1

Is Speculation Destabilizing? 

Celso Brunetti†

Johns Hopkins University 

Bahattin Buyuksahin‡

U.S. Commodity Futures Trading Commission 

This revision/print: April 22, 2009 

First Draft (very preliminary) 

Abstract: The possibility that speculative trading destabilizes or creates a volatile market is 

frequently debated. To test the hypothesis that speculative trading is destabilizing we employ a 

unique dataset from the U.S. Commodity Futures Trading Commission (CFTC) on individual 

positions of speculators. While others have used a more aggregated version of our data, here we test, 

for the first known time, whether speculators cause, in a forecasting sense, price movements and 

volatility in futures markets and, therefore, destabilize markets. Our findings provide evidence that 

speculative trading in futures markets is not destabilizing. In particular, speculative trading activity 

reduces volatility levels. 

Key Words: Speculation, hedge fund, swap dealer, realized volatility, price, Granger-causality 

JEL Codes: C3, G1 

Acknowledgments: We would like to thank Michael Haigh, Jeffrey Harris, Jim Moser, Frank 

Diebold, Jim Overdahl, David Reiffen, Frank Schorfheide and Kirsten Soneson for useful discussions 

and comments. The views expressed on this paper are those of the authors and do not, in any way, 

reflect the views or opinions of the Commodity Futures Trading Commission, its Commissioners or 

its staff. Errors or omissions are the authors’ responsibility. 

† celsob@jhu.edu; ‡ bbuyuksahin@cftc.gov 

 
2

“[...] there is no question that speculators [...] have pushed prices beyond the supply-demand 

fundamentals and into an era of speculative bubble in oil markets [...] hedge funds are 

exploiting recently deregulated energy trading markets to manipulate energy prices.” 

Tyson Slocum, Director, Public Citizen’s Energy Program, 

Capital Hill Hearing Testimony, July 11, 2008 

“[...] swap dealers [...] convinced institutional investors that commodity futures were an 

asset class that would deliver ‘equity like returns’ [...] the result has been a titanic wave of 

speculative money that has flowed into the commodities futures markets and driven up 

prices dramatically.” 

Adam K. White, Director, Research White Knight Research & Trading 

Capital Hill Hearing Testimony, July 10, 2008 

1. Introduction

 The role of speculators, in particular hedge funds, in futures markets has been the 

source of considerable interest as well as controversy, in recent years. The traditional 

speculative stabilizing theory of Friedman (1953), that profitable speculation must involve 

buying when the price is low and selling when the price is high, has come under strong 

criticism. The critique originates from the observation that trading behavior of hedge funds and 

other large speculators can increase the fragility of financial markets leading to a potential destabilization 

of the broader market system. Two of the most important functions of futures markets are the 

transfer of risk and price discovery. In a well functioning futures market, hedgers, who are 

trying to reduce their exposure to price risk, will trade with someone who is willing to 

accept the risk by taking opposing positions. By taking the opposing positions, these traders 

facilitate the needs of hedgers to mitigate their price risk, while also adding to overall 

trading volume, which contributes to the formation of liquid and well-functioning markets. 

One important development in futures markets in recent years is the increased 

participation of speculators. In addition to traditional speculators such as hedge funds, 

other financial institutions, such as commodity swap dealers, have entered commodity 

futures markets. These institutions view commodities as a distinct asset class and allocate a 

portion of the portfolios they manage into futures contracts tied to commodity indices. The 

increased participation of traditional speculators as well as other financial institutions in 

futures markets1 has led to claims that the trading activities of these speculators 

 

1

 Buyuksahin et al. (2009) show that the amount of money invested globally in commodity indices has been steadily 

growing and that the percentage participation of hedge funds and swap dealers in futures and options on futures has 

also been growing. 
3

destabilize markets. Despite these accusations, there has been surprisingly limited 

research on how speculative trading activity may impact prices and volatility. On the one 

hand this is particularly remarkable given the fact that this class of trader is controversial; 

on the other hand a lack of data stands in the way of a formal study of speculative trading 

in markets. 

 The available evidence on the effects of speculation activity is mixed. Speculators, 

and hedge funds in particular, have been examined in several financial distresses, 

including the 1992 European Exchange Rate Mechanism (ERM) crisis, and the 1994 

Mexican peso crisis (Fung and Hsieh, 20002); the 1997 Asian financial crisis (Brown, 

Goetzman and Park, 2000); and perhaps most famously the financial bailout of Long Term 

Capital Management (Edwards, 1999). In some episodes, hedge funds were deemed to have 

significant exposures and more than likely exerted market impact, whereas in other 

episodes they were unlikely to have contributed to destabilization. Brunnermeier and Nagel 

(2004) in their study of hedge funds and the technology bubble concluded that those funds 

did not exert a correcting force on stock prices during the bubble and hence question the 

efficient markets notion that rational speculators stabilize prices. 

 The limited nature of the previous literature on the market impact of speculators 

can be attributed to the difficulty of obtaining data on their trading activities. We do not 

face this problem. In fact, in this paper we employ the Commodity Futures Trading 

Commission (CFTC) Large Trader Reporting System that allows us to identify positions of 

each trader category in each futures contract for every contract maturity on each day. We 

use unique, highly disaggregated, precisely defined, position-level data in five different 

futures markets collected by the US CFTC. These markets are crude oil and natural gas in 

energy futures markets, corn in agricultural futures markets, and three-month Eurodollars 

and mini-Dow in financial futures markets. Choosing commodities and other financial 

derivatives from different sectors allows us to analyze the role of speculators in a variety of 

markets. The choice of these markets is uniquely made. Crude oil, natural gas and corn 

have experienced an amazing increase in price followed by a sudden reduction. We 

investigate whether these price movements are caused by speculation activity. 

 The three-month Eurodollar contract is the most heavily traded futures contract in 

the United States. This market is experiencing high volatility due to the current sub-prime 

crisis. Here we would like to investigate what is the role of speculative activity in the highly 

volatile interest rate market. Finally, the mini-Dow is chosen to represent the market-wide 

 

2 Fung and Hsieh (2000) analyze the impact of hedge funds in several events including the 1994 

Mexican crisis.
4

overall stock market index. We are, therefore, covering a broad spectrum of assets traded in 

organized exchanges. 

 The data period considered in the paper runs from January 2005 until March 2009. 

We select this time period because it covers two important episodes: 1. the rise and the 

subsequent decline in commodity price (especially, energy markets); and 2. the sub-prime 

crisis. Our analysis, therefore, provides insights on the role exerted by speculation activity 

during these important events. 

 Contrary to common claims, we find that speculative activity does not affect prices. 

In addition, we find that speculation activity actually reduces volatility. Specifically, we 

analyze, in a simple multivariate framework, Granger-causality between the daily rate of 

returns of the above mentioned futures contracts and the daily positions of the five most 

important categories of market participants in these markets. With the exception of the 

stock market (mini-Dow), the results unambiguously show that hedge fund activity does not 

Granger-cause returns. In particular, hedge fund activity does not Granger-cause any other 

variable in the system but it is Granger-caused by the other variables in the system. Our 

results suggest that, by taking the reverse positions of other market participants, hedge 

funds provide liquidity to the market. 

 Furthermore, to assess the impact of speculative activity on risk, we construct 

realized volatility measures from high frequency data, and run Granger-causality tests 

between volatility and positions of the five most important trader categories in the crude 

oil, natural gas, corn, Eurodollar and mini-Dow futures contracts. We find evidence that 

swap dealer and hedge fund activities Granger-cause volatility. We, therefore, analyze 

impulse response functions and find that swap dealer and hedge fund activity reduces 

volatility. This result is of particular importance. Lower levels of volatility imply a 

reduction in the overall risk of the markets analyzed. Trading activities of swap dealers, in 

commodity markets, and hedge funds, in all five markets considered in our study, stabilize 

prices and, therefore, help these markets to perform their risk transfer function. 

 Our paper contributes to a reach literature. Empirically, the relationship between 

trader positions and price movement in futures markets has been studied using a highly 

aggregated public report produced by the CFTC called Commitments of Traders (COT). 

Brorsen and Irwin (1987) find no significant relation between price volatility and hedge 

fund positions in COT data, and Brown et al (2000) find no link between fund positions and 

falling currency values around the 1997 Asian financial crisis. Irwin and Yoshimaru (1999) 

also fail to find a link between funds positions and prices. Although these findings are 

suggestive, researchers generally acknowledge that CFTC COT data is highly aggregated; 

therefore results from these studies should be interpreted with caution. Recent research 
5

using disaggregated data from the CFTC Large Trading Reporting System provides further 

evidence on the relationship between trader positions and price movements. Irwin and Holt 

(2004) use CFTC data on large hedge funds and commodity trading advisors for the sixmonth period from April 1994 to October 1994, for 13 different futures markets. They find a 

small but positive relationship between trading volume and volatility. Yet, their study 

suffers from the aggregation problem since they used total hedge fund position as a proxy 

for nearby position. Haigh, Hranaiova and Overdahl (2007) also adopted CFTC data to 

analyze the relationship between positions of different trader categories and price 

movements. Using daily data from August 2003 to August 2004 for crude oil and natural 

gas futures markets, they find that hedge funds enhance the price discovery function of 

futures markets.3 They use directed graph analysis and focus only on the return process 

while we consider both returns and volatility and adopt a different methodology. We also 

employ a richer data set in terms of both markets analyzed and time period covered. 

 Our results are also linked to a vast theoretical literature that starts with Friedman 

(1953). This theory predicts that profitable speculation has a stabilizing effect, since 

speculators buy when the price is low, therefore, increasing depressed prices, and sell when 

the price is high, therefore, decreasing inflated prices. According to Friedman’s theory, 

speculation activity smoothes the price process. Our results confirm this view. Moreover, we 

find evidence that hedge funds take the opposite positions with respect to other market 

participants, therefore providing liquidity to the market. This is in line with Keynes’ (1923) 

view that speculators fill hedgers’ demand-supply imbalances and provide liquidity to the 

market. Our results are also linked to the work of Hirshleifer (1989, 1990). In a general 

equilibrium framework, he shows that speculation lowers hedge premia. We do not measure 

hedge premia directly. However, we find that speculation activity reduces volatility levels 

and lower volatility levels reduce the cost of hedging. 

 The remainder of the paper proceeds as follows. In section 2 we describe our data. In 

section 3 we analyze contemporaneous correlation between return, volatility, and the five 

most important categories of market participants in the crude oil, natural gas, corn, threemonth Eurodollars and mini-Dow futures markets. In section 4 we analyze Grangercausality tests between trader positions and rate of return as well as positions and 

volatility. We conclude in section 5. 

 

 

3 Boyd, Buyuksahin, Haigh and Harris (2009) employ the same data set to analyze the existence of 

herding among hedge funds. They find that the degree of herding in futures markets is similar to 

equity markets and that the moderate level of herding in futures markets serves to stabilize prices. 
6

2. Data 

 We select futures contracts that represent a broad spectrum of assets traded in 

futures markets: energy (crude oil and natural gas), agriculture (corn), interest rates 

(Eurodollar) and stocks (mini-Dow). 

 We analyze a considerable amount of data covering the period of January 3, 2005 

(August 1, 2006 for corn)4 through March 19, 2009. During this time period, the role of 

speculators has been heavily criticized. This is particularly true for energy and agricultural 

markets. In fact, at the beginning of our sample, the futures price of crude oil is just over 

$42, then reaches the staggering price of $146 in July 2008 and goes back to $42 at the end 

of our sample (see Figure 1, row 1). Natural gas also experiences great price variability, 

during our sample. Prices move up from $6 to $15 at the end of 2005, and then return back 

to $6 in 2006, moved up again to $13 in 2008 and settle below $4 in March 2009 (see Figure 

1, row 2). The corn market also experienced a sharp increase in price (from $5 to $16) 

followed by a sharp decline, during our sample. Many have attributed these price 

movements to speculative activity.5 

 For each market analyzed, we use three different data sets: 1) daily futures rate of 

returns; 2) high frequency transaction data which we employ for computing realized 

volatility measures; and 3) data on daily net futures positions of the most important 

categories of market participants in each market. 

 We use futures market data for several reasons. First, futures prices are readily 

available on a tick-by-tick basis. Second, the contracts we analyze are very actively traded, 

and transactions costs are lower in futures markets. Third, the CFTC collects data on the 

market participants’ positions for futures and options but not for the cash market. Fourth, 

numerous studies find that futures markets tend to lead cash markets in terms of price 

discovery.6 This last point is very important as we focus on the impact of speculation 

activity on prices and volatility. 

 For each market we concentrate on the nearby contract (closest to delivery). Before 

maturity (the expiration date), market participants roll over their positions from the nearby 

contract (March 2005, say) to the next-to-nearby contract (June 2005). This behavior 

generates some type of seasonality in the data. To mitigate these problems, the roll-over 

strategy adopted in this paper is to switch to the new contract when the open interest of the 

nearby contract (March 2005) is lower than the open interest of the next-to-nearby contract 

 

4 High frequency data for corn are not available before August 2006. 

5

 Indeed, in the natural gas market over the sample period analyzed, a hedge fund (Amaranth) has been formally 

charged with market manipulation. 

6 See Hausbrouk (2003). 
7

(June 2005).7 Futures contracts rarely involve physical delivery. In fact, they are closed 

before maturity. The roll-over strategy employed in this paper may also solve the delivery 

distortion problems caused by the need of market participants to close their positions before 

the nearby contract expires. 

 In what follows we describe the data in some detail. 

2.1 Futures Market Return Data

 Table 1 provides an overview of the five selected contracts. Crude oil and natural gas 

are listed on the New York Mercantile Exchange (NYMEX). Crude oil is the world's most 

actively traded commodity and the light sweet crude oil traded on NYMEX is the most 

liquid futures contract on a physical commodity. Natural gas accounts for almost a quarter 

of U.S. energy consumption, and the natural gas futures contract traded on NYMEX is 

widely used as a national and international benchmark price. Energy futures contracts are 

traded on both an electronic platform and an open auction. Daily settlement prices refer to 

the prevailing price at 2:30 pm EST, when the open auction closes.8 Futures contracts on 

corn are traded on the Chicago Board of Trade (CBOT). Corn is becoming increasingly 

important for the production of bio-fuel (ethanol). Trading on both the open outcry and 

electronic platform starts at 10:30am and ends at 2:15pm EST (there is also an additional 

electronic trading session from 7:00pm to 7:00am EST). We compute daily returns using 

settlement prices which are set by the exchange at the close of the trading day (2:15pm 

EST). The Eurodollar futures contract is listed on the Chicago Mercantile Exchange (CME) 

and is the most widely traded interest rate futures product in the US. Eurodollar deposits9

play a major role in the international capital market, and are considered the benchmark 

interest rate for corporate funding. The market is open from 8:20am to 3:00pm EST for floor 

trading, while the electronic trading on Globex occurs between 6:00pm (the previous day) 

and 5:00pm EST. The settlement price is derived from trades and quotes occurring between 

2:59pm and 3:00pm EST on Globex. The last market considered is the mini-Dow futures, 

which is traded on CBOT. Mini-Dow futures provide a way to efficiently gain exposure to 

the Dow Jones 30 index, using a small-sized electronic contract (each point is worth $5). 

The contract trades only electronically on Globex from 6:00pm (the previous day) until 

4:15pm EST (there is also an additional session from 4:30pm to 5:30pm EST). 

 Daily returns are constructed as r = p(t) − p(t −1) t , where p(t) is the natural 

logarithm of the settlement price in day t. When we switch contract from the nearby 

 

7 See Gao and Wang (1999). 

8 We only consider days when the market is open for at least five (three for corn) trading hours. 9 Eurodollars are defined as U.S. dollars deposited in commercial banks outside of the U.S.
8

position to the next-to-nearby position, p(t) and p(t −1) refer to the next-to-nearby 

contract. Table 2, column one, reports summary statistics for the return processes. Daily 

returns on crude oil have a negative mean (-11.6% annually) and high standard deviation. 

The unconditional distribution is non-Gaussian with negative skew and kurtosis in excess 

of three. The negative first-order autocorrelation indicates mean reversion. Natural gas 

exhibits a significant negative average daily return (-47% annually) and a very large 

standard deviation (the largest of the five markets). The unconditional distribution of the 

daily rate of returns on natural gas is non-Gaussian. Corn displays the highest average 

daily rate of return over the sample (6.3% annually). The standard deviation dominates the 

mean and the unconditional distribution is close to a Gaussian. Not surprisingly, the 

Eurodollar interest rate has a very low standard deviation and daily average close to zero. 

The Eurodollar returns also exhibit significant first order autocorrelation and excess 

kurtosis. Returns on the mini-Dow have negative daily average (11% annually), negative 

skew and excess kurtosis. The standard deviation of futures returns on the mini-Dow is 

higher than that of the Eurodollar but lower than that of energy products and corn, 

confirming that, over our sample, commodity markets experience large price variations. 

The negative return for the mini-Dow is mainly due to the sub-prime srisis and the 

recession that followed soon after the crisis. 

2.2 High Frequency Transaction Data

 To construct realized volatility measures, we obtained transaction data from the 

Commodity Futures Trading Commission. At the end of each trading day, the CFTC 

receives data on all transactions that occurred in futures and options directly from the 

exchanges. 

 For crude oil and natural gas we consider transactions in both the electronic 

platform and the traditional pit. Energy markets started to trade electronically on 

September 5th 2006. After that day, most of the transactions take place in the electronic 

platform. However, there is still a 30 percent of volume traded on the pit. We, therefore, 

constructed realized volatility measures considering all transactions that took place 

between 9:00 am and 2:30pm EST (see Table 1). 

 In the corn market we only utilize electronic transactions that took place between 

10:30am and 2:15pm EST. In fact, in this market the majority of transactions occur on the 

electronic platform. Pit trading is sometimes infrequent and pit transaction data are 

somehow problematic. Electronic trading on corn starts on August 1st 2006, this is when our 

sample starts. 
9

 For the Eurodollar market we construct realized volatility measures considering 

both electronic and pit transactions that took place between 8:20am and 3:00pm EST. The 

mini-Dow is only traded electronically, and we consider transactions that took place 

between 9:30am and 4:00pm EST, when traded volume is the highest and when the market 

for the underline stocks is most actively trading. 

 We are dealing with very liquid markets. Crude oil and mini-Dow, for example, have 

several days where the number of transactions is over 150,000 (see last row of Table 1). The 

median intertrade duration for all the markets analyzed, is zero seconds.10 

 The use of high frequency data for constructing realized volatility measures could be 

problematic given the bias produced by market microstructure noise. Several solutions have 

been proposed to overcome this problem. In this paper we follow three approaches. 

Barndorf-Nielsen et al. (2008) propose a kernel estimator where the bias correction is 

achieved by taking into account the autocorrelation structure of high frequency data. The 

second approach we follow is that of Andersen et al. (2001) where the bias correction is 

achieved by sampling at relatively lower frequencies. The last approach we implement is 

developed by Zhang et al. (2005) and is referred to in the literature as two scales realized 

volatility. The results of our analysis are qualitatively the same regardless of the realized 

volatility estimator adopted. To conserve space, we only report results for the two scales 

realized volatility estimator.11 Here we describe this estimator in some detail and to do so 

we need to introduce some notation. 

 Let p τ∈t { (τ )} be the natural logarithm of the price process over the time interval t, 

and let [a,b] ⊂ t be a compact interval which is partitioned in m subintervals. In our setup 

the interval [a,b] is a trading day. For a given m, the ith intraday subinterval is given by 

[ , ] 1,

m

i

m

i τ τ − , where a b m

m

m m =τ 0 <τ 1 < ...τ = , and the length of each intraday interval is given by 

m

i

m

i

m Δi = − −1 τ τ . The intraday returns are defined as 

( ) ( ) m

i

m

i

m

ir p p = − −1 τ τ

where i = 1,2,..., m. Realized volatility in day t is the sum of squared intraday returns 

sampled at frequency m

 ( )

2

1

∑=

=

m

i

m

i

m

t RV r (1)

The two scales realized volatility estimator is quite simple. Starting from the first 

observation, set m=300 transactions, say, and compute RV using equation (1). Then, 

starting from the second observation re-compute RV using equation (1) (m is unchanged). 

 

10 Our high frequency data contain information up to the second, but actual transactions are recorded 

in hundredths of a second (centiseconds) or thousands of a second (milliseconds). 

11 Results for the other realize volatility estimators are available from the authors. 
10

In this way, even if m is set to a given sampling frequency, we still make use of all available 

observations (transactions). We then average the estimators obtained on the subintervals. 

Sampling at a relatively lower frequency (say, 5-minute, in calendar time, or every 300 

transactions in transaction time) dramatically reduces the effect of market microstructure 

noise. This benefit is now retained, while the variation of the estimates will be lessened by 

the averaging. When applying equation (1) to all observations (i.e. sampling at the highest 

possible frequency, m=1), we obtain a consistent estimate of the variance of the market 

microstructure noise. The last step in the two scales realized volatility estimator is to 

correct for the bias of the noise, by subtracting from the average estimator the noise 

variance 

 

all

t

k

j

m

t j

TSRV

t RV RV

k

RV = ∑ −γ =1

,

1 (2) 

where k denotes the number of subintervals of size m and, therefore, the number of realized 

volatilities computed using equation (1) and setting m to a lower frequency; γ is a ratio 

between m and the total number of observations in [a,b]; and all RV refers to the realized 

volatility measure in (1) computed using all available data in the interval [a,b]. Intraday 

returns can be constructed using different sampling schemes. In our framework, i,m τ

denotes the time of a transaction. We, therefore, compute the two scales realized volatility 

measure in transaction time (for example, sampling every 300 transactions). In order to 

implement equation (2), we need to choose m, the sampling frequency. Volatility signature 

plots12 provide valuable information about the bias in RV measures in (1) and about 

optimally choosing m in order to correct that bias. For each asset, we construct volatility 

signature plots for each month in our sample and then select m.13 

 Figure 1 depicts prices and two scales realized volatility measures for the five assets 

analyzed in the paper. Crude oil, interest rates (Eurodollar) and the stock market (miniDow) exhibit high volatility in the last part of our sample, after the sub-prime crisis hits the 

economy. We may conjecture that these markets are strongly linked to the overall status of 

the economy. The uncertainty about the sub-prime crisis and the recession is clearly 

evident in the volatility of these assets. Natural gas and corn also exhibit higher volatility 

towards the end of our sample, but these volatility levels are comparable to those computed 

in the first part of our sample. 

 Table 2, column 2, provides descriptive statistics for RV. Energy and corn markets 

show both a very high average volatility and a high variation in volatility levels. This is not 

 

12 Volatility signature plots show average daily realized volatility measures, computed using 

equation (1), against sampling frequencies m - see Andersen, Bollerslev, Diebold and Labys (2000). 13 Details on this procedure can be obtained from the authors. 
11

surprising, given that we are indeed covering the period where commodity markets 

experience dramatic price increases. The lowest volatility is in the Eurodollar market 

followed. All realized volatility measures are stationary and highly persistent. 

2.3 Market Participants’ Positions

The CFTC monitors U.S. futures and options on futures markets through its market 

surveillance program, and since the 1920s, the CFTC (and its predecessors) has been 

utilizing the central tool of market surveillance known as the Large Trader Reporting 

System (LTRS). Following the Commodity Exchange Act (CEA), the CFTC collects and 

stores data from daily reports on market data and position information from the Futures 

Commission Merchants (FCM’s), foreign brokers, exchanges, clearing members, and also 

traders. These reports show the positions of traders that hold contracts above specific levels 

set by the CFTC. These large trader reporting levels include crude oil, 350 contracts; 

natural gas, 200 contracts; corn, 250 contracts; Eurodollar, 3,000 contracts; and mini-Dow, 

1,000 contracts. The total amount of all trader positions reported to the CFTC represents 

approximately 70 – 90 percent of total open interest in any market, while the remainder are 

traders who generally trade a small number of contracts, known as Non-Reportable 

Positions (NRP).14 

When a trader is identified to the CFTC, the trader is classified either as a 

commercial or non-commercial. A trader’s reported futures position is determined to be 

commercial if the trader uses futures contracts for the purposes of hedging as defined by 

CFTC regulations. The non commercial category includes participants who are not involved 

in the underlying cash business – otherwise known as speculators and include hedge funds, 

floor brokers/traders and so forth (see Exhibit 1 in the appendix).15

The Commitment of Traders Report (COT), which utilizes data from the LTRS, 

provides a summary of highly aggregated traders’ position (commercial, noncommercial and 

NRP) as of the close of business on Tuesday for each market in which at least 20 traders 

hold positions. Information released to the public in the form of the COT is highly 

aggregated, but the disaggregated LTRS enables the CFTC surveillance team to monitor 

 

14 Occasionally, the CFTC will raise or lower the reporting levels in specific markets with the 

objective of striking a balance between maximizing effective surveillance and minimizing the 

reporting burden on the futures industry. 

15 Specifically, a reportable trader gets classified as commercial by filing a statement with the CFTC 

(using the CFTC Form 40) that he is commercially “...engaged in business activities hedged by the 

use of the futures and option markets.” However, to ensure that the traders are classified 

consistently and with utmost accuracy, CFTC market surveillance staff in the regional offices checks 

the forms and re-classifies the trader by collecting further information about the trader’s 

involvement in the markets. 
12

individual participants and/or a specific group of market participants. It is these detailed 

groupings within the commercial and non-commercial categories that we analyze in this 

study. Active groupings of participants vary across contract markets but an exhaustive list 

of participants can be found in Exhibit 1 in the appendix. 

Two categories of market participants deserve further discussion: commodity swap 

dealers and hedge funds. We start with the latter. 

There is no consensus on the exact definition of a hedge fund in futures markets and 

the CEA, the statute governing futures trading, does not define hedge funds.16 Accordingly, 

there is not a requirement that hedge funds be categorized in the LTRS. Despite this, many 

hedge fund complexes are registered as Commodity Pool Operators (CPOs),17 Commodity 

Trading Advisors (CTAs)18 and/or Associated Persons (APs),19 who may control customer 

accounts. In addition to these three categories of traders, market surveillance staff at the 

CFTC identifies other participants who are not registered in any of these three categories 

but are known to be managing money (MM). These four categories combined are defined as 

being the hedge fund category (see bottom of Exhibit 1 in the appendix). We actually check 

the names of the funds in these four categories with those listed in press reports as hedge 

funds, and we find that many of the large CPOs, CTAs, APs and MMs are generally 

considered to be hedge funds and hedge fund operators. As such, and to conform with the 

academic literature and common financial parlance, we refer to these four categories 

collectively as hedge funds in our study. 

In commodity markets, commercial commodity swap/derivatives dealers (henceforth, 

swap dealers), play an important, albeit controversial, role. This category of market 

participants uses derivative markets for two main reasons: i) to manage their price 

 

16 However, the SEC notes that a hedge fund is an ‘entity that holds a pool of securities and perhaps other assets, 

whose interests are not sold in a registered public offering and which is not registered as an investment company 

under the Investment Company Act” (p.3. SEC, 2003). 

17 “Commodity Pool Operator (CPO): A person engaged in a business similar to an investment trust or a syndicate 

and who solicits or accepts funds, securities, or property for the purpose of trading commodity futures contracts or 

commodity options. The commodity pool operator either itself makes trading decisions on behalf of the pool or 

engages a commodity trading advisor to do so.” 

Source: Glossary of the CFTC (http://www.cftc.gov/educationcenter/glossary/index.htm). 18 “Commodity Trading Advisor (CTA): A person who, for pay, regularly engages in the business of 

advising others as to the value of commodity futures or options or the advisability of trading in 

commodity futures or options, or issues analyses or reports concerning commodity futures or 

options.” 

Source: Glossary of the CFTC (http://www.cftc.gov/educationcenter/glossary/index.htm). 19 “Associated Person (AP): An individual who solicits or accepts (other than in a clerical capacity) 

orders, discretionary accounts, or participation in a commodity pool, or supervises any individual so 

engaged, on behalf of a futures commission merchant, an introducing broker, a commodity trading 

advisor, a commodity pool operator, or an agricultural trade option merchant.” 

Source: Glossary of the CFTC (http://www.cftc.gov/educationcenter/glossary/index.htm). 
13

exposure originating from their over-the-counter (OTC) business; and ii) to manage their 

transactions with commodity index funds. These funds are often employed by pension funds 

and other large institutions that seek diversification by investing in commodities. For this 

reason commodity index funds hold significant long-only positions, especially in near-term 

futures contracts. The controversy regarding swap dealers owes to the fact that they are 

classified as commercial traders (i.e. hedgers) - indeed these market participants are 

hedging their price exposure - but they are often trading to fulfill the needs of commodity 

index funds that are entering commodity markets to have an exposure in these markets. 

Over our sample, commodity index funds have experienced a significant growth.20

 For each market, we consider four types of traders’ information: i) number of 

contracts held in long positions by a specific trader category (futures long); ii) number of 

contracts held in short positions by a specific trader category (futures short); iii) the 

difference between futures long and futures short positions (net futures positions); and iv) 

net total positions which accounts for both the net futures positions and the net (delta 

adjusted) option positions of each trader. To conserve space we report only results for the 

net futures positions. However, results for futures short, futures long and net total 

positions21 are qualitatively similar to those obtained with the net futures positions. 

 For each market we concentrate on the five largest categories of market 

participants. Hedge funds (HF) and floor brokers/traders (FBT) are common to the five 

markets analyzed. According to the CFTC definition, these two categories of market 

participants are non-commercial – i.e. speculators. In the crude oil, natural gas and corn 

markets, we analyze dealers/merchants (AD) – which include wholesalers, 

exporters/importers, shippers, etc.; swap dealers (AS) – see discussion above; and 

manufacturers22 (AM) – which includes fabricators, refiners, etc.; and producers (AP)23. For 

the Eurodollar market, in addition to hedge funds and floor brokers/traders, we study the 

positions of commercial arbitrageurs or broker/dealers (FA), non-U.S. commercial banks 

(FB) and U.S. commercial banks (FC).24 It is not surprising that domestic and foreign banks 

are very active in this market. Finally, the hedger categories for the mini-Dow are 

arbitrageurs or broker/dealers (FA), financial institutions other than those already 

classified (FO), and hedge funds (FH) – hedge funds that are shown to be hedging. 

 

20 By June 2008, the notional value of commodity index investments tied to U.S. futures exchanges 

exceeded 160 billion dollars (CFTC, 2008). 21 These results are available from the authors. 

22 For crude oil and corn. 

23 For natural gas. 24 For a definition of broker, dealer, floor trader and floor broker, we refer the interested reader to 

the glossary of the CFTC, which can be found on line at 

http://www.cftc.gov/educationcenter/glossary/index.htm. 
14

 Table 1 shows descriptive statistics for the level and first difference of the net 

futures positions of each category of market participants organized by market. Dealers and 

merchants (AD) are net short over our sample, while swap dealers are net long. This is to 

be expected given that commodity index funds hold long positions. Hedge funds are net long 

in crude oil, corn, Eurodollar and mini-Dow, but net short in natural gas. Positions in levels 

are stationary for all markets but the Eurodollar. This market, in fact, is experiencing a 

reduction in volume and open interest since the inception of the sub-prime crisis. 

Interestingly, hedge fund positions in the Eurodollar market are stationary, indicating that 

these market participants are not fleeing the market. Positions in levels exhibit strong 

autocorrelation. Positions in levels measure the stock hold by each market participants in 

each market and are, therefore, a measure of stock. To measure trading activity (flow) we 

compute change in positions. Swap dealers exhibit negative mean and median indicating an 

overall reduction in their positions. This is also true for hedge funds. Swap dealers (AS) and 

hedge funds (HF) exhibit high standard deviation in change in positions - .i.e. they are 

changing their positions often and by large amounts. 

 Table 3 shows the participation rate of each trader category in each market as 

percentage of the total open interest. While in Table 2 we analyze net futures positions of 

market traders, in Table 3 we concentrate on the long and the short components of trader 

positions. Dealers and merchants (AD) are mainly short. This is in line with the needs of 

these market participants to hedge their positions in the underlying commodity. Swap 

dealers hold 40 percent of the long positions in all commodities analyzed. Interestingly, 

hedge funds hold large positions in all five markets, and they are present on both sides of 

the market (long and short positions). The last column of Table 3 shows the percentage 

(mean, maximum and minimum) of total open interest held by the five categories of market 

traders over our sample. Even if we only consider five categories of traders in each market, 

we cover most of the total open interest traded on these markets. 

 We proceed with the correlation analysis. 

3. Unconditional Contemporaneous Correlation

 Our preliminary analysis of the relationship between returns, volatility, and trader 

positions begins by computing correlation coefficients. Table 4 reports, for each market, 

correlation coefficients between returns, realized volatilities, and the positions of the five 

categories of traders analyzed. The top number refers to the positions in levels, while the 

bottom number refers to the change in positions. Positions for dealers/merchants (AD) are 

negatively correlated with rate of returns and positively linked to volatility of natural gas 
15

and corn but negatively linked to volatility of crude oil. There is no evidence of a 

contemporaneous link between swap dealer positions, either in levels or first difference, and 

rate of returns. Swap dealer activity seems to move in the opposite direction of volatility in 

the corn and the natural gas markets but is positively link to volatility levels in the crude 

oil market. Hedge funds have a positive correlation with returns. This implies that hedge 

fund positions move in the same direction as the market. It is also interesting to note that 

hedge fund activity is negatively linked to volatility. An increase in hedge fund activity is 

associated with lower volatility levels. Finally, swap dealer activity and hedge fund activity 

is negatively linked to other traders’ positions. This may suggest that by taking the 

opposite position with respect to the other market traders, swap dealers and hedge funds 

may provide liquidity to the market. This is in line with the results of Haigh, Hranaiova 

and Overdahl (2005) who study the interaction between traders in the natural gas and 

crude oil futures markets. They find that hedge funds provide liquidity to hedgers. This is 

also in line with the theory of speculation as described in Keynes and Hicks, which 

postulates that speculator positions should offset any imbalance of hedger positions. 

 The simple correlation analysis provides three main results. First, swap dealer 

activity is not contemporaneously correlated with returns and is negatively linked to 

volatility. Second, hedge fund activity is positively correlated with returns but negatively 

correlated with volatility. Third, the correlation between hedge fund positions and positions 

of the other market traders is always negative. The same result also holds for swap dealers. 

4. Granger-Causality Analysis 

 The concept of Granger-causality relates to predictions – xt is Granger-causal for zt if 

xt contains useful information for predicting zt. This definition of causality is practical but 

has limitations. In fact, the notion of causality is an old one and goes back to the ancient 

Greek philosophy - Aristotle distinguishes between cause and effect: a cause is an event 

that produces an effect. Unfortunately, Granger-causality does not allow us to distinguish 

between causes and effects. Nonetheless, Granger-causality is easy to compute and provides 

useful information as to whether a trader activity prompts, in a forecasting sense, price 

movements and/or vice versa. 

 We test for Granger causality in the context of Vector AutoRegression (VAR) models. 

Since the variables exhibit heteroskedasticity and serial correlation, we estimate VAR 

models using the Generalized Method of Moments (GMM) and Newey-West robust 

standard errors. We estimate four different sets of VARs. In the first we consider rate of 

returns and positions in levels; in the second we employ rate of returns and change in 
16

positions; in the third we utilize volatility and positions in levels; in the last we consider 

volatility and change in positions.25 We only report results for the optimal lag-length.26

However, we would like to emphasize that these results are very robust and hold regardless 

of the lag structure in the VAR. 

4.1 Crude Oil

 Table 5 provides the results (p-values) of Granger-non-causality tests. The last 

column and the last row of the Table are labeled “all”. In the last column we test whether 

each variable is Granger-caused by all the other variables in the system - i.e. are returns 

(volatilities) Granger-caused by positions? The last row is, on the other hand, testing 

whether each variable is Granger-causing any other variable in the system. In other words, 

we are testing whether each variable is jointly Granger-causing the remaining variables in 

the system. Here we are particularly interested in testing whether swap dealer activity and 

hedge fund activity are Granger-causal for returns and volatility. The null hypothesis is 

that of Granger-non-causality - a p-value greater than 5 percent indicates failure to reject 

the null. 

 Panels A and B in Table 5 refer to Granger-causality tests between rates of returns 

and positions in levels, and in their first difference, respectively. We obtain some 

interesting results. Returns on the crude oil market are not Granger-caused by positions 

(the p-value is equal to 0.148 for levels and 0.199 for change in positions). On the other 

hand, returns Granger-cause positions (the p-value is 0.000). When jointly testing whether 

hedge funds Granger-cause returns or any other variable in the system, we fail to reject the 

null. In fact, hedge fund activity is the only variable which is not jointly Granger-causing 

any other variable in the system (the p-value 0.181 in panel A and 0.086 in panel B) at 5 

percent significance level. This implies that hedge fund positions do not provide any useful 

information for predicting returns or positions of other traders. Moreover, hedge fund 

activity is Granger-caused by the system (the p-value is 0.000 for both levels and change in 

positions). If we pair this result with the negative correlation between hedge fund positions 

and positions of the other traders, it is reasonable to conjecture that by taking the opposite 

side in the market (negative correlation), hedge funds are providing liquidity to the market. 

 

25 We only report results for net futures positions. However, we perform the same analysis also for 

long futures positions, short futures positions and net total (future and options) positions. 26 We select the optimal lag-length using a series of Wald tests (i.e. testing for the significance of the 

parameters of each lag). Given the problems of heteroskedasticity and serial correlation in our data, 

we could not rely on standard Akaike (AIC) and Schwartz information criteria (SIC). Wald tests do 

not pose any penalty to less parsimonious specifications, unlike AIC and SIC. Therefore, the optimal 

lag-length selected is always higher than that selected by AIC and SIC. However, the results do hold 

even when using AIC and SIC. 
17

Our results for swap dealers indicate a feedback effect: swap dealer positions are Grangercaused by the other variables in the system and Granger-cause these variables. 

 The last two panels in Table 5 report Granger-causality tests for volatility and 

traders’ positions (panel C refers to positions in levels while panel D refers to change in 

positions). A stylized empirical finding in the realized volatility literature is that 

logarithmic realized standard deviation is approximately Gaussian. Our realized volatility 

measures confirm this finding.27 In fact, in modeling realized volatility measures in the 

context of VARs, it is customary to use logarithmic realized standard deviation – e.g. 

Andersen et al. (2006). In our analysis we use the three measures of realized volatility 

described in Section 2 and their logarithm realized standard deviations counterparts. To 

conserve space, we only report results for logarithmic two scales realized standard 

deviation in transaction time.28

 Panels C and D show that positions, including those of swap dealers and hedge 

funds, Granger-cause volatility. There is also a feedback effect from volatility to traders’ 

positions. Therefore, it seems that swap dealer and hedge fund trading, among others, are 

moving volatility levels in the crude oil market. To further investigate this issue we 

compute impulse responses. Pesaran and Shin (1998), proposed a technique, termed 

“generalized impulse responses”, which is invariant to the ordering of the variable in the 

VAR and does not require shocks to be orthogonal. Assume that there is a one-standard 

deviation shock to the k-th variable; generalized impulse responses are then computed by 

applying a variable specific Cholesky factor which is derived by placing the k-th variable at 

the top of the Cholesky ordering. Figure 2, row 1, depicts generalized impulse responses of 

volatility to one standard deviation innovation in the level of traders’ positions in the crude 

oil futures market, for the VAR with 5 lags.29 We are particularly interested in the response 

of volatility to a standard deviation shock to commodity swap dealer activity and hedge 

fund activity. The second column in row 1 indicates that swap dealers have no impact on 

volatility. Interestingly, the fourth column in row 1 provides evidence of a short-lived and 

statistically significant reduction in volatility. In other words, a shock in hedge fund 

activity reduces volatility. It is also worth noting that positions of dealers and merchants 

(hedgers) have a positive impact on volatility levels (i.e. they increase volatility). 

 

27 Results not reported. 28 Our results are robust to the different measures of realized volatility considered. 

29 This is the optimal lag-length. Generalize impulse responses for VARs with 4-1 lags are very 

similar to those reported in Figure 2. Response standard errors are computed with 1,000 Monte 

Carlo replications in EViews 6. On the horizontal axis is the number of days after the shock, in this 

instance 10 days. 
18

We also compute impulse responses using the classic Cholesky decomposition which is very 

sensitive to the order of the variables in the VAR. To mitigate this problem we consider 

several orderings of the variables and always find results similar to those reported above.30

 Figure 3 row 1 illustrates impulse responses of volatility to a standard deviation 

shock to change in positions. Swap dealer activity and hedge fund activity have a negative 

impact on volatility – i.e. the trading of these two market participants reduces volatility 

levels. There is also some evidence that dealers and merchants (commercial AD) swell 

volatility.31

4.2 Natural Gas

 Table 6, panels A and B, report Granger-causality test results (p-values) for returns 

and positions in levels, as well as returns and change in positions, respectively. We find 

that rate of returns in the natural gas industry are not Granger-caused by positions, both in 

levels (p-value 0.486) and first difference (p-value 0.571), but positions are Granger-caused 

by rate of returns (p-value 0.001 for positions in levels and 0.000 for change in positions). 

Swap dealer positions are Granger-caused and Granger-cause the other positions in the 

system. There is also evidence that the system is Granger-causing hedge fund activity, but 

hedge fund activity is not Granger-causing the system. It seems that hedge funds are 

reacting to market conditions, and there is no indication that hedge fund activity is moving 

prices and/or positions of other traders. These results are qualitatively similar to those 

obtained in the crude oil market. 

 Panel C in Table 6 shows Granger-causality tests for volatility and positions in 

levels. Volatility is Granger-caused by traders’ positions (p-value 0.016), including hedge 

funds. There is no evidence of a feed-back effect from volatility to trading activity (p-value 

0.633). Figure 2, row 2, shows generalized impulse responses of volatility to a standard 

deviation shock in trader positions. Swap dealers and hedge funds have a short-lived 

negative impact on volatility, in the sense of a reduction of volatility levels (see second and 

fourth columns in Figure 2, row 2). On the other hand, it appears that dealers and 

merchants (AD) and producers (AP) increase volatility levels. 

 The last panel in Table 6 refers to Granger-causality tests for volatility and change 

in trader positions. The results suggest that volatility is marginally not Granger-caused by 

 

30 Impulse responses using the classic Cholesky decomposition are also calculated for all the other 

markets studied in this paper. Results from Cholesky decomposition are similar to generalized 

impulse responses; therefore we do not report them in this paper. 

31 For the change in positions we also compute impulse responses using Cholesky decompositions and 

several orderings of the variables. We always obtain qualitatively similar results. 
19

trading activity (p-value 0.052) and does not Granger-cause trading activity (p-value 0.344). 

Similarly to what we find in the crude oil market, there is evidence that hedge fund trading 

is Granger-caused by the trading of the other market participants but is not causing, in a 

forecasting sense, any variable in the system. Figure 3, row 2, confirms our findings. In 

fact, hedge fund activity does not have any impact on volatility (fourth graph); swap dealers 

seem to slightly reduce volatility (second graph); while dealers and merchants seem to 

increase volatility levels. 

4.3 Corn

 Rate of returns on corn appear to not be affected by trader positions and by the 

change in trader positions. In fact, Table 7 shows that returns are not Granger-caused by 

the trading activity of the five largest categories of traders. Similar to the results for the 

energy market, hedge fund activity is Granger-caused by the system but does not Grangercause the system. This is also true for swap dealer activity in panels B (change in 

positions). 

 There is evidence of a feed-back effect between volatility and trader positions. Hedge 

fund activity is non-causal for the system (p-values 0.070 and 0.148 in Table 7, panels C 

and D, respectively) but is caused by the other variables in the system. Similar results also 

hold for swap dealer positions in the last panel, where changes in positions are considered. 

Impulse responses in Figures 2 and 3, row 3, show that hedge fund activity and swap dealer 

activity do not have a significant effect on volatility. 

4.4 Eurodollar

 Since the inception of the so-called sub-prime crisis, interest rate markets 

experienced a decline in open interest. The Eurodollar futures market is no exception.32

However, it still remains the most liquid futures market in the US in terms of traded 

volumes. For four of the trader types analyzed, net positions (futures or futures and futures 

equivalent options) are non-stationary (the ADF test fails to reject the null of stationarity). 

We, therefore, only analyze changes in positions. Table 8 reports Granger-causality tests for 

the Eurodollar market. In line with the previous results, rate of returns is not Grangercaused by positions (p-value 0.478). Interestingly, hedge fund activity is caused by the other 

variables in the system (p-value 0.001) but does not Granger-cause any variable in the 

system (p-value 0.411). 

 

32 During our sample, futures-only three-month Eurodollars open interest declined from a peak of 12 

million contracts to 9 million contracts. 
20

 Similar to the results for crude oil and natural gas, it seems that volatility in the 

Eurodollar market is Granger-caused by trading activity (p-value 0.025). In particular, 

there is evidence that hedge fund activity is causal to volatility (p-value 0.007). To further 

investigate this issue, we compute generalized impulse responses. Figure 3, row 4, shows 

that four of the five trader categories (including hedge funds) have no effect on the 

Eurodollar volatility while commercial arbitrageurs and brokers/dealers (FA) seem to 

reduce risk in this market. 

4.5 Mini-Dow

 The last market we analyze is the mini-Dow. In this market we have two hedge fund 

categories: hedgers (commercial FH) and speculators (non-commercial HF). It is interesting, 

then, to compare how hedge funds behave when entering the market for different 

purposes.33 

 The results for this market are, in part, different from those obtained in the other 

markets analyzed. In fact, rates of return (see Table 9, panels A and B) are Granger-caused 

by trader positions. This is a feedback effect. Returns also cause, in a forecasting sense, 

positions. It also appears from panel B (change in positions) that speculative hedge fund 

activity is responsible for this causal effect. Positions of commercial hedge funds (hedgers) 

seem to affect rates of return when considering the levels of the positions (p-value 0.000); 

this is no longer the case when considering changes in positions (p-value 0.360). Since this 

is the first market analyzed where rates of return are Granger-caused by positions, we also 

investigate impulse responses. Figure 2, row 4 (positions in levels), and Figure 3, row 5 

(change in positions), depict generalized impulse responses of mini-Dow returns to a shock 

in market positions. Speculative hedge fund activity has a positive impact on returns in the 

sense that it increases return levels. During the time period analyzed, the Dow index, first 

increases from a level of 10,750 to 14,200 in the second half of 2007 and then declines after 

the beginning of the sub-prime crisis and reaches 6,500 at the end of our sample. On 

average, the Dow experienced a negative return over our sample (11% on an annual basis). 

Trading activity of speculative hedge funds (HF) and commercial arbitrageurs and brokers 

and dealers (FA), contribute to reverse the negative trend. On the other hand, commercial 

financial institutions (FO) and floor brokers/traders (FBT) appear to contribute to the 

 

33 Among the five markets analyzed, commercial hedge funds hold significant positions only in the 

mini-Dow. 
21

trend.34 Interestingly, commercial hedge funds (FH) exhibit no significant impact on 

returns. 

 Now we turn our attention to the volatility of the mini-Dow. Table 9, panels C and 

D, show that volatility is Granger-caused by positions (including those of commercial and 

non-commercial hedge funds) but not vice versa. In line with the results for volatility 

dynamics described above, speculative hedge fund activity reduces volatility. It is 

interesting to contrast the graphs in the last two rows of Figures 2 and 3. Traders that 

increase rate of returns and reverse the trend (speculative hedge funds and arbitrageurs 

and brokers and dealers), decrease volatility; while traders that decreased returns 

(financial others), have a net positive effect on volatility. According to classical theory 

(Friedman, 1953) a speculator should buy when the price is falling and sell when the price 

is rising. This implies a trend reversal. Speculative hedge fund activity in this market does 

seem to reverse the trend. Moreover, speculation from hedge funds reduces the risk level in 

this market. We also notice a difference between the effects of trading activity of 

commercial hedge funds (hedgers) and non-commercial hedge funds (speculators). The 

latter category reverses the trend and reduces volatility levels while the former does not 

have any significant effect on returns and volatility. 

4.6 Main Findings

 Is speculation activity causing price movements in energy, agricultural, and 

financial markets? 

 The analysis of Granger-causality between returns and trader activities yields two 

main results. First, returns are not Granger-caused by positions. The only exception is the 

stock market where we find that speculative hedge fund activity has a positive impact on a 

bearish market. Therefore, the answer to the above question is negative for most markets 

analyzed and when we have evidence of a causal effect on returns, we find that speculation 

activity reverses the trend. Second, hedge fund activity is not Granger-causal for returns 

and/or positions of the other market traders, but it is Granger-caused by the other variables 

in the system. Once again, the stock market behaves differently. These results are 

particularly important for the issue we analyze in this paper. In fact, they suggest that 

speculation activity does not destabilize prices, even in markets and times when prices 

reach historical highs. Speculation activity, in general, and hedge fund activity, in 

particular, seems to be responsive to market conditions but is not moving the market, nor is 

it generating trading activity from other traders. We are aware that Granger-causality 

 

34 These results hold for the sub-periods January 2005 – August 2007, and September 2007 – March 

2009. 
22

tests have limitations. However, our results are very robust. Using different data filtrations 

(levels and first differences) and different VAR specifications, we always find that 

speculation activity does not move prices and, in the case of hedge funds, does not cause 

trading activity by other market participants. These findings are in line with previous 

literature. Irwin and Yoshimaru (1999), and Fung and Hsieh (2000), for example, find no 

evidence that hedge fund activity has an impact on market prices. The previous literature, 

however, analyzes highly aggregated data while we are able to precisely identify specific 

categories of traders. In this respect, our results are noteworthy. 

 Overall, the above results seem to provide support to the traditional Keynes-Hicks 

paradigm on the stabilizing role of speculation in financial markets. 

 Is speculation activity increasing risk? It is, in fact, possible that speculation activity 

may not have any impact on prices but it might have an impact on market volatility. 

 With the exception of the corn market, we find that trading activity Granger-causes 

volatility. In particular, there is evidence of causality from hedge fund activity to volatility. 

Further investigation, via generalized impulse responses, shows a statistically significant 

reduction in volatility. In other words, a shock in hedge fund activity reduces volatility. 

These results are very robust. In fact, we adopt three measures of realized variance and 

three measures of logarithmic realized standard deviation, and unambiguously find that if 

hedge fund activity is causal, in a Granger sense, for volatility, this causal relationship 

implies that hedge fund trading reduces volatility levels. Similar results also apply to 

commodity swap dealers in the crude oil and natural gas markets. Lower levels of volatility 

imply that markets are less risky. Less volatile markets are more attractive and perform 

their role of risk transfer. This implies a reduction in the hedge premia, as predicted by 

Hirshleifer (1989, 1990). 

6. Conclusion

 Is speculation activity destabilizing? Our analysis clearly shows that this is not the 

case. We employ a unique dataset that allows us to precisely identify positions of market 

participants in five futures markets. By adopting a very simple but well-established 

technique, Granger-causality, we investigate whether speculation activity is moving prices 

and increasing volatility. In general, we find that speculation activity is not causing any 

price movement, but it has some impact on risk: it reduces risk. We are not ruling out the 

possibility that a single trader (of any category of market participants) might implement 
23

trading strategies that move prices and increase volatility. However, as a whole, 

speculation does not seem to destabilize futures markets. The role of speculation activity in 

financial markets is very important because it allows hedgers to find counterparties to 

hedge their positions and, in general, it allows markets to perform their institutional role. 

Therefore, speculators, in general, and hedge funds, in particular, should not be seen as 

sinful agents. In fact, we find that speculative trading activity has beneficial effect on 

markets. 

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25

Table 1 

Contract Specifications 

 Crude Oil (CL) Natural Gas (NG) Corn (C) EuroDollars (ED) Mini-Dow (YM) 

 

Exchange NYMEX NYMEX CBOT CME CBOT 

Trading Unit 1000 US barrel 10000 mmBtu 5000 bushels Eurodollar time 

deposit having a 

principal value of 

1 million with a 3-

month maturity 

1 mini-sized Dow futures 

 

Trading Hours 

(EST): 

Open Outcry: 

Electronic 

9:00 am-2:30pm 

6:00pm-5:15pm 

9:00am-2:30pm 

6:00pm-5:15pm 

10:30am-2:15pm 

7:00pm-7am and 

10:30am-2:15pm 

8:20am-3pm 

6:00pm-5:00pm 

N/A 

6:00pm-4:15pm, and 

4:30pm-5:30pm 

 

Trading 

months 

Consecutive 

months in the 

current year and 

the next five years 

as well as June 

and December 

contracts are 

beyond sixth year 

Consecutive months 

in the current year 

and the next twelve 

years 

Dec, Mar, May, 

Jul and Sep 

Mar, Jun, Sep, 

Dec, forty months 

in March quarterly 

cycle, and the four 

nearest serial 

contract months 

Mar, Jun, Sep, Dec 

 

Minimum 

Price 

Fluctuations 

$).01 (1cent) per 

barrel ($10 per 

contract) 

$0.01 (1cent) per 

mmBtu ($10 per 

contract) 

1/4 cent/bushel 

($12.50/contract) 

$12.50 per contract 

($6.25 for nearest 

expiring contract) 

Minimum price increment is 

one index point (equal to $5 

per contract). 

 

Settlement 

Type 

Physical Physical Physical Cash Cash 

 

Last Trading 

Day 

Trading 

terminates at the 

close of business 

on the third 

business day prior 

to the 25th 

calendar day of 

the month 

preceding the 

delivery month. 

Trading terminates 

three business days 

prior to the first 

calendar day of the 

delivery month. 

The business day 

prior to the 15th 

calendar day of the 

contract month. 

Futures trading 

shall terminate at 

5:00a.m. (Chicago 

Time on the 

second London 

bank business day 

before the third 

Wednesday of the 

contract month. 

Trading can occur up to 8:30 

a.m. on the 3rd Friday of the 

contract month. 

 

Daily average 

(max-min) 

number of 

contract traded 

39,498 

(182,330-837) 

7,943 

(61,860-302) 

21,041* 

(96,391-942) 

8,355 

(33,641-725) 

73,022* 

(321,700-1,110) 

* Electronic trading only during times when open outcry is trading (9:30am – 4:00pm EST for mini-Dow).
26

Table 2 

Descriptive Statistics

Panel A – Crude Oil (CL) – January 2005-March 2009 – 1047 obs. 

 Returns Volatility AD ∆AD AS ∆ASP AM ∆AM FBT 

Mean -0.0463 3.8032 -61509 -64.214 98528 -159.69 -24744 512.65 -7134.2 

Median 0.0588 2.1706 -61580 306 94960 -492 -25603 272 -7185 

St.Dev. 2.5143 4.5557 25713 6782.5 34882 8207.6 12033 3161.8 8941.7 

Skew -0.1671 2.7523 -0.4610 -0.2125 0.4544 0.0349 0.0441 0.5096 0.2030 

Kurt 6.4251 10.812 3.7731 5.0029 3.3507 3.8687 2.7207 4.8302 4.5732 

AC(1) -0.0878 0.8653 0.9201 0.3441 0.9281 0.4697 0.9125 0.2846 0.8913 -

AC(20) -0.0344 0.7717 0.3377 0.1519 0.1230 0.3021 0.5321 0.2352 0.1854 

ADF 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0005 

Panel B – Natural Gas (NG) – January 2005-March 2009 – 1053 obs. 

 Returns Volatility AD ∆AD AS ∆AS AP ∆AP FBT 

Mean -0.1882 5.2782 -8148.8 89.888 33214 -381.77 -1923.8 6.5489 -2373.2 

Median -0.1569 3.9265 -6887 26 30503 -510 -1663 0.0000 -2318 

St.Dev. 3.0563 4.4653 7681.6 1429.4 20148 2867.1 2486.1 428.41 3937.6 

Skew 0.0966 1.9158 -0.2588 -0.0081 0.4349 -1.4249 -0.4238 1.3370 0.0564 

Kurt 4.4262 8.3137 2.2316 5.2892 2.4758 7.6439 3.1552 17.7895 3.2987 

AC(1) 0.0243 0.3783 0.9498 0.2503 0.9602 0.5307 0.9312 0.2635 0.8767 -

AC(20) 0.0085 0.1789 0.2467 0.0009 -0.1129 -0.1683 0.2740 0.0077 0.0869 

ADF 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0006 

Panel C – Corn (C) – August 2006-March 2009 – 646 obs. 

 Returns Volatility AD ∆AD AS ∆ASP AM ∆AM FBT 

Mean 0.0251 3.1528 -191899 868.23 196425 -328.67 -6447.3 -116.57 -14321 -

Median 0.0000 2.5354 -203709 830.50 205261 -620 -6107 -152.5 -12516 

St.Dev. 2.3031 2.3062 67578 6668.9 71087 7937.1 8007.7 1400 22989 4

Skew -0.1967 2.0833 0.5545 0.2677 -0.0321 0.5368 0.1272 0.4963 0.1265 

Kurt 3.6168 9.4042 2.8670 4.9924 2.3532 7.5340 2.7922 5.6987 2.7297 

AC(1) 0.0150 0.5290 0.9877 0.3853 0.9817 0.6226 0.9608 0.0966 0.9615 

AC(20) -0.0537 0.3394 0.5264 -0.1450 0.5056 -0.0474 0.2556 -0.0006 0.4445 

ADF 0.0000 0.0000 0.0057 0.0000 0.0092 0.0000 0.0000 0.0000 0.0003 

Panel D – Eurodollar (ED) – January 2005-May 2008 – 1045 obs. 

 Returns Volatility FA ∆FA FB ∆FB FC ∆FC FBT 

Mean 0.0003 0.0025 -158630 -555.1 -65481 202.29 -56801 476.92 35828 

Median 0.0000 0.0010 -130246 16 -29143 115 -68800 443 31921 

St.Dev. 0.0591 0.0046 267864 15625 161298 12341 79341 13035 79327 

Skew 0.0148 5.3308 -0.1450 -0.5426 -0.7453 0.6446 0.5171 -1.3694 0.0735 

Kurt 9.6360 44.024 1.6864 6.3110 2.8785 13.700 2.8395 23.445 2.5369 

AC(1) 0.1048 0.5459 0.9950 -0.0084 0.9912 0.1369 0.9764 0.0555 0.9707 -

AC(20) 0.0170 0.2102 0.9044 -0.0420 0.7853 0.0596 0.6550 -0.0065 0.6158 

ADF 0.0000 0.0000 0.1690 0.0000 0.1541 0.0000 0.0539 0.0000 0.3201 

Panel E – Mini-Dow (YM) – January 2005-May 2008 – 1038 obs. 

 Returns Volatility FA ∆FA FH ∆FH FO ∆FO FBT 

Mean -0.0438 1.3029 525.22 116.74 10093 -77.655 -1126.4 12.156 -3844.4 

Median 0.0435 0.3634 -561 222 631.5 -3 -786 10 -4228 

St.Dev. 1.4420 3.1887 15248 2912.3 20662 1749.6 2465.9 547.07 12523 

Skew 0.5730 6.2088 0.1955 -0.3305 1.2705 -0.4312 -1.3895 0.3450 -0.4860 

Kurt 20.355 55.944 2.9972 8.5299 3.7821 46.097 6.6388 11.3671 3.8928 

AC(1) -0.1346 0.7544 0.9712 0.0407 0.9936 0.1505 0.9673 -0.0508 0.9464 -

AC(20) -0.0245 0.5054 0.4724 -0.0348 0.9259 -0.0311 0.4832 -0.0161 0.6726 -

ADF 0.0000 0.0148 0.0007 0.0000 0.0084 0.0000 0.0276 0.0000 0.0196 

ADF: augmented Dickey-Fuller unit root test (p-values). Volatility: two-scale realized volatility in transaction time, Zhang, Mykland and Aït-Sahalia (2005). AD: dealer/merchant; AS: commodity swarbitrageurs or brokers/dealers; FB: non U.S. commercial bank; FC: U.S. commercial bank; FF: pension funds; FH: hedge funds (commercial); FO: financial – other; FBT: floor broker/trader; HF: hedge fund 
27

Table 3 

Long/Short Percentage of Total Open Interest 

Panel A – Crude Oil (CL) 

 

AD AS AM FBT HF 

Total 

Mean Max Min 

Long 0.074 0.417 0.010 0.021 0.233 0.754 0.878 0.524 

Short 0.296 0.064 0.102 0.048 0.224 0.734 0.849 0.576 

 

Panel B – Natural Gas (NG) 

 AD AS AP FBT HF 

Total 

Mean Max Min 

Long 0.074 0.385 0.008 0.024 0.286 0.777 0.912 0.623 

Short 0.159 0.069 0.027 0.046 0.567 0.868 0.999 0.686 

 

Panel C – Corn (C) 

 

AD AS AM FBT HF 

Total 

Mean Max Min 

Long 0.053 0.413 0.034 0.058 0.198 0.756 0.847 0.611 

Short 0.437 0.016 0.048 0.087 0.159 0.746 0.845 0.634 

 

Panel D – €/$ (ED) 

 FA FB FC FBT HF 

Total 

Mean Max Min 

Long 0.1427 0.0877 0.0369 0.0428 0.1250 0.4351 0.6799 0.2113 

Short 0.2413 0.1274 0.0732 0.0201 0.1220 0.5840 0.7981 0.3911 

 

Panel E – Mini-Dow (YM) 

 FA FH FO FBT HF 

Total 

Mean Max Min 

Long 0.2946 0.1625 0.0148 0.1074 0.0979 0.6772 0.8725 0.3463 

Short 0.2800 0.0592 0.0282 0.1493 0.0819 0.5987 0.8029 0.2739 

Total Mean Max Min: mean, maximum and minimum of the sum of the open interest for the five categories of market 

participants. It indicates the percentage of total open interest held by these five categories. 

 
28

Table 4 

Correlations – Net Futures Positions 

Panel A – Crude Oil (CL) 

 AD AS AM FBT HF 

Returns -0.04 

-0.06 

0.03 

0.05 

-0.08* 

-0.14* 

0.01 

-0.08* 

0.10* 

0.32* 

Volatility -0.22* 

-0.03 

0.11* 

0.06 

0.26* 

-0.05 

-0.09* 

0.02 

0.02 

-0.03* 

AS -0.51* 

-0.64* 1.00 

AM 0.25* 

0.25* 

-0.32* 

-0.41* 1.00 

FBT 0.10* 

0.02 

-0.36* 

-0.18* 

-0.03 

0.04 1.00 

HF -0.29* 

-0.23* 

-0.41* 

-0.25* 

-0.32* 

-0.23* 

-0.01 

-0.13* 1.00 

Panel B – Natural Gas (NG) 

 AD AS AP FBT HF 

Returns -0.06* 

-0.18* 

0.02 

0.02 

-0.02 

-0.20* 

-0.14* 

-0.23* 

0.04 

0.18* 

Volatility 0.12* 

0.07* 

-0.01 

-0.06* 

0.19* 

0.05 

-0.03 

0.01 

-0.09* 

0.02 

AS -0.10* 

-0.34* 1.00 

AP 0.22* 

0.09* 

-0.21* 

-0.17* 1.00 

FBT -0.13* 

0.14* 

-0.45* 

-0.18* 

0.11* 

0.05 1.00 

HF -0.27* 

-0.08* 

-0.85* 

-0.62* 

-0.07* 

-0.08* 

0.23* 

-0.30* 1.00 

Panel C – Corn (C) 

 AD AS AM FBT HF 

Returns -0.02 

-0.37* 

0.05 

0.00 

-0.03 

-0.29* 

-0.06 

0.05 

0.00 

0.45* 

Volatility 0.32* 

0.01 

-0.38* 

-0.07 

-0.07 

-0.05 

0.15* 

0.08* 

-0.05 

-0.01 

AS -0.69* 

-0.54* 1.00 

AM -0.09* 

0.34* 

-0.27* 

-0.23* 1.00 

FBT 0.02 

0.05 

-0.55* 

-0.46* 

0.39* 

0.02 1.00 

HF -0.64* 

-0.51* 

0.01 

-0.13* 

0.19 

-0.31* 

0.22* 

-0.09* 1.00 

Panel D – Eurodollar (ED) 

 FA FB FC FBT HF 

Returns -0.20* -0.07* 0.04 0.01 0.19* 

Volatility -0.04 0.01 0.01 -0.02 -0.03 

FB -0.07* 1.00 

FC -0.08* -0.01 1.00 

FBT -0.02 0.12* -0.06 1.00 

HF -0.33* -0.09* -0.22* 0.13* 1.00 

Panel E – Mini-Dow (YM) 

 FA FH FO FBT HF 

Returns 0.04 

0.13* 

0.05 

0.00 

-0.06* 

-0.30* 

-0.06* 

-0.10* 

0.08* 

0.21* 

Volatility -0.17* 

-0.01 

-0.20* 

0.02 

0.16* 

-0.01* 

0.16* 

-0.03 

-0.20* 

0.00 

FH -0.32* 

-0.12* 1.00 

FO 0.09* 

-0.09* 

-0.02 

0.02 1.00 

FBT -0.11* 

-0.06* 

-0.48* 

-0.48* 

-0.31* 

0.03 1.00 

HF -0.24* 

-0.50* 

0.47 

-0.00 

-0.17* 

-0.25* 

-0.47* 

-0.23* 1.00 

* significance at 10% level. The top number refers to net futures positions in levels while the bottom number 

refers to the first difference. The €/$ positions in levels are not stationary. Therefore, no correlation is reported. 
29

Table 5 

Granger non-Causality Test: p-values –Crude Oil (CL) 

Panel A: Returns and Net Futures Positions in Levels – Optimal Lags-Length (5) 

Returns AD AS AM FBT HF All 

Returns 0.546 0.621 0.276 0.091 0.624 0.148 

AD 0.000* 0.026* 0.226 0.492 0.058 0.000* 

AS 0.004* 0.402 0.000* 0.155 0.645 0.000* 

AM 0.000* 0.012* 0.000* 0.025* 0.023* 0.000* 

FBT 0.035* 0.371 0.108 0.288 0.350 0.000* 

HF 0.021* 0.199 0.009* 0.142 0.264 0.000* 

All 0.000* 0.002* 0.000* 0.000* 0.008* 0.181 

Panel B: Returns and Net Futures Positions in First Difference – Optimal Lags-Length (4) 

Returns AD AS AM FBT HF All 

Returns 0.253 0.362 0.211 0.218 0.533 0.199 

AD 0.000* 0.011* 0.317 0.420 0.016* 0.000* 

AS 0.001* 0.195 0.000* 0.217 0.590 0.000* 

AM 0.000* 0.004* 0.000* 0.052 0.003* 0.000* 

FBT 0.017* 0.353 0.011* 0.416 0.252 0.000* 

HF 0.013* 0.427 0.030* 0.433 0.380 0.000* 

All 0.000* 0.001* 0.000* 0.000* 0.067 0.086 

Panel C: Volatility and Net Futures Positions in Levels – Optimal Lags-Length (5) 

Volatility AD AS AM FBT HF All 

Volatility 0.191 0.064 0.086 0.063 0.127 0.001* 

AD 0.024* 0.089 0.188 0.093 0.001* 0.000* 

AS 0.000* 0.206 0.000* 0.540 0.841 0.000* 

AM 0.340 0.000* 0.000* 0.018* 0.023* 0.000* 

FBT 0.035* 0.089 0.088 0.468 0.206 0.000* 

HF 0.052 0.272 0.004* 0.064 0.148 0.000* 

All 0.000* 0.000* 0.000* 0.000* 0.000* 0.004* 

Panel D: Volatility and Net Futures Positions in First Difference – Optimal Lags-Length (5) 

Volatility AD AS AM FBT HF All 

Volatility 0.066 0.001* 0.062 0.025* 0.072 0.000* 

AD 0.223 0.064 0.209 0.117 0.000* 0.000* 

AS 0.001* 0.185 0.000* 0.242 0.557 0.000* 

AM 0.556 0.000* 0.000* 0.023* 0.000* 0.000* 

FBT 0.063 0.124 0.098 0.394 0.596 0.000* 

HF 0.079 0.453 0.028* 0.086 0.284 0.000* 

All 0.007* 0.000* 0.000* 0.000* 0.001* 0.000* 

* indicates rejection of the null of non-Granger causality at 5% level. 

 
30

Table 6 

Granger non-Causality Test: p-values –Natural Gas (NG) 

Panel A: Returns and Net Futures Positions in Levels – Optimal Lags-Length (4) 

Returns AD AP AS FBT HF All 

Returns 0.629 0.578 0.426 0.205 0.796 0.483 

AD 0.357 0.004* 0.031* 0.961 0.301 0.000* 

AP 0.104 0.097 0.000* 0.805 0.053 0.000* 

AS 0.000* 0.163* 0.000* 0.495* 0.311 0.000* 

FBT 0.010* 0.098* 0.000* 0.275 0.219 0.000* 

HF 0.044 0.049* 0.000 0.395 0.957 0.000* 

All 0.001* 0.000* 0.000* 0.000* 0.847 0.061 

Panel B: Returns and Net Futures Positions in First Difference – Optimal Lags-Length (3) 

Returns AD AP AS FBT HF All 

Returns 0.448 0.403 0.503 0.345 0.666 0.571 

AD 0.206 0.013* 0.009* 0.913 0.841 0.000* 

AP 0.124 0.301 0.000* 0.908 0.303 0.000* 

AS 0.000* 0.385 0.000* 0.502 0.535 0.000* 

FBT 0.019* 0.106 0.001* 0.194 0.308 0.000* 

HF 0.036* 0.025* 0.000* 0.652 0.889 0.000* 

All 0.000* 0.000* 0.000* 0.000* 0.923 0.240 

Panel C: Volatility and Net Futures Positions in Levels – Optimal Lags-Length (4) 

Volatility AD AP AS FBT HF All 

Volatility 0.393 0.119 0.557 0.080 0.037* 0.016* 

AD 0.839 0.001* 0.025* 0.993 0.654 0.000* 

AP 0.168 0.053 0.000* 0.631 0.147 0.000* 

AS 0.725 0.112 0.000* 0.476 0.121 0.000* 

FBT 0.763 0.004* 0.000* 0.353 0.063 0.000* 

HF 0.325 0.023* 0.000* 0.173 0.991 0.000* 

All 0.633 0.000* 0.000* 0.000* 0.806 0.051 

Panel D: Volatility and Net Futures Positions in First Difference – Optimal Lags-Length (3) 

Volatility AD AP AS FBT HF All 

Volatility 0.974 0.476 0.065 0.066 0.667 0.052 

AD 0.951 0.001* 0.014* 0.996 0.865 0.000* 

AP 0.169 0.105 0.000* 0.975 0.244 0.000* 

AS 0.391 0.538 0.000* 0.819 0.139 0.000* 

FBT 0.477 0.154 0.001* 0.286 0.344 0.000* 

HF 0.044 0.015* 0.000* 0.354 0.976 0.000* 

All 0.344 0.002* 0.000* 0.000* 0.879 0.437 

* indicates rejection of the null of non-Granger causality at 5% level. 

 
31

Table 7 

Granger non-Causality Test: p-values –Corn (C)

Panel A: Returns and Net Futures Positions in Levels – Optimal Lags-Length (5) 

Returns AD AM AS FBT HF All 

Returns 0.358 0.847 0.634 0.863 0.650 0.462 

AD 0.032* 0.280 0.100 0.433 0.921 0.001* 

AM 0.914 0.600 0.000* 0.922 0.792 0.000* 

AS 0.072 0.310 0.000* 0.062 0.689 0.000* 

FBT 0.801 0.138 0.001* 0.220 0.045* 0.000* 

HF 0.313 0.131 0.003* 0.059 0.962 0.001* 

All 0.228 0.007* 0.000* 0.000* 0.134 0.078 

Panel B: Returns and Net Futures Positions in Levels – Optimal Lags-Length (5) 

Returns AD AM AS FBT HF All 

Returns 0.285 0.777 0.799 0.823 0.394 0.442 

AD 0.004* 0.388 0.544 0.591 0.892 0.002* 

AM 0.633 0.745 0.910 0.899 0.861 0.960 

AS 0.355 0.607 0.000* 0.352 0.948 0.000* 

FBT 0.799 0.056 0.001* 0.059 0.047* 0.000* 

HF 0.240 0.327 0.001* 0.101 0.957 0.000* 

All 0.100 0.018* 0.000* 0.563 0.483 0.158 

Panel C: Volatility and Net Futures Positions in Levels – Optimal Lags-Length (5) 

Volatility AD AM AS FBT HF All 

Volatility 0.273 0.923 0.678 0.152 0.164 0.008* 

AD 0.041* 0.220 0.083 0.233 0.898 0.001* 

AM 0.103 0.645 0.000* 0.884 0.666 0.000* 

AS 0.544 0.665 0.000* 0.044* 0.778 0.000* 

FBT 0.198 0.038* 0.000* 0.267 0.047* 0.000* 

HF 0.726 0.117 0.004* 0.138 0.991 0.001* 

All 0.012* 0.003* 0.000* 0.000* 0.103 0.070 

Panel D: Volatility and Net Futures Positions in First Difference – Optimal Lags-Length (5) 

Volatility AD AM AS FBT HF All 

Volatility 0.711 0.892 0.266 0.174 0.330 0.020* 

AD 0.034* 0.337 0.147 0.439 0.893 0.010* 

AM 0.222 0.722 0.816 0.940 0.795 0.868 

AS 0.431 0.774 0.000* 0.246 0.906 0.000* 

FBT 0.011* 0.022* 0.000* 0.046* 0.045* 0.000* 

HF 0.839 0.168 0.001* 0.234 0.973 0.000* 

All 0.004* 0.013* 0.000* 0.158 0.500 0.148 

* indicates rejection of the null of non-Granger causality at 5% level. 
32

Table 8 

Granger non-Causality Test: p-values – Eurodollar (ED)

Panel A: Returns and Net Futures Positions in First Difference – Optimal Lags-Length (4) 

Returns FA FC FB FBT HF All 

Returns 0.174 0.203 0.226 0.165 0.315 0.478 

FA 0.380 0.727 0.671 0.051 0.924 0.144 

FC 0.254 0.511 0.861 0.139 0.270 0.196 

FB 0.381 0.897 0.001* 0.100 0.654 0.000* 

FBT 0.731 0.020* 0.173 0.439 0.228 0.239 

HF 0.548 0.005* 0.301 0.594 0.005* 0.001* 

All 0.495 0.000* 0.001* 0.169 0.004* 0.411 

Panel B: Volatility and Net Futures Positions in First Difference – Optimal Lags-Length (5) 

Volatility FA FC FB FBT HF All 

Volatility 0.053 0.085 0.104 0.058 0.007* 0.025* 

FA 0.278 0.192 0.370 0.668 0.279 0.059 

FC 0.642 0.015* 0.754 0.285 0.211 0.373 

FB 0.575 0.153 0.021* 0.079 0.001* 0.000* 

FBT 0.054 0.573 0.147 0.592 0.047* 0.084 

HF 0.111 0.846 0.196 0.946 0.002* 0.002* 

All 0.239 0.015* 0.001* 0.198 0.000* 0.000* 

* indicates rejection of the null of non-Granger causality at 5% level. 

 
33

Table 9 

Granger non-Causality Test: p-values – Mini-Dow (YM)

Panel A: Returns and Net Futures Positions in Levels – Optimal Lags-Length (2) 

Returns FA FO FH FBT HF All 

Returns 0.082 0.074 0.652 0.672 0.127 0.036* 

FA 0.089 0.184 0.696 0.377 0.026* 0.000* 

FO 0.188 0.083 0.000* 0.023* 0.046* 0.000* 

FH 0.022* 0.493 0.000* 0.494 0.756 0.000* 

FBT 0.813 0.469 0.314 0.826 0.672 0.228 

HF 0.064 0.146 0.185 0.444 0.150 0.003* 

All 0.011* 0.108 0.000* 0.000* 0.063 0.080 

Panel B: Returns and Net Total Positions in First Difference – Optimal Lags-Length (3) 

Returns FA FO FH FBT HF All 

Returns 0.057 0.010* 0.221 0.471 0.071 0.026* 

FA 0.097 0.818 0.590 0.658 0.007* 0.000* 

FO 0.165 0.261 0.518 0.171 0.191 0.232 

FH 0.070 0.588 0.000* 0.434 0.283 0.000* 

FBT 0.237 0.762 0.219 0.918 0.350 0.521 

HF 0.090 0.102 0.444 0.332 0.250 0.004* 

All 0.038* 0.089 0.000* 0.360 0.362 0.008* 

Panel C: Returns and Net Futures Positions in Levels – Optimal Lags-Length (4) 

Volatility FA FO FH FBT HF All 

Volatility 0.053 0.061 0.224 0.712 0.079 0.002* 

FA 0.829 0.519 0.534 0.631 0.004* 0.003* 

FO 0.116 0.441 0.000* 0.086 0.357 0.000* 

FH 0.264 0.756 0.000* 0.460 0.239 0.000* 

FBT 0.267 0.968 0.319 0.983 0.451 0.239 

HF 0.601 0.145 0.460 0.311 0.442 0.050* 

All 0.397 0.258 0.000* 0.000* 0.294 0.005* 

Panel D: Volatility and Net Futures Positions in First Difference – Optimal Lags-Length (4) 

Volatility FA FO FH FBT HF All 

Volatility 0.042* 0.162 0.270 0.909 0.043* 0.007* 

FA 0.741 0.799 0.153 0.651 0.008* 0.001* 

FO 0.370 0.590 0.640 0.275 0.335 0.435 

FH 0.069 0.138 0.000* 0.474 0.389 0.000* 

FBT 0.016* 0.583 0.213 0.118 0.530 0.011* 

HF 0.359 0.134 0.770 0.348 0.325 0.043* 

All 0.110 0.089 0.000* 0.010* 0.617 0.008* 

* indicates rejection of the null of non-Granger causality at 5% level. 
34

Figure 1 

Price and Realized Volatility

 

0

10

20

30

40

0

50

100

150

200

1/3/2005 1/3/2006 1/3/2007 1/3/2008 1/3/2009

Volatility

Price

Crude Oil

Price

Volatility

0

20

40

0

10

20

1/3/2005 1/3/2006 1/3/2007 1/3/2008 1/3/2009

Volatility

Price

Price Natural Gas

Volatility

0

5

10

15

20

0

5

10

1/3/2005 1/3/2006 1/3/2007 1/3/2008 1/3/2009

Volatility

Price

Corn

Price

Volatility

0.00

0.02

0.04

0.06

90

95

100

1/3/2005 1/3/2006 1/3/2007 1/3/2008 1/3/2009

Volatility

Price

Price Eurodollar

Volatility

0

20

40

60

0

5000

10000

15000

1/3/2005 1/3/2006 1/3/2007 1/3/2008 1/3/2009

Volatility

Price

Mini Dow

Price

Volatility
35

Figure 2 

Generalized Impulse Response of Volatility/Returns to One S.D. Innovations 

in Trader Positions in Levels 

 

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AD

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AS

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AM

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to HF

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FBT

Crude Oil - Volatility

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AD

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AS

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AP

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to HF

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FBT

Natural Gas - Volatility

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AD

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AS

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AM

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to HF

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FBT

Corn - Volatility

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to FA

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to FH

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to FO

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to HF

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to FBT

Mini Dow - Returns

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FA

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FH

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FO

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to HF

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FBT

Mini Dow - Volatility
36

Figure 3 

Generalized Impulse Response of Volatility/Returns to One S.D. Innovations 

in Traders Positions in First Difference 

 

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AD

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AS

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AM

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to HF

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FBT

Crude Oil - Volatility

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AD

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AS

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to AP

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to HF

-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FBT

Natural Gas - Volatility

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Res pons e of Volatility to AD

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Res pons e of Volatility to AS

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Res ponse of Volatility to AM

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Res ponse of Volatility to HF

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Res ponse of Volatility to FBT

Corn - Volatility

-.10

-.05

.00

.05

.10

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FA

-.10

-.05

.00

.05

.10

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FB

-.10

-.05

.00

.05

.10

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FC

-.10

-.05

.00

.05

.10

1 2 3 4 5 6 7 8 9 10

Response of Volatility to HF

-.10

-.05

.00

.05

.10

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FBT

Eurodollar - Volatility

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to FA

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to FH

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to FO

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to HF

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of Returns to FBT

Mini Dow - Returns

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FA

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FH

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Response of Volatility to FO

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Res ponse of Volatility to HF

-.050

-.025

.000

.025

.050

.075

.100

1 2 3 4 5 6 7 8 9 10

Res pons e of Volatility to FBT

Mini Dow - Volatility
37

Appendix 

List of the trader sub-categories in the CFTC’s large-trader reporting system (LTRS) 

Commercial Non-Commercial 

Code Description Code Description 

18 Co-Operative AP Associated Person 

AD Dealer/Merchant CPO Commodity Pool Operator 

AM Manufacturer CTA Commodity Trading Advisor 

AO Agricultural/Natural Resources – Other FB Floor Broker 

AP Producer FCM Futures Commission Merchant 

AS Commodity Swaps/Derivatives Dealer FT Floor Trader 

FA Arbitrageur or Broker/Dealer IB Introducing Broker 

FB Non U.S. Commercial Bank MM Managed Money 

FC U.S. Commercial Bank 

FD Endowment or Trust NR No Registration 

FE Mutual Fund 

FF Pension Fund 

FG Insurance Company 

FH Hedge Fund 

FM Mortgage Originator 

FO Financial – Other 

FP Managed Account or Pool 

FS Financial Swaps/Derivatives Dealer 

FT Corporate Treasurer 

LF Livestock Feeder 

LO Livestock – Other 

LS Livestock Slaughterer 

Hedge Funds (HF) 

CPO Commodity Pool Operator 

CTA Commodity Trading Advisor 

AP Associated Person 

MM Managed Money (subset) 

Floor Broker and Traders (FBT) 

FB Floor Broker 

FT Floor Trader 

CFTC weekly Commitment of Traders (COT) Reports aggregate these sub-categories in two broad 

groups (except for agricultural which also has index traders): “Commercials”, who have declared an 

underlying hedging purpose, and “Non-commercials”, who have not. “Dealer/Merchant” includes 

wholesalers, exporter/importers, crude oil marketers, shippers, etc. “Manufacturer” includes refiners, 

fabricators, etc. “Agricultural / Natural Resources – Other” may include, for example, end users. 

“Commodity Swaps/Derivatives Dealers” aggregate all reporting “Swaps/Derivatives Dealers” and 

“Arbitrageurs or Broker Dealers”, two categories that were merged in the CFTC’s internal reporting 

system part-way through our sample period. “Hedge Funds” aggregate all reporting Commodity Pool 

Operators, Commodity Trading Advisors, Associated Persons controlling customer accounts as well as 

other Managed Money traders. “Floor Brokers & Traders” aggregate all reporting floor brokers and 

floor traders. NR represents those traders that have not yet been categorized or do not fit any other 

category. Note: FH under the Commercial category includes hedge funds in financial contracts that are 

shown to be hedging.