Finance and Economics Discussion Series

Divisions of Research & Statistics and Monetary Affairs

Federal Reserve Board, Washington, D.C.

Does Speculation Affect Spot Price Levels? The Case of Metals

with and without Futures Markets

George M. Korniotis

2009-29

NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary

materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth

are those of the authors and do not indicate concurrence by other members of the research staff or the

Board of Governors. References in publications to the Finance and Economics Discussion Series (other than

acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.
Does Speculation Affect Spot Price Levels? 

The Case of Metals with and without Futures Markets 

George M. Korniotis* 

Board of Governors of the Federal Reserve System 

Washington DC 

Abstract: This paper finds no evidence that speculative activity in futures markets 

for industrial metals caused higher spot prices in recent years. The empirical analysis 

focuses on industrial metals with and without futures contracts and is organized 

around two key themes. First, I show that the comovement between metals with and 

without futures contracts has not weakened in recent years as speculative activity has 

risen. Specifically, the annual and quarterly price growth rates of the two metal 

categories have been positively correlated with their growth rates experiencing a 

structural shift by the end of 2002. This comovement is driven by economic 

fundamentals because world GDP growth is strongly correlated with metal price 

growth, especially after 2002. The structural change in 2002 is also consistent with 

supply and demand information found in industry newsletters. In the second set of 

results, I focus more directly on financial speculation and spot price inflation. I use the 

S&P Goldman-Sachs Commodity Index returns to proxy for the volume of speculative 

activity and I show that these returns are unrelated to metal prices. The final test 

follows storage models, which suggest that speculation can affect spot markets only if 

it leads to physical hoarding. Focusing on metals with established futures markets, I 

find no evidence of physical hoarding because inventory growth is found to be 

negatively correlated with price growth rates. 

* Date of Draft: May 26, 2009. I would like to thank Sean Campbell, Michael Gibson, Erik Heitfield, Andrei 

Kirilenko, Michael Palumbo, Trevor Reeve, Paul Reverdy, Steve Sharpe, Robert Vigfusson, Nick Walraven, and 

Pat White for their useful feedback. Also, Alexander Marland provided excellent research assistance. The 

analysis and conclusions herein are mine and should not be taken to indicate any endorsement by the Board of 

Governors or its staff. Phone: (202) 452-2581, Email: George.M.Korniotis@frb.gov. 
1

1. Introduction 

The role of financial speculators in the market place has been debated by academics and 

practitioners since the inception of futures markets. Following the seminal work of Kaldor 

(1939), the literature has primarily focused on whether speculative activity in the futures 

markets stabilizes (i.e. reduces the variance) of commodity spot prices. Part of the literature 

finds that the introduction of futures contracts destabilizes the spot market (Finglewski 

(1981), Simpson (1985), Hart and Kreps (1986), Newbury (1987), Stein (1987)). On the other 

extreme, Cox (1976), Turnovsky (1983), and Turnovsky and Campbell (1985) support the 

view that speculation is welfare improving because it reduces the variability of spot prices. 

In the midst of this debate, the issue of whether speculation has a direct effect on the level of 

spot prices has been ignored. Recently however, commodity prices and speculative activity 

rose dramatically. Buyuksahin, Haigh, and Robe (2008) report that by 1999 about 5 billion 

dollars were invested in vehicles tracking the Standards and Poor Goldman Sachs Commodity 

Index (SPGSCI). By the third quarter of 2008, the investments linked to five prominent 

commodity indices, including the SPGSCI, rose to 140 billion. The increased participation of 

financial investors in the futures markets has sparked a debate on whether speculation led to 

the spike in commodity spot prices for some agricultural and energy products.1

 

Financial investors did not only seek exposure to agricultural and energy product prices 

because many financial investors participated in the futures markets through commodity index 

funds. Such funds hold futures in a variety of products because they track commodity indices 

like the SPGSCI.2

 Therefore, if the trading activities of financial investors caused spot price 

appreciation, they should have affected the prices of most products in the index funds. In this 

 

1

 This highly publicized debate has been the topic of a hearing in front of the U.S. Senate Committee on 

Homeland Security and Governmental Affairs. The hearing took place on May 20, 2008 and its title was 

“Financial Speculation in Commodity Markets: Are Institutional Investors and Hedge Funds Contributing to 

Food and Energy Price Inflation?” 

2

 Currently, the SPGSCI contains 24 commodities from all commodity sectors: six energy products, five 

industrial metals, eight agricultural products, three livestock products and two precious metals. 
2

study, I take up this issue and investigate the potential impact of speculation on commodity 

spot prices of metals, a prominent category in commodity index funds. Metals offer a unique 

“natural” experiment because there are metals with established futures contracts (which are 

included in commodity funds) and metals with no futures contracts (and thus not included in 

commodity funds). 

My analysis offers a comparison between the spot (cash) prices across industrial metals with 

and without futures contracts and it is organized around two key themes. To begin with, 

industrial metals are primarily used in the manufacturing sector in a complementary fashion. 

For example, they are typically used in the form of alloys.3

 Being complements to one another 

implies that their spot prices should be positively correlated. Therefore, if speculative activity 

in the futures markets were directly affecting the physical markets of the traded metals (i.e. 

the metals for which there are futures contracts available), then the positive correlation 

between the price changes of traded and non-traded commodities should weaken. Based on 

this prediction, the first set of empirical findings investigates the comovement between traded 

and non-traded metals. 

I test the comovement hypothesis by studying the time patterns of metals with and without 

futures contracts. Over the period 1991 to 2008, I find that the correlation of metal price 

growth rates was consistently positive and did not decrease after 2000. I also show that the 

prices for metals with and without futures contracts increased substantially after 2002. The 

2002 rise in prices is economically and statistically significant according to the structural 

break statistical tests developed by Andrews and Ploberger (1994).4

To understand the causes behind the comovement and upward shift in prices after 2002, I 

study the potential role of supply and demand factors in two ways. First, I use the world GDP 

growth rate to capture world economic activity. I find that world growth rate is positively 

correlated with metal price growth. Also, similar to metal price growth rates, world growth 

 

3

 For an extensive discussion on alloys see “Constitution of Binary Alloys,” 1958, McGraw-Hill. 4

 A recent study by Deutsche Bank (2008) also finds that traded and non-traded commodities experienced a 

dramatic price hike after 2000. 
3

started to steadily rise after 2002. Therefore, accounting for world growth reduces the 

statistical significance of the structural break in metal spot prices. 

Second, I study the supply and demand information that was available to the metals markets 

from September 1, 2003 to April 1, 2004. During this six month period all metal prices rose. I 

use the search engine Factiva to identify reports from industry newsletters with information 

on metal production, inventories, demand, etc. I use the number of reports as my information 

proxy. I find that for both traded and non-traded metals, the price-increasing news reports 

(i.e., news related to disruption of production, rising production costs, etc.), outnumber pricedecreasing news reports (i.e. news related to increase in inventories, drop in consumption, 

etc). Thus, fundamental information about the metal markets could explain the acceleration of 

metal prices after 2002. 

The previous findings demonstrate that the complementary relationship between traded and 

non-traded metal remained strong even after 2002 when speculative activity rose. Next, I 

study the link between speculation in futures markets and spot price appreciation more 

directly. For this test, I focus on the S&P Goldman-Sachs Commodity Index (SPGSCI). The 

SPGSCI is a weighted average of many commodity spot prices. It is tracked by many 

financial instruments and its returns are representative of the earrings related to investing in 

commodity futures contracts. 

Ideally, I would like to examine if the volume in SPGSCI related instruments (measured by 

net open interest) had any impact on metal prices. Unfortunately, the public open interest data 

provided by the Commodity Futures Trading Commission (CFTC) are very limited. With this 

limited data, I show that the realized returns of the SPGSCI (which are available for all the 

years in my sample) are positively related to the net open interest of the SPGSCI contract 

traded on the Chicago Mercantile Exchange (CME). This is a reasonable finding because high 

returns from investing in the futures markets should attract more financial investors in the 

futures markets. Using the realized returns as a proxy for volume, I show that the SPGSCI 
4

return is unrelated to the price appreciation of metals. On the one hand, it cannot explain the 

shift in prices in 2002, and in general, it is not correlated to metal price growth rates. 

The final test relies on implications related to no-arbitrage/storage models of commodity 

prices (Pindyck (2001)). In these models, speculative activity in the futures market can affect 

spot market price if it causes hoarding of inventories from the physical market, i.e. suppliers 

of commodities restrict supply to the physical markets and enter into futures contracts with 

speculators. In the presence of physical hoarding, contrary to standard supply and demand 

models, inventory formation is associated with spot price appreciation. To test the latter 

hypothesis, I focus on metals with futures markets. I proxy for their inventory changes with 

the growth of world-wide commercial stocks reported in the World Metals Statistics 

Yearbook. My analysis finds no evidence of physical hoarding. In particular, inventory 

growth is negatively correlated with price growth. Also, this negative relationship is present 

even after 2002. 

Overall, the current paper is among the first to show that the run up in spot metal prices after 

2003 is related to economic fundamentals and not to speculation by financial investors. The 

evidence relies on several empirical findings. First, consistent with the fact that non-precious 

metals are used in a complementary fashion, I find that their price growth rates are positively 

correlated. Their comovement is also magnified by a common structural break at the 

beginning of 2003. The structural break is related to supply and demand factors and it 

occurred around the same time as the acceleration in world economic activity. In addition, the 

return to the SPGSCI, a proxy for the volume in speculative activity in the futures markets, is 

unrelated to metal price growth rates. Finally, consistent with storage models, the negative 

relationship between inventory growth and price growth of trade metals has not been affected 

by speculation. 

Beyond the contribution to the debate on speculation, the paper makes several important 

contributions to the literature on commodity prices. To begin with, it complements the 

existing studies on futures markets. It shows that speculation does not affect the level of spot 
5

prices because there is no evidence of physical hoarding. In addition, the evidence supports 

the predictions of various storage models. For instance, consistent with Turnovsky (1983) and 

Chari, Jagannathan and Jones (1990), I find that traded metals exhibit lower variability and 

spot price appreciation compared to non-traded metals. 

The rest of the paper is organized as follows. Section 2 describes the commodity price data. 

Section 3 presents graphical evidence on the behavior of the spot price growth rate indices for 

traded and non-traded commodities. It also deals with the statistical significance of the 

structural break in the price growth series. Section 4 tests whether this break can be explained 

by economic (supply and demand) fundamentals. Section 5 looks into the relationship 

between metal prices, the return of the SPGSCI, and inventory growth. Finally, Section 6 

provides a short literature review and Section 7 concludes the discussion. 

2. Data and Methodology

My empirical analysis focuses on quarterly and annual price growth rates which are based on 

daily spot price data I obtained from Bloomberg. I supplement the Bloomberg data with data 

from the website of the U.S. Geological Survey.5 Specifically, I collect data on non-precious 

metals. The class of traded metals (i.e. commodities with established futures markets) 

includes copper, aluminum, lead, nickel, tin, and zinc. I choose the latter five commodities 

because they have standardized and widely traded contracts on either the Chicago Mercantile 

Exchange (CME) or the London Metal Exchange (LME).6

 The class of non-traded 

commodities (i.e. commodities without any futures markets) includes steel, manganese, 

cadmium, cobalt, tungsten, rhodium, ruthenium, and molybdenum.7

 These commodities are 

truly non-traded because there no futures contracts for them on the organized exchanges. 

 

5

 These data can be found at http://www.usgs.gov. 

6

 On the LME there are futures contracts on copper, aluminum, lead, nickel, tin, and zinc. On the CME there are 

futures contracts for copper and aluminum. 

7

 The LME started a futures contract for steel at the second quarter of 2008. Moreover, it announced that 

contracts for cobalt and molybdenum will become available at some point in the second quarter of 2009. 
6

Moreover, they cannot be indirectly traded because futures contracts on their alloys are also 

not available.8

The paper examines industrial metals because there are sufficient commodities in both the 

traded and non-traded classes to allow a meaningful comparison of the two commodity 

classes. This is not the case for other commodity classes like the agricultural products; in this 

instance most of them have established futures markets. Apart from data constraints, nonprecious metals are typically used in tandem. Therefore, they are complementary goods and 

their prices should move together. I use this prediction to test whether speculation has affected 

this fundamental complementary relationship. 

Apart from using the growth rates of individual metals, the comparison between the traded 

and non-traded metals uses growth rate indices. The index for each commodity class uses 

daily spot prices and it is calculated in three steps. First, I calculate the quarterly (annual) 

price at quarter (year) t of commodity i, (Pt,i) by a time-series average of all available daily 

prices in quarter (year) t. Second, I compute the quarterly (annual) price growth rate, dPt,i, 

using the difference in natural logarithms, [ln(Pt,i) – ln(Pt-1,i)], which is multiplied by a 100. 

Third, I obtain the value of the growth rate index at t using the simple (not weighted) crosssectional average of dPt,i across the commodities in either the traded and non-traded class. 

The time period of the study is from 1991 to 2008 for the annual data. I choose this period 

because the daily price data for almost all non-traded commodities have many missing values 

prior to 1991. Because of missing values in the case of ruthenium, the quarterly data cover the 

1992(Q4) to 2008(Q4) period. I do not use monthly or daily data because there are many 

missing values for the non-traded metals. Therefore, monthly and daily growth rates cannot be 

meaningfully computed. 

 

8

 It is possible that there are private futures contracts traded in the over-the-counter (OTC) market for the 

commodities with no established futures contracts. Because most speculative activity is related to instruments 

that track indices of traded commodities, ignoring private deals on non-traded commodities should not affect my 

analysis. 
7

To set the stage for the main empirical analysis, I present simple descriptive statistics for the 

individual metals and their indices in Table 1. I find that over the full sample period both 

traded and non-trade metals experienced price inflation. However, the price of non-traded 

ones rose substantially more. For example, the average annual growth rate for traded is 4.2% 

while for the non-traded is 7.2%. Moreover, the standard deviation of most non-traded metals 

is higher than most traded ones. These results are consistent with the model of Turnovsky 

(1983) and the baseline model of Chari, Jagannathan and Jones (1990). 

3. Comovement across Non-Precious Metals 

Using the metal price growth rates, I develop my empirical analysis around two themes. First, 

starting with this section, I study the comovement between traded and non-traded metals. In 

the second set of tests, I focus more directly on the potential link between speculation and 

spot prices. 

3.1 Economic Intuition 

In this section, I develop and test my first hypothesis, which is based on the intuition from no 

arbitrage/storage models. In these models, financial investors can affect spot price levels if 

their behavior prompts producers and storers of goods to hoard supply from the physical 

markets.9

 Assume that rising speculative activity in the futures markets leads to an increase in 

today’s futures contract price, Ft,T, where T is the delivery day. Responding to high futures 

prices, stores enter into futures contract with financial investors. Because storers sell their 

goods for futures delivery, inventory levels rise and the supply of goods to the physical 

markets declines. Physical hoarding then leads to higher spot prices. At the delivery date, T, 

 

9

 This analysis follows, among others, Fama and French (1988), Bailey and Chan (1993), Pindyck (2001), and 

Nielsen and Schwartz, (2004). 
8

the futures price further increases as it converges to the new high spot price.10 Thus, physical 

hoarding implies a positive relationship between inventory growth and spot price inflation.11

Directly testing the above scenario is very difficult. It requires a great deal of precise trading 

and inventory data. Because commodity markets are international and trading takes place on 

regulated and unregulated markets (for example, over-the-counter markets) it is impossible to 

gather all the relevant data. Moreover, in the case of inventories, it is not clear what the 

appropriate definition of inventory is. Probably, inventory numbers should include 

commodities in storage and account for reserves in the ground. But the latter component 

cannot be measured precisely. 

3.2 Traded and Non-Traded Metals 

In this paper, I sidestep the aforementioned data difficulties by offering a comparison between 

traded and non-traded metals. To begin with, because non-precious industrial metals are 

complementary goods, their prices should be positively correlated. If speculative activity in 

the futures markets induces storers and producers to increase their inventories of traded 

goods, the complementary relationship between traded and non-traded industrial metals 

should weaken. This would imply that the positive correlation across their price growth rates 

should fall. 

For example, take the case of the aluminum-manganese (Al-Mn) alloy.12 Assume that 

speculative activity in aluminum futures contracts has lead to physical hoarding of aluminum 

from the aluminum cash markets. The declining supply of aluminum raises the spot price of 

aluminum and its alloys. Because the Al-Mn alloy is now more expensive, its demand falls, 

which reduces the demand for manganese (the non-traded component of the alloy). 

Manganese becomes cheaper and, all else equal, its spot price is now negatively correlated 

with the spot price of aluminum. 

 

10 The convergence of the futures and spot prices upon delivery, know as the convergence property, follows from 

the no-arbitrage assumption. 

11 Samuelson (1966) was among the first to make this argument in his classic work on intertemporal price 

equilibria. 

12 For more information on this alloy, see “Constitution of Binary Alloys,” 1958, McGraw-Hill, pages 110-114 
9

This intuition gives rise to my first hypothesis: 

Hypothesis 1: If the participation of financial investors in futures markets 

affects the spot market, then the complementary (positive) relationship 

across traded and non-traded industrial metals should weaken.

Figure 1: Annual Growth Rates 

-40

-30

-20

-10

0

10

20

30

40

50

199119921993199419951996199719981999200020012002200320042005200620072008

Traded Index

Non-Traded Index

Annual Price Growth Rates (x 100)

The figure depicts the time-series for the growth rates of spot price indices for traded and non-traded metals. The 

growth rates are calculated at the annual frequency. All growth rates are multiplied by a hundred. The shaded 

area highlights the period during which spot metal prices increase considerably.

3.2 Time Patterns of Traded and Non-Traded Metals 

Next, I test Hypothesis 1, which implies that the correlation between traded and non-traded 

metals should fall due to speculative activity in the futures markets. I use graphical evidence 

and simple descriptive statistics. The annual and quarterly time-series of the traded and nontraded growth rate indices are presented in Figures 1 and 2 respectively. Figure 3 presents 
10

rolling correlations between the indices and the individual metals. Finally, Table 1 reports 

descriptive statistics for the two indices and their individual components. 

Figure 2: Annualized Quarterly Growth Rates 

-210

-185

-160

-135

-110

-85

-60

-35

-10

15

40

65

90

115

2000 Q12000 Q32001 Q12001 Q32002 Q12002 Q32003 Q12003 Q32004 Q12004 Q32005 Q12005 Q32006 Q12006 Q32007 Q12007 Q32008 Q12008 Q3

Traded Index

Non-Traded Index

Annualized Quarterly Price Growth Rates (x 100)

The figure depicts the time-series for the growth rates of price indices for traded and non-traded metals. The 

growth rates are quarterly and they are annualized (i.e. multiplied by four). All growth rates are multiplied by a 

hundred. The shaded area highlights the period during which spot metal prices increase considerably.

Inconsistent with Hypothesis 1, I find that traded and non-traded metals move in tandem even 

in recent years. For example, both traded and non-traded metals appreciated around 2003 and 

then depreciated in 2008. See Figures 1 and 2. Moreover, the correlation between the metal 

categories has been positive and stable. For example, as depicted in Figure 3, over 2000 to 

2008 the annual rolling correlation between the traded and non-traded growth rate indices has 

been stable and always close to 0.70. Similarly, the average of the rolling correlations 

between each traded metal growth rate with each non-traded metal remained close to its 
11

average value of 0.30. The positive and stable correlation across the price growth rates is 

consistent with the fact that industrial metals are complementary goods. 

Figure 3: Rolling Correlations 

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

20002002200320042005200620072008

Rolling Correlation Between Traded and Non-Traded Indices

Average Rolling Correlation Across All Traded and Non-Traded Commodities

Correlation Coefficients Across Annual Growth Rates

The figure depicts the time-series for the rolling correlations between the growth rates of spot price indices for 

traded and non-traded metals. It also includes the average of the rolling correlations between each traded metal 

with each non-traded metal. The growth rates are calculated at the annual frequency. The rolling correlation in 

year t uses data from (t – 9) to t. 

3.2 Common Appreciation in Prices 

Apart from the comovement across the price growth rates, the other salient feature in Figures 

1 and 2 is that the level of the traded and non-traded indices rose significantly after 2003. For 

example, as shown in Table 1, over the 1991 to 2002 period the mean annual growth rate of 

the traded and non-traded indices was -3.7% and -4.4%, respectively. However, after 2002 the 

level of these mean growth rates rose dramatically. The traded index grew by 11.9% and the 

non-traded index grew 22.7%. See Table 1 for these descriptive statistics. Moreover, the 
12

structural break in the indices is not driven by a subset of metals as all metals exhibited a 

dramatic price appreciation after the end of 2002. As shown in Table 1, the shift in prices is 

present across all metals. 

The structural break in both the traded and non-traded metals reinforces the fact that these 

industrial metals are complementary to one another. More importantly, their complementary 

relationship is strong even in recent years when financial investors have become an important 

investor category in commodity futures markets. It is also interesting that non-traded metals 

have experienced a more dramatic price appreciation compared to traded metals. By and 

large, the aforementioned findings do not support Hypothesis 1 and the conjecture that the 

participation of financial investors in the futures markets has affected the level of spot metal 

prices. 

3.3 Formal Structural Break Tests 

The previous section provided simple graphical evidence for a structural break in the growth 

rates of metals around the 2002 to 2003 period. In this section, I formally test the structural 

break using the two metal growth rate indices. First, I follow Andrews (1993) and estimate the 

date of the break points. Then, following Andrews and Ploberger (1994), and Hansen (1997), 

I test whether the break points are statistically significant. I conduct this analysis for both the 

annual and the quarterly growth rate indices and report the results in Table 2. Because of the 

dramatic drop in commodity prices in 2008, I also consider the case in which the 2008 data is 

excluded for the sample period. 

First, in Panel A for Table 2, I report the tests with 2008 data being included in the sample. In 

the case of the annual data, the estimated break date is 2002 for the index of traded metals and 

2003 for the index of non-traded metals. Moreover, according to the ExpF and AveF tests by 

Andrews and Ploberger (1994), the change in the mean of the indices on the estimated break 

dates are statistically significant. Specifically, their p-values, which are computed as in 

Hansen (1997), are always less than 0.07. In the case of the quarterly data, the estimated break 

date for the traded index is at the fourth quarter of 2001 and for the non-traded index is at the 
13

fourth quarter of 2002. However, only the shift in the mean value of the non-traded index is 

statistically significant. 

The weak statistical significance of the structural break in the case of quarterly growth rates 

might be related to the fact that in the fourth quarter of 2008 metal prices plummeted. As 

depicted in Figure 2, the growth rate of the traded and non-traded index in the third quarter of 

2008 was -0.61% and -0.43%, respectively. These growth rates fell dramatically in the fourth 

quarter of 2008; they both came very close to -2%. It is therefore possible that this decline in 

the growth rates is biasing the structural break tests. 

Next, I exclude 2008 and re-run the structural break tests. As reported in Table 2, Panel B, the 

evidence in favor of a break becomes stronger and the results with annual data are now 

aligned with those with quarterly data. For example, in the case of the annual data, the 

estimated break date is again 2002 for the index of traded metals and 2003 for the index of 

non-traded metals. In the case of the quarterly data, the estimated break date for the traded 

index is fourth quarter of 2002 and for the non-traded index is third quarter of 2003. Finally, 

the shift in the mean value of the growth rate indices is statistically significant across both 

indices and data frequencies. 

Overall, the formal structural break tests echo the graphical evidence in Figures 1 and 2. They 

show that in the beginning of 2003 metals with and without established futures market 

underwent a common structural break. The break roughly happened in the same period, which 

is consistent with the two metal classes being complementary to each other. 

3.4 Panel Regression Analysis 

In this Section, I further explore the structural break finding and I estimate a series of panel 

regressions. The panel regressions are estimated by pooling the annual and annualized 

quarterly spot price growth rates of the individual commodities instead of the growth rates of 

indices. I use the individual commodity data to exploit all their time-series and cross-sectional 

variation. This approach is more efficient than estimating the panel regressions with the 
14

growth rates of the indices because the indices smooth out cross-sectional differences within 

the traded and non-traded commodity classes. 

In the case of the annual growth rates, dPt,i, I estimate two panel regressions. The regressions 

include a series of dummy variables, which are designed to test whether there are differences 

between the levels of price growth rates before and after 2002. The regression models are: 

(a) dPt,i = α1D02 + α2D03 + β1dPt-1,i ,

(b) dPt,i = α3(DTR× D02) + α4(DTR× D03) + α5(DNTR× D02) + α6(DNTR× D03) + β2dPt-1,i . 

Above, DTR is a dummy variable that takes the value of one if commodity i is traded, and zero 

otherwise. Similarly, DNTR is a dummy variable that takes the value of one if commodity i is 

not traded, and zero otherwise. D02 (D03) is a dummy variable that takes the value of one if 

year t is prior (after) to 2003, and zero otherwise. I use the coefficient estimates from the 

regression models to test for the structural break in 2002. In particular, I test whether the 

differences (α2 – α1), (α4 – α3) and (α6 – α5) are statistically different from zero. 

In the case of quarterly data, I estimate regressions similar to regressions (a) and (b) above. 

However, the quarterly regressions include seasonal dummy variables for quarters 1 to 3. The 

time period for annual data is 1992 to 2008 and for quarterly data is 1993(Q1) to 2008(Q4). 

The regressions are estimated with OLS and the estimation results are reported in Table 3. 

The results with annual growth rates are in columns 1 and 2, while the results with quarterly 

data are in columns 3 and 4. The quarterly growth rates are annualized (i.e. multiplied by 

four) before the estimation. 

3.5 Estimation Results 

In the first set of regressions, columns 1 and 3, I examine whether the mean spot price growth 

rates rise after the end of 2002. Consistent with the Andrews and Ploberger (1994) tests, I find 

that after 2002 the growth rates across all commodities increase and the difference between 
15

the post- and pre-2003 periods are statistically significant. In particular, the difference 

between the coefficient estimates on the D02 and D03 dummy variables (D03 - D02) is 0.28 (tstatistic = 5.38) and 0.09 (t-statistic = 1.91) for annual and quarterly data, respectively. Thus, 

both traded and non-traded commodities underwent a structural change at the end of 2002. 

As shown in Table 1, the growth rate increase from 2003 and onwards is higher for nontraded than for traded commodities. To further examine this observation, in regression 2 and 

4, I include interaction terms of the DTR and DNTR dummy variables with the structural break 

D02 and D03 dummy variables. 

The results from regressions 2 and 4 confirm the finding from Table 1. In the case of annual 

growth rates, even if the difference between the interaction terms (DTR× D03) and (DTR× D02) 

is statistically significant (difference = 0.20, t-statistic = 2.66), it is smaller in magnitude than 

the difference between (DNTR× D03) and (DNTR× D02) (difference = 0.34, t-statistic = 5.01). 

Similarly, in the case of quarterly data, the difference between the interaction terms 

(DTR× D03) and (DTR× D02) is smaller (difference = 0.05, t-statistic = 0.69) than the difference 

between (DNTR× D03) and (DNTR× D02) (difference = 0.12, t-statistic = 1.97) 

In general, the panel regression analysis confirms that the prices of traded and non-traded 

metals move in tandem with both metal categories experiencing a structural break. 

Consequently, the participation of financial investors in the futures markets of the traded 

metals has not broken the complementary relationship between traded and non-traded metals. 

Moreover, the fact that non-traded metals experience a much higher price appreciation than 

traded ones after 2002 casts further doubt that speculators are the cause of the spike in 

commodity prices. In all, there is again no evidence to support Hypothesis 1. 
16

4. Economic Fundamentals and Metal Prices

The previous analysis has established the comovement of metal prices. In this section, I take a 

closer look at metal markets and examine whether the patterns in metal price growth rates are 

related to economic fundamentals. I approach this question in two distinct ways. First, I 

account for the level of world economic activity to test Hypothesis 2: 

Hypothesis 2: If supply and demand factors drive metal spot prices, world 

economic activity should be correlated to the price growth rate of metals. 

Second, I collect news reports from industry newsletters. My goal is to compute a proxy for 

the fundamental information (i.e. information related to supply and demand factors) that was 

when metal prices started to appreciate. With the information proxy, I test the following 

hypothesis: 

Hypothesis 3: If supply and demand factors drive metal spot prices, price 

increasing news should outnumber price decreasing news during the onset 

of the metal price inflation in 2003. 

4.1 Accounting for World Economic Activity 

The commodity markets are international markets and thus are affected by changes in the 

world economy. I proxy for world economic activity using the world per capita GDP growth 

published by the World Bank in the World Development Indicators (WDI). 

As depicted in Figure 4, world per capita growth was about 0.6% in 2002. This percentage 

rose to 1.4% in 2003 and it has been about 2.3% over the 2003 to 2008 period. The rise in 

world economic activity in 2003 coincides with the structural break in metal spot price growth 

rates detected at the beginning of 2003. It is therefore very likely that the appreciation of 

commodity prices is related to economic fundamentals. 
17

Next, I formally test Hypothesis 2 by adding world per capita GDP growth in the annual 

regressions in Table 3.13 The new regression results are reported in Table 4 and they are 

divided into two groups. In the first set of regressions (1 and 2), world growth is added to the 

control variables to test the significance of world economic activity across the whole sample 

period. The second set of regressions (3 and 4) examines whether the correlation between 

metal price growth rates and world economic activity changes from 2003 onwards. In 

particular, in regressions 3 and 4 the control variables include the interaction terms of world 

per capita GDP growth with the D02 and D03 dummy variables. 

Figure 4: Annual Per Capita World GDP Growth Rate, 1991 - 2008 

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

199119921993199419951996199719981999200020012002200320042005200620072008

Per Capita Growth Rate (x 100)

The figure depicts world per capita GDP growth (%). The data are from the World Bank. The growth rate for 

2008 is the projection by the World Bank. The shaded area highlights the period during which spot commodity 

prices increase considerably. 

 

13 The world GDP growth is not available at the quarterly frequency and I therefore only consider the annual 

regressions in this section. 
18

The results from Regressions 1 and 2 demonstrate that world per capita GDP growth is an 

important determinant of the price growth of metals. To begin with, its coefficient estimates 

are significant and positive. For example, in Regression 1 the coefficient estimate and tstatistic on world growth is 0.14 and 4.73, respectively. Moreover, in the presence of world 

growth the evidence for the structural break in the price growth rates weakens. In Regression 

1, the estimate (t-statistic) on the D03 dummy variable becomes negative and equal to -0.08 

(0.99). In the absence of world growth, it was 0.24 and its t-statistic was 5.75. See Table 3, 

Regression 1. Similarly, the difference between D03 and D02 becomes 0.15 (it is 0.28 in Table 

3, Regression 1) and its t-statistic drops to 2.57 (from 5.38 in Table 3, Regression 1). 

The previous results provide supporting evidence for Hypothesis 2, which posits that 

fundamental factors drive metal prices. I further test Hypothesis 2 by testing whether the 

strength of the relationship changes before and after 2002, the year of the structural break. 

The findings in Regressions 3 and 4 show that the coefficient estimates on the interaction 

terms of world growth with the D02 and D03 dummy variables are always significant. For 

example, in Regression 2, the estimate (t-statistic) on the D02 interaction term is 0.12 (3.48), 

while the estimate on the D03 interaction term is 0.24 (3.62). 

Interestingly, world growth is more correlated with metal price growth rates after 2002, since 

the estimate on the D03 interaction term is double the estimate on the D02 interaction term. 

Also, when we allow for a structural break in the coefficient on world per capita GDP growth, 

the shift in the means of the price growth rates are no longer statistically significant. Such a 

result indicates that the dramatic rise in metal prices at the end of 2002 must be related to 

fundamental supply and demand factors. 

4.2 Supply and Demand Information 

The inclusion of world GDP in the panel models demonstrates that a substantial component of 

the variation in metal prices can be explained by world economic activity. Next, I test 

Hypothesis 3 and collect news reports from Factiva to compute a proxy for the type of 

information that was available to market participants. In particular, I focus on industry 
19

newsletters, like Platt’s Metal Week, to ensure that the articles I identify provide specialized 

information for the metals’ market. For feasibility, I focus on the period from September 01, 

2003 to April 01, 2004. During this 6-month period all metal prices were rising. 

For each metal, I execute two searches. The first search is designed to capture news reports 

that should be related to prices increasing. This search identifies reports that include phrases 

about a) rising demand or consumption of a metal, b) decline in inventories, or production, or 

reserves, or supply for a metal. For example, in the case of zinc, I search for articles that 

includes phrases like “disruption in production of zinc” and “inventories of zinc have been 

declining.” 

The second search is designed to capture news reports that should be related to prices 

declining. This search identifies reports that include phrases about a) declining demand or 

consumption of a metal, b) increase in inventories, or production, or reserves, or supply for a 

metal. For instance, in the case of tin, I search for reports including phrases like “demand for 

tin has been decreasing,” “tin production rose.”14 I conjecture that the number of news reports 

generated by each search is a proxy of the market perception about the state of supply/demand 

fundamentals. 

The results of the Factiva searches are reported in Table 5. First, we see that there are more 

news reports for traded than for non-traded metals. Moreover, consistent with Hypothesis 3, 

the price increasing news reports outnumber the price decreasing ones. For example, in the 

case of copper, there are 32 more news report related to the price of copper rising. Overall, the 

Factiva news reports support Hypothesis 3 and the argument that metal prices respond to 

fundamental news in the metal markets. 

In general, the panel regressions and the Factiva news reports indicate that the prices of traded 

and non-traded metals are driven by economic fundamentals. Their reliance on common 

supply and demand forces implies that their prices should be positively correlated. Such 

 

14 The exact code used for the news searches is available from the author upon request. 
20

comovement is also consistent with the fact that industrial metals are used as complementary 

goods in the manufacturing sector. 

5. Does Commodity Index Trading Affect Commodity Cash Prices? 

The findings presented thus far support the view that the patterns in metal prices are primarily 

driven by economic fundamentals. In this Section, I test the potential role of speculation in 

futures markets on spot commodity markets more directly. This analysis is organized around 

three themes. First, I show that the earnings from investing in futures contracts can proxy for 

the volume of speculative activity in the futures markets. Second, I examine whether the 

volume proxy is related to metal spot prices. Finally, I focus on traded metals and test if the 

relationship between inventory changes and price growth rates has changed after 2002. 

5.1 S&P Gold-Sachs Commodity Index 

Financial investors can gain exposure to commodity price changes by investing in the futures 

markets. The most typical investment strategy has been to invest in products that track 

commodity indices like the S&P Goldman-Sachs Commodity Index (SPGSCI). The SPGSCI 

represents an unleveraged, long-only investment in a broad array of commodity futures. An 

investor can implement the index with SPGSCI instruments, like the SPGSCI futures contract 

traded on the Chicago Mercantile Exchange (CME). 

I use the SPGSCI total return data to proxy for returns representative of investments in U.S. 

commodities and I test the following hypothesis: 

Hypothesis 4: Speculative activity in futures markets rises when the 

returns from investing in futures contracts are high. Thus, if speculation in 

futures markets is driving commodity spot prices, there should be a positive 

relation between the total return of investing in futures contracts and 

commodity spot prices. 
21

Figure 5: Annual Growth Rates, 1991 - 2008 

-65

-55

-45

-35

-25

-15

-5

5

15

25

35

45

55

199119921993199419951996199719981999200020012002200320042005200620072008

SPGSCI (x 100) (SPGSCI - FF MKT) (x 100) Growth in Open Interest Spreading (x 10)

Annual Growth Rates

The figure depicts three annual rates. First, the rate of return of the SPGSCI is calculated from daily total return 

data from Bloomberg. Second, the excess rate of return is the difference between the SPGSCI rate of return and 

the CRSP value-weighted market return CRSP (FF MKT). Third, the growth in open interest spreading variable 

is the growth of net positions of all non-commercial traders reporting to the CFTC. This variable is only 

available from 1993 to 2000 and 2007 to 2008. Because the growth in open interest spreading is very volatile, for 

easy visualization, the first two variables are multiplied by a hundred and the last one by ten. The shaded area 

highlights the period during which spot metal prices increase considerably.

5.2 The Volume-Return Correlation 

To test Hypothesis 4, I collect daily price data for the SPGSCI total return index from 

Bloomberg. I average all the price data within a year to compute an annual price index, Pt. I 

calculate the annual rate of return as 100 × ln(Pt / Pt-1). For robustness, I also calculate an 

excess SPGSCI return. It is given by the difference between the SPGSCI annual return and 

the CRSP value-weighted index of all stocks listed on CRSP. The CRSP index is from the 

web site of Kenneth French.15

 

15 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 
22

The return data are a good proxy for the variation in financial investor participation (volume) 

in the futures markets. I establish the volume-return connection using open interest data for 

the SPGSCI futures contract traded on the Chicago Mercantile Exchange. I obtain the open 

interest data from the CFTC Commitments of Traders Reports.16 From these reports, I collect 

the variable called “non-commercial positions-spreading,” which aggregates the net (long – 

short) positions of each non-commercial trader reporting to the CFTC. I focus on the noncommercial category because it includes financial investors like hedge funds.17

The non-commercial positions-spreading variable for the SPGSCI futures contract is only 

available from 1992 to 2000 and 2006 to 2008. I average the reported weekly data within each 

year and I compute an annual spreading variable. Then, I compute its growth rate to capture 

the variation in financial investor participation in the futures markets. 

The open interest growth rate is depicted in Figure 5 together with the rate of return and 

excess return of the SPGSCI. The figure shows that there is a positive relationship between 

open interest growth and returns. In untabulated results, I find that the correlation between the 

SPGSCI total rate of return and the growth of the open interest by non-commercial traders is 

positive (0.43). The correlation with the excess return is also positive (0.13). Thus, the returns 

for the SPGSCI are a reasonable volume proxy in the futures markets. 

5.3 SPGSCI Returns and Spot Metal Prices 

Unfortunately, the open interest data are not available from 2001 to 2005, the period 

containing the structural break in metal prices. Therefore, I proxy for the volume of 

speculative activity in the futures markets using the SPGSCI returns. 

The SPGSCI rate and excess return are plotted in Figure 5. The plot shows that the SPGCI 

return is highly volatile; its minimum value is -31% and its maximum value is 41%. The 

excess return is even more volatile; its minimum value is -56% and its maximum value is 

 

16 Open interest data are a good proxy of the intensity of participation because they measure the total number of 

futures contracts long or short in a delivery month or market that has been entered into and not yet liquidated by 

an offsetting transaction or fulfilled by delivery 

17 For more details see www.cftc.gov/marketreports/commitmentsoftraders/index.htm 
23

52%. Nevertheless, the average returns between 2002 and 2007 have been high and around 

10%. Gorton and Rouwenhorst (2006) also find that by 2004 an equal-weighted return index 

of commodity futures earned about 9% more than the respective commodity spot price index. 

The graphical evidence in Figure 5 suggests that Hypothesis 4 is not supported by the data. As 

we see in the figure, the SPGSCI rate and excess return are not consistently rising during the 

period 2002 to 2004. Unlike the metal price growth rates, there does not appear to be a 

structural break in the two return series. 

I complement the graphical evidence and formally test Hypothesis 4 by including the SPGSCI 

rate and excess return in the panel regressions from Section 4. The goal of the regression 

analysis is to test whether the SPGSCI returns can explain the structural break at the end of 

2002. I report the new regressions in Table 6. Regressions 1 and 2 include SPGSCI rate of 

return and Regressions 3 and 4 include SPGSCI excess return over the CRSP return. 

The regression results in Table 6 strongly reject Hypothesis 4. To begin with, the rate and 

excess return of the SPGSCI have no explanatory power for the metal price growth rates. For 

instance, in Regression 1 its t-statistic is only 0.85. In untabulated results, I estimate 

regressions in which I constrain the SPGSCI return to only affect the traded metals. I find that 

even in these regressions the speculative activity proxy has no impact on spot price growth 

rates. 

Apart from being insignificant, the inclusion of the SPGSCI returns in Regressions 3 and 4 

does not weaken the importance of world growth for metal prices. In the case of Regression 3, 

the estimate on the world growth and D02 interaction term is positive (= 0.12) with a high tstatistic (= 3.48). Similarly, the estimate on the world growth and D03 interaction term is 0.24 

and its t-statistic is 3.62. 

Taken together, the results in Table 6 strongly reject Hypothesis 4. They suggest that financial 

investor participation in the futures markets, proxied by the SPGSCI returns, is not related to 
24

the price appreciation of metals after 2002. Moreover, the failure to find supporting evidence 

for Hypothesis 4 reinforces the evidence for Hypotheses 2 and 3. 

5.4 Inventory Formation and Price Inflation 

The economic theory behind storage models suggests that the only way speculation in the 

futures markets can affect spot commodity prices is by leading to physical hoarding. If that 

were the case, inventory growth and price growth would be positively correlated. All else 

equal, if speculation is irrelevant these growth rates should be negatively correlated. This 

intuition gives rise to my final hypothesis: 

Hypothesis 5: If speculative activity in futures markets affects the spot 

commodity prices, then the negative relationship between inventory 

formation and spot price changes should weaken. 

The discussion in Section 3.1 argued that data limitations are a major hurdle in testing 

Hypothesis 5. Nevertheless, given the importance of inventory fluctuations in noarbitrage/storage models, I use the available inventory data to test Hypothesis 5. Specifically, 

I focus on traded metals because data on world inventories of non-traded metals are not 

consistently collected. The inventory data are from the 2005 and 2008 World Metals Statistics 

Yearbooks published by the World Bureau of Metal Statistics. The data are annual, they cover 

the period from 1995 to 2007, and they refer to world total commercial stocks. With the 

annual growth rates of the commercial stocks, I estimate panel regressions, which I report in 

Table 7. 

In Regressions 1 and 2, the inventory growth rate is included in the set of explanatory 

variables. I find that conditional on lag price growth and world per capita GDP growth, the 

coefficient estimates on the inventory growth rate are negative and significant. Therefore, 

inventory growth is related to metal prices decreasing, a prediction of standard supply and 

demand models with no physical hoarding. Next, in Regressions 3 and 4, I test whether price 

growth responds differently to inventory growth before and after 2002. Specifically, I include 
25

in the regression interaction terms of inventory growth with the D02 and D03 dummy variables. 

The interaction terms have negative coefficient estimates and their magnitudes are very 

similar. Moreover, they are statistically insignificant. Therefore, there is no supporting 

evidence that the relationship between inventories and metal prices has changed after 2002, 

the year when prices spiked. 

Overall, the evidence in Table 7 does not support Hypothesis 5. My findings suggest that 

fluctuations in supply and demand in physical markets (as captured by inventory fluctuations) 

are driving the prices of traded metals. Even if the inventory data are not free of measurement 

errors, it is important that their growth rate is negatively correlated with the price growth 

rates. 

6. Related Research 

In this section, I survey the recent literature on speculation. Because the debate about 

speculation leading to spot price inflation is a recent one, there are relatively few studies on 

the issue. These studies nevertheless find no convincing evidence that speculation in the 

futures markets has led to spot price inflation. Next, I review some academic studies as well 

as reports from regulatory agencies. 

One of the salient findings of the paper is that at the end of 2002 the prices of both traded and 

non-traded metals have been rising substantially. Haigh, Hranaiova, and Oswald (2005) also 

find that the initial appreciation in most commodity spot prices started in 2002. These authors 

note that spot price changes have led to changes in investor interest and not the other way 

around. Brunetti and Buyuksahin (2009) also show that speculative activity did not anticipate 

price changes. Moreover, they use detailed data on open interest and find that speculative 

activity in the futures market did not destabilize these markets. 
26

My empirical results rely on long term price changes, which are captured by annual price 

growth rates, and show that economic fundamentals are driving these long term growth rates. 

Domaski and Heath (2007) argue that in the short term it is possible that financial investors 

can indirectly affect inventory decisions through future prices. To the extent that taking long 

positions in futures markets leads to higher futures prices, the value of holding inventory for 

future delivery increases. Under this scenario, storers might be tempted to increase inventory 

levels in the short term. 

In the long term, however, inventory decisions should be primarily driven by factors affecting 

the real supply and demand of the underlying goods. Currently, there is no direct evidence 

that storers and producers have been ignoring supply and demand factors and have been 

accumulating inventories betting on the prediction that futures prices will continue to 

appreciate. To the contrary, the evidence in Section 5.4 suggests that storers and producers 

have been making inventory decisions based on supply and demand conditions in the physical 

commodity markets. 

 

Even if there is no relationship between investor participation in futures markets and spot 

(physical) prices, Haigh, Harris, Overdahl, and Robe (2007) find that speculation has affected 

the futures markets themselves. In particular, they focus on the New York Mercantile 

Exchange's WTI sweet crude oil futures. They show that the prices of one-year and two-year 

futures have become cointegrated with the price of near-month futures, for the first time ever, 

since mid-2004. 

In a related study, Buyuksahin, Haigh, and Robe (2008) investigate the comovement of 

commodity and equity investment returns. They use the Standard and Poor’s S&P 500 return 

and the SPGSCI total return to proxy for the representative performance in U.S. equities and 

commodities. They find that the correlation between the two return indices has been very 

stable in the last fifteen years. 
27

Finally, due to the public attention drawn to commodity markets, the International 

Organization of Securities Commissions (IOSCO) set up a Task Force to investigate the role 

of speculation by financial investors in the futures market. The Task Force reviewed recent 

reports from various international agencies. Consistent with my findings, the Final Report 

(March 2009) concluded that economic fundamentals, rather than speculative activity, are the 

most plausible cause for the recent price appreciation in commodity prices.18

One report cited by the IOSCO is a Staff report by the CFTC.19 It publishes the results of the 

June 2008 special call for data from over-the-counter (OTC) swap and commodity index 

markets. Evidence in the study shows that during December 31, 2007 to June 30, 2008, the 

behavior of crude oil prices and speculative activity were negatively correlated. During this 

period, while crude oil prices were increasing, speculative activity by commodity index 

traders reflected a net decline of futures equivalent contracts. 

 

7. Conclusion 

Do financial investors affect the physical commodity markets through their participation in 

the futures markets? To answer this question, I study industrial metals with and without 

futures markets. My empirical analysis evolves around two themes: comovement of metal 

prices and more direct tests of the impact of speculation on spot markets. 

The comovement hypothesis is motivated from the fact that industrial metals are typically 

used by the manufacturing sector in a complementary fashion. Therefore, if supply and 

demand forces are the primary driver of their price changes, their long-term price patterns 

should move in tandem. Using annual and quarterly price growth rates for the period 1991 to 

2008, I find that traded and non-traded metals are positively correlated. Moreover, both metal 

classes experience a structural change by the end of 2002. 

 

18 The IOSCO report is at www.iosco.org/library/pubdocs/pdf/IOSCOPD285.pdf. 19 See www.cftc.gov/stellent/groups/public/@newsroom/documents/file/cftcstaffreportonswapdealers09.pdf. 
28

The comovement across industrial metals is also supported by additional evidence. To begin 

with, I find that all metal prices are correlated to world per capita GDP growth, which can 

explain the shift in metal prices after 2002. Also, using news report, I assess the type of 

supply and demand information that was available about non-precious metals during the onset 

of metal price appreciation. I find that price increasing news reports outnumber price 

declining news reports. 

The first set of tests suggests that fundamental information is driving metal prices. The 

remaining tests confirm this conclusion by finding no direct link between speculation and spot 

prices. First, I show that the total return of the SPGSCI, a proxy for the intensity of 

speculative activity in the futures markets, has no explanatory power for metal price growth 

rates. Finally, I search for evidence of physical hoarding by focusing on the relationship 

between traded metals and inventory levels. In line with my previous finding, inventory 

growth is negatively correlated with price growth rates suggesting that storers were not 

accumulating stocks due to high futures prices. 

Taken together, the results indicate that in recent years the relationship between futures and 

physical commodity markets for industrial metals was not disturbed by financial investors. 

Instead, commodity spot prices changes are driven by world economy activity and financial 

investors are merely responding to these price changes. This conclusion is strongly confirmed 

by the economic developments in 2008. As shown in Figure 4, world fundamentals worsened 

in 2008 with world per capita GDP growth falling to 1.7%. The slow down in world economic 

activity reduced the demand for metals and their price plummeted. For example, the price 

growth rate of traded metals fell by 19.18%. See Figure 1. In response, as depicted in Figure 

5, speculative activity in the futures market declined with the net open interest for the 

SPGSCI futures contract falling by 171%. 
29

References 

Andrews, D. W. K, 1993, “Tests of Parameter Instability and Structural Change with 

Unknown Change Point,” Econometrica 61, 821—856. 

Andrews, D. W. K. and W. Ploberger, 1994, “Optimal Tests When a Nuisance Parameter is 

Present Only Under the Alternative,” Econometrica 62, 1383—141. 

Bailey W., and K. C. Chan, 1993, “Macroeconomic Influences and the Variability of the 

Commodity Futures Basis,” Journal of Finance 48, 555—573. 

Brunetti, C., and B. Buyuksahin, 2009, “Is Speculation Destabilizing?” Working Paper. 

Buyuksahin, B, M. S. Haigh, and M. A. Robe, 2008, “Commodities and Equities: A Market of 

One?” SSRN Working Paper, No. 1069862. 

Deutsche Bank, “Commodities and the Role of Speculators,” May 9, 2008, Commodities 

Special, Global Markets Research, Commodities Research. 

Fama E. F., and K. R. French, 1988, “Business Cycles and the Behavior of Metals Prices,” 

Journal of Finance 43, 1975—1093. 

Gorton, G. and Rouwenhorst, K. G., 2006, “Facts and fantasies about commodity futures,” 

Financial Analysts Journal 62, 47–68. 

Haigh, M, J. Hranaiova, and J. Oswald, 2005, “Price dynamics, price discovery and large 

futures trader interactions in the energy complex”, Working Paper, U.S. Commodity 

Futures Trading Commission. 

Haigh, M. S., J. H. Harris, J. A. Overdahl, and M. A. Robe, 2007, “Market Growth, Trader 

Participation and Derivative Pricing, Working Paper, U.S. Commodity Futures Trading 

Commission. 

Hansen, B. E., 1997, “Approximate Asymptotic P Values for Structural-Change Tests,” 

Journal of Business and Economic Statistics 15, 60—67. 

Nielsen M. J., and E. S. Schwartz, 2004, “Theory of Storage and the Pricing of Commodity 

Claims,” Review of Derivatives Research 7, 5—24. 

Pindyck, R. S., 2001, “The Dynamics of Commodity Spot and Futures Markets: A Primer,” 

Energy Journal, 22, 1-29. 
30

Samuelson, P. A., 1966, “Intertemporal Price Equilibrium: A Prologue to the Theory of 

Speculation,” in The Collected Papers of Paul A. Samuelson, Vol. II, J. E. Stiglitz, ed., 

Cambridge: M.I.T. Press, 946—84. 
31

Table 1: Descriptive Statistics for Price Growth Rates 

Avg STDV Avg STDV Avg STDV Avg STDV Avg STDV Avg STDV

Traded

Copper 5.3 0.2 -4.5 0.2 24.9 0.2 2.8 0.6 -4.6 0.4 15.5 0.8

Aluminum 2.5 0.2 -1.6 0.2 10.7 0.1 2.1 0.4 0.4 0.3 5.1 0.4

Lead 5.2 0.3 -4.9 0.2 25.5 0.3 4.2 0.5 -3.7 0.3 17.7 0.7

Nickel 4.8 0.3 -2.3 0.3 18.9 0.4 2.5 0.7 -0.2 0.5 7.1 0.9

Tin 6.1 0.2 -3.5 0.1 25.3 0.3 4.0 0.4 -4.6 0.3 18.9 0.6

Zinc 1.1 0.3 -5.6 0.2 14.6 0.5 -0.8 0.5 -5.5 0.4 7.2 0.7

Traded Index 4.2 0.2 -3.7 0.1 20.0 0.2 2.5 0.4 -3.0 0.3 11.9 0.6

Non-Exchange Traded

Steel 6.8 0.2 -2.3 0.2 25.1 0.3 5.3 0.7 0.4 0.4 13.7 1.0

Manganese 7.0 0.4 -5.7 0.1 32.6 0.6 9.2 0.6 -5.4 0.2 34.1 0.9

Cadmium 2.4 0.6 -13.1 0.6 33.4 0.5 5.4 1.0 -1.8 1.0 17.7 1.1

Cobalt 7.5 0.4 -2.9 0.4 28.4 0.4 0.9 0.8 -11.2 0.6 21.7 0.9

Tungsten 7.8 0.3 -1.1 0.3 25.6 0.4 9.2 0.6 -0.9 0.5 26.6 0.7

Rhodium 3.4 0.5 -12.4 0.5 34.8 0.5 -2.0 1.1 -12.4 0.9 15.7 1.3

Ruthenium 9.2 0.6 0.7 0.5 26.3 0.8 11.6 1.0 5.0 0.8 22.8 1.2

Molybdenum 13.2 0.5 1.7 0.4 36.3 0.5 13.0 1.0 3.5 1.0 29.3 0.9

Non-Traded Index 7.2 0.3 -4.4 0.2 30.3 0.2 6.6 0.5 -2.8 0.3 22.7 0.6

1991 - 2008

Annual Growth Rates

1992 - 2008

Annualized Quarterly Growth Rates

1991 - 2002 2003 - 2008 1992 - 2002 2003 - 2008

The table reports descriptive statistics for the growth rates of spot price indices for traded and non-traded metals 

as well as individual metals. The sample averages are denoted by “Avg” and the sample standard deviations by 

“STDV”. The annual data cover the 1991 to 2008 period. The quarterly data cover the 1992(Q4) to 2008(Q4) 

period. The quarterly growth rates are annualized (i.e., multiplied by four). All growth rates are multiplied by a 

hundred.
32

Table 2: Structural Break Tests 

Panel A: Full Sample

Date ExpF AveF Date ExpF AveF

Index of Traded 2002 1.786 3.105 2001 (Q4) 0.582 1.005

0.067 0.054 0.338 0.325

Index Non-Traded 2003 3.054 5.244 2002 (Q4) 1.488 2.521

0.014 0.010 0.095 0.084

Panel B: Excluding 2008

Date ExpF AveF Date ExpF AveF

Index of Traded 2002 3.128 4.768 2002 (Q4) 5.124 8.016

0.013 0.015 0.001 0.001

Index Non-Traded 2003 2.893 4.622 2003 (Q3) 4.946 7.870

0.017 0.017 0.001 0.001

Annual Data Annualized Quarterly Data

Annual Data Annualized Quarterly Data

The table reports structural break tests. The date of the break (reported underneath the column titled “Date”) is 

the date in which the Andrews’ (1993) F-test of no break is maximized. The ExpF and AveF columns report two 

tests of structural break following Andrews and Ploberger (1994). Underneath the test statistics (reported in 

smaller font) are their p-values computed as in Hansen (1997). In Panel A, the sample period includes 2008, and 

in Panel B, 2008 is excluded from the analysis.
33

Table 3: Panel OLS Regressions 

(1) (2) (3) (4)

D02 -0.04 0.20

-1.24 4.10

D03 0.24 0.29

5.76 5.38

DTR x D02 -0.02 0.20

-0.44 3.42

DTR x D03 0.18 0.25

2.99 3.65

DNTR x D02 -0.05 0.20

-1.26 3.63

DNTR x D03 0.29 0.32

5.28 5.11

Adjusted R-squared 0.13 0.13 0.14 0.14

D03 - D02 0.28 0.09

5.38 1.91

(DTR x D03) - (DTR x D02) 0.20 0.05

2.66 0.69

(DNTR x D03) - (DNTR x D02) 0.34 0.12

5.01 1.97

Annual Regressions Quarterly Regressions

Panel A: Coefficient Estimates and T-Statistics

Panel B: Difference Between Estimates 

The table reports OLS coefficient estimates and t-statistics (beneath the estimates and in smaller font) in Panel 

A. Panel B reports the difference between estimates and their t-statistics (beneath the differences and in smaller 

font). The sample periods are 1992 to 2008 and 1993(Q1) to 2008(Q4) for annual and quarterly data, 

respectively. To conserve space, I omit the coefficient estimates of the lagged spot price growth rates (included 

in regressions 1 to 3) and of the seasonal dummy variables (included in regressions 4 to 6). The quarterly growth 

rates are annualized. 
34

Table 4: Panel OLS Regressions with Annual Price Growth Rates 

(1) (2) (3) (4)

D02 -0.22 -0.19

-4.62 -3.65

D03 -0.08 -0.30

-0.99 -1.93

DTR x D02 -0.21 -0.18

-3.57 -2.85

DTR x D03 -0.14 -0.36

-1.52 -2.23

DNTR x D02 -0.24 -0.20

-4.38 -3.54

DNTR x D03 -0.03 -0.25

-0.40 -1.62

(World GDP Growth)t 0.14 0.14

4.73 4.73

(World GDP Growth)t x D02 0.12 0.12

3.48 3.48

(World GDP Growth)t x D03 0.24 0.24

3.62 3.62

Adjusted R-squared 0.20 0.20 0.21 0.21

D03 - D02 0.15 -0.11

2.57 -0.66

(DTR x D03) - (DTR x D02) 0.07 -0.18

0.91 -1.06

(DNTR x D03) - (DNTR x D02) 0.20 -0.05

2.90 -0.30

Panel A: Coefficient Estimates and T-Statistics

Panel B: Difference Between Estimates

The table reports OLS estimates and t-statistics (beneath the estimates and in smaller font) in Panel A. Panel B 

reports the difference between estimates and their t-statistics (beneath the differences and in smaller font). The 

time period for the regressions is 1991 to 2008. To conserve space, I omit the coefficient estimates on the lagged 

spot price growth. world growth is given by world per capita GDP growth published n the World Development 

Indicators (WDI). 
35

Table 5: Factiva News Reports 

Price Increasing News Price Decreasing News Net

Traded

Copper 39 7 32

Aluminum 18 9 9

Lead 10 5 5

Nickel 19 7 12

Tin 18 5 13

Zinc 23 1 22

Non-Exchange Traded

Steel 17 5 12

Manganese 5 0 5

Cadmium 3 2 1

Cobalt 5 3 2

Tungsten 4 0 4

Rhodium 4 2 2

Ruthenium 4 0 4

Molybdenum 16 9 7

The table reports the number of news reports in industry newsletters that included news related to metal prices 

increasing and metal prices decreasing. The news reports are identified using the search engine Factiva over the 

period from September 1, 2003 to April 1, 2004. The column “Net” reports the difference between price 

increasing and price decreasing news. 
36

Table 6: Panel OLS Regressions with Annual Price Growth Rates

(1) (2) (3) (4)

DTR x D02 -0.02 -0.19 -0.03 -0.17

-0.51 -2.78 -0.66 -2.83

DTR x D03 0.17 -0.33 0.18 -0.36

2.58 -1.94 2.82 -2.22

DNTR x D02 -0.05 -0.21 -0.06 -0.20

-1.35 -3.39 -1.49 -3.52

DNTR x D03 0.27 -0.23 0.28 -0.26

4.67 -1.36 5.05 -1.61

dP t - 1, i 0.06 0.06 0.07 0.05

0.96 0.88 1.09 0.82

Rate of Return (GSCI)t 0.12 -0.07

0.85 -0.42

Excess Rate of Return (GSCI)t -0.0011 0.0001

-0.94 0.08

(World GDP Growth)t x D02 0.13 0.12

3.09 3.44

(World GDP Growth)t x D03 0.23 0.24

3.41 3.59

(DTR x D03) - (DTR x D02) 0.19 -0.15 0.21 -0.18

2.44 -0.76 2.70 -1.06

(DNTR x D03) - (DNTR x D02) 0.32 -0.02 0.34 -0.05

4.68 -0.08 5.04 -0.31

Panel A: Coefficient Estimates and T-Statistics

Panel B: Difference Between Estimates

The table reports OLS estimates and t-statistics (beneath the estimates and in smaller font) in Panel A. Panel B 

reports the difference between estimates and their t-statistics (beneath the differences and in smaller font). The 

time period for the regressions is 1991 to 2008. To conserve space, I omit the coefficient estimates on the lagged 

spot price growth. The annual rate of return of the SPGSCI is calculated from the daily total return on the index 

obtained from Bloomberg. The excess rate of return is the difference between the SPGSCI rate of return and the 

CRSP value-weighted return of all stocks on CRSP. World growth is given by world per capita GDP growth 

published in the World Development Indicators (WDI). 
37

Table 7: Panel OLS Regressions with Annual Growth Rates

(1) (2) (3) (4)

Intercept -0.15 -0.15

-2.96 -2.76

D02 -0.15 -0.15

-2.71 -2.47

D03 -0.17 -0.17

-1.23 -1.22

dP t - 1, i -0.22 -0.22 -0.22 -0.22

-2.03 -2.02 -2.00 -1.99

(World GDP Growth)t x D02 0.07 0.07 0.07 0.07

2.34 2.17 2.18 1.97

(World GDP Growth)t x D03 0.19 0.20 0.19 0.20

6.59 3.36 6.52 3.31

(Inventory Growth)t -0.23 -0.23

-2.19 -2.17

(Inventory Growth)t x D02 -0.24 -0.24

-1.53 -1.51

(Inventory Growth)t x D03 -0.22 -0.21

-1.57 -1.54

Adjusted R-squared 0.58 0.57 0.57 0.57

The table reports OLS estimates and t-statistics (beneath the estimates and in smaller font). The time period for 

the regressions is 1997 to 2007. I only use data on metals with established futures markets. World growth is 

given by world per capita GDP growth published in the World Development Indicators (WDI). Inventory growth 

is the growth rate of total commercial stocks for the World Bureau of Metal Statistics.