative activity causes price run-ups in crude oil futures, we need a way to
examine two time series and test whether a change in one affects the other. A popular test is the Granger causality test
16
(Granger, 1969). This test creates an econometric bivariate time series model to measure the feedback given to one time
series by another. The goal of Granger tests is to construct measures of causal lag and causal strength.
Let Ut be all the information in the universe accumulated since time t - 1 and let Ut - Yt denote all this information apart from
specified series Yt. If σ2(X|U) < σ2 (X|U - Y ), then
Y is said to be causing X, or Yt →Xt.
If
σ2 (X|U) < σ2 (X|U - Y );
σ2 (Y |U) < σ2 (Y|U - X);
then we say that feedback is occurring, by Yt , Xt, Xt is causing Yt, and Yt is causing Xt.
If Yt → Xt then the causality lag m is the least value of k such that
σ2 (X|U - Y (k)) < σ2 (X|U - Y (k + 1)).
While Granger (1969) established the theory for causality testing, later research supplemented the theory with
application. Gelper and Croux (2007) create out-of-sample tests of Granger causality using multiple time series. The intent is
to determine whether the forecast of one variable can be improved by the correlation to another time series. An effective
Granger causality test will predict an unknown data point in time using the values of a different time series. In the past, most
Granger causality tests have been with univariate time series, however this study uses simulation methods to analyze and
calibrate multivariate time series. Here is the aim is to test whether a time series xt has incremental predictive power for
predicting a second set of time series yt." Gelper and Croux (2007, p. 3320). The authors performed tests of Granger causality
of consumer confidence indexes of four countries (Belgium, Germany, France, and the Netherlands) on retail sales in those
counties with the null hypothesis that φ1 = φ 2, both being rectangular matrices of time series parameters. The study used
three out-of-sample testing procedures for Granger causality: (1) directly compare squared forecast errors, (2) multivariate
regression, and (3) canonical correlations. Simulations indicated that regression-based tests were the most powerful. Swanson
and Granger (1997), Hiemstra and Jones (1994), Engle and Granger (1987), Chen, Rangarajan, Feng, and Ding (2004).
Bhar and Hamori (2004) applied causality tests to gold futures prices and volume. This is a good parallel to crude oil
futures pricing because, although the commodity being analyzed is different, the methodology of this study can be applied to
17
information arrival in the crude oil market. The authors studied the correlation between trading volume price change in gold
futures. The methodology is based on a VAR specification of trading volume and price changes, then they applied a Granger
causality test in an attempt to detect the relationship and its direction. A similar study was conducted with crude oil futures
and index funds by Irwin and Sanders (2011). The authors tested the theory that commodity index funds were linked to the
price run-up of 2007-2008. They performed Granger causality tests which failed to show a causal link between daily returns
or volatility in the crude oil futures contracts with positions of energy exchange-traded index funds. The source of their data
was the Commitments of Traders (COT) reports. While the CFTC does not disclose the positions held by individual trading
firms, they have instead, categorized traders within the system as those who are largely hedgers (commercials) and those that
are largely speculators (non-commercials).
Sims (1980) wrote extensively about Granger causality and VAR, but not specifically about crude oil. Bekiros and
Diks (2008) examined the relationship between crude oil spot and futures prices by using nonlinear causality methods and the
Granger causality test. Sari, Hammoudeh, Chang, and McAleer (2011) examined causality of energy markets on the grains
markets, looking at liquidity and market depth.
Serra, Zilberman, Goodwin, and Gil (2008) looked at nonlinearities of US corn, ethanol, and crude oil markets.
VAR
Toda and Phillips (1993) write about vector autoregression and causality and advised against Granger causality test when
there is cointegration and stochastic trends. The aim was to test for causality among subsets of multiple time series using
previously published models. They conclude with a warning that using a Granger causality test may be unreliable when there
are stochastic trends within the time series system, yet other types of Wald tests may be more reliable. Furthermore, they
found that causality tests are valid asymptotically as χ2 criteria only when there is sufficient cointegration with respect to the
variables whose causal effects are being tested" (p. 1387). Finding this cointegration requires creating a matrix that is part of
the cointegrating matrix, which would suffer from a simultaneous equation bias Stock, J. H., & Watson, M. W. (2001).
Johansen, S. (1991).
18
Chapter 3
Methodology
This chapter focuses on the methodology and data to assess the level of causality of large speculative activity on the price of
crude oil futures. The beginning of the chapter provides an overview of the questions and hypotheses being examined. Next,
the chapter turns to methods and data to address those questions. The methods section begins with a brief discussion of the R
statistical software environment. This software, and some additional libraries, will be used for statistical analysis and testing
in this study. It is well-established software widely used in econometrics (Racine & Hyndman, 2002). Specific econometric
and statistical methods and their use in the study are discussed next. The selected methods, moving averages, vector
autoregression, and Granger causality are described with focus on operational aspects and their implementations in this study.
Following that is a detailed discussion of the data for use in the study. Key variables and what they represent are described,
and then data sources to measure the variables are noted and presented in Appendix A. In some cases, the variables
themselves require construction using methodologies that require explanation. Such explanations are provided in the data
sections, or in additional appendices.
Research Questions, Testable Hypotheses, Approaches
There are three research questions/testable hypotheses.
Research Question 1: Are large speculators causing the price of crude oil to rise?
H01: An increase in large speculator long open interest does not Granger-cause an increase in crude oil price.
HA1: An increases in large speculator long open interest Granger-causes an increase in crude oil price.
Research Question 2: Are increases in crude oil prices caused by dollar weakness?
H02: A decrease in dollar strength does not have a significant correlation or a Granger causality to an increase in
crude oil price.
19
HA2: A decrease in dollar strength has a significant correlation or a Granger causality to an increase in crude oil
price.
Research Question 3: Are increases in crude oil prices caused by precious commodity price changes?
H03: An increase in gold or silver prices does not have a significant correlation or a Granger causality to increases in
crude oil price.
HA3: An increase in gold or silver prices has a significant correlation or a Granger causality to increases in crude oil
price.
These hypotheses will be assessed using a Granger causality test with varying lags to the time series. Additionally, a vector
autoregressive test will be run on several combinations of the three time series vectors in each of the three research questions.
The econometric methods discussed below and the data series allowed for quantifications and tests. The assessments rest on
the sign, statistical significance, and magnitude of the correlation. The fundamental approach will start with an exploratory
analysis and tests to clarify relationships in the data, and to detect statistical issues. The overall conclusion regarding the
questions of the study is expected to emerge through a statistically significant measure of Granger causality. The initial
exploratory econometric work will be done with autoregression and time series decomposition of open interest, price, and
volume.
Econometric Methods Used for Testing and Approaches
The methods used for estimation and testing in this study will be consistent with, or extensions of those found in the
literature. The next section contains an explanation of the statistical/econometrics software package used for the analytics.
After that, will be a discussion of the specific methods used in this study and exactly how they are used are addressed.
The econometric and statistical work in this study will be done using the R statistical environment and other
statistical packages related to time series analysis. R is a widely-used econometric statistical package that is based on the S
language, which was developed at Bell Laboratories, formerly AT&T, now Lucent Technologies, by John Chambers and
colleagues (What is R?, n.d.). It is cited and used in econometrics research (Pfaff, 2012b). Packages will include software
20
libraries for database access, Granger causality, and VAR (James & DebRoy, 2012; Hothorn & Zeileis, 2012; Champely,
2012; R Core Team, 2012; Pfaff, 2012a)
The R statistical environment does not include all functions upon initial installation. There are hundreds of libraries
that contain functions to perform data loading, analysis, and statistical methods. The libraries, or packages, are open-source
and may be downloaded from a common software repository at no charge. Because of the nature of open source, the
packages are constantly tested, reviewed, and deployed by practitioners who may improve the functions.
Periods for Testing and Data Frequency
The bulk of the empirical work in this study will cover the period from 1986-2012. Both the sample period and data
frequency were influenced by data availability. As discussed further below, the data frequency will be weekly and some
analysis may be adjusted to monthly observations to reduce volatility. The period of the years 1986-2012 affords a significant
number of data points for time series analysis. The sample size is expected to be greater than n=1300.
Data/Variables Used for Testing
The variables used in the time series study will include,
1. CFTC Commitment of Traders Report: large speculator long open interest, large speculator short open interest, large
hedger long open interest, and large hedger short open interest
2. Crude oil prices: WTI and Brent
3. Gold prices, London fixing
4. 1 and 3 month LIBOR rates
Measures of open interest will need to be split out by large speculator and large hedger, and may also include a new category
that the CFTC introduced in 2009, money manager. It is worth noting that some speculating firms are also hedgers, as in the
case of a petroleum firm. Money managers are a category on their own, and are worth studying because they are not hedgers,
which may affect their ability to manipulation the futures markets.
21
Research Design and Approach
Since the aim of the study is to measure levels of autocorrelation of price changes and differences in contract open interest
during rapid price movements, the study will seek outputs of Granger causality, VAR coefficients, and tests for
multicollinearity. Initially, the study will use variables consisting of price, volume, long open interest, short open interest, and
an autoregression measure. In the event that some variables appear redundant or present an unrealistically high correlation, a
test for multicollinearity will help eliminate some independent variables.
When examining Granger causality of one time series to another, or whether time series A “Granger causes” time
series B, there will be several tests using different lags, different sets of time series, and a test for feedback, that is, whether
time series A affects time series B, which later affects time series A – a feedback.
An autoregression, as the name implies, is the estimation of a variable by its own past values. I intend to study the
extent to which autocorrelation changes during high-volume trading periods, and during periods of open interest
accumulation. For instance, does an increase in non-commercial long open interest Granger cause prices to increase? I will be
using a method to change model parameters or models themselves.
Setting and Sample
The population from which the sample will be drawn is weekly crude oil prices, weekly contract open interest and volume,
and weekly gold prices for the period 1986 to 2011. This is the period where crude oil futures contract trading was at its most
active. The CFTC Commitment of Traders report is published weekly, from which we obtain contract open interest. This
weekly time series provides enough data points annually to allow for the measurement of trends. Since the open interest
statistics are weekly, the other time series will be collected or adjusted to a weekly time series. The number of observations
for each time series is approximately 1,300.
Instrumentation and Materials
Raw data will be available from public sources, generally the web sites of government agencies. Contract open interest will
come from the Commodity Futures Trading Commission (CFTC) in their weekly Commitment of Traders report. Crude oil
and gold prices and trading volume will come from the St. Louis Federal Reserve Bank website. Since the data will be
collected from different web sites, gathering the data is a relatively simple task. However, combining time series, and
22
cleaning data that might have errors or be missing, may require more work. These tasks can be achieved by using the R
statistical environment, which facilitates in the collection, cleaning, querying, and reporting of the time series.
Data Collection and Analysis
I propose a time series model that uses Granger causality to conduct hypothesis tests of whether increased speculative trading
“causes” price run-ups. When the Granger causality test is performed with two different time series, it creates a hypothesis
test generating a Wald test comparing the unrestricted model in which y is explained by the lags (up to n order) of x and y and
the restricted model in which x is only explained by the lags of y F-statistic with a certain significance. If the result is
significant then one time series is said to Granger cause the other. As an initial model, I will examine if non-commercial long
open interest Granger causes oil futures prices to increase (Granger, 1969, 1988). I will test other combinations of time series
variable, including changing the time series lag period (Chao, Corradi, & Swanson, 2001).
Measures taken for protection of participants rights
All of the data are public secondary data obtained from government agencies, futures exchanges, or statistical bureaus. Since
they are aggregated at their respective sources, participants are unknown, and their exact contribution to any given time series
is only available to the government agency, exchange, or bureau collecting the data.
23
References
Baffes, J., & Haniotis, T. (2010). Placing the 2006/08 commodity price boom into perspective. World Bank Policy Research
Working Paper 5371 . Retrieved from http://elibrary.worldbank.org/content/workingpaper/10.1596/1813-9450-
5371. doi: 10.1596/1813-9450-5371
Baffes, J. & Kaltsas, I., 2004. “Cotton Futures Exchanges: Their Past, their Present, and their Future,” Quarterly Journal of
International Agriculture, 43: 153-176.
Baumeister, C., & Peersman, G. (2009, December). Sources of the volatility puzzle in the crude oil market. Papers of Faculty
of Economics. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract id=1471388 doi:
10.2139/ssrn.1471388
Bekiros, S., & Diks, C. (2008). The relationship between crude oil spot and futures prices: Cointegration, linear and nonlinear
causality. Energy Economics, 30 (5), 2673-2685.Retrieved from
http://www.sciencedirect.com/science/article/pii/S0140988308000492
Bhar, R., & Hamori, S. (2004). Information flow between price change and trading volume in gold futures contracts.
International Journal of Business, 3 (1), 45-56. Retrieved from
http://www.ijbe.org/table%20of%20content/pdf/vol3-1/vol3-1-06.pdf
Chen, Y., Rangarajan, G., Feng, J., & Ding, M. (2004). Analyzing multiple nonlinear time series with extended Granger
causality. Physics Letters A, 324(1), 26-35.
Commodity Futures Trading Commission (CFTC). (2012). Mission & responsibilities. Retrieved from
http://www.cftc.gov/About/MissionResponsibilities/index.htm
24
Chakraborty, A., & Yilmaz, B. (2004, January). Informed manipulation. Journal of Economic Theory, 114 (1), 132-152.
Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0022053103001017 doi: 10.1016/S0022-
0531(03)00101-7
Champely, S. (2012, February). Package `pwr'. CRAN, 1-21. Retrieved from http://cran.r-
project.org/web/packages/pwr/index.html
Chao, J., Corradi, V., & Swanson, N. (2001). An out of sample test for Granger causality. Macroeconomic Dynamics.
Retrieved from http://fmwww.bc.edu/RePEc/es2000/0362.pdf
Charles, A. & Darne, O (2009). The efficiency of the crude oil markets: Evidence from variance ratio tests. Energy Policy, 37
(11), 4267-4272. Retrieved from http://www.sciencedirect.com/science/article/pii/S0301421509003802
doi:10.1016/j.enpol.2009.05.026
Chatfield, C. (2003). The analysis of time series: an introduction (6th ed.). Chapman & Hall/CRC.
Chatterjea, A., Cherian, J. A., & Jarrow, R. A. (1993). Market Manipulation and Corporate Finance: A New Perspective.
Financial Management, 22 (2), 200-209. Retrieved from http://www.jstor.org/stable/10.2307/3665870
CME Group. (2012, April). CFTC -Commitments of Traders Report Update. , 1-5. Retrieved from
http://www.cmegroup.com/education/files/COT FBD Update 2012-4-26.pdf
Crawford, G., & Sen, B. (1996). Derivatives for Decision Makers: Strategic Management Issues. Hoboken, NJ: Wiley.
Davidson, P. (2008). Crude Oil Prices: Market Fundamentals or Speculation? Challenge, 51 (4), 110-118. doi: 10.2753/0577-
5132510406
25
EIA. (2012). What drives crude oil prices? Retrieved from http://www.eia.gov/finance/markets/
Engdahl, F. (2008). Perhaps 60% of today’s oil price is pure speculation. Centre for Research on Globalization, May, 2.
Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing.
Econometrica: journal of the Econometric Society, 251-276.
Fagan, S., & Gencay, R. (2008, January). Liquidity-induced dynamics in futures markets. Munich Personal RePEc Archive.
Retrieved from http://mpra.ub.uni-muenchen.de/6677/
Federal Reserve Economic Data (2012). Crude Oil: West Texas Intermediate (WTI) - Cushing, Oklahoma (DCOILWTICO).
Retrieved from http://research.stlouisfed.org/fred2/series/DCOILWTICO?cid=32217
Gelper, S., & Croux, C. (2007, April). Multivariate out-of-sample tests for Granger causality. Computational Statistics &
Data Analysis, 51 (7), 3319-3329. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0167947306003501
doi:10.1016/j.csda.2006.09.021
Granger, C. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37 (3),
424-438. Retrieved from http://www.jstor.org/stable/1912791 doi: 10.1016/0304-4076(88)90045-0
Granger, C. (1988). Some recent development in a concept of causality. Journal of Econometrics, 39 (1-2), 199-211.
Retrieved from http://www.sciencedirect.com/science/article/pii/0304407688900450
Haigh, M. S., Hranaiova, J., & Overdahl, J. A. (2005, April). Price dynamics, price discovery and large futures trader
interactions in the energy complex. Commodity Futures Trading Commission. Retrieved from
http://cftc.gov/files/opa/press05/opacftc-managed-money-trader-study.pdf
26
Hiemstra, C., & Jones, J. D. (1994). Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation.
The Journal of Finance, 49(5), 1639-1664.
Hothorn, T., & Zeileis, A. (2012, May). Package `lmtest'. CRAN, 1-46. Retrieved from http://cran.r-
project.org/web/packages/lmtest/index.html
Irwin, S. H., & Sanders, D. R. (2011). Testing the masters hypothesis in commodity futures markets. Energy Economics, 34
(1), 256-269. Retrieved from http://www.sciencedirect.com/science/article/pii/S0140988311002362
doi:10.1016/j.eneco.2011.10.008
Jabir, I. (2001). “The shift in US oil demand and its impact on OPEC’s market share.” Energy Economics 23(6): 659-666.
James, D. A., & DebRoy, S. (2012, February). Package `RMySQL'. CRAN, 1-44. Retrieved from http://cran.r-
project.org/web/packages/RMySQL/index.html
Jarrow, R. A. (1992, September). Market Manipulation, Bubbles, Corners, and Short Squeezes. The Journal of Financial and
Quantitative Analysis, 27 (3), 311-336. Retrieved from http://www.jstor.org/stable/2331322?origin=crossref
doi:10.2307/2331322
Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models.
Econometrica: Journal of the Econometric Society, 1551-1580.
Jones, C. T. (1990). “OPEC behaviour under falling prices: Implications for cartel stability.” The Energy Journal 90(11):
117-129.
Just, D. R., & Just, R. E. (2008, December). Monopoly Power, Futures Market Manipulation, and the Oil Price Bubble.
Journal of Agricultural & Food Industrial Organization, 6 (2), 1-29. Retrieved from
http://www.bepress.com/jafio/vol6/iss2/art2/?sending=10444 doi:10.2202/1542-0485.1241
27
Kaufmann, R. K., & Ullman, B. (2009, July). Oil prices, speculation, and fundamentals: Interpreting causal relations among
spot and futures prices. Energy Economics, 31 (4), 550-558. Retrieved from
http://linkinghub.elsevier.com/retrieve/pii/S0140988309000243 doi:10.1016/j.eneco.2009.01.013
Kaufmann, R. K., S. Dees, P. Karadeloglou, and M. Sanchez (2004). “Does OPEC Matter? An Econometric Analysis of Oil
Prices.” The Energy Journal 25(4): 67-90.
Krugman, P. (2008a). New York Times, "The Oil Nonbubble" May 12, 2008.
http://www.nytimes.com/2008/05/12/opinion/12krugman.html?adxnnl=1&adxnnlx=1313772097-
AuBuBZnwcRO5mXjkeGDu5w
Krugman, P. (2008b). Speculation and Signatures. June 24, 2008.
http://www.princeton.edu/~pkrugman/Speculation%20and%20Signatures.pdf
Krugman, P. (2008c). New York Times, “Fuels on the Hill” June 27, 2008.
http://www.nytimes.com/2008/06/27/opinion/27krugman.html?adxnnl=1&adxnnlx=1314108385-
yhxV8MHXB+EV8yte4gHadg
Mead, W. J. (1979). The performance of government in energy regulations. The American Economic Review, 69(2), 352-
356.
Medlock III, K. B., & Jaffe, A. M. (2009). Who is in the oil futures market and how has it changed?. Houston, TX: Mimeo.
James Baker III Institute for Public Policy, Rice University.
Mobert, J. (2007). Crude Oil Price Determinants. Publications of Darmstadt Technical University, Institute of Economics
(VWL).
28
Mobert, J. (2009, December). Do speculators drive crude oil prices? Dispersion in beliefs as a price determinant (Tech. Rep.).
Deutsche Bank Research. Retrieved from http://ideas.repec.org/p/zbw/dbrrns/32e.html
OECD. (2012). OECD: Statistics from A to Z. Retrieved from
http://www.oecd.org/document/0,3746,en_2649_201185_46462759_1_1_1_1,00.html
OPEC. (2012). OPEC: Market indicators. Retrieved from http://www.opec.org/opec_web/en/data_graphs/334.htm
Pfaff, B. (2012a, July). Package `vars'. CRAN, 1-52. Retrieved from http://cran.r-project.org/web/packages/vars/index.html
Pfaff, B. (2012b, July). VAR, SVAR and SVEC Models: Implementation Within R Package vars. , 1-32. Retrieved from
http://cran.r-project.org/web/packages/vars/vignettes/vars.pdf
Pierce Jr., R. J. (2003, January). Market Manipulation and Market Flaws. The Electricity Journal, 16 (1), 39-46. Retrieved
from http://linkinghub.elsevier.com/retrieve/pii/S1040619002004165 doi:10.1016/S1040-6190(02)00416-5
R Core Team. (2012, May). Package `foreign'. CRAN, 1-23. Retrieved from http://cran.r-
project.org/web/packages/foreign/index.html
Racine, J., & Hyndman, R. (2002). Using R to teach econometrics. Journal of Applied Econometrics, 17 (2), 175-189. doi:
10.1002/jae.657
Ripple, R. (2006). Energy Futures Market Trading Versus Physical Commodity Usage: A playground for manipulation or a
miscalculation? Macquarie Economics Research Papers. Retrieved from http://www.econ.mq.edu.au/research.htm
Sanders, B. (2011a). CFTC report reveals rampant speculation in oil markets. Retrieved from
http://sanders.senate.gov/newsroom/news/?id=E802998A-8EE2-4808-9649-0D9730B75EA4
29
Sanders, B. (2011b). End oil speculation. Retrieved from http://sanders.senate.gov/newsroom/news/?id=680AA76F-E41E-
4256-977A-FF943CFDEE7B
Sari, R., Hammoudeh, S., Chang, C., & McAleer, M. (2011). Causality Between Market Liquidity and Depth for Energy and
Grains.
Serra, T., Zilberman, D., Goodwin, B. K., & Gil, J. M. (2008, July). Nonlinearities in the US corn-ethanol-oil price system..
Retrieved from http://ageconsearch.umn.edu/bitstream/6512/2/464896.pdf
Sims, C. (1980, January). Macro-Economics and Reality. Econometrica, 48 (1), 1-48. doi: 10.2307/1912017
Smith, J. L. (2009). World Oil: Market or Mayhem? The Journal of Economic Perspectives, 23 (3), 145-164. Retrieved from
http://www.ingentaconnect.com/content/aea/jep/2009/00000023/00000003/art00009
Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. The Journal of Economic Perspectives, 15(4), 101-115.
Swanson, N. R., & Granger, C. W. (1997). Impulse response functions based on a causal approach to residual
orthogonalization in vector autoregressions. Journal of the American Statistical Association, 92(437), 357-367.
Toda, H. Y., & Phillips, P. C. B. (1993, November). Vector Autoregressions and Causality. Econometrica, 61 (6), 1367-
1393.
Tsay, R. (2005). Analysis of financial time series (2nd ed.). Wiley-Interscience. What is R? (n.d.). http://http://www.r-
project.org/. (Accessed: 01/13/2013)