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The Growing Interdependence Between Financial and Commodity Markets

Discussion paper by Mayer, Jörg/UNCTAD, 2009

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Financial investment has become increasingly important on commodity exchanges. This paper distinguishes two types of financial investors and emphasizes differences in their position taking motivation and price impacts. Index traders follow a passive strategy holding virtually only long positions. Money managers trade on both sides of the market and attempt to maximize short-term returns. Regression analysis indicates that: (i) index trader positions are particularly influenced by roll returns, while money managers emphasize spot returns; and that: (ii) money managers moved from emphasizing diversification to a more speculative strategy by taking commodity positions that are positively, rather than negatively, related to developments in equity markets. Granger-causality tests indicate that these differences translate into different price impacts: (i) index trader positions have a causal price impact particularly for agricultural commodities; and (ii) money managers had a causal impact during the sharp increases in the prices for some non-agricultural commodities.

No. 195
October 2009


THE GROWING INTERDEPENDENCE BETWEEN
FINANCIAL AND COMMODITY MARKETS






The GrowinG inTerdependence beTween
Financial and commodiTy markeTs


Jörg Mayer


No. 195
October 2009


Acknowledgement: Part of this paper draws on the author’s contributions to UNCTAD (2009a and 2009b).
The author is grateful to Dietrich Domanski, Chris Gilbert, Ugo Panizza, and seminar participants at the
University of London for comments on earlier drafts. Juan Pizzaro provided excellent research assistance
and econometric advice. The opinions expressed are solely those of the author and do not necessarily reflect
the views of UNCTAD or its Member States.


UNCTAD/OSG/DP/2009/3




ii


The opinions expressed in this paper are those of the author and are not to be taken as the official views
of the UNCTAD Secretariat or its Member States. The designations and terminology employed are also
those of the author.


UNCTAD Discussion Papers are read anonymously by at least one referee, whose comments are taken
into account before publication.


Comments on this paper are invited and may be addressed to the author, c/o the Publications Assistant,
Macroeconomic and Development Policies Branch (MDPB), Division on Globalization and Development
Strategies (DGDS), United Nations Conference on Trade and Development (UNCTAD), Palais des Nations,
CH-1211 Geneva 10, Switzerland (Telefax no: (4122) 9170274/Telephone no: (4122) 9175896). Copies
of Discussion Papers may also be obtained from this address.


Discussion Papers are available on the UNCTAD website at http://www.unctad.org.


JEL classification: F36, O13, G11, G14




iii


contents


Page


Abstract ...................................................................................................................................................... 1


i. inTroducTion ................................................................................................................................. 1


ii. The increasinG presence oF Financial invesTors
in commodiTy markeTs ............................................................................................................. 3


iii. Financial invesTmenT and commodiTy markeT eFFiciency .................................. 6


iv. posiTion TakinG oF diFFerenT Types oF Financial invesTors ................................ 8
A. Index traders ...................................................................................................................................... 9
B. Non-commercial traders excluding index traders ........................................................................... 11
C. Spread positions of non-commercial traders ................................................................................... 14
D. The size of individual financial positions ....................................................................................... 15


v. The impacT oF FinancializaTion on posiTion TakinG
and price developmenTs ........................................................................................................ 17
A. The impact of return and diversification motives on position taking ............................................. 17
B. Financial position taking and commodity price developments ....................................................... 20


vi. conclusions .................................................................................................................................. 23


reFerences ............................................................................................................................................ 25


Figure


1 Futures and options contracts outstanding on commodity exchanges,
December 1993–June 2009 .................................................................................................................... 3
2 Notional amount of outstanding over-the-counter commodity derivatives,
December 1998–December 2008 ........................................................................................................... 3
3 Estimated net long positions in agricultural products of commodity index traders
on United States commodity exchanges, January 2006–July 2009 ..................................................... 10
4a Net long positions of index traders on United States commodity exchanges,
per cent of total open interest, selected agricultural commodities ....................................................... 11
4b Estimated net long positions of index traders on United States commodity exchanges,
per cent of total open interest, selected non-agricultural commodities ............................................... 11
5 Financial positions and prices, selected commodities, January 2002–July 2009 ................................. 12
6 Share of non-commercial spread positions in total open interest,
selected commodities, 1997–2009 ....................................................................................................... 15


Table


1 Futures and options market positions, by trader group, selected agricultural commodities,
averages for 52 weeks prior to price peak during period January 2006–July 2009 .............................. 16
2 Regression results: non-commercial positions .................................................................................... 18
3 Regression results: index-trader positions ............................................................................................ 19
4 Granger-causality tests: commodity prices and financial positions, January 2006–June 2009 ............ 21
5 Granger-causality tests: commodity prices and financial positions, 52 weeks prior to price
peak during period January 2006–June 2009 ....................................................................................... 22






The GrowinG inTerdependence beTween
Financial and commodiTy markeTs


Jörg Mayer
UNCTAD


(joerg.mayer@unctad.org)


Abstract


Financial investment has become increasingly important on commodity exchanges. This paper
distinguishes two types of financial investors and emphasizes differences in their position taking
motivation and price impacts. Index traders follow a passive strategy holding virtually only long
positions. Money managers trade on both sides of the market and attempt to maximize short-term
returns. Regression analysis indicates that: (i) index trader positions are particularly influenced by
roll returns, while money managers emphasize spot returns; and that: (ii) money managers moved
from emphasizing diversification to a more speculative strategy by taking commodity positions that
are positively, rather than negatively, related to developments in equity markets. Granger-causality
tests indicate that these differences translate into different price impacts: (i) index trader positions
have a causal price impact particularly for agricultural commodities; and (ii) money managers had
a causal impact during the sharp increases in the prices for some non-agricultural commodities.


i. inTroducTion


Much of the recent commodity price developments have been attributed to changes in fundamental supply
and demand relationships. However, the extreme scale of the price changes since 2002, and the fact that
prices increased and subsequently declined across all major categories of commodities, suggests that,
beyond the specific functioning of commodity markets, broader macroeconomic and financial factors that
operate across a large number of markets need to be considered to fully understand recent commodity
price developments.


A major new element in commodity trading is the greater importance of financial investment on commodity
exchanges. Financial investors regard commodities as an asset class (comparable to equities, etc) and do
not necessarily trade on the basis of fundamental supply and demand relationships in specific commodity
markets. If financial investment has a price impact, commodity price developments will no longer merely
reflect changes in fundamentals but also be subject to influences from financial markets.1 As a result,
market participants with a commercial interest in physical commodities (i.e. producers and consumers)
will face increased uncertainty about the reliability of signals emanating from the commodity exchange
exchanges. Managing the price risk of market positions and making storage, investment and trading
decisions will become more difficult.


1 This paper focuses on potential effects on price levels. For potential effects on price volatility see Domanski
and Heath (2007), IMF (2008a) and UNCTAD (2009b).




2


A range of studies has explored the extent to which financial investment has affected recent commodity
price developments. The studies published before early 2009 usually found little evidence to support
this hypothesis.2 More recently, however, there appears to be an emerging consensus, at least among
policymakers, that financial investors have affected commodity price developments to an extent that
warrants a tightening of supervision and regulation of commodity futures exchanges (Gensler, 2009;
United States Senate, 2009).3


Most existing studies on the link between financial investment and commodity price developments have
concentrated on a specific type of financial investors: Domanski and Heath (2007) and IMF (2006) focus
on so-called ‘non-commercial’ market participants (i.e. money managers such as hedge funds), whereas
Gilbert (2009) and Masters (2008, 2009) emphasize so-called ‘index traders’ (i.e. financial investors that
try to replicate the returns of a particular commodity futures index). IMF (2008b) looks at the impact on
commodity price developments of both these types of financial investors but does not explore differences
in their trading behaviour and restricts the analysis of index-trader effects on prices to those agricultural
commodities for which data are readily available.


The major new element of this paper is the explicit distinction between these two types of financial
investors – money managers and index traders – in terms of both their trading motivations and their
impact on commodity price developments. The paper also presents novel estimates of index trader
positions in four non-agricultural markets (copper, gold, natural gas and crude oil) for which no official
data are available.


The main empirical finding of this paper is that index traders appear to have affected the prices of a wide
range of commodities over the past three and a half years, while those of non-commercial traders excluding
index traders have tended to affect the prices mainly of non-agricultural commodities when their prices
were increasing sharply. The empirical findings also point to an increased positive relationship between
financial investor positions on commodity futures exchanges and equity market developments, and to a
decreased importance of hedging against dollar depreciation as a determinant of position taking. Both
these changes have created a greater interdependence between financial and commodity markets.


The remainder of the paper is structured as follows. The next section briefly discusses aggregate evidence
on financial investment in commodity markets and the general motivation that underlies such investment.
Section III discusses two channels through which financial investors may cause the functioning of
commodity exchanges to deviate from an ideal efficient market – information failures and weight-of-
money effects. Section IV assesses the importance of different types of financial investors on commodity
exchanges. Section V conducts an econometric exploration of the trading motivations of both money
managers and index traders, as well as Granger-causality tests of the impact of their position taking on
commodity price developments. Section VI concludes.


2 See, for example, IMF (2006, 2008a, 2008b) and (CFTC, 2008a). For a sharply contrasting view see Masters
(2008). The careful econometric studies by Domanski and Heath (2007) and Gilbert (2008) provide more mixed
results.
3 Confronting the various arguments made by academics and policymakers regarding the impact of speculation
on oil price developments, the recent paper by Khan (2009: 8) also concludes “that speculation drove an oil price
bubble in the first half of 2008.”




3


ii. The increasinG presence oF Financial
invesTors in commodiTy markeTs


Most financial investors in commodities take positions on commodity futures and options markets.4
Financial investors have been active on such markets since the early 1990s. However, in the aftermath
of the dot-com crash on equity markets in 2000, their involvement increased, rising dramatically in early
2005, as reflected in aggregate measures of financial investment in commodity markets: the number of
futures and options contracts outstanding on commodity exchanges worldwide rose more than threefold
between 2002 and mid-2008 (figure 1), and, during the same period, the notional value of commodity-
related contracts traded over the counter (OTC) (i.e. contracts traded bilaterally, and not listed on any
exchange) increased more than 14-fold, to $13 trillion (figure 2).5 Financial investments in commodities
fell sharply starting in mid-2008 before picking up again more recently.


Financial investors in commodity futures markets regard commodities as an asset class, comparable to
equities, bonds, real estate, etc. They take positions in commodities as a group based on the risk-return
properties of portfolios that contain commodity futures relative to those that are confined to traditional
asset classes. This strategy supposes that commodities have a unique risk premium which is not replicable


4 Financial investors can gain exposure on commodity markets also through spot market activities (i.e. buying and
accumulating physical commodities in inventories). This strategy mainly aims at hedging against inflation and it is
usually confined to the relatively small markets for precious metals such as gold and silver. It is more difficult to pursue
this physical market strategy for other commodities, especially because of the greater storage costs they entail.
5 The Bank for International Settlements (BIS) is the only source that regularly provides publicly available
information about OTC commodity trading. However, commodity-specific disaggregation is not possible with these
data. Notional amount refers to the value of the underlying commodity. However, traders in derivatives markets
do not own or purchase the underlying commodity. Hence, notional value is merely a reference point based on
underlying prices.


Figure 1


Futures and options contracts
outstanding on commodity exchanges,


december 1993–June 2009


(Number of contracts, millions)


0


10


20


30


40


50


60


1993 1995 1997 1999 2001 2003 2005 2007 2009


Source: BIS, Quarterly Review, September 2009, table 23B.


Figure 2


notional amount oF outstanding over-the-
counter commodity derivatives,


december 1998–december 2008


(Trillions of dollars)


0


2


4


6


8


10


12


14


1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
(June)


2008


Other commodities


Other precious metals


Gold


Source: BIS, Quarterly Review, September 2009, table 22A.




4


by combining other asset classes, and that they form a fairly homogenous class which can be put together
through a few representative positions (Scherer and He, 2008). Long-term empirical evidence in fact
indicates that commodity futures contracts exhibit the same average return as investments in equities, but
over the business cycle their return is negatively correlated with that on equities and bonds. Moreover,
the returns on commodities are less volatile than those on equities or bonds, because the pair-wise
correlations between returns on futures contracts for various commodities (e.g. oil and copper, or oil and
maize) traditionally have been relatively low (Gorton and Rouwenhorst, 2006).6


Contrary to equities and bonds, commodity futures contracts also have good hedging properties against
inflation (i.e. their return is positively correlated with inflation). This is because commodity futures
contracts represent a bet on commodity prices, such as those of energy and food products that have a
strong weight in the goods baskets used for measuring current price levels. Also, futures prices reflect
information about expected changes in commodity prices, so that they rise and fall in line with deviations
from expected inflation.


Furthermore, investing in commodity futures contracts may provide a hedge against changes in the
exchange rate of the dollar. Most commodities are traded in dollars and commodity prices in dollar terms
tend to increase as the dollar depreciates. However, IMF (2008b: 63) shows that measured in a currency
basket, commodity prices are generally less correlated with the dollar and the sign of the correlation is
reversed. This suggests that changes in the value of the dollar against other currencies may partly explain
the negative correlation between the prices of dollar-denominated commodities and the dollar.


Two broad groups of financial investors on commodity exchanges may be distinguished according to
differences in position taking motivation.7 Money managers, such as commodity trade advisors and
commodity pool operators who may operate hedge funds, have short-term investment horizons and
take positions on both sides of the market. This enables them to earn positive returns in both rising and
declining markets. Some money managers may conduct research on individual commodities and thus react
to changes in commodity market fundamentals. But the great majority rely on computerized ‘technical’
tools, such as trend identification algorithm and investment rules (which may include various kinds of
arbitrage trades). These technical tools may be calibrated to signals from commodity markets alone or
also include signals from other asset markets. Computerized technical trading often automatically triggers
changes in position taking and hence risks creating trends that the programmes then identify and follow.
This can result in herd behaviour and price bubbles.


Money managers have been active on commodity exchanges for many years. However, it is likely that they
changed their trading behaviour. Traditionally, they may have taken positions on commodity exchanges
for strategic reasons with a view to diversifying their portfolios. But with the growing acceptance –
including as a reaction to the influential study by Gorton and Rouwenhorst (2006) – of the notion that
commodities as an asset class are a quasi natural hedge of positions on equity markets they may have
given less priority to such diversification motives and concentrated on return considerations. Section V
below will further discuss this potential change in position taking behaviour.


6 These salient features are based on data for periods in which few investors were actually following this strategy.
Whether these features have prevailed also in more recent periods remains untested.
7 Financial investors must be distinguished from traditional speculators. Traditional speculators have normally
been the counterparties of producers and consumers of the physical commodities who use futures markets to offset
price risk. Traditional speculators react to changes in commodity market fundamentals and mostly trade in only
one or two commodities on which they have intimate knowledge. Hence, position taking by traditional speculator
does not transmit signals from financial to commodity markets.




5


The presence on commodity exchanges of the second type of financial investors – index traders – is a
relatively recent phenomenon. Index traders follow a passive investment strategy (i.e. they take no view
on the performance of individual commodities). Financial investors gain exposure in commodity indexes
by entering into a bilateral financial agreement, usually a swap, with a bank or another large financial
institution.8 The investor purchases parts in a commodity index from the bank. The bank in turn hedges
its exposure resulting from the swap agreement through futures contracts on a commodity exchange.


Financial investment in commodity indexes involves only “long” positions (i.e. pledges to buy commodities)
and relates to forward positions (i.e. no physical ownership of commodities is involved at any time).9
Index funds buy forward positions (often relating to futures contracts with a remaining maturity of about
75 working days, i.e. roughly three calendar months), which they sell as expiry approaches (at about
25 working days, i.e. roughly one calendar month, prior to expiry of the contract) and use the proceeds
from this sale to buy forward positions again. This means that investors who own, say, the October crude
oil contract will sell that contract and buy the December contract before delivery begins on the October
contract. Then they will later “roll” from November into January, and so on. This process – known as
“rolling” – gives rise to a roll yield which is positive when the prices of futures contracts are progressively
lower in the distant delivery months (i.e. in a “backwardated” market) and negative when the prices of
futures contracts with longer maturities are progressively higher (i.e. in a “contango” market).10 This
specific characteristic of index trading implies that roll yields are of particular importance to position
taking by index traders.


Two common indexes are the Standard & Poor’s Goldman Sachs Commodity Index (S&P GSCI) and the
Dow Jones-Union Bank of Switzerland Commodity Index (DJ-UBSCI) (previously called Dow Jones-
American International Group Commodity Index, DJ-AIGCI).11 These indexes are composites of futures
contracts on a broad range of commodities (including energy products, agricultural products and metals)
traded on commodity exchanges.


8 There are three basic types of instruments related to commodity indexes (United States Senate, 2009: 83–88).
Commodity index swaps are the most common index instrument. Of relatively smaller importance are exchange-
traded funds and exchange-traded notes that offer index-related shares for sale on a stock exchange.
9 A long position is a market position that obligates the holder to take delivery (i.e., to buy a commodity). This
contrasts with a short position, which obligates the holder to make delivery (i.e., to sell a commodity). The aggregate
of all long open positions is equal to the aggregate of all short open positions. For individual traders, net long
positions are total long positions minus total short positions. Open interest is the total number of futures contracts,
long or short, in a market that has been entered into and not yet liquidated by an offsetting transaction or fulfilled
by delivery.
10 The roll yield can be positive independent of the term structure of futures contract when in the period during which
index traders hold a contract the increase in spot prices is sufficiently high to fully compensate the contango.
11 In the S&P GSCI, weights are based on five-year averages of relative world production quantities; energy
products usually account for about two thirds of the total index. In the DJ-UBSCI, weights are also based on five-
year averages but primarily rely on the relative amount of trading activity of a particular commodity; weights are
limited to 15 per cent for individual commodities and to one third for entire sectors in order to allow for a greater
degree of diversification across commodities.




6


iii. Financial invesTmenT and commodiTy
markeT eFFiciency


Scepticism is often expressed with regard to the link between financial investment and commodity price
developments. This scepticism is usually based on the efficient market hypothesis.12 According to this view,
prices perfectly and instantaneously respond to all available information relevant to a freely operating
market. Market participants continuously update their expectations from inflowing public and private
information. This means that prices will move either when new information becomes publicly available
(e.g. when harvest forecasts or changes in oil production are announced), or when private information is
incorporated in prices through transactions. When this is the case, the value of a futures contract will be
an unbiased estimate of the spot price on the delivery date specified in the futures contract. Policymakers,
especially central bankers, commonly base part of their decisions on this feature as they use the price of
commodity futures contracts as a proxy for the market’s expectations of future commodity spot prices
(Svensson, 2005; Greenspan, 2004).


There are at least two reasons why the efficient market hypothesis may fail in relation to commodity
markets, at least in the short run, so that the value of futures contracts will not serve this price discovery
purpose.13 First, changes in market positions may occur in response to factors other than information
about market fundamentals. Second, individual market participants may make position changes that are
so large relative to the size of the market that they move prices (the so-called “weight-of-money” effect).
Significant impacts of these two factors will cause mechanisms that would prevent prices from moving
away from levels determined by fundamental supply and demand factors – the efficient absorption of
commodity-related information and sufficiently strong price elasticity of supply and demand – to be
relatively weak on commodity markets.14


12 Another source of scepticism, which was widely discussed in the blogosphere in the first half of 2008, relates
to an argument introduced by Krugman (2008) in relation to oil prices. According to this argument speculative
activity that drives prices above fundamental equilibrium levels will cause market imbalances and excess supply,
which eventually must result in inventory accumulation. Oil inventories had not increased, so that, according to this
reasoning, speculation cannot have played a role in the 2008-oil-price hike. However, Khan (2009: 5) argues that
data on oil inventories are notoriously poor (most non-OECD countries, which account for almost half of world
demand for crude oil and include very large consumers such as China, do not report data on oil inventories, and
oil stored in tankers distorts the inventory data reported by OECD countries) so that one should not draw strong
inferences from such data. More generally, the short-run elasticity of commodity supply and demand is extremely
low, so that only very sharp and lasting price changes can be expected to trigger significant supply and demand
responses and related changes in inventories. Hence, the accumulation of inventories will occur only gradually and
spot prices will overshoot during this process.
13 The study by the United States Senate (2009) on the wheat market emphasizes a third mechanism, namely the
lack of convergence between the price of wheat futures contracts and the price of wheat in the cash market. The
study found “significant and persuasive” evidence that index traders were one of the major causes for this lack of
price convergence upon contract expiration.
14 As a result, futures prices are less accurate forecasts than simple alternative models such as a random walk without
drift, i.e. expecting no change from current spot prices (see Alquist and Kilian, 2007, for such evidence related to
oil prices). Bernanke (2008) also highlights the difficulty in obtaining a meaningful gauge for future commodity
price movements from signals obtained from commodity futures markets and emphasizes the importance of finding
alternative approaches to forecasting commodity market movements.




7


To examine how different sorts of information may influence market positions, it is useful to group market
participants into three categories based on differences in their rationale for position taking: informed
traders, uninformed traders and noise traders.15


Informed traders rely on information about current market fundamentals and on forecasts of future market
conditions. On commodity markets, informed traders are those market participants who have an interest
in the physical commodity (i.e. producers and consumers) and use commodity futures exchanges to hedge
price risk, as well as traditional speculators that have usually been their counterparties in hedging. Both
these types of traders have intimate knowledge of specific commodity markets and base their position
taking on fundamentals. Informed traders, nonetheless, face two difficulties in assessing commodity
market developments: (i) medium- and longer-term commodity supply and demand conditions are
subject to considerable uncertainty for example because of unknown depletion rates of non-renewable
resources and unknown effects of climate change on agricultural production; and (ii) inventory data,
which provide valuable signals for short-term price expectations, suffer from significant measurement
errors (Gorton, Hayashi and Rouwenhorst, 2007; Khan, 2009), and data on current global commodity
supply and demand conditions are published with long time lags and are frequently revised. Therefore,
informed traders must formulate price expectations on the basis of partial and uncertain data. This may
lead them to focus on a small number of available signals, with the attendant risk of herding and copying
the behaviour of others. Alternatively, it may cause traders to consider past price movements themselves
as a good guide to future developments.


Noise traders trade for broader strategic reasons, and make position changes irrespective of prevailing
conditions on commodity markets. On commodity markets, index traders and money managers that
calibrate their technical tools to signals from other asset markets behave like noise traders: they change
their total positions in commodities based on information relating to other asset markets but which has
no relevance for commodity markets. In addition, index traders tend to change the composition of their
positions in commodities in response to different price changes for different commodities with a view to
maintaining a specific commodity’s predetermined weight in a commodity index. It is difficult for other
traders to judge whether market prices are changing because of the position changes of the noise traders
or as a response to new information about market fundamentals.


Uninformed traders, who glean information on future price developments from current and past price
movements, are particularly exposed to such situations. They follow what may be called “momentum
strategies” – buying commodities that have experienced rising prices and selling those that have
underperformed. Uninformed traders observe price movements but are unable to identify whether price
changes were caused by informed or noise trading. Hence, they risk misinterpreting a noise trader’s
position change as a genuine price signal and, by incorporating this signal into their trading strategy,
perpetuate the “informational” value of this signal across the market. Given that uninformed traders
often use similar trend identification techniques, they run the risk of collectively generating the trends
that they then individually identify and follow. On commodity markets, money managers who calibrate
their trade identification algorithms and investment rules to signals from commodity markets are likely
to behave like momentum traders.


One effect of momentum trading that uses statistical analysis tools for position taking is that the resulting
changes in positions can be anticipated by other market participants. Thus it provides continued arbitrage
possibilities. Informed traders, who know the fundamental value of a commodity, will try to benefit from
such profit opportunities. Informed traders working for financial institutions will do this in order to meet


15 The following discussion is based on recent financial market models that show how speculators can affect prices
beyond the very short term (Harrison and Kreps, 1978; De Long et al., 1990; Banerjee, 2009; Cao and Ou-Yang,
2009).




8


their institutions’ short-term performance targets or reporting requirements, even if doing so implies going
against signals from long-term fundamental supply and demand factors (de Long et al., 1990). This can
lead to speculative bubbles. The same kind of snowball effect can be created by financial investors when
they react to signals from other, non-commodity markets.


A second reason why the efficient market hypothesis may fail on commodity markets relates to the fact
that the number of counterparties (especially those with an interest in physical commodities) and the size
of their positions are less than perfectly price elastic. Thus, large orders may face short-term liquidity
constraints and cause significant price shifts. This implies the possibility of a “weight-of-money” effect:
position changes which are large relative to the size of the total market have a temporary, or even a
persistent, price impact. As a result, market participants that take large positions can move prices. This
possibility is particularly high in commodity markets where the short-run price elasticity of both production
and consumption is very low, and hence the physical adjustment mechanisms of markets are weak. As a
result, in tight markets with minimum inventory levels, the relevance of expectations based on longer-
term fundamental factors sharply declines, which makes it difficult to determine a market price solely
on the basis of fundamentals.


The weight-of-money effect relates primarily to index-based investment. Index-trader positions can be
large relative to the size of the entire market, as shown below. One reason for their relatively large size
relates to the fact that index traders take virtually only long positions and that they take positions across
many commodities in proportions that depend only on the weighting formula of the particular index,
independent of the specific market conditions for the individual commodities contained in the index.
Hence, large positions taken by index traders implies a significant risk that the weight-of-money effect
will exacerbate the price impact of trading in response to factors other than information about commodity
market fundamentals. Moreover, informed traders who know that buy-side investment is dominated by
index-based investment will demand a higher risk premium to engage in short positions with a view to
compensating the risk that large index-based buying may push prices to ever higher levels. Both these
mechanisms can cause speculative bubbles.


iv. posiTion TakinG oF diFFerenT Types oF Financial invesTors


Making the analytical distinction between informed, uninformed and noise traders, discussed in the
previous section, is straightforward in principle but in practice making this separation is not easy. The
Commodity Futures Trading Commission (CFTC) – the institution mandated to regulate and oversee
commodity futures trading in the United States – publishes trading positions in anonymous and summary
form in its weekly Commitments of Traders (COT) reports. The CFTC classifies market participants as
“commercial” if they are hedging an existing exposure, and as “non-commercial” if they are not.16 The
main purpose of these reports is to improve transparency about activity in futures markets. However, it
is widely perceived that, as a consequence of the growing diversity of futures market participants and
the greater complexity of their activities, the COT data may fail to fully represent such activity (CFTC,
2006a). This is because those hedging, and therefore defined as commercial market participants, have
normally been considered as being those entities that use transactions in futures contract to reduce risk
in the conduct of a commercial enterprise. However, many market participants who report positions as
hedges, and who therefore fall under the “commercial” category, are in fact commodity swap dealers, such


16 More precisely, among the types of firms engaged in business activities that can be hedged and therefore
classified as “commercial” by the CFTC are merchants, manufacturers, producers, and commodity swaps and
derivative dealers. The CFTC classifies as “non-commercial” all other traders, such as hedge funds, floor brokers
and traders, and non-reporting traders (i.e. those traders whose positions are below the reporting thresholds set by
an exchange).




9


as commodity index traders. These dealers have no commercial interest in the physical commodities – they
hedge to offset financial positions. If their underlying positions were held directly as commodity futures
contracts (rather than being intermediated through OTC swap agreements), they would be categorized
as “non-commercial”.


Responding to these concerns, in 2007 the CFTC started to issue a Supplementary Report with data on
positions of commodity index traders (CITs) for 12 agricultural commodities (CFTC, 2006b).17 The index
trader positions include both pension funds, previously classified as non-commercial traders, and swap
dealers, that had been classified as commercial traders. According to the CFTC (2009), CITs generally
replicate a commodity index, but may belong to either the commercial or non-commercial category.


The remainder of this section examines: (i) the net long position of index traders; (ii) the net long positions
of non-commercial traders excluding index traders; (iii) spread positions of non-commercial traders; and
(iv) the size of individual financial positions.


a. index traders


The data published in the COT Supplementary Report can be used to estimate the development of index-
based positions in agricultural products on United States commodity exchanges. To prevent different price
movements for different commodities from unduly influencing the results, the estimation is based on data
on the number of contracts, and to better reflect its evolution over time it is expressed as index numbers.
The estimation results suggest that the size of net long positions of index traders in agricultural products
on United States commodity exchanges more than doubled between January 2006 and May 2008 (see
figure 3).18 Index trader positions recorded sharp rises in the first quarter of 2006 and between the fourth
quarter of 2007 and the second quarter of 2008. They fell sharply in the third and fourth quarters of 2008
but, starting in March 2009, have rebounded to their levels of end-2006.


The question arises as to whether the above index for agricultural commodities is representative also of
index trader positions in non-agricultural products. There are three main reasons why this may be the
case. First, the weights of individual commodities in the commodity futures indexes change only slowly
and marginally. Second, the data on total index-trader positions that CFTC (2008b: 36) reports on the
basis of a questionnaire to which 34 swap dealers and index funds responded indicate that the weights of
individual commodities in terms of notional value are indeed fairly stable; the weights of the 12 agricultural
products on the basis of which the above index was calculated moved from 36.6 per cent in December


17 These 12 commodities are: feeder cattle, live cattle, cocoa, coffee, cotton, lean hogs, maize, soybeans, soybean
oil, sugar, Chicago wheat and Kansas wheat. The reports have so far not included similar data for energy and metals
markets because inter alia many swap dealers in metals and energy futures contracts have physical activities on
their own account so that it is difficult to separate hedging from speculative activities (CFTC, 2008b: 48–49).
18 This estimation mainly follows the methodology used to calculate the ‘Corazzolla index’ in Gilbert (2009).
However, the Corazzolla index maintains the individual product weights calculated for 3 January 2006 throughout
the sample period. By contrast, for the estimation in this paper these weights are adjusted weekly for changes in
the value of the futures contracts for the individual commodities and annually for changes in the weight of each
of the 12 commodities in the S&P-GSCI and DJ-UBSCI. The annual re-weighting is based on the assumption that
all index traders follow the energy-heavy S&P GSCI or the agriculture-heavy DJ-UBSCI, with an imposed fixed
market share of 50 per cent each of the S&P GSCI and the DJ-UBSCI. These methodological differences change
the estimation results for agricultural positions only marginally. But they are necessary to make similar estimations
for those commodities that are not covered by the COT Supplementary Report (see below).




10


2007 to 36.7 per cent in March 2008 and
36.1 per cent in June 2008.19 Third, the
development of index trader positions in
crude oil calculated by Masters (2009: 13)
closely resembles that of the index shown
in figure 3.


An application of the estimated index to
non-agricultural commodities that allows
an assessment of index traders’ market
shares in terms of number of contracts
(i.e. the variable that will be used in the
econometric estimations below) requires
an estimate of index trader positions at the
beginning of the sample period (3 January
2006). This may be done by calculating the
notional value of a futures contract on that
date, combined with the assumption that all
index traders follow the S&P GSCI or the
DJ-UBSCI with an imposed fixed market
share of 50 per cent each of the S&P GSCI
and the DJ-UBSCI.


The results of this estimation for four
non-agricultural commodities – copper,
gold, natural gas and crude oil (West Texas
Intermediate, WTI) – expressed as shares
in total open interest is shown in figure 4.


The figure also shows the same measure, based on actual data from the COT Supplementary Report,
for four agricultural products (maize, soybeans, soybean oil, and wheat traded on the Chicago Board of
Trade). The figure indicates that the importance of index positions in the open interest on United States
commodity exchanges has been relatively stable, except for copper, but also that its size strongly diverges
across commodities (figure 4). Index positions are particularly important in the copper (30–75 per cent)
and wheat markets (35–50 per cent), account for about 15–30 per cent in the markets for crude oil, maize,
soybeans and soybean oil, but are of comparatively low importance in the gold and natural gas markets
(about 10 per cent).20


19 The fact that the number of survey respondents remained unchanged over the sample period implies that the
absence of significant changes in commodity weights may be taken as representative of total index trader positions.
By contrast, the fact that the absolute size of these weights of agricultural products are close to that of agricultural
products in the agriculture-heavy DJ-UBSCI, but more than double that in the, at least as important, S&P GSCI,
indicates that the proportions of the data reported by CFTC (2008b: 36) cannot be taken as representative of index
trader positions on United States commodity exchanges. Indeed, the data reported by CFTC (2008b) comprise
more than index trader positions on commodity exchanges and also include exchange-traded funds and exchange-
traded notes, which are traded on stock exchanges, as well as indirect investment through over-the-counter swap
agreements with financial firms.
20 It should be noted that these data refer only to United States commodity exchanges. For the copper market the
London Metal Exchange (LME) is more important than the New York Mercantile Exchange (NYMEX) to which
the data in figure 4b refer. Thus, the data indicated in figure 4b are likely to overestimate index trader investment
in the copper market overall. However, this is of little importance in the present context because arbitrage between
the LME and NYMEX markets is near perfect.


Figure 3


estimated net long positions in agricultural
products oF commodity index traders on


united states commodity exchanges,
January 2006–august 2009


(Index numbers January 2006 = 100)


100


120


140


160


180


200


220


3/1/06 3/7/06 3/1/07 3/7/07 3/1/08 3/7/08 3/1/09 3/7/09


Source: Author's calculations based on data from Bloomberg, Goldman
Sachs, Dow Jones Indexes, and CFTC.




11


b. non-commercial traders excluding index traders


To gauge the importance of index-trader positions relative to those taken by other financial investors it is
useful to compare the net long positions of index traders with those of non-commercial market participants.
For agricultural commodities, the required data are readily available form the CFTC Supplementary
Report. For non-agricultural commodities, the above estimation of index-trader positions needs to be
complemented by an estimation regarding the share of index-trader positions included in the commercial
trader category and that included in the non-commercial trader category. Assuming that the distribution
of index-trader positions between the commercial and non-commercial trader categories is the same
for agricultural and non-agricultural commodities, this may be done by calculating such a measure for
agricultural commodities, weighted by their importance in index-trader positions, and applying this
measure to the non-commercial positions that the CFTC reports for non-agricultural commodities.


Figure 4a


net long positions oF index traders on united states
commodity exchanges, per cent oF total open interest,


selected agricultural commodities


Source: Author's calculations based on CFTC databases.


Figure 4b


estimated net long positions oF index traders on united states
commodity exchanges, per cent oF total open interest,


selected non-agricultural commodities


Source: Author's calculations based on CFTC databases.


0


10


20


30


40


50


60


70


80


3/1/06 3/7/06 3/1/07 3/7/07 3/1/08 3/7/08 3/1/09 3/7/09


Soybeans Soybean oil


0


10


20


30


40


50


60


70


80


3/1/06 3/7/06 3/1/07 3/7/07 3/1/08 3/7/08 3/1/09 3/7/09


Wheat Chicago Corn


0


10


20


30


40


50


60


70


80


3/1/06 3/7/06 3/1/07 3/7/07 3/1/08 3/7/08 3/1/09 3/7/09


Gold Copper


0


10


20


30


40


50


60


70


80


3/1/06 3/7/06 3/1/07 3/7/07 3/1/08 3/7/08 3/1/09 3/7/09


Crude oil Natural gas




12


This calculation indicates that on average 85–90 per cent of index-trader positions are included in the
commercial trader category.

Figure 5 compares, for the period January 2006–July 2009, the net long positions taken by index traders
with those taken by non-commercial traders excluding index traders for the four agricultural and the
four non-agricultural commodities already considered above. It indicates that index-trader positions
are significantly higher for all the selected commodities, except gold. The estimated relatively low
importance of index trader positions on gold futures exchanges suggests that for precious metals financial
investors may prefer acquiring commodities positions by buying and accumulating physical commodity
in inventories.21

Figure 5 also shows, for the period January 2002–December 2005 (i.e. the period before index trader data
become available), net long non-commercial positions for the eight commodities and compares, for the
period January 2002–July 2009, the evolution of financial positions with that of commodity prices. This


21 According to Barclays Capital (cited in Financial Times, 3 September 2009: 24), precious metals account for
three fourths of exchange-trade products which, in turn, account for one third of managed commodity assets (which
is only slightly less than index trading). Exchange-traded products are backed by physical inventories.


Figure 5


Financial positions and prices, selected commodities, January 2002–July 2009


Wheat (Chicago Board of Trade)


-100


-50


0


50


100


150


200


250


1/1/02 7/1/03 6/1/04 4/1/05 3/1/06 2/1/07 1/1/08 6/1/09


0


200


400


600


800


1 000


1 200


1 400


Maize (Chicago Board of Trade)


-200


-100


0


100


200


300


400


500


1/1/02 7/1/03 6/1/04 4/1/05 3/1/06 2/1/07 1/1/08 6/1/09


0


100


200


300


400


500


600


700


800


Soybeans (Chicago Board of Trade)


-100


-50


0


50


100


150


200


250


1/1/02 7/1/03 6/1/04 4/1/05 3/1/06 2/1/07 1/1/08 6/1/09


0


200


400


600


800


1 000


1 200


1 400


1 600


1 800


Soybean oil (Chicago Board of Trade)


-60


-40


-20


0


20


40


60


80


100


1/1/02 7/1/03 6/1/04 4/1/05 3/1/06 2/1/07 1/1/08 6/1/09


0


10


20


30


40


50


60


70


80


Net long non-commercial positions; futures and options contracts (thousands)
Net long non-commercial positions excl. CIT; futures and options contracts (thousands)
Net long CIT positions; futures and options contracts (thousands)
Price (right scale)




13


comparison provides only scant evidence of a correlation between position and price changes.22 While
there clearly are periods and commodities where positions and prices have moved together, especially
during the recent downturn and occasionally during the previous price upturn, there are other times when
positions have not risen during periods of rapid price appreciation. For example, in the wheat market
there was no increase in either non-commercial positions or index trader positions during the steep price
increase from mid-2007 to the end of the first quarter of 2008. By contrast, during the same period there
appears to have been a positive correlation between market positions and prices in the maize and soybean
markets, while the evidence is mixed for the soybean oil, gold and natural gas markets.


For oil and copper, financial positions declined along with prices in the second half of 2008. Evidence
for the earlier increases in copper prices is more mixed: net long positions by non-commercial traders


22 The absence of any systematic difference in price developments between commodities that are traded on futures
exchanges and those that are not is sometimes cited as further evidence for an absence of any significant impact of
financial investors on price developments (ECB, 2008: 19).


Figure 5 (concluded)


Financial positions and prices, selected commodities, January 2002–July 2009


Source: Author's calculations based on weekly data from Bloomberg and CFTC.
Note: CIT = commodity index traders.
Price refers to $/barrel for crude oil, cents/bushel for wheat, maize and soybeans, $/million British thermal unit (mmBtu)


for natural gas and cents/lb for copper and soybean oil.


Crude oil, light sweet (NYMEX)


-100


0


100


200


300


400


500


600


700


1/1/02 7/1/03 6/1/04 4/1/05 3/1/06 2/1/07 1/1/08 6/1/09


0


20


40


60


80


100


120


140


160


Copper (COMEX)


-30


-20


-10


0


10


20


30


40


50


60


70


1/1/02 7/1/03 6/1/04 4/1/05 3/1/06 2/1/07 1/1/08 6/1/09


0


50


100


150


200


250


300


350


400


450


Gold (COMEX)


0


20


40


60


80


100


120


140


160


180


200


1/1/02 7/1/03 6/1/04 4/1/05 3/1/06 2/1/07 1/1/08 6/1/09


0


200


400


600


800


1 000


1 200


Natural gas (NYMEX)


-300


-250


-200


-150


-100


-50


0


50


100


150


200


1/1/02 7/1/03 6/1/04 4/1/05 3/1/06 2/1/07 1/1/08 6/1/09


0


2


4


6


8


10


12


14


16


18


Net long non-commercial positions; futures and options contracts (thousands)
Estimated net long non-commercial positions excl. CIT; futures and options contracts (thousands)
Estimated net long CIT positions; futures and options contracts (thousands)
Price (right scale)




14


excluding index traders declined during the period of the sharpest price increase – roughly from the
beginning of 2004 through mid-2006 – but there appears to be a much closer correlation for the period
early-2007–early-2008. For oil, positions by non-commercial traders excluding index traders exhibited
strong volatility, even as oil prices rose almost continuously from the beginning of 2007 through the
second quarter of 2008. By contrast, there appears to be a fairly close correlation between the estimated
net long index-trader positions and oil price development over the entire period (January 2006–July 2009)
for which such positions could be estimated.


Figure 5 also indicates that, since the beginning of 2009, there has been an increase in the net long
positions of both index traders and non-commercial participants excluding index traders. This suggests
that after the strong decline in their positions during the second half of 2008, both these groups are once
again taking large positions on commodity markets.


While the evidence in figure 5 does not point to a long-standing correlation between position and price
changes across the selected eight commodities, for most of them some correlation is present over sub-
periods, as peaks and turning points seem to occur around the same time across the two series. This
suggests that any analysis of a relationship between position and price changes may be sensitive to the
choice of time period. This issue will be analysed more closely in section V below.


c. spread positions of non-commercial traders


The above evidence on net long positions gives an incomplete picture of speculative activities by
non-commercial traders. According to Alquist and Kilian (2007: 34) a “natural measure of the relative
importance of speculative activities is the number of non-commercial spread positions expressed as a
percentage of the reportable open interest positions”. Holding spread positions in a specific commodity
implies the purchase of a futures contract regarding one delivery month against the sale of a futures
contract for another delivery month of the same commodity.23 This means that spread positions can
indicate overall speculative activity but, depending on how they are constructed, such positions may
reflect speculation on rising or falling prices.


There has been a marked and sustained increase in the importance of non-commercial spread positions
in energy markets, as well as in some agricultural markets (figure 6). In the crude oil market, spread
positions started to increase sharply in late-2003 as well as in early-2005, and the percentage share of
spread positions in total open interest has remained at historically high levels. In the natural gas market,
spread positions increased in mid-2001 and this increase intensified in mid-2004. Between early-2006 and
mid-2008, there was a strong and sustained increase in the importance of spread positions in the markets
of the four agricultural commodities shown in the figure. For copper and gold, spread positions have been
more volatile. As noted by Alquist and Kilian (2007), there have been other spikes in speculative activity
in the past, but the recent increases in non-commercial spread positions are unprecedented over the past
20 years: between 2005 and 2008, the percentage shares in question doubled for oil, gas and soybean
oil, and they increased four-fold for gold, copper, wheat, corn, and soybeans. This indicates substantial
speculative behaviour in all these markets.


23 This implies that taking spread positions is particularly important in markets with strong seasonality.




15


D. The size of individual financial positions


A primary concern often expressed with respect to the financialization of commodity trading relates to
the magnitude of index trader activity, combined with the fact that such traders tend to take only long
positions. Table 1 provides evidence of the relative share of both long and short positions held by different
trader categories in those agricultural markets for which the CFTC has been publishing disaggregated
data for January 2006 onwards.24 For each commodity, the data cover the 52-week period prior to the date
when the price for the respective commodity reached its peak. They clearly show that index traders are
present almost exclusively in long positions, and that they account for a large portion of the open interest
in some food commodity markets. Indeed, over the periods covered by the data, the relative shares of
index traders in total long positions in cotton, live cattle, feeder cattle, lean hogs, and wheat traded on
the Chicago Board of Trade (CBOT) were significantly larger than the positions of commercial traders
in those commodities, while they were roughly of equal size for maize, soybeans and wheat traded on
the Kansas City Board of Trade (KCBOT).25


24 Using data on bank participation in futures markets, Sanders, Irwin and Merrin (2008: 9) show that index trader
activity in grain markets started in 2003, and that the most rapid increase in trader positions occurred between early
2004 and mid-2005. Given that the CFTC’s index trader data start only in 2006, they cannot reflect these events.
25 UNCTAD (2009b: 64) shows evidence for the period 2006–2008 with qualitatively identical results.


Figure 6


share oF non-commercial spread positions in total open interest,
selected commodities, 1997–2009


Source: CFTC database.


0


10


20


30


40


50


60


7/1/97 7/1/99 7/1/01 7/1/03 7/1/05 7/1/07 7/1/09


Soybeans Soybean oil


0


10


20


30


40


50


60


7/1/97 7/1/99 7/1/01 7/1/03 7/1/05 7/1/07 7/1/09


Wheat Corn


0


10


20


30


40


50


60


7/1/97 7/1/99 7/1/01 7/1/03 7/1/05 7/1/07 7/1/09


Gold Copper


0


10


20


30


40


50


60


7/1/97 7/1/99 7/1/01 7/1/03 7/1/05 7/1/07 7/1/09


Oil Gas




16


While the number of index traders is relatively small, their average long position is very large (middle panel
of table 1), sometimes more than 10 times the size of an average long position held by either commercial
or non-commercial traders. Positions of this order are likely to have sufficiently strong financial power
to influence prices (Capuano, 2006). As a result, speculative bubbles may form and price changes can
no longer be interpreted as reflecting fundamental supply and demand signals. All of this can have an


Table 1


Futures and options market positions, by trader group,
selected agricultural commodities, averages For 52 weeks
prior to price peak during period January 2006–July 2009


(Per cent and number of contracts)


Long positions


Percentage share in total positions Average position size
Speculative


limits


Commodity
Non-


commercial
Com-


mercial Index
Non-


reporting
Non-


commercial
Com-


mercial Index


Maize 42.5 24.7 22.5 10.4 1 225 1 632 16 069 22 000
Soybeans 45.6 19.4 23.5 11.5 749 1 088 6 566 10 000
Soybean oil 40.0 28.0 22.6 9.5 949 1 765 4 347 6 500
Wheat CBOT 41.2 12.0 39.0 7.9 612 866 8 065 6 500
Wheat KCBOT 37.3 24.5 22.0 16.2 746 684 1 994 6 500
Cotton 45.4 18.6 27.9 8.1 448 1 113 4 407 5 000
Live cattle 37.8 9.8 44.8 7.6 661 389 5 380 5 150
Feeder cattle 48.2 11.1 27.3 13.5 323 147 524 1 000
Lean hogs 36.6 10.1 43.2 10.1 474 1 045 4 417 4 100


Short positions


Percentage share in total positions Average position size
Speculative


limits


Commodity
Non-


commercial
Com-


mercial Index
Non-


reporting
Non-


commercial
Com-


mercial Index


Maize 33.4 49.5 1.2 15.9 638 2 583 1 659 22 000
Soybeans 31.7 51.8 0.8 15.7 273 2 006 610 10 000
Soybean oil 24.1 69.7 0.8 5.4 423 3 755 631 6 500
Wheat CBOT 42.3 41.3 3.0 13.4 564 1 910 1 239 6 500
Wheat KCBOT 18.2 59.5 0.2 22.1 421 1 094 216 6 500
Cotton 36.5 58.6 1.0 4.0 326 3 348 658 5 000
Live cattle 34.3 45.0 0.8 19.9 498 968 383 5 150
Feeder cattle 34.4 20.2 1.5 44.0 166 159 222 1 000
Lean hogs 40.5 42.5 1.5 15.6 481 2 166 599 4 100


Source: CFTC and author’s calculations; speculative limits from Sanders, Irwin and Merrin (2008: 25).
Note: Dates of price peaks are as follows: maize: 17 June 2008; soybeans: 4 March 2008; soybean oil: 4 March 2008; wheat


CBOT: 11 March 2008; wheat KCBOT: 11 March 2008; cotton: 4 March 2008; live cattle: 2 September 2008; feeder cattle:
4 September 2008; lean hogs: 12 August 2008.




17


extremely detrimental effect on normal trading activities and market efficiency, despite position limits
that exist to contain speculation.26


During the periods reflected in the table, index traders actually exceeded speculative position limits
in wheat contracts on the CBOT, as well as in live cattle and lean hogs contracts, and for the other
commodities they came much closer to these limits than did the other trader categories (right-hand
panel of table 1). Exceeding speculative limits is perfectly legal for index traders, as they are generally
classified as commercial traders, and therefore are not subject to speculative position limits set by the
CFTC. But, as noted by Sanders, Irwin and Merrin (2008: 8), “it does provide some indirect evidence
that speculators or investors are able to use … [existing] instruments and commercial hedge exemptions
to surpass speculative limits”.


v. The impacT oF FinancializaTion on posiTion TakinG
and price developmenTs


A. The impact of return and diversification motives on position taking


This section provides an econometric estimation of the relationship between the activity of financial
investors on commodity exchanges and possible motivating determinants. The objective of this
examination is to obtain a general sense of the motivations that underlie financial investment. During the
period 1997–2001, commodity price developments were relatively smooth and financial investments in
commodity markets were low. Booms in commodity prices and financial investments started roughly in
2002, commodity prices and index trader investments sharply increased in 2007 and peaked roughly in
mid-2008. This analysis therefore distinguishes three periods: January 1997–December 2001, January
2002–December 2006, and January 2007–June 2008.


Given that two types of financial investors with different motivating determinants have been active
on commodity markets, the estimations are done on two different dependent variables: (i) the share of
net long non-commercial positions in total open interest; and (ii) the share of net long CIT-positions in
total open interest. The positions of some index traders are included in the positions reported for non-
commercial traders, but available data do not allow subtracting these index trader positions from total
non-commercial positions either for the non-agricultural commodities included in the analysis or for the
agricultural commodities prior to 2006. However, this is likely to affect the results only marginally because,
as already mentioned, data for the agricultural commodities and the period since January 2006 indicate
that only about 10–15 per cent of index trader positions are from the non-commercial trader category.


The estimations are done for four agricultural products (maize, wheat, soybeans and soybean oil) and
four non-agricultural products (copper, gold, crude oil and natural gas). Following Domanski and Heath
(2007), the explanatory variables included in the analysis reflect motivations related to either return or
diversification. Return motivations are captured by: (i) the percentage change in the price of the futures


26 Speculative position limits define the maximum position, either net long or net short, in one commodity
futures (or options) contract, or in all futures (or options) contracts of one commodity combined, that may be held
or controlled by one entity during different periods of trading. Speculative position limits are meant to reduce
the likelihood that a single entity can obtain positions large enough to manipulate the market. The CFTC or the
exchange on which the respective contract is traded can grant commercial entities with large merchandising needs
a hedge exemption from these limits so that they can obtain futures markets position large enough to match their
underlying physical commodity needs. The responsibility for enforcement of speculative position limits is shared
between the CFTC and the futures exchanges. The CFTC establishes speculative position limits only on a limited
group of agricultural commodities. For all other commodities, futures exchanges establish their own limits ‘where
necessary and appropriate’ given certain statutory rules (for details, see CFTC, 2008b: 55–56).




18


contract for which delivery has started (return); (ii) the size of the roll return (roll), defined as twelve-
month moving averages of the difference in the values of the first and the third futures contracts following
that on which delivery has started, divided by the price of the first contract; (iii) volatility (volatility),
defined as twelve-month moving averages of the standard deviation of monthly percentage changes in
three-month futures prices; and (iv) the opportunity cost of investing in commodities (interest), defined
as the three-month world interest rate (proxied by averaging the interest rates of Canada, Germany,
Japan, Sweden, the United Kingdom and the United States). The expected sign of the coefficients on the
variables return and roll is positive (greater investment is correlated with larger returns), negative for
interest (a higher interest rate implies higher opportunity costs) and undetermined for volatility (higher
volatility may improve returns but lowers risk-adjusted returns). Diversification motivations are captured


Table 2


regression results: non-commercial positions


Dependent variable: share of net long non-commercial positions in total open interest


Return Roll Volatility Interest Correlation Inflation Dollar Adjusted R2


Expected sign + + +/- - - + +


January 1999–december 2001


Gold 1.20*** 50.08** -7.79 14.16 -6.80 25.61*** -42.51 0.56
Copper 0.83*** -60.59*** -1.38 -4.51 -27.77 7.01 -2.00 0.64
Crude oil 0.02 -0.41 0.68** 1.59 -4.90** 5.41*** 10.66 0.53
Natural gas 0.02 -0.86* 1.42 -0.63 4.18 7.16 13.72 0.30
Corn 0.41*** 1.04 -2.38 2.90 7.15 6.73*** -36.93* 0.29
Wheat 0.13 3.24 -0.54 8.33** 22.00*** 8.71*** -37.40** 0.17
Soybeans 0.41*** 3.77 -1.70 2.39 -3.25 22.75*** 42.44*** 0.60
Soybean oil 0.25* 1.00 1.16 2.61 -13.02** 4.78 -5.15 0.13


January 2002–december 2004


Gold 0.60** -13.91 -4.34 -14.08 -11.52* -2.25 50.19 0.30
Copper 0.38 12.58** -8.31*** -27.69** -4.09 1.99 0.86 0.20
Crude oil 0.02 -3.12** 3.19*** -26.38*** 8.03** -6.63*** 35.94*** 0.53
Natural gas 0.05** -1.76 -0.01 -4.34 2.31 -4.06** 18.44 0.40
Corn 0.66*** -0.53 -4.14** 6.92 32.73*** 1.96 44.98 0.37
Wheat 0.74*** -1.12 10.10* 6.40 17.55* -22.97*** 30.67 0.36
Soybeans -0.06 -3.04 -3.73*** -0.99 -24.55** 17.67*** 39.66 0.58
Soybean oil 0.29 -2.12 -6.18*** 31.35** -6.01 39.53*** 57.76 0.53


January 2005–June 2008


Gold 0.29* 21.57 1.64 8.60 -10.55 -10.04 -32.96 0.06
Copper 0.22*** -9.29*** -2.66*** -16.52*** 7.42 16.27 35.14*** 0.82
Crude oil 0.08** 1.92** -1.24 3.00** -1.28 6.08*** -21.79** 0.28
Natural gas 0.05*** -0.67*** -0.38 4.87* -7.85 17.84*** -60.62*** 0.69
Corn 0.05 -2.18*** -0.35 10.17*** 3.24 -6.85 14.29** 0.67
Wheat 0.10 -1.57** 0.40 6.68** 5.46 -1.40 61.66** 0.39
Soybeans 0.18** -1.05 4.84*** 14.99*** 1.85 -12.33 -15.72 0.71
Soybean oil 0.51* -4.18*** 2.69* 18.59*** 13.02*** 31.25** -54.20* 0.45


Source: Author’s calculations based on data from Bloomberg and CFTC.
Note: Results based on Newey-West standard errors and covariance (lag truncation=12).
*** Significantatthe1percentlevel.
** Significantatthe5percentlevel.
* Significantatthe10percentlevel.




19


by: (i) the twelve-month moving average of the correlation between the variable return and the percentage
change in the S&P 500 equity price index (correlation); (ii) expected inflation (inflation), defined as the
difference between nominal and real 10-year United States bonds; and (iii) the dollar-euro exchange rate
(dollar). The expected sign of the coefficient on correlation is negative (assuming that investors take
commodity positions to diversify and hedge their positions in equity markets) and positive for inflation and
dollar (assuming that investors hedge against inflation and a depreciation of the dollar exchange rate). The
estimation uses monthly data (including the roll variable precludes the use of weekly data).27 All independent
variables are lagged once. Given that a variable definition based on moving averages implies an overlapping
observation structure, the estimation is based on Newey-West adjusted standard errors and covariance.


A comparison of the results reported in tables 2 and 3 leads to two broad observations. First, return
considerations are substantial determinants of position taking by both index traders and non-commercial
traders. Index trader positions are strongly influenced by roll returns: the coefficient on the roll return
variable is highly statistically significant for five of the eight commodities, always with the expected sign.
By contrast, non-commercial trader positions are mainly determined by spot returns: the coefficient on the
spot return variable is statistically significant for several of the eight commodities across the three sample
periods, and it has the expected sign in the vast majority of the estimations. This finding reflects the difference
in trading motivations between the two types of financial investors, with index traders emphasizing a passive
long-only strategy in which rolling futures contracts from one month to another is a key characteristic, and
non-commercial traders following a more active strategy with position taking on both sides of the market
in which benefits from short-term price movements is a key determinant of profitability.


Second, diversification objectives appear to have given way to more speculative motives. For the first
sample period (January 1999–December 2001), the results in table 2 are in line with those reported by
Gorton and Rouwenhorst (2006) as the vast majority of the significant coefficients on correlation and


27 The results reported for the third period in table 4 are robust to using net long non-commercial positions excluding
index trader positions and the period January 2006–June 2008.


Table 3


regression results: index-trader positions


Dependent variable: share of net long index-trader positions in total open interest


Return Roll Volatility Interest Correlation Inflation Dollar Adjusted R2


Expected sign + + +/- - - + +


January 2006–June 2008


Gold -0.02 -3.78 0.90** -2.03 -5.03** 4.94* 10.23 0.51
Copper -0.18* 5.41* 2.71*** 14.40*** 1.83 -8.99 44.50*** 0.83
Crude oil -0.05*** -0.64 2.53*** -1.28 2.65** -1.74 13.33 0.50
Natural gas 0.00 0.19*** -0.08 -0.97 2.30** -4.46*** 7.19 0.83
Corn 0.07 1.58*** -1.26*** -2.55*** -1.24 12.37*** -24.25*** 0.59
Wheat -0.14*** 0.77 0.56 -3.22*** 1.90 -0.04 -47.66 0.45
Soybeans -0.05*** 1.62** -0.75** -0.30 0.06 7.64** 2.91 0.47
Soybean oil -0.06** 4.80*** 0.43*** 2.10** -0.53 2.68** -19.36*** 0.77


Source: Author’s calculations based on data from Bloomberg and CFTC.
Note: Results based on Newey-West standard errors and covariance (lag truncation=12).
*** Significantatthe1percentlevel.
** Significantatthe5percentlevel.
* Significantatthe10percentlevel.




20


inflation have the expected sign. They indicate that non-commercial traders were taking commodity
positions to hedge against adverse developments in equity markets and inflation rates. By contrast, for
the third sample period (January 2005–June 2008), the results indicate that non-commercial traders were
taking positions that were positively, rather than negatively, related to developments in equity markets
and that they served as a hedge against dollar depreciation much less than in the first period.


Taken together the results indicate the importance of return motives for financial investors, as well as the
rising importance of speculative position taking. But they also show that the two categories of financial
investors follow different investment strategies. These results raise the question as to how changes in the
scale and character of involvement of financial investors in commodity markets have affected the price
dynamics of these markets. This will be the focus of the remainder of this section.


B. Financial position taking and commodity price developments


Granger causality tests have often been used to examine causal lead and lag dynamics between changes
in the positions of financial investors on commodity futures exchanges and changes in commodity prices.
Most existing studies that use such Granger-causality tests have not found evidence of a systematic
impact on prices of positions taken by non-commercial traders. To the contrary, they have tended to find
a statistically significant causal relationship between the movement of commodity futures prices and
measures of position changes (see, e.g. IMF, 2008b).


The results of these studies suffer from a number of data problems, including the aggregation of trader
positions across maturities, the fact that weekly data cannot identify very short-run effects even though
intra-week, or even intra-day, trading activity may be significant, and, most importantly, the fact that
they usually concentrate on non-commercial traders thereby ignoring the bulk of index trader positions.
However, their most important shortcoming is that they do not distinguish between the effects of index
traders and those of non-commercial traders excluding index traders.


Distinguishing between these two types of financial investors to examine causal lead and lag dynamics
between position taking and price developments may be based on the Autoregressive Distributed Lag
(ADL) model suggested by Gilbert (2008). Accordingly, the following equation was estimated:



∑∑∑
=



=



=


− ++++=
3


1


3


1


3


1
0


j
tjtj


j
jtj


j
jtjt zxrr εγβαα



(1)


where rt is the weekly change in the price of the commodity’s nearby futures contract, xt is the weekly
change in net long positions of index traders, zt is the weekly change in the net long positions of non-
commercial traders excluding index traders, and εt is an error term. The estimation was done for four
agricultural commodities – maize, soybeans, soybean oil and wheat – and for the non-agricultural
commodities – copper, natural gas, gold and crude oil – for which both the index trader positions and the
positions of other non-commercial traders were estimated, as explained above. Three null hypotheses
were tested for each of the 8 commodities:


(1) Index positions do not Granger-cause prices: 0: 321
1
0 === βββH


(2) Non-commercial positions excluding index positions do not Granger-cause prices:


0: 321
2
0 === γγγH


(3) Index positions and non-commercial positions excluding index positions have identical effects on


prices: jjH γβ =:40 (j=0,…,3)


The first null hypothesis was used to test also for reverse causality.




21


Given the evidence from figure 5 above that the correlation between position and price changes may be
stronger in some periods than in others, and the fact that index traders follow a long-only strategy and
hence tend to exert upward pressure on prices, the Granger-causality tests were done for two periods:
the first period starts in January 2006, when the index-trader data begin, and ends in June 2009, when
commodity prices appear to have terminated their downward overshooting and started a relatively stable
sideward movement; the second period spans the 52-week period prior to the price peak of the specific
commodity.28


Regarding the entire period January 2006–June 2009, there is strong evidence that changes in the
positions of index traders, as well as those of non-commercial traders excluding index traders, Granger
cause commodity price changes (table 4). The results indicate that index traders cause changes in the
prices for soybeans, soybean oil, copper and crude oil, while they suggest a causal impact of position
changes by non-commercial traders excluding index traders only for copper. The results also show that
the positions of the two types of financial investors have non-identical effects for prices in soybeans,
soybean oil and crude oil. The tests on reverse causality reveal that it is only index position taking in
gold that is affected by price development in a statistically significant way. This result is not surprising
because, as already mentioned, financial investment in physical inventories (which is not reflected here) is
much more important for precious metals such as gold than in other commodities. There is no indication
for reverse causality between prices and the positions of non-commercial traders excluding index traders
for any of the eight commodities.


28 For the agricultural products the same dates as in table 3 were used, while the dates for the non-agricultural
products are as follows: copper: 22 April 2008 (given that the absolute price peak of 23 May 2006 is too early in
the sample period to allow for meaningful test results); gold: 18 March 2008; natural gas: 1 July 2008; crude oil:
1 July 2008.


Table 4


granger-causality tests: commodity prices and Financial positions,
January 2006–June 2009


Index positions
do not Granger-


cause prices


Non-commercial
positions


excluding index
positions do not
Granger-cause


prices


Index positions
and non-


commercial
positions


excluding index
positions have
identical effects


on prices


Prices do not
Granger-cause
index positions


Prices do not
Granger-cause
non-commercial


positions
excluding index


positions


F3,169 F3,169 F3,169 F3,172 F3,172


Maize 1.22 1.13 1.70 1.42 0.40
Soybeans 3.27** 0.61 2.49* 1.50 0.07
Soybean oil 2.76** 0.08 2.57* 0.29 0.97
Wheat 0.65 0.53 0.21 0.33 0.30
Copper 2.54* 3.46** 0.12 0.37 0.86
Gold 1.91 0.95 1.89 5.30*** 0.78
Natural gas 0.54 1.88 0.67 1.42 0.60
Crude oil 4.79*** 2.08 5.28* 1.08 1.00


Source: Author’s calculations based on data from Bloomberg and CFTC.
Note: *** Significantatthe1percentlevel.
** Significantatthe5percentlevel.
* Significantatthe10percentlevel.




22


Taken together, these results suggest a significant impact of financial investment on price developments
in a wide range of commodities over the past three and a half years. This significant impact of index
trader positions on price developments, combined with the absence of reverse causality, and the fact that
index traders do not take positions based on commodity market fundamentals, may also be interpreted
as indicating that the efficient market hypothesis fails in relation to commodity markets.


Turning to the 52-week period prior to a commodity’s price peak, overall the results indicate that index
traders affected the prices of agricultural commodities, while non-commercial traders excluding index
traders affected the prices of non-agricultural commodities, especially copper and crude oil (table 5). There
is some evidence for reverse causality between prices and index trader positions for gold, but also for
wheat and crude oil though the respective coefficients have a fairly low level of statistical significance.29
Similarly to the results on the entire sample periods, the results in table 5 also suggest that the price effects
of the two types of financial investors are not identical.


29 With respect to crude oil, it could be argued that this reverse causality between spot prices and index trader
positions is related to the term structure of futures contracts. The crude oil market was in contango for much of the
period during which oil prices strongly increased. This implies that the roll return on crude oil futures contracts
was negative unless a strong increase in the value of oil futures across all contract maturities occurred and that it
was large enough to compensate for the contango in the term structure. This condition for earning positive returns
might have led index traders to adopt a more careful attitude and pay greater attention to actual commodity price
developments. This would also explain why the test results indicate a causal impact of index trader positions on oil
prices during the entire sample period (when they supposedly followed their characteristic passive position taking
strategy) but not during the period of the particularly sharp price increase (when they might have paid more attention
to actual spot market developments).


Table 5
granger-causality tests: commodity prices and Financial positions,


52 weeks prior to price peak during period January 2006–June 2009


Index positions
do not Granger-


cause prices


Non-commercial
positions


excluding index
positions do not
Granger-cause


prices


Index positions
and non-


commercial
positions


excluding index
positions have
identical effects


on prices


Prices do not
Granger-cause
index positions


Prices do not
Granger-cause
non-commercial


positions
excluding index


positions


F3,42 F3,42 F3,42 F3,45 F3,45


Maize 3.59** 1.86 3.03** 1.29 0.58
Soybeans 2.46* 1.16 2.98** 0.57 0.90
Soybean oil 4.65*** 2.46* 5.42*** 0.15 1.26
Wheat 0.99 1.26 0.37 2.21* 1.07
Copper 0.86 3.28** 1.33 0.99 0.35
Gold 0.33 0.04 0.33 3.29** 0.17
Natural gas 1.16 1.91 1.22 1.47 0.23
Crude oil 0.33 2.70* 0.84 2.33* 1.42


Source: Author’s calculations based on data from Bloomberg and CFTC.
Note: *** Significantatthe1percentlevel.
** Significantatthe5percentlevel.
* Significantatthe10percentlevel.




23


The above results, nonetheless, are conservative estimates of the impact of financial investment on
commodity price development. They cannot account for the impact of non-commercial traders’ spread
positions, given that the price impact of any spread position depends on the specific way how long
and short positions are combined. As noted above, the share of spread positions in open interest has
strongly increased particularly in energy markets, and it is likely that this increase has impacted price
developments.


vi. conclusions


The increasing importance of financial investment in commodity trading appears to have caused commodity
futures exchanges to function in such a way that prices may deviate, at least in the short run, quite far
from levels that would reliably reflect fundamental supply and demand factors. Financial investment
weakens the traditional mechanisms that would prevent prices from moving away from levels determined
by fundamental supply and demand factors – efficient absorption of information and physical adjustment
of markets. This weakening increases the proneness of commodity prices to overshooting and heightens
the risk of speculative bubbles occurring.


The main result of the empirical investigation in this paper is that a distinction needs to be made between
the two types of financial investors in commodity exchanges (money managers and index traders) in
order to account for their impact on commodity price developments. The positions of index traders appear
to have affected the prices of a wide range of commodities over the past three and a half years, while
those of non-commercial traders excluding index traders have tended to affect the prices mainly of non-
agricultural commodities when their prices were increasing sharply. The increased positive relationship
between financial investor positions on commodity futures exchanges and equity market developments,
and the apparent decreased importance of hedging against dollar depreciation as a determinant of position
taking, have created a greater interdependence between financial and commodity markets.


These effects of the financialization of commodity futures trading have made the functioning of commodity
exchanges increasingly contentious. They risk reducing the participation of commercial users because
commodity price risk hedging becomes more complex and expensive. They also cause greater uncertainty
about the reliability of signals emanating from the commodity exchanges with respect to making storage
decisions and managing the price risk of market positions. It has therefore become necessary to consider
how the functioning of commodity futures exchanges could be improved so that they can continue to
fulfil their role of providing reliable price signals to producers and consumers of primary commodities
and contributing to a stable environment for development.


Regulatory changes designed to keep pace with commodity market developments, in particular the
participation of new trader categories such as index funds, play a key role in this respect. It is indispensable
to broaden and strengthen the supervisory and regulatory powers of mandated commodity market
regulators. The ability of any regulator to understand what is moving prices and to intervene effectively
depends upon its ability to understand the market and to collect the required data. Such data are currently
not available, particularly for off-exchange derivatives trading. Yet such trading and trading on regulated
commodity exchanges have become increasingly interdependent. Hence, comprehensive trading data
need to be reported to enable regulators to monitor information about sizeable transactions, including
in similar contracts traded over the counter that could have an impact on regulated futures markets. In
addition to more comprehensive data, broader regulatory mandates might be required. Supervision and
regulation of commodity futures markets may need to be enhanced, particularly with a view to enabling
regulators to counter unwarranted impacts from off-exchange trading on commodity exchanges.


A substantial part of commodity futures trading is executed on exchanges located in the United States,
which the Commodity Futures Trading Commission (CFTC) is mandated to regulate. It is therefore




24


encouraging to see that the new CFTC-chairman Gary Gensler (2009: 2–3) in his address to the United
States Senate Subcommittee on Financial Services on 2 June 2009 recognized that we “experienced …
an asset bubble in commodities” and “commodity index funds and other financial investors participated
in the commodity asset bubble” and that the United States government has taken first legislatory action
to address some of the problems that appear to have impaired the proper functioning of commodity
futures exchanges.




25


reFerences


Alquist R and Kilian L (2007). What do we learn from the price of crude oil futures? Discussion Paper No. 6548.
London, Centre for Economic Policy Research (CEPR).


Banerjee S (2009). Learning from prices and the dispersion in beliefs. Mimeo. Kellogg School of Management.
Northwestern University, 31 July.


Bernanke B (2008). Outstanding issues in the analysis of inflation. Speech delivered at the Federal Reserve Bank
of Boston’s 53rd Annual Economic Conference. Chatham (Mass), 9 June.


Cao HH and Ou-Yang H (2009). Differences of opinion of public information and speculative trading in stocks and
options. Review of Financial Studies, 22(1): 299–335.


Capuano C (2006). Strategic noise traders and liquidity pressure with a physically deliverable futures contract.
International Review of Economics and Finance, 15(1): 1–14.


CFTC (2006a). Comprehensive Review of the Commitments of Traders Reporting Program. Federal Register,
71(119): 35627–35632.


CFTC (2006b). Commodities Futures Trading Commission Actions in Response to the “Comprehensive Review
of the Commitments of Traders Reporting Program”, 5 December. Available at: http://www.docstoc.com/
docs/873643/Comprehensive-Review-of-the-Commitments-of-Traders-Reporting-Program.


CFTC (2007). Report on the oversight of trading on regulated futures exchanges and exempt commercial markets.
Available at: http://www.cftc.gov/stellent/groups/public/@newsroom/documents/file/pr5403-07_ecmreport.pdf.


CFTC (2008a). Interim Report on Crude Oil. Interagency Task Force on Commodity Markets. Washington,
DC, CFTC. Available at: http://www.cftc.gov/stellent/groups/public/@newsroom/documents/file/
itfinterimreportoncrudeoil0708.pdf.


CFTC (2008b). Staff Report on Commodity Swap Dealers & Index Traders with Commission Recommendations.
Washington, DC, CFTC. Available at: http://www.cftc.gov/stellent/groups/public/@newsroom/documents/
file/cftcstaffreportonswapdealers09.pdf.


CFTC (2009). About the commitments of traders reports. Available at: http://www.cftc.gov/marketreports/
commitmentsoftraders/cot_about.html.


De Long JB, Shleifer A, Summers LH and Waldmann RJ (1990). Noise Trader Risk in Financial Markets. Journal
of Political Economy, 98(4): 703–738.


Domanski D and Heath A (2007). Financial investors and commodity markets. BIS Quarterly Review, March: 53–67.
ECB (2008). Monthly Bulletin, Frankfurt, European Central Bank, September.
Gensler G (2009). Testimony before the United States Senate Subcommittee on Financial Services and General


Government, Committee on Appropriations. Available at: http://www.cftc.gov/stellent/groups/public/@
newsroom/documents/speechand testimony/opagensler-2.pdf.


Gilbert CL (2008). How to understand high food prices. Mimeo. University of Trento and University of London,
17 November.


Gilbert CL (2009). How to understand high food prices. Mimeo. Paper prepared for the 2009 ICABR conference,
Ravello, June.


Gorton G and Rouwenhorst KG (2006). Facts and fantasies about commodity futures. Working Paper No. 10595.
National Bureau of Economic Research (NBER), March.


Gorton G, Hayashi F and Rouwenhorst KG (2007). The fundamentals of commodity futures returns. Working
Paper No. 13249. National Bureau of Economic Research, July.


Greenspan A (2004). Oil. Remarks to the National Italian American Foundation. Washington, DC, 15 October.
Available at: http://www.federalreserve.gov/boarddocs/speeches/2004/200410152/default.htm.


Harrison JM and Kreps DM (1978). Speculative investor behaviour in a stock market with heterogeneous expectations.
Quarterly Journal of Economics, 92(2): 323–336.


IMF (2006). World Economic Outlook, Autumn. Washington, DC, International Monetary Fund.
IMF (2008a). World Economic Outlook, Autumn. Washington, DC, International Monetary Fund.




26


IMF (2008b). Global Financial Stability Report, Annex 1.2. Washington, DC, International Monetary Fund.
Khan MS (2009). The 2008 oil price “bubble”, Policy Brief 09/19. Peterson Institute for International Economics.


Washington, DC, August.
Krugman P (2008). The oil nonbubble. New York times, 12 May.
Masters MW (2008). Testimony before the United States Senate Committee of Homeland Security and Government


Affairs. Washington, DC, 20 May.
Masters MW (2009). Testimony before the Commodities Futures Trading Commission, 5 August.
Sanders DR, Irwin SH and Merrin RP (2008). The adequacy of speculation in agricultural futures markets: too


much of a good thing? Marketing and Outlook Research Report 2008–02. Department of Agriculture and
Consumer Economics. University of Illinois at Urbana-Champaign. Forthcoming in Review of Agricultural
Economics.


Scherer B and He L (2008). The diversification benefits of commodity futures indexes: a mean-variance spanning
test. In: Fabozzi FJ, Füss R and Kaiser DG, eds. The Handbook of Commodity Investing. Hoboken (NJ),
Wiley: 241–265.


Svensson LEO (2005). Oil prices and ECB monetary policy. Princeton University. Mimeo. Available at: http://www.
princeton.edu/svensson/papers/ep501.pdf.


UNCTAD (2009a). The Global Economic Crisis: Systemic Failures and Multilateral Remedies. United Nations
publications, Sales No. E.09.II.D.4, New York and Geneva.


UNCTAD (2009b). Trade and Development Report 2009. United Nations publications, Sales No. E.09.II.D.16,
New York and Geneva.


United States Senate (2009). Excessive Speculation in the Wheat Market. United States Senate Permanent
Subcommittee on Investigations. Washington, DC, 24 June. http://levin.senate.gov/newsroom/supporting/2009/
PSI.WheatSpeculation.062409.pdf.




27


No. Date Author(s) Title


194 June 2009 Andrew Cornford Statistics for international trade in banking services:
Requirements, availability and prospects


193 January 2009 Sebastian Dullien Central banking, financial institutions and credit creation
in developing countries


192 November 2008 Enrique Cosio-Pascal The emerging of a multilateral forum for debt
restructuring: The Paris Club


191 October 2008 Jörg Mayer Policy space: What, for what, and where?
190 October 2008 Martin Knoll Budget support: A reformed approach or old wine in new


skins?
189 September 2008 Martina Metzger Regional cooperation and integration in sub-Saharan Africa
188 March 2008 Ugo Panizza Domestic and external public debt in developing


countries
187 February 2008 Michael Geiger Instruments of monetary policy in China and their


effectiveness: 1994–2006
186 January 2008 Marwan Elkhoury Credit rating agencies and their potential impact on


developing countries
185 July 2007 Robert Howse The concept of odious debt in public international law
184 May 2007 André Nassif National innovation system and macroeconomic policies:


Brazil and India in comparative perspective
183 April 2007 Irfan ul Haque Rethinking industrial policy
182 October 2006 Robert Rowthorn The renaissance of China and India: implications for the


advanced economies
181 October 2005 Michael Sakbani A re-examination of the architecture of the international


economic system in a global setting: Issues and proposals
180 October 2005 Jörg Mayer and


Pilar Fajarnes
Tripling Africa’s Primary Exports: What? How? Where?


179 April 2005 S.M. Shafaeddin Trade liberalization and economic reform in developing
countries: structural change or de-industrialization?


178 April 2005 Andrew Cornford Basel II: The revised framework of June 2004
177 April 2005 Benu Schneider Do global standards and codes prevent financial crises?


Some proposals on modifying the standards-based
approach


176 December 2004 Jörg Mayer Not totally naked: textiles and clothing trade in a quota
free environment


175 August 2004 S.M. Shafaeddin Who is the master? Who is the servant? Market or
Government?


174 August 2004 Jörg Mayer Industrialization in developing countries: some evidence
from a new economic geography perspective


173 June 2004 Irfan ul Haque Globalization, neoliberalism and labour
172 June 2004 Andrew J. Cornford The WTO negotiations on financial services: current


issues and future directions
171 May 2004 Andrew J. Cornford Variable geometry for the WTO: concepts and precedents
170 May 2004 Robert Rowthorn and


Ken Coutts
De-industrialization and the balance of payments in
advanced economies


uncTad Discussion PaPers


/...




28


No. Date Author(s) Title


169 April 2004 Shigehisa Kasahara The flying geese paradigm: a critical study of its
application to East Asian regional development


168 February 2004 Alberto Gabriele Policy alternatives in reforming power utilities in
developing countries: a critical survey


167 January 2004 Richard Kozul-Wright
and Paul Rayment


Globalization reloaded: an UNCTAD Perspective


166 February 2003 Jörg Mayer The fallacy of composition: a review of the literature
165 November 2002 Yuefen Li China’s accession to WTO: exaggerated fears?
164 November 2002 Lucas Assuncao and


ZhongXiang Zhang
Domestic climate change policies and the WTO


163 November 2002 A.S. Bhalla and S. Qiu China’s WTO accession. Its impact on Chinese
employment


162 July 2002 Peter Nolan and
Jin Zhang


The challenge of globalization for large Chinese firms


161 June 2002 Zheng Zhihai and
Zhao Yumin


China’s terms of trade in manufactures, 1993–2000


160 June 2002 S.M. Shafaeddin The impact of China’s accession to WTO on exports of
developing countries


159 May 2002 Jörg Mayer,
Arunas Butkevicius
and Ali Kadri


Dynamic products in world exports


158 April 2002 Yılmaz Akyüz and
Korkut Boratav


The making of the Turkish financial crisis


157 September 2001 Heiner Flassbeck The exchange rate: Economic policy tool or market price?
156 August 2001 Andrew J. Cornford The Basel Committee’s proposals for revised capital


standards: Mark 2 and the state of play
155 August 2001 Alberto Gabriele Science and technology policies, industrial reform and


technical progress in China: Can socialist property rights
be compatible with technological catching up?


154 June 2001 Jörg Mayer Technology diffusion, human capital and economic
growth in developing countries


153 December 2000 Mehdi Shafaeddin Free trade or fair trade? Fallacies surrounding the theories
of trade liberalization and protection and contradictions in
international trade rules


152 December 2000 Dilip K. Das Asian crisis: distilling critical lessons
151 October 2000 Bernard Shull Financial modernization legislation in the United States –


Background and implications
150 August 2000 Jörg Mayer Globalization, technology transfer and skill accumulation


in low-income countries
149 July 2000 Mehdi Shafaeddin What did Frederick List actually say? Some clarifications


on the infant industry argument
148 April 2000 Yılmaz Akyüz The debate on the international financial architecture:


Reforming the reformers


Copies of UNCTAD Discussion Papers may be obtained from the Publications Assistant, Macroeconomic
and Development Policies Branch (MDPB), Division on Globalization and Development Strategies (DGDS),
United Nations Conference on Trade and Development (UNCTAD), Palais des Nations, CH-1211 Geneva 10,
Switzerland (Fax no: +41(0)22 917 0274/Tel. no: +41(0)22 917 5896).


Discussion Papers are accessible on the website at http://www.unctad.org.




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