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The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. In addition to the commencement of the financialization era in oil markets, there have been structural changes in the global oil market. Financial instruments are communicating information about future conditions much more rapidly than in the past. Prices from long and short-duration contracts have started moving more together. Abrupt changes in supply and demand, influenced by such events and trends as the financial crisis of 2008-09, uncertainty about China’s economic growth rate, the Libyan uprising, the Iranian Nuclear standstill, and the Deepwater Horizon oil spill, change expectations and current prices. Although volatility appears greater over this period, financialization makes price discovery more robust. Most empirical economic studies suggest that fundamental factors shaped the expectations over 2004-08, although financial bubbles may have emerged just prior to and during the summer of 2008. This review represents a broad survey of economic research and literature related to the structure and functioning of the world oil market. The theories and models of oil demand and supply reviewed here, although imperfect in many respects, offer a clear and well-defined perspective on the forces that are shaping the markets for crude oil and refined products. Much work remains to be done if we are to achieve a more complete understanding of these forces and the trends that lie ahead. The contents that follow represent an assessment of how far we have come and where we are headed. Around the world governments, businesses and consumers share a vital interest in the benefits that flow from an efficient, well-functioning oil market. It is hoped, therefore, that the discussion in this review will find a broad audience.
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Oil Price Drivers and Movements: The Challenge for Future Research
Hillard Huntington, Saud M. Al-Fattah, Zhuo Huang, Michael Gucwa, and Ali Nouria
Chartered Alternative Investment Analyst Association®
Q1 2014, Volume 2, Issue 4
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Alternative Investment Analyst Review
Oil Price Drivers and Movements: The Challenge for Future Research
Research Review
CAIA Member Contribution
CAIA Member Contribution
Research Review
Hillard Huntington
Energy Modeling Forum, Huang Engineering Center, Stanford University
Saud M. Al-Fattah
King Abdullah Petroleum Studies and Research Center (KAPSARC) and Saudi Aramco
Zhuo Huang
National School of Development, Peking University, Beijing, China
Michael Gucwa
Management Science and Engineering, Huang Engineering Center, Stanford University
Ali Nouri Dariani
Management Science and Engineering, Huang Engineering Center, Stanford University
Oil Price
Drivers and
for Future
Alternative Investment Analyst Review
1. Introduction
e complexity of the world oil market has increased
dramatically in recent years and new approaches
are needed to understand, model, and forecast oil
prices today. In addition to the commencement of the
nancialization era in oil markets, there have been
structural changes in the global oil market. Financial
instruments are communicating information about
future conditions much more rapidly than in the past.
Prices from long and short-duration contracts have
started moving more together. Abrupt changes in supply
and demand, inuenced by such events and trends
as the nancial crisis of 2008-09, uncertainty about
Chinas economic growth rate, the Libyan uprising,
the Iranian Nuclear standstill, and the Deepwater
Horizon oil spill, change expectations and current
prices. Although volatility appears greater over this
period, nancialization makes price discovery more
robust. Most empirical economic studies suggest that
fundamental factors shaped the expectations over 2004-
08, although nancial bubbles may have emerged just
prior to and during the summer of 2008.
With increased price volatility, major exporters are
considering ways to achieve more price stability to
improve long-term production and consumption
decisions. Managing excess capacity has historically
been an important method for keeping world crude oil
prices stable during periods of sharp supply or demand
shis. Building and maintaining excess capacity in
current markets allows greater price stability when
Asian economic growth accelerates suddenly or during
periods of supply uncertainty in major oil producing
regions. OPEC can contribute to price stability more
easily when members agree on the best use of oil
production capacity.
Important structural changes have emerged in
the global oil market aer major price increases.
Partially motivated by governments' policies, major
developments in energy and oil eciencies occurred
aer the oil price increases of the early and the late
1970s, such as improvements in vehicle fuel eciency,
building codes, power grids, and energy systems. On
the supply side, seismic imaging and horizontal drilling,
as well as favorable tax regimes, expanded production
capacity in countries outside OPEC. Aer the oil price
increases of 2004-08, investments in oil sands, deep
water, biofuels, and other non-conventional sources of
energy accelerated. Recent improvements in shale gas
production could well be transferred to oil-producing
activities, resulting in expanded oil supplies in areas that
were previously considered prohibitively expensive. e
search for alternative transportation fuels continues
with expanded research into compressed natural gas,
biofuels, diesel made from natural gas, and electric
In spite of these advances, some aspects of the world
oil market are not well understood. Despite numerous
attempts to model the behavior of OPEC and its
members, there exists no credible, veriable theory about
the behavior of this 50 year-old organization. OPEC has
not acted like a monolithic cartel, constraining supplies
to raise prices. Empirical evidence suggests that at
some times, members coordinate supply responses and
at other times they compete with each other. Supply-
restraint strategies include slower capacity expansions,
as well as curtailed production from existing capacity.
Regional political considerations and broader economic
goals beyond oil are inuential factors in a country’s oil
decisions. Furthermore, the economies and nancial
needs of OPEC members have changed dramatically
since the 1970s and 1980s.
is review represents a broad survey of economic
research and literature related to the structure and
functioning of the world oil market. e theories
and models of oil demand and supply reviewed here,
although imperfect in many respects, oer a clear and
well-dened perspective on the forces that are shaping
the markets for crude oil and rened products. Much
work remains to be done if we are to achieve a more
complete understanding of these forces and the trends
that lie ahead. e contents that follow represent an
assessment of how far we have come and where we
are headed. Around the world governments, busineses
and consumers share a vital interest in the benets that
ow from an ecient, well-functioning oil market. It is
hoped, therefore, that the discussion in this review will
nd a broad audience.
2. Price Volatility and Uncertain Conditions
Oil prices have uctuated widely since 2004. Brent
crude oil prices rose from $29 to $38 per barrel (annual
averages) between 2003 and 2004. ey rose steadily
until 2008, reaching a record near $147 per barrel in July
2008. is price spike reected extremely strong Asian
economic growth, combined with certain geopolitical
events. Prices collapsed below $33 within the next few
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months as the world economy spun downward into
nancial disarray. ey spurted back to levels above
$80 per barrel in 2010, as the economies in Asia and
elsewhere recovered. Additional price increases in 2011
beyond $100 per barrel were prompted by continued
Asian growth and supply uncertainty mounted with the
Arab uprising and the Libyan disruption. Continued
fears about the nancial system and future economic
growth lingered in August 2011, causing world oil
prices to begin their retreat once again.
ese conditions have created massive uncertainty
about where future oil prices will be headed and what
factors create these dramatic price movements. Peak
oil arguments abound during an era when non-OPEC
oil production has increased only modestly despite the
record-high prices. Turmoil dominates the political
landscape in the Middle East, fueling additional concerns
about the security of oil supplies. Most disconcerting to
both oil producing and oil-consuming nations has been
the nancialization of oil, where nancial motives and
trading permeate oil transactions and make physical
markets appear less important.
is uncertainty creates very signicant problems for
major oil-consuming countries that are trying to recover
from nancial disintegration, as well as investors who
are considering long-term allocations to commodities.
It also raises important concerns for major oil-
producing countries with ample resources. Should
they expand capacity to supply growing economies
and at what rate? How much spare oil capacity should
be maintained to oset sudden oil-market surprises—
unexpectedly higher economic growth, political unrest
in oil-producing regions, or major oil spills in oshore
drilling areas? Fundamental factors should be important
for both capacity decisions, but these uncertainties have
eroded the belief that these factors still operate in the
same way that they have in the past.
Capacity expansion inuences both short and long-
term market operations. First, greater capacity allows
more future production to meet growing demand.
ese decisions require an understanding of long-
term market conditions. Second, additional capacity
can also build surplus capacity for market imbalances.
ese decisions require an understanding of short-term
market conditions. Although the distinction between
the short and long term can be ambiguous, we dene
the short-term to include horizons of three years or less.
Oil markets are not easy to understand and projections
of future oil prices have not been accurate consistently.
If fundamental supply and demand analysis and oil
market modeling have any benets, it would appear
to be in their ability to organize complex information
eciently and to provide better understanding of how
oil markets perform. For this reason, it is sensible to
emphasize these characteristics, rather than to focus on
their suitability for forecasting.
3. Long-Run Oil Price Drivers and Models
Oil represents a substantial proportion of global energy
demand. As the world’s most highly traded international
commodity, oil will continue to play a large role in
meeting energy demand in the future. Over the long
run, the price of oil will be inuenced by four major
trends: (1) global economic growth, (2) demand-side
technological progress and eciency gains, (3) new
alternative energy sources, and (4) the changing costs of
production. e depletion of easily extracted resources
is pushing production into more technologically
demanding elds, lower-quality crudes, and higher-cost
operating environments. At the same time, dramatic
improvements in technology are expected to continue
to reduce the cost of nding and producing oil from
such reserves. Government policies will have important
impacts on the costs of both petroleum products and
competitive energy sources. Understanding how
production, consumption, and the price of oil will
change over the coming decades is of vital interest to
both oil-producing and oil-consuming nations, with
strong implications for energy policy, economic growth,
climate-change policy, and international stability.
3.1. Oil Demand: Drivers and Trends
Generally speaking, when the world economy as a
whole experiences growth, oil demand will increase.
e existence of this fundamental relationship is
uncontested, but its strength varies between regions and
will be moderated by many factors with the potential
to curb demand, such as fuel-saving technologies, fuel-
switching to dierent forms of primary energy, and
policies designed to constrain carbon dioxide emissions.
Much of the recent growth in global oil consumption
(which rose by 1.5% per year between 1985 and 2008)
occurred outside the OECD nations. As a percent
of world consumption, the emerging nations’ share
has grown from 37.6% to 44.5% over this period.
Developing economies are expected to continue being
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the primary drivers of the growth in global oil demand.
e hypothesized energy and environmental Kuznets
curve, which views investments in energy eciency
as a luxury good that become more aordable and
widespread as developing economies mature and
prosper, teaches us that continued strong economic
growth in China and elsewhere may work paradoxically
to restrain the growth rate of demand—if only in the
longer run.
3.1.1. Growth and Industrialization
Per-capita oil demand grows at the same rate as the
economy in many emerging economies, so long as other
factors like prices do not change. Many countries are
experiencing rapid increases in vehicle penetration and
ownership rates as incomes rise. Based on estimates as of
1973, oil income elasticities exceeded unity throughout
the developing regions of the world, and approached a
level of 2 in the poorest nations. is implies that oil
demand should increase at least as fast as GDP in the
developing world, holding constant energy prices and
technological progress. In the poorest Asian nations,
oil demand should expand nearly twice as fast as GDP
(Medlock and Soligo 2001 and Van Benthem and
Romani 2009).
In contrast, per-capita oil demand grows more slowly
than GDP within the OECD, even before the impact of
potentially rising prices is factored in. Vehicle ownership
per person has stabilized and consumers are beginning
to purchase alternative-fuel vehicles in these countries.
Gately and Huntington (2002) estimate that the long-run
income elasticity to be 0.55 in the more mature OECD
countries, implying that oil demand may increase only
about half as fast as GDP in the industrialized portions
of the world (again, abstracting from the impact of
potential changes in prices, regulation, and technology).
3.1.2. Oil Demand and Technical Progress
Whereas pure price-substitution implies reversibility,
technological progress that is induced by price increases
creates an irreversible and unidirectional eect that is
not easily unwound, even when prices return to previous
levels. Several distinct processes drive technical changes
that inuence oil demand. e rst is exogenous change
that is largely unrelated to specic changes in the price
of oil or economic conditions. For example, airplane
designs incorporated signicant improvements in fuel
eciency, even prior to the price shocks of the 1970s. In
an endogenous process, rising oil prices are the specic
incentive that drives technical change. Automobile
companies, for example, revamped their vehicle eets
aer the 1970s to make passenger cars more fuel
ecient; even when oil prices declined aer 1985, those
design innovations were never eliminated.
3.1.3. Alternative Vehicles and Competitive Fuels
Limited historical evidence exists by which to measure
the strength and potential of inter-fuel substitution
among competing fuels. In many countries, petroleum-
based fuels appear to have no strong or viable competitor
for powering transportation. at may be changing
as countries have begun to make commitments to
vehicles fueled by compressed natural gas, biofuels, and
electrication. Additionally, companies may increasingly
pursue gas-to-liquid processes as a technological option
that substitutes relatively inexpensive natural gas for oil
in the production of diesel fuels. Energy security and
climate mitigation policies may accelerate these oil-
reduction trends.
3.1.4. Demand Response to Oil Prices
If future oil supplies are expected to be scarcer than
today, future oil prices will rise and curb some of the
growth in demand; but, by how much? is question
has probably attracted more of the attention of energy
economists and commodity investors than any other
issue during the last few decades. A major conclusion
consistent with the ndings of most studies is that
the longer-run demand response to any gasoline
price increases occurring over the next twenty years
is likely to be several times larger than the short-term
response that is initially apparent (Dahl and Sterner
1991 and Goodwin, Dargay et al. 2004). e response
of consumption to price is the combined eect of many
dierent decisions. Utilization decisions impact the
gasoline market by reducing trac activity and the
number of miles driven by households. Over a longer
period, household response to higher prices is also
magnied as the vehicle eet is retired and replaced.
e price elasticity of oil demand seems to be declining
lately within the United States and perhaps more
broadly within the OECD. Many countries outside of
the OECD maintain large fuel subsidies that impose
a wedge between crude and product prices (Arze
del Granado et al. 2010). Removal of those subsidies,
which have become quite expensive to maintain, would
increase fuel prices to the end-user and thereby reduce
future oil demand. e lack of data and estimates for
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the emerging countries limits our ability to foresee how
these changes will inuence oil markets in greater detail.
3.2. Oil Supply Availability and Costs
Despite signicant gains achieved via enhanced oil
recovery technologies, conventional oil supplies are
diminishing in many elds located outside of the
Middle East. Development of unconventional resources
to oset this decline will be very important, but the cost,
availability, and scale of resources such as Alberta's oil
sands are as yet unknown. At the same time, oil supply
prospects, even from conventional resources, may
improve in certain areas. New oil may be discovered in
relatively unexplored regions and reserve appreciation
in known resource basins remains an important source
of new additions. Technical progress will probably
continue to reduce exploration and development
costs signicantly, as well as to enhance the safety and
security of operations that extend further into frontier
areas. Governments may reduce oil supply barriers
by rolling back production royalties and taxes, and by
easing constraints on leasing and acreage.
3.2.1. Resources and Geological Availability
Oil resources are scattered across the globe in formations
with very dierent characteristics. Based upon its world
oil assessment of 2000, the United States Geological
Survey (2003) estimated that there were 1,898 billion
barrels of remaining conventional oil and natural gas
liquids, excluding cumulative volumes that had already
been produced. ese geological estimates are based
upon likely discoveries, given the prevailing oil prices
and available technologies present in 2000.
ese conventional resources are supplemented
by considerably larger volumes of unconventional
resources—heavy oil, oil sands, and oil shale—that
require specialized extraction technologies and
signicant processing before the oil can be sold. Aguilera
et al. (2009) estimate that the combined volume of
conventional and unconventional oil would last for 132
years if production increased by 2% per year.
3.2.2. Resource Costs
For economists evaluating market conditions, resource
costs, rather than total reserves, determine whether
scarcity prevails. Many geological estimates do not
distinguish between resources that are inexpensive to
extract and those that are much more costly to develop
and produce. To ll this gap, a useful concept is the
resource availability curve—a schedule that represents
the total known resource base that could be developed
at each successively higher-cost level.
Aguilera et al. (2009) derive an availability curve for
conventional and unconventional petroleum resources.
ey estimate 7 TBOE (trillion barrels of oil equivalent)
of conventional resources and 4 TBOE of heavy oil,
5 TBOE of oil sands, and 14 TBOE of oil shale with
average production costs usually considerably higher
than the comparable costs for conventional oil. e cost
estimates for these unconventional petroleum resources
are very uncertain and subject to change. To be useful,
any long-run cost estimates should reect production
expanded to scale and the considerable learning that
will accumulate through experience in developing these
resources. Oil prices may well overshoot these long-run
cost estimates during intervening years when additional
unconventional sources are not yet large enough to
meet growing demand.
3.2.3. Oil Supply from Competitive Regions
Producers outside the major exporting countries are
generally considered as competitive price takers. Market
prices must cover the marginal cost of producing the last
unit of these supplies, including both the direct expenses
and the rms’ opportunity cost of drilling for oil, rather
than engaging in another economic activity. If resource
depletion is a factor, each supplier will also consider the
opportunity cost of current extraction relative to future
production. At higher prices, rms can justify exploring
for and extracting more costly resources, and doing so
earlier rather than later.
Two major trends are driving oil supply from regions
outside of OPEC: the depletion of reserves that are easy
to extract and the improvement of oil exploration and
production technologies. e combined eects have
led to an increase in mega-projects aimed at resources
that were formerly inaccessible, either commercially or
technically. Such projects include the Alberta oil sands,
the deep water resources of the Gulf of Mexico, and the
pre-salt deposits oshore of Brazil.
3.3. OPEC
e major oil exporters are suciently large to inuence
as well as to respond to price. ey have market power.
However, the extent to which market power has
been exercised is less certain. e previous empirical
literature leaves many questions regarding the impact
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of decisions and actions taken by OPEC. e data
tends to support multiple competing theories, without
denitively excluding any particular behavioral model.
Analysts choose their favorite hybrid; they seldom test
all versions. ere is clearly room for additional research
on the nature of OPEC and its evolution.
3.4. Long-term Models
e long-run behavior of the oil market has received
considerable study through the application of computer
models. Models can be classied by many dierent
criteria, but we nd it helpful to distinguish structural
models from computational models. Both approaches
take fundamental microeconomic theories about
the objectives, constraints, and behaviors of market
actors into consideration at their core. ese theories
are distilled into a mathematical structure, allowing
for interaction between the actors within a specic
market context. e primary distinction between the
two categories is the level of complexity and detail;
computational models have signicantly more detailed
representations of the market at the cost of model run
time. ey also have increased data requirements and
may oer less straightforward interpretations.
Research into the formal modeling of the oil market
began largely as a response to the oil crisis of 1973. e
initial goal was to understand the role of OPEC decision
making and its impact on the market price. Since that
time, as the oil market has changed, and the research
community has become more international, structural
models have been applied in analyzing a wide range of
issues involving oil. e major structural approaches
include simulation, optimization, and game-theoretic
In simulation models, the behavior of actors in the
market is represented by a specic function contingent
on market conditions. is function can be based
either on some rule-of-thumb (such as a target price
or target capacity utilization rule), or on historical
econometric estimates of past behavior. Depending on
the researcher’s focus, the behavior of dierent agents
may be described in various levels of detail. In general,
OPEC is given more complex behavior, while non-
OPEC producers follow a simple supply curve, oen
one that exhibits constant price elasticity. Of course, the
researcher’s goal is to develop rules or functions that are
descriptive of actual behavior.
In an optimization model, at least one agent actively
chooses its behavior to maximize an objective function,
typically related to prot or welfare. For models of the
oil market, the optimizing agent is generally assumed to
be OPEC, or some subset of that organization. OPEC
chooses a level of production to maximize the present
value of prots, while taking how the resulting price
will inuence the decisions of competitive producers
and consumers into account. While some models may
have sophisticated representations of the limits to the
knowledge available to the optimizing agent, in many
cases, the optimizer is given complete foresight of
the future path of the market. With the optimization
approach, the researcher seeks to understand what a
market player must do to obtain his best outcome.
In game-theoretic models, two or more agents are
assumed to have market power, or at least some inuence
on each other’s welfare. ey attempt to take actions
that are optimal, given their anticipation of what the
other agent will do. Each agent is also assumed to take
into account the strategic behavior of other actors in the
market. A game-theoretic approach may be useful when
it is necessary to explicitly consider the consequences
of rivalry and competition between dierent large
players in the market—for instance, when evaluating
the incentives for individual OPEC members to deviate
from established production quotas.
Computational models share many attributes with
structural models and are largely distinguished by the
sheer number of details included. e complexity of the
models makes them costly to build and maintain and the
level of detail makes it dicult to establish the impact of
any one model choice. However, computational models
facilitate certain types of analysis that are impossible with
a structural model: detailed impacts upon individual
stakeholders, specic technological scenarios, and
full policy analysis. Moreover, one approach to
computational modeling, so-called computable general
equilibrium models, has been used extensively to
investigate fuel substitution opportunities and the
broader energy sector impacts of global greenhouse-gas
emissions policies. Computational models also facilitate
the division of labor in the modeling eort by dividing
the project into distinct sub-modules.
Due to their cost and complexity, computational models
are relatively scarce, but with cheaper computer power,
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they are becoming more common. Still, computational
models are typically conned to institutions such
as the International Energy Agency and the Energy
Information Administration.
3.5. Research Gaps in Long-term Oil Markets
While there has been signicant eort representing the
long-term behavior of oil markets using models of all
sorts, a great deal of work remains to be done. As new
technologies are developed, demand grows, new kinds of
resources are exploited, and relationships in the market
change; the theories and models we apply to the oil
market need constant re-evaluation. A few topics stand
out as signicant open questions in our understanding
of the long-term behavior of the market going forward:
demand behavior, modern OPEC behavior, producer
welfare, and resource depletion.
3.5.1. Demand Behavior
With the exception of the large computational models,
most oil models do not have a very sophisticated or
detailed representation of the demand side of the market.
Understanding demand dynamics would be useful not
only in explaining recent price movements, but also in
exploring the impacts this degree of demand variation
has on oil-producing nations. Marked variation, but
especially unpredictability, of demand presumably
aects the welfare of producers, not just consumers,
and may change the nature of their capacity investment
decisions. ree major topics in demand behavior stand
out as top candidates for further exploration: (1) the
high rate of demand growth in developing countries,
(2) the asymmetric response of oil demand to price
changes, and (3) the role of technology in altering the
energy intensity of oil-consuming activities.
3.5.2. Security and Climate Policy
Closely aligned with demand issues is the inclusion
of other energy market dynamics that produce viable
substitutes for oil-based products for transportation.
Governments are adopting policies to accelerate the
shi consumption away from oil through mandates,
taxes, and subsidies—all in response to concerns about
energy security and global climate change. To derive
meaningful results, the broadening range of available
substitutes for petroleum-based fuels requires the
simultaneous evaluation of multiple fuel markets, rather
than oil-only analysis.
3.5.3. Modern OPEC Behavior
In the late 1970s, OPEC was modeled by many to be a
monopolist in the world oil market. One author once
referred to it as a clumsy” cartel (Adelman 1980). Models
developed in late 1970s and early 1980s examined a
number of dierent theories regarding OPEC’s behavior
and market power. However, OPEC and its members
have evolved through time and observations gleaned
from the 1970s are now outdated.
In the most recent two decades, the global view of OPEC
has changed. OPEC is no longer considered denitively
as a cartel that exercises market power by regulating
output. Smith (2009) suggests that OPEC has been
restraining investment in new oil production capacity
in recent years and thereby has contributed to higher
prices in a market with very rapid demand growth.
Although research eorts to study OPEC’s behavior
either econometrically or theoretically have diminished
compared to prior years, there remains a need for new
theoretical models describing OPEC; these models
should be tested with detailed data culled from recent
3.5.4. Producer Welfare
Many of the market-power models treat OPEC
production decisions as if they were made by a prot-
maximizing rm or cartel. When trying to understand
the impact of OPEC production decisions on global oil
prices and consuming nations, such a formulation may
be an adequate approximation of the decisions made
by the organization. However, in reality, as sovereign
nations, both political and economic concerns drive
decision making. Oil-producing nations may constrain
prices in order to maintain favorable relationships with
other nations, or they may sell oil at a discount in their
home market to benet domestic consumers. It may
make more sense to view the nations as maximizing
welfare rather than maximizing prot.
Unfortunately, when moving from models that consider
prot to ones that try to measure welfare, modeling
techniques increase in complexity and require greater
information on the national economy as a whole.
While some models (De Santis 2003) have previously
approached this important question, a great deal of
work remains to be done in this area.
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3.5.5. Resource Depletion
Oil reserves are nite and production will become more
expensive and perhaps eventually hit a peak (Hubbert
1962). It remains unclear when such a peak will occur
and whether it will be based on a lack of available
resources or the lack of sustained crude oil demand. In
fact, the threat of peak oil has loomed over the horizon
since the dawn of the petroleum age, but consistent
resource discoveries, unconventional resources, and
technological breakthroughs have so far managed to
expand oil supplies and may continue to mitigate crude
resource scarcity for the foreseeable future. As discussed
elsewhere (Smith 2012), it is not even clear that a peak
in the production of oil, if it does occur, would be a
harbinger of impending scarcity.
4. Short-Run Oil Price Drivers and Models
Generally speaking, conclusions regarding the short-
run behavior of oil prices are even less certain than our
knowledge of the factors that drive long-term trends.
In large part, this is due to the relatively short history
of investigation into short-run uctuations, as well as
recent changes in the composition and liquidity of short-
term oil markets that have only begun to be sorted out.
Modeling short-run changes in the oil market requires
dierent techniques, depending upon the specic issue
under investigation. Financialization, in particular, has
made the oil futures and other derivatives market more
liquid and perhaps more inuential, while the number
of participants in nancial markets has increased
because of hedging and investment opportunities. e
use of high-frequency data may be required to consider
all the relevant details in short-term models, but much
of that data is not available in the public domain. e
primary goal of short-term models is to provide a better
understanding of short-term price movements and
to create short-term forecasts. In contrast with long-
term models, short-term models do not usually seek
to determine what the future equilibrium path for oil
prices will look like. Instead, they attempt to forecast
prices or price changes that are expected to be observed
in the near future. is is typically attempted with the
help of reduced-form models that estimate parameters
of statistical models that best describe short-run price
movements without considering the fundamental forces
of supply and demand. Short-term models also use the
powerful nancial theories concerning arbitrage and
risk-taking in an attempt to infer market expectations
from observed future prices.
4.1. Critical Observations
During the previous decade, the oil market experienced
signicant short-term upsets, one the most important of
which was the boom-bust price cycle during 2008. at
particular episode challenged the ability of conventional
models to provide adequate explanations and forecasts
of oil prices. Many studies have looked to nd structural
explanations, but there still is no consensus on the
underlying economic causes. In addition to the high
levels of price, a higher level of volatility has been
observed in the oil market in recent years. For the rst
time, a change of $100 per barrel in only four months
was observed in oil prices from July to November 2008.
ese trends are not limited to the oil market; nancial
activity and turmoil in commodity markets in
general have increased. e volume of investment in
commodity index funds, overall futures market trading
activity (as revealed by the open interest in all contract
maturities), and correlations among commodity prices,
as well as between commodities and equities, have
increased by varying degrees. Forward curves have
become substantially atter at times, indicating that
futures prices at varying maturity dates are now moving
more closely with each other and also with spot prices.
Financialization may act as a double-edge sword; it
increases market liquidity and facilitates price discovery
and risk management. However, it also creates more
opportunities for some traders who would attempt to
distort and manipulate futures prices.
Against this backdrop, there appear two overriding
challenges for the modeling community. First, is the
need to examine whether futures trading causes articial
movements in the spot price of oil or not, and, if so,
to trace out the expected remedial eect of alternative
regulatory reforms. Second, is to assess if and how
nancial variables can be used to forecast future price
paths more accurately than methods that are based on
fundamental analysis alone.
4.2. Fundamental Drivers
Certain economic factors have played a fundamental
role in recent price changes. Supply and demand
shocks, together with the continuous ow of news
and uncertainty that surrounds them, are the primary
drivers underlying short-run oil price dynamics. e
impact of these shocks is magnied by the low elasticities
of both short-run oil supply and demand. Hamilton
(2009) demonstrates that under specic assumptions
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about the elasticities of supply and demand, one can
explain the 2008 boom-bust price cycle just by using
fundamental supply and demand factors. A caveat is
that the price predictions drawn from such models are
extremely sensitive to the specic magnitude of the
presumed elasticities. Nevertheless, short-term supply
and demand drivers are believed to be able to describe
most of the observed price changes. is section reviews
these fundamental drivers.
4.2.1. Short-term Supply Drivers
During 2005-08, available inventories were depleted
while major oil-producing countries held low levels
of spare capacity. Considering the inverse relationship
between spare capacity and spot oil prices, and the
inelastic supply in the short-term, this has led to higher
price levels.
Short-term supply shocks have also inuenced the oil
market. e Deepwater Horizon oil spill in the Gulf
of Mexico and the revolution in Libya are two recent
examples of such events. Consider rst the deep water
Horizon spill. e methods of obtaining liquid fuels are
becoming increasingly reliant on advanced and capital
intensive technologies. From deep water drilling, to
the processing of oil sands, to advances in rening, the
oil market is changing its risk prole. While engineers
are constantly working to perfect control systems and
reduce the chance of failures, the potential for damage
from any single catastrophe is increasing. Furthermore,
with the increasing lumpiness of production from the
trend towards more complex megaprojects, the supply
impact of a single outage (or addition) is increasing,
potentially leading to greater price volatility (Skinner
4.2.2. Short-term Demand Drivers
e short-run demand for oil is also relatively price
inelastic. ere are four main reasons for this. First, oil
consumption levels cannot change quickly, due to the
existing stock of vehicles and other equipment that uses
oil. Second, in the OECD countries, oil consumption
is less responsive to price changes because the share of
consumers energy expenditures as a fraction of their
total incomes is relatively low. ird, oil demand in
developing countries is largely driven by steady income
growth and industrialization. Fourth, the demand
impacts of crude oil price changes are in many cases
oset by government subsidies or taxes.
Macroeconomic news also inuences oil prices. As
incomes increase and economies expand, more energy
will be used for transportation, heating, and cooling.
Hicks and Kilian (2009) utilize a direct measure of
global demand shocks based on revisions of professional
forecasts of real GDP growth. ey show that recent
forecast surprises are associated primarily with
unexpected growth in emerging economies. According
to this line of research, markets have been repeatedly
surprised by the strength of this growth.
Finally, U.S. foreign exchange and interest rates exert an
inuence on the price of oil. e price of oil (in USD)
increased by more than 600% from January 2002 to
July 2008. e same increase in terms of the Euro was
less than 300% as the Euro gained strength during that
interval. As this example suggests, depreciation of the
U.S. currency may either lead or at least contribute to an
increase in oil prices (which are typically expressed in
U.S. dollars). Fluctuations in interest rates inuence the
value of oil in the future relative to its value today, which
can lead to changes in production, consumption, and
storage decisions. In addition, changes in interest rates
prompt changes in the prices of nancial derivatives
4.2.3. News and Information Signals
In nancial markets, the price is believed to reect
all publicly available information. Newly released
information about future events will have a proportionate
impact on today’s price. All kinds of news are relevant:
information regarding the economic growth of dierent
countries, the prices of other commodities, currency
rates, major countriesstock market movements, signs
of geopolitical unrest or uprisings, unexpected severe
weather conditions and natural catastrophes, and many
other factors. e ow of information can change prices
frequently and sharply. However, to have any impact,
the news must be credible.
Previous research shows that not all announcements
made by major players in the oil market (OPEC, IEA,
etc.) are credible. To better understand short-run price
movements, it is important to distinguish between
relevant, credible announcements and ones that are
ignored by the market. An important step in conducting
this analysis is to consider the incentives of the issuers
of information: specically, whether those objectives
are aligned with the truthful revelation of information.
A signaling framework and a forecast model can be
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used to simulate the eect of new announcements and
analyze their incentives.
4.3. Price Forecasting Approaches
Most short-term oil market models focus exclusively
on oil price and its statistical time series properties. In
contrast, structural models explicitly specify and attempt
to estimate the impact of changes in oil demand and/
or supply. is distinction means that short-term price
models are mainly limited to the task of forecasting,
rather than providing economic interpretation of the
sort required for policy analysis. Despite the rather
large number of recent short-term price models that
have appeared in the literature, signicant opportunities
remain for further study.
4.3.1. Reduced Form Models
Reduced form models take advantage of nancial and
structural data and employ econometric tools to build
a model and estimate its parameters. ese models
are usually applied to forecast a specic variable (e.g.,
world oil price). ey dier primarily in terms of the
complexity of the statistical structure that is assumed
to t the data. ree contrasting approaches have been
used to study oil price data: 1) time-series analysis,
e.g. autoregressive moving average (ARMA), general
autoregressive conditional hetereoskedastic (GARCH),
2) Structural Vector Autoregression (SVAR) and 3)
non-parametric regression, e.g. articial neural network
(ANN) (Jammazi and Aloui, 2012). Each approach has
its own advantages and, at the conceptual level, no one
approach is superior to the others. erefore, the choice
among the models should be dictated by the observed
statistical properties of the time series involved in the
If the reduced form models are applied for purposes
beyond simple forecasting, however, serious problems
arise. ese problems center on the concept and
interpretation of causality,a term that plays an oen
misunderstood role in many short-run time-series
analyses. Causality is, of course, central to the study of
policy analysis. To be successful, policy makers must be
able to anticipate the consequences of their actions. Will
trading limits cause volatility to decrease? Will producing
from the strategic petroleum reserve cause prices to
decline? And so on. e cause and eect relationships
that are implicit in these questions represent something
stronger than the statistical tendency of two variables to
move together, which is not evidence that an exogenous
change in one variable will cause another resulting
change in the other variable. erefore, it is essential
when contemplating short-term forecasting models to
understand that a nding of “Granger–causality,” which
is based on patterns of correlation, neither proves nor
disproves that a fundamental causal relationship links
one variable to another. To the extent that a fundamental
structure is added to the short-term approach in the
form of a SVAR, it is also important to keep in mind
that the structure that is assumed to link the variables
in a causal chain is typically dictated by convenience, as
when a diagonal pattern of variable exclusions is adopted
in order to permit the model to be solved recursively;
or, when a priori constraints are imposed on the size
of key parameters to achieve identication of causal
relations. Of course, if the constraints are untested and
inappropriate, so may be the causal relations.
In summary, short-term statistical models will continue
to ourish, based in part on the availability of additional
high-frequency pricing data and in part due to increased
scrutiny of nancial investors in the oil market. It will be
imperative for both the producers and consumers of this
research to keep in mind the fundamental limitations of
these time-series methods and to tailor their inquiries
to questions that can properly be answered with the
tools at hand.
4.3.2. Financial Models
Financial models are a more recent brand of oil price
models that extend statistical analysis to some of the
newer time series (futures prices and options) with
guidance from relevant hypotheses developed in the
theory of nance. Since options and futures contracts
convey information about the future, they have been
considered as a rst step in incorporating nancial data
in oil models. However, futures prices may include a risk
premium that varies through time, and therefore, they
do not represent a simple expectation about the price
that will prevail in the future. It has been shown, for
example, that “no change” forecasts are more accurate
than forecasts based on futures prices (Alquist, Kilian
et al. 2010).
Pagano and Pisani (2009) document signicant time-
variation in the risk premium and use the degree of
capacity utilization in U.S. manufacturing and oil
inventory levels as proxies for this variation. ey
demonstrate how one can nd expected future prices
based on the combination of futures prices and the risk
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premium. us, if one could model and forecast the risk
premium when combined with market data, it should
be possible to obtain estimates of future expected prices.
Given the potential value of this ability to producers and
consumers alike, further research into determinants of
the risk premium seems warranted.
4.3.3. Structural/Financial Hybrid Models
Hybrid models, combinations of structural and
nancial models, are motivated by the need to produce
short-term forecasts that are more consistent with
supply and demand frameworks. ese models are
calibrated to base-case forecasts of a long-term model,
with outcomes that are adjusted based on the ow of
new market information and short-term economic
responses. Relevant new market information would
include price observations from futures markets,
forecasts in demand growth, and supply shocks (e.g.,
the reduction in Libyas production during 2011).
Hybrid modeling requires estimates of both short-
term and long-term elasticities with which to simulate
price responses. e model takes information signals as
input and generates price and quantity paths as output.
In light of the Ecient Market Hypothesis, the market
responds instantly to the new forthcoming information.
Further eorts to incorporate theories of commodities
and storage might lead to models capable of forecasting
inventory changes and the movement of futures prices
as well. (e.g., Routledge, Seppi, and Spatt 2000).
4.3.4. Modeling Volatility
Market price volatility can be estimated either from
backward-looking historical data or from forward-
looking nancial derivatives using implied volatility
(Szakmary et al. 2003). Indeed, even for models where
volatility is not of direct concern, a researcher might
need to know how volatility and price shocks lead to
changes by consumers and producers. For example, the
use and production of oil are heavily tied to existing
capital stock and capacity investments. Price shocks,
even over a relatively short time frame, can have lasting
impacts on demand and supply for years to come
through their impact on capital investment. Monte
Carlo methods and articial neural network technology
could be applied to simulate supply and demand shocks
and to estimate the benets of major producers adopting
strategies that stabilize prices, but that is all dependent
on rst developing an understanding of how the use of
excess capacity and stockpiles inuences volatility.
4.4. Analytical/eoretical Models and Insights
Financial aspects of oil markets are not well explored.
Studies are still trying to conrm a range of theoretical
hypotheses about the operation of the nancial markets
and to identify the most important nancial drivers.
ese include models that do not try to simulate
or forecast the whole oil market. Instead, they use
partial equilibrium or econometric techniques in an
attempt to understand short-term market movements
more accurately and to distinguish among competing
theoretical hypotheses.
During 2000-08, when oil prices were increasing,
investments in commodities markets also increased
signicantly. is triggered the question of whether the
price rise of 2008 represented a nancial bubble of some
kind or not. Brunnermeier (2009) denes a speculative
bubble as characterized by the following elements: (1)
prices are higher than the fundamental value, (2) a
group of investors buys the asset based on the belief or
sentiment that they can sell it to others later at a higher
price, and (3) such beliefs or sentiments cannot be
supported by fundamental factors.
Studies on the role of nancialization can be categorized
into two groups: conceptual models and statistical
tests. e former type of analysis consists of deductive
arguments for accepting or denying the hypothesis that
an increase in nancial activity will cause prices to rise
more than what fundamental factors would dictate. e
logical validity of these arguments rests solely on the
underlying assumptions independent of any empirical
evidence. e latter type of analysis focuses on
quantitative relationships between trending variables to
nd statistical patterns of predictability.
A few studies cite conceptual arguments to advance
the claim that excessive investment in commodity
index funds might have played a role in creating the
bubble. However, conceptual analysis alone cannot
establish the strength or magnitude of the eect. us,
additional empirical research is needed to clarify the
picture. Certain conceptual relationships remaining
so far are still rather inscrutable, even aer they have
been quantied. For example, Tang and Xiong (2010)
nd a link between increased price correlations among
dierent commodities and the growing volume of
commodity index investments. However, there is
no indication, theoretical or otherwise, that higher
correlations are good or bad.
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In any event, elevated correlations are not evidence of
a bubble. Regarding the possibility of a bubble induced
by nancialization, it is useful to remember that price
movements in futures markets with rising index
fund investment have not been moving uniformly
upward (Irwin, Sanders et al. 2009). Moreover,
Headey and Fan (2008) show that prices of many
non-nancial commodities—commodities that were
not nancialized—displayed similar dynamics as the
nancialized commodities, despite having no inux of
speculative nancial investors. ere is considerable
room for additional research into the price movements
of all commodities, whether they are nancialized or
not. e parallel movements suggest the presence of
some common factors beyond nancialization and
research is needed to identify and measure the inuence
of those factors. It seems likely that any progress in this
direction will depend on a more complete appreciation
of the role of common demand shocks, inventories, and
convenience yields.
4.5. Statistical Tests
Researchers commonly perform statistical causality
tests to describe the temporal relationship between
speculators’ trading activity, oil price movements, and
volatility. As noted above, these tests using Granger
causality establish causality not in a structural sense,
but only conrm whether the observed movement in
one variable precedes the changes observed in another
variable. e dierence is important: although one
might move a picnic inside just before it starts raining,
moving the picnic doesn’t cause the rain. With that caveat
in mind, and using non-public data, the Interagency
Task Force on Commodity Markets (2008) studied
the dynamic relation between daily price changes and
changes in the positions of various categories of traders.
ey found that some trader positions can be predicted
as a response to price changes, but not the reverse.
Sanders and Irwin (2011) nd evidence that larger long
positions by index traders Granger-cause lower market
volatility. is result is contrary to the popular belief
that index traders’ activities increase market volatility.
ere is some additional evidence on the other side
of the argument and the conicting conclusions are
an invitation to pursue further both conceptual and
empirical research into the causes of commodity price
movements. One example is the group of studies that
evaluate whether or not statistical characteristics of
oil price movements match the pattern of an explosive
bubble. In contrast with explosive bubbles that
prevailed for several months in the copper and nickel
markets, Gilbert (2010) nds only weak evidence for an
explosive bubble in the oil market, and that it appears to
have endured for only a few days in July 2008. Even so,
do we know what caused it, or why it subsided? Further,
a few studies report some evidence, conceptual as well
as empirical, that nancial activities were driving the
oil price away from its fundamental value during 2008.
Although fundamental factors are important in his
analysis, Einloth (2009) suggests that speculators may
have been building inventories from March to July in
2008 based upon evidence that spot prices rose further
aer convenience yields had begun to fall. Singleton
(2011) nds signicant empirical support that investor
ows inuenced excess returns from holding oil future
contracts of dierent maturities, aer controlling for a
number of other exogenous factors.
In summary, there have been many studies, but as
yet no absolute consensus on the causes of the oil
price boom and bust of 2008. Although there exists
only limited statistical evidence that the price cycle
represented a speculative bubble caused by an inux
of nancial traders, the matter remains the subject of
great debate among researchers, policy makers, and the
general public. e value of any further work that helps
to clarify this issue would be substantial.
4.6. Prescriptive Models
Short-term modeling is a relatively new approach. Some
studies have tried to build a theoretical framework for
interactions between the nancial markets and the
physical markets. ese prescriptive studies usually
simplify the details of the actual market and examine
various phenomena that would be expected to occur
under certain conditions. e main goal of this
deductive approach is to understand how the market
works, rather than forecasting or simulating with high
For example, Deaton and Laroque (1996) and
Routledge, Seppi, and Spatt (2000) consider storage
agents in the commodities' markets and determine
how the levels of inventories should change with
uncertainty and how forward curves should behave in
such settings. Routledge, Seppi, and Spatt interpret the
concept of convenience yield as an option that storage
agents will exercise at an optimal time. Allaz (1992)
develops a generic commodity market model (1992) to
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demonstrate that, depending on the relative strength of
the hedging and strategic motives, a producer’s optimal
position in forward markets may be either short or
long. Brandts, Pezanis-Christou, and Schram (2008)
study of Cournot (quantity) competition and Liski and
Monteros (2006) inquiry into a potential link between
forward contracting and collusion are further examples.
e stylized nature of modeling that lies behind all
of these studies invites extensions that explore the
robustness of the ndings to more realistic depictions
of the agents who trade in these markets.
5. e Role of Saudi Arabia in the Global Oil Market
e inuence of Saudi Arabia on the global oil market
is indisputable. Saudi Arabias role and decision
parameters since the discovery and production of oil in
the Kingdom have been determined by dierent factors.
Al-Moneef (2011) discussed this issue and highlighted
four important factors.
e rst factor is the size and production life of Saudi
Arabias oil reserves. For the past y years, Saudi
Arabia has had very large crude oil reserves, equivalent
to 20% of the worlds proven reserves.
e second factor is the diversity of Saudi Arabia’s
export outlets. Saudi Arabia is exporting to the U.S.,
Europe, and the Far East. is diversity of outlets (and
crude types exported) oers Saudi Arabia marketing
exibility and highlights the international consequences
of its policies. In addition, Saudi Arabia is exporting its
oil to the rest of the world from two domestic terminals
located on its eastern and western coastlines.
e third aspect is the Kingdoms large crude oil
production capacity. Saudi Arabia maintains a large
excess capacity that is available to face supply disruptions
and demand surges. Saudi Arabias excess capacity in the
past three decades since 1980 averaged 60% of OPEC’s
(and of the world’s) excess production capacities, while
its share in OPEC’s and the worlds production averaged
32% and 12% respectively during the period. is
unused capacity averaged 35% of Saudi oil production
during the 1982-1990 period, 13% during the 1990s,
and 14% in this decade. OPEC’s averages over these
three periods were 17%, 6%, and 4%, respectively.
While the other OPEC members' excess capacities depend
on market conditions, Saudi Arabia made an ocial policy
since the mid-1990s, of maintaining 1.5-2 MBD excess oil
production capacity at all times. Saudi Arabias role has
been very useful to soen the impact of major oil supply
interruptions, such as the Iran-Iraq war, Iraqs invasion of
Kuwait, the Venezuela crisis in 2003, Hurricane Katrina in
2005, and the Libyan crisis in 2011. ese actions helped in
lessening oil market volatility and stabilizing oil prices.
e fourth facet is the role of oil in Saudi Arabia’s
economy. For the past three decades, oil has represented
35% of Saudi Arabias GDP, 84% of its government
revenues, and 90% of its merchandise exports. ese
rates explain the high interdependence between the
Kingdom’s domestic and international oil policies.
ese four factors have pushed Saudi Arabia to develop
its own oil industry through its national oil company
Saudi Aramco. e company was created through
the purchase by Saudi Arabia of the assets of the four
American companies operating in the Kingdom. Saudi
Aramco was entrusted with the tasks of managing and
developing the hydrocarbon resources of the Kingdom
to achieve its development objectives, executing the
government energy policies, and developing the
technical skills needed in that sector.
Saudi Arabias oil policies are geared towards eciency
and sustainability, which involves stable oil markets and
an ecient oil industry that is able to play a strong role in
the oil sector. In the face of environment uncertainties,
Saudi Arabia is investing in research and development
projects such as research centers, universities, and
Regarding the role of Saudi Arabia in OPEC, it has been
as important for OPEC as OPEC has been for Saudi
Arabia (as suggested by R. Mabro, Oxford Institute for
Energy Studies 2001). e roles of OPEC and Saudi
Arabia have evolved in line with market changes.
Such changes include the diversity of market players,
the inuence of the nancial markets on the physical
markets, the energy policies of consumer countries, and
climate change, as well as energy security concerns.
Since the inuence of the nancial market on the physical
oil market is increasing, Saudi Arabia has acknowledged
the new market reality and adopted a policy of urging
the international community to exert some regulatory
oversight, as well as transparency measures, over the
means of transactions in such markets. In order to
stabilize the market, Saudi Arabia has been collaborating
with international organizations such as OPEC and
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IEA to reach better predictability. e Kingdom is also
promoting the strengthening of the producer-consumer
dialogue, among other things, by strengthening the role
of the International Energy Forum (IEF), created in 2003
and entrusting it to coordinate the Joint Oil Data Initiative
(JODI) to enhance the ow of timely and accurate oil
data worldwide.
In the early 1990’s, Saudi Arabia realized that the
challenges of climate change would add to oil supply and
demand uncertainties and so it integrated its climate-
change policy with its oil policy. It also considers
energy security as a two-dimensional concern: supply
security (the availability, diversity, and reliability of
energy supplies at all times) and demand security (the
predictability, eciency, and growth of energy demand
in line with economic growth).
Saudi Arabia is expected to continue playing a
dominant role in stabilizing the global oil market. e
Kingdom will continue its eorts to ensure sustainable
oil supply to the world with stabilized long-term prices
at reasonable levels. At the same time, it will go on
with its investments in the oil and gas sectors to ensure
adequate supplies and sustainable economic growth.
It is expected to maintain an excess capacity of 1.5 to
2 MBD to face supply crises eciently. Finally, Saudi
Arabias oil policy will be dened in dialogue with other
producers and consumers to address the environmental,
investment, and price volatility challenges as a whole.
6. Conclusion
e complexity of the world oil market has increased
dramatically in recent years and new approaches are
needed to understand, model, and forecast oil prices
today. In addition to the rapid nancialization of the
oil market, many fundamental structural changes have
aected physical markets for oil. Financial instruments
now communicate information about expected changes
in the underlying fundamentals much more rapidly
than in the past, so the implications of both nancial
and physical developments are clearly linked.
Casual evidence of the closer relation between nancial
and physical markets may be found everywhere. e
prices of long- and short-dated contracts have started
moving more closely together. Sudden supply and
demand adjustments, including those related to China's
economic growth, the Libyan uprising, and the Deepwater
Horizon oil spill have changed expectations in ways that
aect both current and futures prices. Although volatility
appears to have increased, nancialization has arguably
made price discovery more robust and expectations
more transparent. Most empirical economic studies
suggest that expectations regarding fundamental drivers
and their future trends shaped prices during the 2004-08
cycle, although over-exuberant expectations cannot be
ruled out completely, based on available evidence.
With increased price volatility, major exporters are
now considering ways to provide more price stability,
which is needed to improve long-term production and
consumption decisions. Managing excess capacity,
primarily within OPEC, but also in the strategic stockpiles
held by major consuming nations, has historically been
an important factor in keeping world crude oil prices
stable during periods of sharp demand and supply shis.
To what extent would the expansion of excess capacity
alter market expectations in the current environment?
Would the result be greater price stability in the face of
uncertainties regarding, for example, the rate of Asian
economic growth, the debt crisis in some European
countries, the restoration of Libyan production, and
heightened tensions between Iran and the West? OPEC
can pursue price stabilization strategies more eectively
if the causes and consequences of volatility are better
understood and if OPEC members can coordinate on
the use of additional oil production capacity.
Within the context of long-term oil price drivers, the role
of Saudi Arabia in the energy market is quite important.
Maintaining and expanding Saudi crude oil capacity, if
undertaken, would provide a supply cushion to lessen
oil price volatility. Unfortunately, one does not know the
magnitude of these eects because there is uncertainty
about the parameters inuencing supply and demand
behavior. History tells us that there have been periods
when expansions in Saudi output stabilized oil prices,
for example, in 1991 during the rst Gulf War. It also
reveals that there have been other periods when oil
prices continued rising despite Saudi expansion, for
example in 2008 leading up to the nancial crisis.
In evaluating any decision regarding the use of excess
capacity, it is important to know what other factors are
moving oil prices at the same time. Another important
aspect of Saudi Arabia’s role in the oil market involves
continued oil exploration eorts and the development
of new elds that would allow production to keep
pace with the growing global oil demand in the long
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run. Saudi Arabia’s role also includes the maintenance
of excess capacity that could be released immediately
in periods when oil shortages suddenly emerge in the
Apart from the short-run consequences of price
volatility, we must learn from the important structural
changes that have occurred in oil markets aer major
price increases, because similar events are likely
to happen again in the future. Partially motivated
by government policies, the automobile industry
dramatically improved vehicle fuel eciency in the mid
to late 1970s. Seismic imaging and horizontal drilling
in the early 1980s expanded production capacity in
countries outside OPEC. Recent improvements in
shale gas technologies are now being extended to shale
oil as well, resulting in expanded oil supplies in areas
recently considered prohibitively expensive. e search
for alternative transportation energy sources continues
with expanded research into compressed natural gas,
biofuels, diesel made from natural gas, and electric
vehicles. Which of these factors, or others, will produce
the next game-changing impact on the oil industry?
Many fundamental aspects of the world oil market
remain unclear. Aer 40 years of research, there exists
no credible, veriable theory about the behavior and
inuence of OPEC. It is evident that OPEC members do
not consistently act like a monolithic cartel. Empirical
evidence suggests that at times members coordinate
supply reductions and at other times they compete
with each other. Output can be managed either by
production in the short-term, or by limiting investment
to expand capacity in the longer-term. Clearly, these
are complementary strategies, but how can or should
they be coordinated? Regional political considerations
and broader economic goals beyond oil also must enter
the calculations of each OPEC member country. ese
inuences have also changed rapidly as the economies
of OPEC members have been transformed dramatically
during the past two decades; nancial needs for
exporting oil now weigh heavily in their decision-
making and their actions continue to have a strong
eect on the rest of the world.
is paper is based on research supported by King Abdullah
Petroleum Studies and Research Center (KAPSARC) and
contained in Huntington et al (2012, 2013), which provides a
much more extensive set of references than we could produce
here. We would like to acknowledge the careful comments
and review on our previous work by Bassam Fattouh, James
Hamilton, James Smith, Fred Joutz, Majid Moneef, and
Awwad Al-Harthi. We also benetted from the valuable
advice from and discussion with John P. Weyant, Stephen P.A.
Brown, James L. Sweeney, and participants at the Oil Metric
Forum workshop in Washington, D.C. on September 16-17,
2010, and the National Energy Policy Institute Conference
on OPEC at 50: Its Past, Present and Future in a Carbon-
Constrained World in Tulsa, OK on March 23, 2011. All
responsibility for the contents of this paper belongs to the
principal authors, and none of the views and conclusions
can be attributed to any of the above individuals or the King
Abdullah Petroleum Studies & Research Center (KAPSARC).
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Author Bios
Hillard Huntington is Executive Director of Stanford
University’s Energy Modeling Forum, where he
conducts studies to improve the usefulness of models
for understanding energy and environmental problems.
In 2005 the Forum received the prestigious Adelman-
Frankel Award from the International Association
for Energy Economics for its “unique and innovative
contribution to the eld of energy economics.
Educated in economics at Cornell University and the
State University of New York, he is a Senior Fellow
and a past-President of the United States Association
for Energy Economics and a member of the National
Petroleum Council. He was also Vice-President for
Publications for the International Association for
Energy Economics and a member of the American
Statistical Associations Committee on Energy Data.
Previously, he served on a joint USA-Russian National
Academy of Sciences Panel on energy conservation
research and development.
Saud M. Al-Fattah is a Corporate Planning Consultant
Prior to this position, he was Director and Fellow of
Global Energy Markets and Economics Research at the
King Abdullah Petroleum Studies and Research Center
(KAPSARC), seconded from Saudi Aramco from
2008 to 2013. Saud has more than 25 years of working
experience with Saudi Aramco. His areas of specialty
include: Reservoir management, Energy economics,
Articial intelligence and data mining, Energy markets
modeling, Operations research and management, and
Strategy management.
Saud has been granted a U.S. patent, published several
technical papers, and authored and co-authored
three books: Articial Intelligence and Data Mining
Applications in the E&P Industry,” “Carbon Capture
and Storage: Technologies, Policies, Economics, and
Implementation Strategies,” and “Innovative Methods
for Analyzing and Forecasting World Gas Supply”.
Saud is a technical editor for the Energy Policy Journal,
SPE Reservoir Evaluation & Engineering Journal, and
Journal of Natural Gas Science and Engineering.
Saud is a member of the Society of Petroleum Engineers
(SPE), the International Association for Energy
Economics (IAEE), the Arab Energy Club, the European
Association of Geoscientists & Engineers (EAGE), and
Research Review
Alternative Investment Analyst Review
Tomouh. He is also a member of the SPE Articial
Intelligence & Petroleum Analytics Subcommittee.
at Saudi Aramco.
Saud earned his Ph.D. (with distinction) from Texas
A&M University, Texas, and M.Sc. and B.Sc. degrees
(with honors) from King Fahd University of Petroleum
and Minerals (KFUPM), Saudi Arabia, all in petroleum
engineering. He also earned Executive MBA degree
(with honors; rst-in-class) from Prince Mohammad
Bin Fahd University (PMU), Saudi Arabia.
Zhuo Huang is currently an assistant professor at the
National School of Development, Peking University,
in Beijing, China. He has a PhD in Economics from
Stanford University. Zhuo Huang acknowledges
nancial support from National Natural Science
Foundation of China (71201001) and Ministry of
Education Humanities and Social Sciences Youth Fund
Michael Gucwa is a PhD candidate in the Stanford
Management Science and Engineering department
whose dissertation focuses on the impact vehicle
automation will have on personal mobility, energy
consumption, and land use. His work is supported
by the Precourt Energy Eciency Center at Stanford
( which “works to understand and
overcome market, policy, technology, and human
behavioral barriers to economically ecient reductions
of energy use and to inform public and private
policymaking.” Michael has a M.S. in Management
Science and Engineering and a B.S. in Math and
Computational Science, both from Stanford.
Ali Nouri Dariani is a PhD candidate in the Stanford
Management Science and Engineering department,
Stanford University. His dissertation focuses on price
dynamics in commodities markets and structural
models of short-term stochastic price movements. Ali
has a B.Sc. and M.Sc. in Electrical Engineering from
University of Tehran, Iran.
Although the financialization of commodity markets has recently become a broadly discussed phenomenon, its implications for commodity investors remain unknown to a large extent. This paper focuses on whether the potential benefits of passive investment strategies in the commodity futures markets would be expected to continue with the increased financialization of the commodity futures markets. Firstly, a regression analysis was conducted in order to examine the correlation between financialization and the disappearance of roll yields. Secondly, after finding such a correlation, we assume that this correlation was driven by the increased financialization causing the decline in roll yields. Thirdly, simulation-based methods were implemented to examine the revised efficient frontiers after accounting for the potential impact of financialization on roll yields. Empirical research was based on returns on various asset classes and other related variables over the period between 1990 and 2012. As noted, the computations indicate that market financialization may have resulted in a decline in roll returns. This decrease in roll yields, should it continue, is of a potential magnitude that this would undermine the legitimacy of including commodity futures in traditional stock-and-bond portfolios.
Full-text available
The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. In addition to the commencement of the financialization era in oil markets, there have been structural changes in the global oil market. Financial instruments are communicating information about future conditions much more rapidly than in the past. Prices from long and short duration contracts have started moving more together. Sudden supply and demand adjustments, such as the financial crisis of 2008-2009, faster Chinese economic growth, the Libyan uprising, the Iranian nuclear standstill or the deepwater horizon oil spill, change expectations and current prices. The daily Brent spot price fluctuated between $30 and above $140 per barrel since the beginning of 2004. Both fundamental and financial explanations have been offered as explanatory factors. This paper selectively reviews the voluminous literature on oil price determinants since the early 1970s. It concludes that most researchers attribute the long-run oil price path to fundamental factors such as economic growth, resource depletion, technical advancements in both oil supply and demand, and the market organization of major oil petroleum exporting countries (OPEC). Short-run price movements are more difficult to explain. Many researchers attribute short-run price movements to fundamental supply and demand factors in a market with very little quantity response to price changes. Nevertheless, there appears to be some evidence of occasional financial bubbles particularly in months leading up to the financial collapse in 2008. These conflicting stories will not be properly integrated without a meeting of the minds between financial and energy economists.
Full-text available
During the 1970s, oil market models offered a framework for understanding the growing market power being exercised by major oil producing countries. Few such models have been developed in recent years. Moreover, most large institutions do not use models directly for explaining recent oil price trends or projecting their future levels. Models of oil prices have become more computational, more data driven, less structural and increasingly short run since 2004. Quantitative analysis has shifted strongly towards identifying the role of financial instruments in shaping oil price movements. Although it is important to understand these short-run issues, a large vacuum exists between explanations that track short-run volatility within the context of long-run equilibrium conditions. The theories and models of oil demand and supply that are reviewed in this paper, although imperfect in many respects, offer a clear and well-defined perspective on the forces that are shaping the markets for crude oil and refined products. The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. There are several kinds of models have been proposed, including structural, computational and reduced form models. Recently, artificial intelligence was also introduced. This paper provides: (1) model taxonomy and the uses of models providing the motivation for its preparation, (2) a brief chronology explaining how oil market models have evolved over time, (3) three different model types: structural, computational, and reduced form models, and (4) artificial intelligence and data mining for oil market models.
Economists have studied various indicators of resource scarcity but largely ignored the phenomenon of "peaking" due to its connection to non-economic (physical) theories of resource exhaustion. I consider peaking from the economic point of view, where economic forces determine the shape of the equilibrium extraction path. Within that framework, I ask whether the timing of peak production reveals anything useful about scarcity. I find peaking to be an ambiguous indicator. If someone announced the peak would arrive earlier than expected, and you believed them, you would not know whether the news was good or bad. However, I also show that the traditional economic indicators of resource scarcity (price, cost, and rent) fare no better, and argue that previous studies have misconstrued the connection between changes in underlying scarcity and movements in these traditional indicators.
This paper explores the impact of investor flows and financial market conditions on returns in crude oil futures markets. I argue that informational frictions and the associated speculative activity may induce prices to drift away from "fundamental" values, and may result in price booms and busts. Particular attention is given to the interplay between imperfect information about real economic activity, including supply, demand, and inventory accumulation, and speculative activity in oil markets. Furthermore, I present new evidence that there were economically and statistically significant effects of investor flows on futures prices, after controlling for returns in the United States and emerging-economy stock markets, a measure of the balance sheet flexibility of large financial institutions, open interest, the futures/spot basis, and lagged returns on oil futures. The largest impacts on futures prices were from intermediate-term growth rates of index positions and managed-money spread positions. Moreover, my findings suggest that these effects were through risk or informational channels distinct from changes in convenience yield. Finally, the evidence suggests that hedge fund trading in spread positions in futures impacted the shape of term structure of oil futures prices.
This paper examines the possible price impact of speculative bubbles and index-based investment activity on commodity futures prices over 2006–2008. I look specifically at crude oil, three non-ferrous metals (aluminium, copper and nickel) and three agricultural commodities (wheat, corn and soybeans). There is significant evidence for periods of explosive bubble behaviour in the copper market where I find three separate bubbles. I also identify a bubble in the soybeans market. The evidence for bubble behaviour is weaker for crude oil and nickel. Aluminium, corn and wheat appear to have been bubble-free. I also examine the effects of index-based investment on the same markets. There is strong evidence that index-based investment did contribute to the rises in oil and metals prices over 2006–2008 but weaker evidence for similar effects on grains prices. The maximum impact may have been to raise prices by the order of 15 per cent.
Oil price prediction has usually proved to be an intractable task due to the intrinsic complexity of oil market mechanism. In addition, the recent oil shock and its consequences relaunch the debate on understanding the behavior underlying the expected oil prices. Combining the dynamic properties of multilayer back propagation neural network and the recent Harr A trous wavelet decomposition, a Hybrid model HTW-MPNN is implemented to achieve prominent prediction of crude oil price. While recent studies focus on the determination of the best forecasting model by comparing various neural architectures or applying several decomposition techniques to the ANN, the new insight of this paper is to target the issue of the transfer function selection providing robust simulations on both in sample and out of sample basis. Based on the work of Yonaba, H., Anctil, F., and Fortin, V. (2010) “Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Stream flow forecasting”. Journal of Hydrologic Engineering, April, 275–283, we use three variants of activation function namely sigmoid, bipolar sigmoid and hyperbolic tangent in order to test the model's flexibility. Furthermore, the forecasting robustness is checked through several levels of input–hidden nodes. Comparatively, results of HTW-MBPNN perform better than the conventional BPNN. Our conclusions add a major attribute to the previous studies corroborating the Occam razor's principle, especially when simulations are constructed through training and testing phases simultaneously. Finally, more eligible forecasting power is found according to the wavelet oil price signal which appears to be the closest to the real anticipations of future oil price fluctuations.