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Abstract

Speculative trading in energy and commodity markets has been blamed for increased volatility, price distortions, and market inefficiency, with negative effects on the real economy. We take a new approach to investigate the impact of speculative trading using macroeconomic announcements and high-frequency data. We study the impact of twenty-six macroeconomic announcement releases on energy commodities (crude oil, natural gas) as our baseline case, which we contrast with metals (gold, silver, copper, and palladium). We find that increased speculative trading lessens the impact of macroeconomic surprises on futures markets, as measured by price drift, volatility, and bid-ask spreads. Our full-sample results show that increased trading by speculators improves liquidity and price discovery, while reducing volatility. We document a damping effect on volatility that is stronger for procyclical commodities such as crude oil and natural gas than for precious metals such as gold, which is a safe haven. In sub-sample analysis where we separate the effects of money managers and swap dealers, we find that the positive effects that we document are driven by money managers. Since traditional market participants prefer stability, our results suggest a beneficial impact of increased trading and speculation.

JEL Classification: G13 - Contingent Pricing; Futures Pricing; option pricing; G14 - Information and Market Efficiency; Event Studies; Insider Trading; Q41 - Energy: Demand and Supply; Prices; Q43 - Energy and the Macroeconomy

Keywords

energy markets crude oil futures high-frequency speculation trading commercial volatility macroeconomic announcements

1. Introduction

The appeal of energy and other commodities as an asset class has grown since the Commodity Futures Modernization Act of 2000 (CFMA). By partially deregulating derivatives, the CFMA has made it easier to trade commodity futures contracts for investment purposes. Rather than invest in physicals or in shares of commodity-linked firms, investors can use futures to gain exposure to “commodity beta” (Boons et al. 2014). This evolution is particularly relevant for energy markets, where futures trading volume has grown tremendously. For instance, the total trading volume of commodity derivatives was 137.3 bn contracts in 2023, which is 64% more than in 2022 (Futures Industry Association, 2024). An important reason for this trend is that commodities have periodically benefited from bull cycles (most notably in 2004–2008), attracting a growing number of speculators and institutional investors. As a result, the commodities asset class has become an important but volatile revenue source for trading firms and investment banks. Three firms, Goldman Sachs, Citi, and Macquarie earned together $20 bn from commodities trading in 2022, much of it from energy-related contracts.¹

Is the presence of more financial investors harmful to traditional market participants, such as hedgers? While many think so, the evidence is unclear. The idea that poorly informed investors can disrupt markets has a long history (Shleifer and Summers 1990) and has been revived in a recent theoretical literature on financialization (Basak and Pavlova 2016; Goldstein and Yang 2022). These papers are motivated by the commodity price run-up of 2004 to 2008, which occurred shortly after the CFMA was passed (Domanski and Heath 2007). Critics argue that the activities of financial investors, who are not directly involved in producing or processing commodities, can distort prices and increase volatility.² These critics claim that energy and commodity markets have become more sensitive to financial market fluctuations, and less to supply and demand fundamentals. Energy futures markets have attracted attention due to the popular perception that large price swings affect the real economy (e.g., through higher gasoline and heating costs) (Cheng and Xiong 2014). Research, however, generally does not support this claim (Baumeister and Kilian 2014). Whether or not these fears are justified, policymakers have taken notice and the CFTC has progressively implemented rule changes such as new position limits.

A large empirical literature debates the causes of periodic price and volatility run-ups in commodity and energy markets. Researchers emphasize the importance of differentiating between speculative traders and passive investors (e.g., index traders). The latter category of traders is more recent and trades energy and commodity contracts for diversification purposes rather than for speculative profit. While Singleton (2014) suggests that financial investors may be to blame for higher energy prices, Kilian and Murphy (2014) use a structural model to show that speculation can be ruled out as a cause of the oil price surge during 2003 to 2008 – even though speculative demand played a role in previous oil price spikes. Further evidence against the hypothesis that index traders are responsible for the sharp increase in commodity prices is provided by Irwin and Sanders (2011, 2012). In a different strand of the literature, Büyükşahin and Harris (2011) use Granger causality tests and daily data to investigate whether speculators increase crude oil futures prices. They find little evidence to support that claim. Also using daily position-level data, Brunetti et al. (2016) show that speculators reduce price volatility in commodity and energy markets. Reviewing this early literature, Fattouh et al. (2013) conclude that speculation is unlikely to explain the commodity and energy bull cycle of 2004 to 2008.

Recent research provides new theoretical grounds to establish how trading activity could affect energy and commodity prices (Basak and Pavlova 2016; Goldstein and Yang 2022). The subsequent empirical literature, however, does not reach a consensus. Henderson et al. (2015) use data on commodity-linked notes to show that uninformed trading flows affect commodity prices, but Ready and Ready (2022) argue that the economic magnitude of this effect is too small to matter. Other recent papers find instances of futures price overshooting, reversals, and greater noise in markets (Da et al. 2024). They also find that commodities seem to display higher correlations with equities and with each other (Kang et al. 2023).

Thus, our main contribution is to provide sharply identified evidence on the impact of speculative trading on energy (crude oil and natural gas) and metal markets (gold, silver, copper, and palladium), with additional evidence on sub-categories of traders. We focus on speculation rather than financialization, which has been linked to the trading activities of passive (index) traders (Tang and Xiong 2012). Specifically, our paper investigates the impact of speculative trading of sub-categories of non-commercial traders. Our rationale is that speculative trading is likely to reflect informed trades, which is our focus, in contrast to index traders who are considered uninformed. Energy commodities serve as our baseline case due to their economic importance and high trading volumes, while metals offer a useful comparison, particularly as gold is perceived as a safe-haven asset. We use high-frequency (five-minute) data to measure the instantaneous reaction of commodity futures returns, volatility, and bid-ask spreads to the surprise component in macroeconomic announcement releases (Andersen et al. 2007; Kurov et al. 2019). The data runs from April 4th, 2007, to February 11th, 2024. Our framework also accounts for the time-varying intensity of speculative trading activity. This study builds on Kilian and Vega (2011), who find no evidence, at a daily frequency, that energy prices react to macroeconomic announcements. By using intraday data, we can better identify the impact of specific macro surprises. We also avoid a common criticism of event study methods, namely that using daily frequency data may reduce the power of statistical tests and could lead the researcher to misattribute the effect of a specific announcement, as other market events occur the same day (Kothari and Warner 2007).

We find evidence of beneficial effects (price stability and market efficiency) from increased trading activity in energy and commodity markets. Our first finding is a damping effect on price reactions: while macro surprises generate a positive abnormal return for good news (and negative for bad news), the magnitude of this reaction is significantly weaker when speculative trading is higher. Second, we find a similar damping effect on volatility reactions. While all surprises (good or bad) generate a volatility increase, this reaction is lessened when the futures market shows more speculative trading. Third, we document lower bid-ask spreads when speculative trading is higher, controlling for the surprise environment. Fourth and last, these beneficial effects are linked to the trading activities of money managers. In contrast, increased trading by swap dealers appears to have an amplifying effect on reactions to macro surprises. These new insights are made possible by investigating this issue using a new angle, namely their sensitivity to macroeconomic surprises, and with high-frequency data. Our findings have important implications for energy market investment and regulation, and to the broader debate about speculation in energy and commodity markets.

By investigating a broad range of traders in energy and commodity markets, our findings also extend the work of Brunetti et al. (2016). They find that financial investors, especially money managers and hedge funds, help commodity markets by supplying liquidity, reducing volatility, and generally improving market efficiency. Moreover, our results relate to Cheng et al. (2015) who show that financial investors, being better informed about markets, contribute to price discovery and liquidity. This is particularly relevant for energy markets, where accurate price discovery is crucial for physical market participants and investors. Thus, speculative traders help markets by distributing and assimilating new information into prices. These insights are valuable given the ongoing energy transition and the importance of efficient price discovery in energy markets. Two papers are probably closest to ours. First, Brunetti et al. (2016) who find that hedge funds add liquidity to commodity markets, resulting in more efficient prices and lower volatility. They argue that it is merchant positions (i.e., hedgers) that are linked to greater volatility, and that the presence of hedge funds allows for faster and more efficient price discovery. Second, using daily data, Kilian and Vega (2011) study how energy prices react to macroeconomic announcements. Our paper extends this work to high frequency data.

2. Background

Price discovery in commodity markets occurs mainly in futures markets and is affected by informational frictions around supply and demand. Thus, risk sharing and information discovery represent a potential channel for financial investors to generate distortions in energy and commodity markets (Cheng and Xiong 2014). In their model, Basak and Pavlova (2016) predict that the increased presence of financial actors can increase commodity futures volatility, as well as correlations between commodity and equity returns. Goldstein and Yang (2022) also argue that under some conditions, a greater presence of financial investors can be harmful to commodity markets. Theory shows how a change in the participant mix in commodity markets could generate undesirable distortions, but the empirical literature is far from settled (see Table 1). Singleton (2014) argues that trading activity by financial investors creates informational frictions, leading commodity prices to become more volatile and to diverge from their fundamental values. Using a no-arbitrage argument, however, Hamilton and Wu (2014) show that the positions of commodity traders included in index funds cannot be used to achieve excess returns in futures markets. Ready and Ready (2022) show that while index traders do have a positive price impact, it is much too small to explain the apparent price distortions or bull cycles observed since 2004. In addition, financial investors do not have a uniform impact on market liquidity. Investors affect liquidity risk by either providing liquidity to meet the hedging needs of other traders or consuming liquidity when they trade for their own needs (Kang et al. 2020b). Indeed, Brunetti and Reiffen (2014) show using data on commodity trader positions that index traders provide insurance against price risk.

Table 1.

Summary of the Literature: Effect of Non-Commercial Trading Activity on Commodity Futures Volatility.

References	Proxy used for financialization or speculation	Impact on volatility
Chang et al. (1997)	CFTC’s definition of speculators	Increase
Daigler and Wiley (1999)	CFTC’s definition of speculators
Irwin and Holt (2004)	Set speculators
Tang and Xiong (2012)	Commodity index trader (CIT) positions
Irwin and Brorsen (1987)	Amount of money invested in traded futures funds	No change
Irwin and Yoshimaru (1999)	Trading volume of large-commodity pool operators
Bryant et al. (2006)	CFTC’s definition of speculators
Haigh et al. (2007)	Number and positions of commodity pool operators and hedge funds
Brunetti et al. (2016)	Net positions of hedge funds and floor brokers	Decrease
Aulerich et al. (2012)	Commodity index trader (CIT) positions	Decrease

Note. This table summarizes the findings of a range of studies on the effect of financialization and speculation on commodity futures volatility. The impact on volatility is categorized as Positive, Neutral, or Negative based on the results reported by each study. The proxies used for financialization or speculation include definitions and positions from the CFTC, set speculators, commodity index trader (CIT) positions, trading volume, and net positions of hedge funds and floor brokers.

2.1. Macroeconomic Announcements

Surprises in macroeconomic announcements affect financial markets, whether in stocks (Scholtus et al. 2014) or in bonds (Fleming and Remolona 1997). In a key study, Balduzzi et al. (2001) find that seventeen public news releases affect bond prices, trading volume, and bid-ask spreads. Karali and Ramirez (2014) show that energy futures markets exhibit asymmetric responses to macroeconomic news, with significant volatility spillovers between natural gas and crude oil markets. Cao et al. (2024) document a time-varying relationship between U.S. monetary policy and crude oil prices, finding that unexpected oil price increases can push monetary policy from expansionary to restrictive stance. Kang et al. (2020a) further show that after 2004, short-term oil price volatility is driven by industrial production, term spreads, and credit spreads, along with traditional market factors.

The literature on commodity-specific announcements is smaller and less conclusive. Hollstein et al. (2020) look at how different economic variables affect the term structure of commodity futures volatility. They show that speculation and jobs-related macro variables have the largest impact on volatility. Zhu et al. (2022) further show that stock market anomalies can be explained to some extent by oil price shocks, separately from the effect of other macroeconomic variables and investor sentiment. While the literature finds a clear impact of macroeconomic announcements on stock and bond prices, there is no clear answer as to whether they affect commodity futures prices, or whether increased trading by financial participants accentuates these reactions. This issue is especially relevant for energy markets, given their macroeconomic importance. Our research provides new insights by using high-frequency data, expanding the set of announcements, and considering a time-varying measure of speculative trading intensity to capture trading activities for each of the commodities in the sample.

3. Data

We now present a detailed description of our data. Since this paper relies on several types of data, we describe: (i) how to obtain the macroeconomic announcement surprises, (ii) the commodity futures data, and (iii) how to capture speculative trading activity.

3.1. Data on Macroeconomic Announcements

The macroeconomic announcement release data are obtained from Bloomberg and Refinitiv Eikon. We collect information on twenty-two announcements that are standard to the literature (see e.g., Andersen et al. 2003). Our sample for macroeconomic announcements is matched to our high-frequency data and therefore runs from April 2nd, 2007 to February 11th, 2024. The announcements belong to ten categories: Income, Employment, Industrial Activity, Investment, Consumption, Housing Sector, Government, Net Exports, Inflation, and Forward-looking. Most of the announcements are released on a monthly basis. Table 2 summarizes the announcements and provides more detail such as the number of observations, release frequency, source, unit of measure, and time of release. Bloomberg provides analyst forecasts for all announcements, as well as the actual value of the announcement release. For all announcements except the Consumer Price Index and Initial Jobless Claims releases, a positive surprise will be interpreted by investors as signaling a strong economy (Fleming and Remolona 1997). In addition, we include energy sector-specific announcements published by the U.S. Energy Information Administration. The first is the weekly crude oil storage report, which provides an update on the quantity of crude oil held in storage in the U.S. The second is the weekly natural gas storage report. We do not include OPEC announcements, as they cannot be reliably used in a high-frequency econometric design (Känzig 2021) (Table 2).³

Table 2.

List of Macroeconomic Announcements in Our Sample.

Announcement	Frequency	Source	Unit	Time
GDP advance	Quarterly	BEA	%	8:30
GDP preliminary	Quarterly	BEA	%	8:30
GDP final	Quarterly	BEA	%	8:30
Personal income	Monthly	BEA	%	8:30
ADP employment	Monthly	ADP	Number of jobs	8:15
Initial jobless claims	Weekly	ETA	Number of claims	8:30
Non-farm employment	Monthly	BLS	Number of jobs	8:30
Factory orders	Monthly	BC	%	10:00
Industrial production	Monthly	FRB	%	9:15
Construction spending	Monthly	BC	%	10:00
Durable goods orders	Monthly	BC	%	8:30
Advance retail sales	Monthly	BC	%	8:30
Consumer credit	Monthly	FRB	USD	15:00
Personal consumption	Monthly	BEA	%	8:30
Building permits	Monthly	BC	Number of permits	8:30
Existing home sales	Monthly	NAR	Number of homes	10:00
Housing starts	Monthly	BC	Number of homes	8:30
New home sales	Monthly	BC	Number of homes	10:00
Pending home sales	Monthly	NAR	%	10:00
Trade balance	Monthly	BEA	USD	8:30
Consumer price index	Monthly	BLS	%	8:30
Producer price index	Monthly	BLS	%	8:30
CB Consumer confidence index	Monthly	CB	Index	10:00
UM Consumer sentiment	Monthly	TR/UM	Index	9:55
Weekly Crude Oil Stock	Weekly	EIA	Number of barrels	10:30
Weekly Natural Gas Stock	Weekly	EIA	Number of cubic feet	11:00

Note. This table shows the category, frequency, source, unit of measure, and release time for each macroeconomic announcements. ADP = Automatic Data Processing, Inc.; BC = Bureau of the Census; BEA = Bureau of Economic Analysis; BLS = Bureau of Labor Statistics; CB = Conference Board; ETA = Employment and Training Administration; FRB = Federal Reserve Board; ISM = Institute for Supply Management; NAR = National Association of Realtors; TR/UM = Thomson Reuters/University of Michigan; USDT = U.S. Department of the Treasury.

It is common practice in this literature to use the standardized surprise of an announcement rather than its realized value to quantify the unexpected component of the release. To calculate surprises, we follow Balduzzi et al. (2001). Let $A_{kt}$ be the realized value (i.e., release) of macroeconomic announcement $k$ at time $t$ , and let $E_{kt}$ be the median value of all Bloomberg analyst forecasts for announcement $k$ at time $t$ . To standardize the surprise, we divide the raw surprise $(A_{kt} - E_{kt})$ by $σ_{k}$ , the sample standard deviation of the surprise for announcement $k$ . Thus, equation (1) describes the standardized surprise for announcement $k$ at time $t$ :

S_{kt} = \frac{A_{kt} - E_{kt}}{σ_{k}}

(1)

The sample period is used to compute $σ_{k}$ , as in Balduzzi et al. (2001) and Kurov et al. (2019).⁴ Table 3 presents the minimum, 1st quartile, median, mean, third quartile, and maximum of the surprise for each announcement.

Table 3.

Descriptive Statistics for the Standardized Surprise Calculated for Each of the Macroeconomic Announcements.

Announcements	Nb. obs.	Min.	1st Qu.	Med.	Mean	3rd Qu.	Max.
Initial jobless claims	825	−3.407	−0.0720	−0.007	0.068	0.065	22.672
ADP Employment	202	−2.751	−0.0640	0.008	0.046	0.078	12.880
CB Consumer	201	−2.635	−0.4638	0.093	0.102	0.872	2.412
Advance retail sales	202	−4.028	−0.3661	−0.092	0.023	0.183	8.879
Building permit	198	−2.375	−0.5356	0.025	0.082	0.627	3.205
Construction spending	202	−3.054	−0.5912	−0.099	−0.130	0.493	4.335
Consumer_credit	202	−2.055	−0.5217	0.104	0.061	0.619	3.131
Consumer price index	201	−3.483	−0.6966	0.000	−0.035	0.697	4.180
Durable goods orders	193	−2.702	−0.5757	0.022	0.037	0.531	6.688
Existing home sales	202	−4.729	−0.4627	0.000	−0.067	0.488	2.467
Factory orders	202	−3.040	−0.3378	0.000	0.081	0.507	2.534
GDP	185	−2.589	−0.3698	0.000	−0.026	0.370	2.958
Housing starts	199	−2.285	−0.6178	0.000	0.042	0.624	3.401
Industrial production	385	−4.773	−0.5727	0.000	−0.080	0.573	2.291
Michigan Sentiment Index	202	−3.922	−0.4100	0.036	−0.049	0.463	3.244
New home sales	202	−3.062	−0.3466	0.116	0.118	0.631	3.562
Non-farm employment	201	−0.892	−0.0564	0.005	0.078	0.071	13.169
Pending home sales	202	−2.949	−0.4244	0.022	0.075	0.581	5.674
Personal consumption	201	−3.666	−0.3666	0.000	−0.026	0.367	2.566
Personal income	201	−1.079	−0.0771	0.000	0.095	0.077	13.108
Producer price index	188	−3.168	−0.5760	0.000	0.083	0.864	2.880
Trade balance	202	−1.831	−0.1801	−0.018	0.013	0.207	2.359

Note. This table presents descriptive statistics for the standardized surprise $(A_{kt} - E_{kt}) / σ_{kt}$ for each of the macroeconomic announcements. The column (Nb. Observations) shows the number of individual surprises that can be calculated over the whole analysis period. The columns (Min.), (1st Qu.), (Median), (Mean), (3rd Qu.), and (Max) present respectively the minimum value, the first quartile, the median, the mean, the third quartile, and the maximum value for the standardized surprise of each macroeconomic announcement.

3.2. Commodity Futures Price Data

For intraday data on commodity futures prices, we use Barchart’s API.⁵ Our dataset for prices contains some of the most economically significant commodity futures contracts traded in the U.S. We use a high-frequency price series that runs from April 2nd, 2007 to February 11, 2024. Among these contracts, crude oil and natural gas are pro-cyclical, while gold and silver behave as safe havens. High-grade copper and palladium are industrial metals used in the manufacturing of consumer products.

For each of the commodities in our sample, price returns $R_{t}$ are calculated as the log return over a five-minute period $(τ = 5)$ beginning at time $t$ . The database provides the futures contract close price ( $p_{t}^{close}$ ) of each five-minute period. Thus, $R_{t}$ is obtained as in equation (2):

R_{t}^{t + τ} = \ln (\frac{p_{t + τ}^{close}}{p_{t}^{close}}) = \ln (p_{t + τ}^{close}) - \ln (p_{t}^{close})

(2)

Descriptive statistics for the five-minute log returns are presented in Table 4. The most extreme outlier observations belong to crude oil, while gold has the fewest outliers.⁶

Table 4.

Descriptive Statistics: Five-Minute Intraday Futures Price Returns.

Commodity futures	Min (%)	1st Qu. (%)	Mean (%)	3rd Qu. (%)	Max (%)
Crude Oil (CL = F)	−33.91	−0.0441	0.000	0.0447	41.64
Gold (GC = F)	−2.782	−0.0241	0.0001	0.0244	3.064
Copper (HG = F)	−4.534	−0.0363	−0.0001	0.0365	8.877
Natural Gas (NG = F)	−6.735	−0.0528	−0.0003	0.0532	15.62
Palladium (PA = F)	−13.350	−0.034	0.0001	0.0348	9.467
Silver (SI = F)	−7.504	−0.0394	0.0001	0.0415	4.242

Note. Shows descriptive statistics of the five-minute intraday returns, for each commodity futures. The columns (Min.), (1st Qu.), (Median), (Mean), (3rd Qu.), and (Max) present respectively the minimum value, the first quartile, the median, the mean, the third quartile, and the maximum value for the five-minute intraday returns.

3.3. Measures of Speculative Trading Activity and Trader Categories

The index of speculative trading is constructed using data in the Commitment of Traders (CoT) Report published weekly by the Commodity Futures Trading Commission (CFTC). The data provided by the CFTC includes the number of positions held by different types of participants in commodity markets. The CFTC separates trader types as follows: Commercials refer to trader-reported futures positions which the trader claims are used for hedging purposes, while Non-Commercials is obtained by subtracting the total long and short commercial positions from the total open interest.⁷ We use the following information presented in the $CoT$ report: for a futures contract $i$ , the number of long and short positions held by Non-Commercial traders are $S L_{i}$ and $S S_{i}$ , respectively, while for Commercial traders they are $H L_{i}$ and $H S_{i}$ .⁸

The specific measure we use follows Hedegaard (2011), who suggests an index of speculative activity computed as the ratio of net long speculative positions over total open interest ( $NL S_{i}$ ):

NL S_{i} = \frac{S L_{i} - S S_{i}}{O I_{i}}

(3)

In addition to computing $NLS$ using the full sample data, we use disaggregated data from the CFTC to compute the NLS index separately for money manager (MM) and swap dealer (SD) positions, which allows for additional empirical analysis. Please see Table 5 for descriptive statistics of the NLS variable. The data on money manager and swap dealer positions come from Quandl’s API.⁹Money managers typically refer to Non-Commercial market participants who are involved in managing funds and investing in commodity futures and options markets (Fishe and Smith 2012).¹⁰ Their activities are influenced by financial and economic factors related to commodities. Money managers are often considered to be more informed investors because they actively manage portfolios and adjust their positions based on market information and analysis. Swap dealers are considered as Non-Commercial traders by the CFTC. They typically use futures contracts to hedge risk generated by their swap positions. Swap dealers have been studied in relation to index investors, as their positions are distinct from those of other market participants. Their activities are influenced by the need to manage large exposures and to facilitate trading for clients. While money managers regularly take long or short futures positions, swap dealers mainly take long positions (Fishe and Smith 2012). Hedgers, who we exclude from the analysis, tend to take short positions. Additional details on data construction, alternative proxies, robustness checks, and supplementary figures are provided in the Online Supplemental Material.

Table 5.

Descriptive Statistics: Computed Value of the NLS Proxy (Respectively: Full Sample, Money Managers-only Sample, Swap Dealers-only Sample).

Statistics	CL	GC	HG	SI	PA	NG
$NLS$
Min.	−0.1667	−0.4505	−0.3238	−0.1364	−0.6246	−0.2745
1st Qu.	0.0358	0.2285	−0.1067	0.1547	0.3181	−0.1731
Median	0.0642	0.2397	0.0235	0.2163	0.3056	−0.0563
Mean	0.0774	0.1780	0.0278	0.2123	0.2685	−0.0579
3rd Qu.	0.1834	0.4012	0.1417	0.3334	0.5652	−0.0321
Max.	0.2941	0.5269	0.4413	0.5748	0.7343	0.0794
$NL S_{MM}$
Min.	−0.0370	−0.2353	−0.2727	−0.2303	−0.6005	−0.2471
1st Qu.	0.0358	0.2177	0.0238	0.1550	0.3919	−0.0345
Median	0.1059	0.2177	0.0238	0.1550	0.3919	−0.0345
Mean	0.1011	0.2125	0.0350	0.1425	0.3294	−0.0371
3rd Qu.	0.1834	0.4012	0.1417	0.3334	0.5652	−0.0321
Max.	0.2051	0.4563	0.3923	0.4477	0.7330	0.1867
$NL S_{SD}$
Min.	−0.2645	−0.4092	−0.2263	−0.2263	−0.3640	−0.0689
1st Qu.	−0.1119	−0.1457	0.0238	−0.0255	−0.0251	0.1091
Median	−0.1119	−0.1457	0.2398	−0.0255	−0.0251	0.1091
Mean	−0.0897	−0.1612	0.2395	−0.0276	0.0086	0.1087
3rd Qu.	0.1847	0.1290	0.3923	0.3334	0.5652	0.2743
Max.	0.1847	0.1290	0.3923	0.3334	0.5652	0.2743

Note. This table provides descriptive statistics of the NLS proxy for speculative trading intensity for each commodity futures contract in our sample. The lines (Min.), (1st Qu.), (Median), (Mean), (3rd Qu.), and (Max) present respectively the minimum value, the first quartile, the median, the mean, the third quartile, and the maximum value. CL = crude oil; GC = gold; HG = high-grade copper; SI = silver; PA = palladium; NG = natural gas.

4. Econometric Framework and Methods

4.1. Modeling the Impact of Surprises on Returns

Our high-frequency regression model is based on Kurov et al. (2019).¹¹ We run the following regression using the specification in equation (4):

R_{t}^{t + τ} = α + \sum_{m = 1}^{22} γ_{m} S_{m, t} + δ X_{j} + \sum_{m = 1}^{22} θ_{m} (S_{m, t} \cdot X_{j}) + β R_{t - τ}^{t} + ϵ_{t}

(4)

where $R_{t}^{t + τ}$ is the continuously compounded futures return from time $t$ to $t + τ$ , $S_{mt}$ is the surprise for macroeconomic announcement $m$ published at time $t$ , and $X_{j}$ is the NLS speculative trading intensity variable, which is updated at a weekly frequency, with $j$ the index for the week. The impact of macro announcements on commodity futures returns can be assessed by looking at the $γ_{m}$ coefficient in the mean equation, while the $δ$ coefficient controls for the level of speculative trading intensity as it relates to futures returns. The key coefficient to help answer our main research question is $θ_{m}$ , which relates the effect of time-varying speculative trading intensity on the impact of the news release. The regression is estimated using a two-step weighted least squares (WLS) procedure. To account for heteroskedasticity, we construct a volatility estimate by means of an exponential moving average, using the regression residuals obtained in the first step. This auxiliary regression is presented in equation (5), with a smoothing parameter $α = 0.9$ and a starting parameter value set to $σ_{1} = ϵ_{t}$ :

σ_{t} = α σ_{t - 1} + (1 - α) | ϵ_{t} |

(5)

After obtaining $σ_{t}$ for each observation, we apply the transformation $w_{t} = {\hat{σ_{t}}}^{- 2}$ to obtain the WLS regression weight. Then, we multiply each variable by $w_{t}$ and run an OLS regression to estimate the model.

4.2. Modeling the Impact of Surprises on Volatility

To estimate the volatility equation, we use a GARCH specification, as it is well known that the variance of commodity futures returns displays time variation and clustering (see e.g., Brunetti and Reiffen 2014). We specify a GARCH (1,1) model and extend the equation by including our NLS speculative intensity proxy as well as the macroeconomic news surprise variables. First, we estimate the mean equation (6):

R_{t}^{t + τ} = α + \sum_{m = 1}^{22} γ_{m} S_{m, t} + β R_{t - τ}^{t} + ϵ_{t}

(6)

Then, we estimate the following equation for conditional variance:

σ_{t}^{2} = α_{0} + α_{1} σ_{t - 1}^{2} + α_{2} ϵ_{t}^{2} + \sum_{m = 1}^{22} Φ_{m} D_{m, t} + β X_{j} + \sum_{k = 1}^{n} ϕ_{k} I_{kt} + \sum_{h = 1}^{23} ρ_{h} D_{h}

(7)

where $I_{k, t} = D_{m, t} \cdot X_{j}$ and $D_{m, t}$ is a dummy variable for macro announcement $m$ . The latter equals 1 if an announcement takes place at time $t$ (five-minute frequency) and equals 0 otherwise. $X_{j}$ is the NLS speculative intensity variable as defined earlier. The $ρ_{h}$ coefficient captures intraday periodicity, while the $D_{h}$ dummy equals 1 at hour $h$ and 0 otherwise. The impact of macro announcement $m$ on conditional variance is captured by the $Φ_{m}$ coefficient in equation (7), while the $β$ coefficient shows the impact of the speculative intensity variable $X_{j}$ . Finally, the $ϕ_{k}$ coefficient shows the interaction effect from speculative trading and the macro surprise $m$ . The standard errors are computed using the Newey-West heteroskedasticity and autocorrelation consistent (HAC) estimator with automatic lag selection, following the procedure outlined in Newey and West (1994).This methodology mirrors the approach used in Andersen et al. (2003, 2007) and Kurov et al. (2019) to study announcement effects in other asset classes. In unpublished results, we consider the mixed-data sampling (MIDAS) approach proposed by Ghysels et al. (2004). We find that the results are similar.

4.3 Modeling the impact on bid-ask spreads

Speculative trading could make markets more efficient by improving information. We test this hypothesis by measuring the effect of macro surprises on the futures price bid-ask spread in high-frequency regressions. The bid-ask spread is widely recognized as a measure of market efficiency. A narrower spread suggests less uncertainty about the asset’s true value and reflects lower transaction costs, improved liquidity, and lower information asymmetry (Roll 1984). Furthermore, Chordia et al. (2008) show that the bid-ask spread is an indicator of market quality and market efficiency. Since a smaller spread is associated with a more efficient price discovery process, an increase in the quality of market information should decrease the spread. Therefore, we estimate the following equation, where the relative bid-ask spread is defined as $(As k_{t} - Bi d_{t}) / Mi d_{t}$ :

Sprea d_{t} = α + \sum_{m = 1}^{22} γ_{m} D_{m, t} + δ X_{j} + \sum_{m = 1}^{22} θ_{m} (D_{m, t} \cdot X_{j}) + β Sprea d_{t - τ} + ϵ_{t} .

(8)

In equation (8), $Sprea d_{t}$ is the relative bid-ask spread measured at five-minute frequency $t$ using the high-frequency data and $Sprea d_{t - τ}$ is the lagged spread. In addition, $\sum_{m = 1}^{22} γ_{m} D_{m, t}$ accounts for the macroeconomic announcements, where each dummy variable $D_{m, t}$ is multiplied by its respective coefficient $γ_{m}$ . We also include the speculative trading intensity variable $X_{j}$ with its coefficient $δ$ . The interaction terms $\sum_{m = 1}^{22} θ_{m} (D_{m, t} \cdot X_{j})$ capture the effect of speculative trading intensity on the bid-ask spread at the time of a release, with each term multiplied by its respective coefficient $θ_{m}$ . To test whether increased speculative trading activity improves informational efficiency at the time of a macroeconomic announcement, we check whether the sign on $θ_{m}$ is negative and significant, thus reducing the spread.

5. Results

5.1. The Impact of Surprises on Cumulative Abnormal Returns

We begin by documenting the impact of macroeconomic announcement surprises on commodity futures returns. The impact of surprises is shown in the following graphs of high-frequency cumulative abnormal returns (CARs). Figures 1 to 6 show CARs for each commodity as measured over a window of sixty minutes before to sixty minutes after a macroeconomic announcement release. The figures are constructed similarly to those shown in Kurov et al. (2019). The magnitude of the CARs after announcement releases is comparable to those shown in their paper for stock index and Treasury futures. Unlike them, however, we do not see evidence of a pre-announcement drift.

Figure 1.

Cumulative abnormal return of crude oil sixty minutes before and after macroeconomic announcements.

Figure 2.

Cumulative abnormal return of gold sixty minutes before and after macroeconomic announcements.

Figure 3.

Cumulative abnormal return of copper sixty minutes before and after macroeconomic announcements.

Figure 4.

Cumulative abnormal return of silver sixty minutes before and after macroeconomic announcements.

Figure 5.

Cumulative abnormal return of palladium sixty minutes before and after macroeconomic announcements.

Figure 6.

Cumulative abnormal return of natural gas sixty minutes before and after macroeconomic announcements.

A red line denotes the average CAR for announcement releases that are seen as negative surprises (i.e., worse than anticipated news), while a green line denotes the average CAR for positive surprises. For brevity, we discuss only the CARs for crude oil and gold, as they are representative of pro-cyclical energy markets and safe haven assets. Figure 1 shows the average CAR for crude oil futures. The CAR increases following a positive surprise and decreases following a negative one, confirming that crude oil is a pro-cyclical commodity. In contrast, Figure 2 shows that gold futures react in the opposite manner. The red line indicates that CAR is positive after bad news, while the green line shows that CAR is negative after good news.

5.2. Macroeconomic Surprises, Speculative Trading Activity and Futures Returns

Table 6 presents the results of high-frequency regressions that explain commodity futures returns immediately after a macroeconomic announcement release. Our discussion focuses on coefficients that are statistically significant at the 5 percent level.¹² We begin with energy commodities, which serve as our baseline case. The $γ_{m}$ coefficient shows the immediate impact of a macro surprise on commodity futures returns. For crude oil futures, we find that several macroeconomic announcements exhibit significant effects. Consider for instance Initial Jobless Claims, for which a greater than expected value indicates bad economic news. The table shows that for this announcement, $γ_{m}$ is negative, indicating that crude oil prices tend to drop in response to unexpected increases in jobless claims. This result is consistent with the expectation that higher jobless claims signal weaker economic conditions, which reduce the demand for crude oil. The corresponding $θ_{m}$ coefficient for Initial Jobless Claims is positive, however, suggesting that increased speculative trading mitigates the negative impact of bad macroeconomic news on crude oil prices. This damping effect suggests that markets are better informed as a result of the increased participation of speculators. Indeed, the announcement release creates a smaller surprise and a smaller shock (Tables 6, 7).

Table 6.

Effects of Macro Announcements and Speculative Trading Intensity (NLS) on Futures Returns – Full Sample.

Commodities	Crude oil		Gold		Copper		Silver		Palladium		Natural gas
Announcements	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$
Macroeconomic news announcements
Initial jobless claims	−0.198***	0.879***	0.700***	−1.340***	−0.449***	1.988***	−0.007	−0.015	0.029**	−0.221***	−0.003	−0.109
ADP Employment	0.343***	−1.068**	−1.187***	2.525***	0.581***	−2.370***	−0.026***	0.169	0.008	−0.142	−0.115***	−2.798***
CB Consumer	0.117***	−0.469***	−0.059***	0.070*	0.080***	−0.152**	0.000	0.006	−0.068***	0.116**	0.029	−0.014
Advance retail sales	0.200***	−0.745***	−0.229***	0.447***	0.104***	−0.425**	0.013	−0.086	−0.018	0.007	−0.027	−0.471
Building permit	0.000	0.040	−0.027**	0.037	0.063***	−0.157**	0.007	−0.004	−0.002	−0.037	0.043*	0.362**
Construction spending	0.022	−0.146	−0.041**	0.092**	0.040*	−0.144*	−0.001	−0.189***	0.010	−0.003	−0.029	−0.179
Consumer credit	−0.016	0.083	−0.014*	0.030	−0.016*	0.062	0.002	0.024	0.011	−0.010	−0.012	−0.112
Consumer price index	0.084***	−0.293**	−0.172***	0.310***	0.269***	−0.759***	−0.030***	−0.023	−0.142***	0.231***	0.013	0.150
Durable goods orders	0.153***	−0.738***	−0.073***	0.147***	0.064***	−0.149*	−0.011*	0.000	−0.054***	0.145***	−0.003	−0.100
Existing home sales	0.087***	−0.592***	−0.020	0.050	−0.022	0.108	−0.022***	−0.069	0.012	−0.045	0.041	0.219
Factory orders	−0.001	0.010	−0.029	0.003	−0.060**	0.146	0.004	0.025	−0.019	0.000	0.107***	0.685***
Gross domestic product	0.046*	−0.184	−0.161***	0.242***	0.170***	−0.331***	0.009	−0.005	−0.054***	0.008	−0.036	−0.337*
Housing starts	0.032	−0.127	−0.063***	0.114***	0.071***	−0.149**	0.005	0.006	0.002	−0.036*	0.023	0.383**
Industrial production	0.021	−0.154	−0.001	−0.071	−0.013	−0.116	−0.009	−0.008	−0.022	0.021	−0.023	−0.023
New home sales	0.099***	−0.436**	−0.069***	0.136***	−0.033*	−0.041	−0.024***	0.087*	−0.008	0.022	−0.003	−0.084
Non-farm employment	1.404***	−5.111***	−3.194***	6.812***	1.389***	−6.044***	−0.027***	0.624**	0.000	0.153	−0.218***	−6.294***
Pending home sales	0.073***	−0.349**	−0.018	0.003	−0.013	−0.023	−0.017**	−0.111	−0.009	0.031	−0.024	−0.399
Personal consumption	−0.015	0.068	−0.036**	0.063*	0.009	−0.058	0.003	0.087	0.013	−0.050	0.004	0.369
Personal income	0.009	−0.136	−0.072	0.150	0.254***	−1.052**	−0.017*	0.279**	−0.013	−0.022	−0.028	−0.820
Producer price index	0.035*	−0.236**	−0.077***	0.150***	−0.015	−0.042	−0.005	−0.013	−0.020	0.045	−0.049*	−0.223
Trade balance	0.003	−0.056	−0.052**	0.119**	−0.011	0.041	−0.004	−0.132	−0.039	0.077	−0.047	−0.451
Crude Oil Weekly inventory	−0.077***	−0.396***
Natural Gas Weekly inventory											−0.365***	−0.077
Observations	1,193,455		1,190,001		1,180,816		1,138,696		749,168		1,101,836
$R^{2} (%)$	.198		.204		.101		.0290		.0246		.118

Note. This table presents estimates of equation (4), $R_{t}^{t + τ} = α + \sum_{m = 1}^{22} γ_{m} S_{m, t} + δ X_{t, i} + \sum_{m = 1}^{22} θ_{m} (S_{m, t} \cdot X_{t}) + β R_{t - τ}^{t} + ϵ_{t}$ using the method proposed by Kurov et al. (2019) and speculative trading intensity variable $X_{t} = NL S_{t}$ . The period covered is from 2007-04-01 to 2024-02-11. The $γ_{m}$ coefficients capture the instantaneous change in the futures return when an announcement has just occurred. The $θ_{m}$ coefficients capture the instantaneous change in return when an announcement has just occurred in conjunction with the level of speculative trading intensity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels (two–tailed), respectively. Standard errors are Newey–West HAC.

Table 7.

Effects of Macro Announcements and Speculative Trading Intensity (NLS) on Futures Conditional Variance – Full Sample.

Commodities	Crude oil		Gold		Copper		Silver		Palladium		Natural gas
Announcements	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$
Macroeconomic news announcements
Initial jobless claims	0.101***	−0.436***	0.047***	0.020	0.117***	−0.188***	0.025***	−0.056***	0.118***	−0.114***	−0.012	−0.021
ADP Employment	0.028	−0.200	0.064***	−0.084***	0.053***	−0.030	0.028***	0.034	−0.029*	0.015	0.016	0.057
CB Consumer	0.104***	−0.414***	0.031***	0.012	0.045***	0.028	0.028***	−0.103**	0.061***	−0.099***	−0.008	−0.013
Advance retail sales	0.169***	−0.862***	0.096***	−0.056*	0.124***	−0.054	0.028***	−0.156***	0.114***	−0.169***	0.058***	0.251*
Building permit	0.105***	−0.540***	0.046***	−0.060*	0.061***	−0.193***	0.005	−0.074*	0.077***	−0.070***	0.022	−0.026
Construction spending	0.153***	−0.627***	0.095***	−0.119***	0.086***	−0.098	0.041***	−0.190***	0.046***	0.013	−0.019	−0.321**
Consumer credit	0.048*	−0.261*	0.008	−0.003	0.017	0.004	0.003	0.039	−0.006	0.034	0.002	0.014
Consumer price index	0.072**	−0.067	0.139***	−0.075*	0.266***	−0.529***	0.094***	−0.375***	0.182***	−0.274***	0.027	0.039
Durable goods orders	0.098***	−0.411***	0.023**	0.016	0.042***	0.022	0.008	−0.024	0.068***	−0.037	−0.022	−0.256*
Existing home sales	0.048*	−0.059	0.019*	0.041	0.038***	−0.030	0.022***	−0.132***	0.048***	−0.067**	0.022	−0.113
Factory orders	0.088***	−0.311**	−0.007	0.162***	0.018	0.151**	0.026***	−0.132***	0.040***	−0.040	−0.030	−0.695***
Gross domestic product	0.116***	−0.618***	0.047***	0.053	0.100***	−0.063	0.025***	0.017	0.110***	−0.121***	−0.051**	−0.309**
Housing starts	0.113***	−0.607***	0.037***	−0.031	0.062***	−0.190***	0.008	−0.095**	0.060***	−0.049	0.018	0.040
Industrial production	0.096***	−0.638***	0.019*	0.003	0.021	−0.007	0.002	−0.048	0.020	−0.071**	−0.033	−0.187
New home sales	0.101***	−0.527***	0.041***	0.015	0.058***	0.028	0.025***	−0.001	0.068***	−0.115***	−0.025	−0.229*
Non-farm employment	0.365***	−1.206***	0.236***	0.047	0.381***	−0.141**	0.129***	−0.156***	0.173***	−0.050	0.028	−0.354**
Pending home sales	0.109***	−0.494***	0.020*	−0.016	0.025*	0.021	0.028***	−0.008	0.059***	−0.085***	−0.014	−0.308**
Personal consumption	0.006	0.021	0.062***	−0.081**	0.102***	−0.268***	0.007	−0.008	0.111***	−0.148***	0.024	−0.376**
Personal income	0.007	0.047	0.091***	−0.150***	0.125***	−0.341***	0.013**	−0.023	0.094***	−0.109***	0.003	−0.432***
Producer price index	0.097***	−0.350**	0.017	0.085**	0.090***	−0.099	0.011*	−0.051	0.120***	−0.067**	0.008	0.005
Trade balance	0.078***	−0.128	−0.007	0.206***	0.003	0.393***	0.009	0.030	0.103***	−0.087***	0.018	0.112
Crude Oil Weekly invention	0.005	0.624***
Natural Gas Weekly inventory											0.531***	−0.687***
Observations	1,193,455		1,190,00		1,180,816		1,138,696		749,168		1,101,836
$R^{2} (%)$	7.065		8.064		7.396		7.057		7.629		13.60

Note. This table presents estimates of equation (7) using the speculative trading intensity variable $NL S_{t}$ . The equation is $σ_{t}^{2} = α_{0} + α_{1} σ_{t - 1}^{2} + α_{2} \in_{t}^{2} + \sum_{m = 1}^{22} ϕ_{m} D_{m, t} + β X_{i, t} + \sum_{k = 1}^{n} ϕ_{k} I_{k t} + \sum_{h = 1}^{23} ρ_{h} D_{h}$ where $I_{kt} = D_{m, t} \cdot X_{i, t}$ and $D_{m, t}$ is a macro announcement dummy variable for release $m$ . The period covered is from 2007-04-01 to 2024-02-11. The $Φ_{m}$ coefficients capture the instantaneous change in the conditional variance when an announcement has just occurred. The $φ_{m}$ coefficients capture the conditional variance when an announcement has just occurred in conjunction with the level of speculative trading intensity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels (two–tailed), respectively. Standard errors are Newey–West HAC.

In the case of natural gas futures, we find that the results across announcements are less frequently significant. Increased speculative trading tends to increase the magnitude of the surprise’s effects, when the results are significant. An exception is the natural gas market-specific announcement release (inventories), for which the coefficient is negative but not significant. Overall for natural gas futures, we do not find as much support that speculative trading dampens the effect of macroeconomic announcements on returns. The reason why the results for natural gas futures are less conclusive is most likely that this futures contract displays greater volatility, that it has a lower trading volume (Irwin and Sanders 2012), and it has less speculative trading activity (thus, fewer informed traders) (Büyükşahin and Robe 2014).

Copper is a pro-cyclical, industrial commodity, so it is expected that the results for copper futures should resemble those for crude oil. The results are highly significant for many announcements. If we look at announcements such as ADP Employment and CB Consumer Confidence, which are “good news,” the $γ_{m}$ coefficients are positive, confirming copper’s pro-cyclical nature and its ties to industrial production. The corresponding $θ_{m}$ coefficients are negative, consistent with the damping effect that we document for energy commodities. Thus, the main finding for copper futures is that, as with crude oil, increased speculative trading intensity has the effect of weakening the impact of a macro surprise.

Gold is considered to be a safe haven asset (Baur and Lucey 2010). Therefore, it is expected that the reaction of gold futures returns to macro surprises will be the opposite to what we have found for crude oil, natural gas, and copper. This is indeed what we find: gold futures returns are lower after “good news” and higher after “bad news.” These results support the idea that energy commodities are pro-cyclical, while gold is a safe haven asset. In particular, the positive $γ_{m}$ coefficient for Initial Jobless Claims indicates that gold prices rise in response to unexpected increases in jobless claims, as investors seek safety in gold positions during economic uncertainty. The $θ_{m}$ coefficient is negative, indicating that speculative trading activity tempers this flight to safety, leading to less pronounced price increases.In the case of the ADP Employment release, the $γ_{m}$ coefficient is negative for gold futures, suggesting that strong employment figures reduce gold prices as investors move away from safe-haven assets towards pro-cyclical assets, such as energy commodities. The positive $θ_{m}$ coefficient suggests that speculative trading reduces the extent of this price drop. The CB Consumer Confidence and Advance Retail Sales announcements also generate negative $γ_{m}$ coefficients for gold futures and positive $θ_{m}$ coefficients at a 10 percent level, providing additional evidence of a moderating influence of speculative trading on the reaction of gold to economic news. The main finding is therefore that whether a commodity is pro-cyclical or a safe haven asset, increased speculative trading has a damping effect on the reactions to macro surprises.

The last two commodities in our sample, silver and palladium futures, behave more like gold futures. We find that for Initial Jobless Claims for palladium and ADP Employment in silver, significant $γ_{m}$ coefficients are found in directions consistent with their status as safe haven assets. The $θ_{m}$ coefficients for silver and palladium also indicate a damping effect on price reactions. Taken together, our results show that the damping effect of speculative trading is stronger in energy markets, suggesting that the beneficial effects of financial participants are particularly important for energy commodities.

5.3. Macroeconomic Surprises, Speculative Trading, and Volatility

Table 7 presents regression results to explain the conditional variance of high-frequency commodity futures returns after macroeconomic announcements. We first examine our baseline assets, energy commodities, as volatility in energy markets is of particular concern given their economic importance and direct impact on consumer prices. For crude oil, macroeconomic surprises generally lead to an increase in conditional variance, as the $Φ_{m}$ coefficients are consistently positive. For instance, a surprise in Initial Jobless Claims significantly increases crude oil volatility. Natural gas exhibits similar patterns, with some variations in magnitude and a greater impact on inventory-related announcements. This result suggests that unexpected economic news generates greater uncertainty and price fluctuations in energy markets. In contrast, the interaction coefficients $ϕ_{m}$ , which inform us about the impact of speculative trading, tend to be negative for the two energy commodities. The implication is that increased trading activity by speculative traders lowers volatility following a macro surprise. Taking Initial Jobless Claims as an example, we find that higher levels of speculation reduce the impact of news on volatility in crude oil futures markets. Thus, speculative trading can act as a stabilizing force by damping the heightened volatility that occurs after a surprise in macroeconomic news.

Comparing these results with those for other commodities, we find that gold, copper, silver, and palladium also show positive $Φ_{m}$ coefficients across various announcements, indicating increased volatility following macro surprises. However, the magnitude of these effects is generally smaller than what we see in energy markets, especially in the case of precious metals. The corresponding $ϕ_{m}$ coefficients, measuring the damping effect of financial investor activity, are negative across commodities and announcements, and the strongest effects are found in crude oil futures markets. This pattern holds for other macroeconomic announcements as well. The coefficients for ADP Employment, CB Consumer Confidence, and Advance Retail Sales, for instance, generally indicate increased volatility after surprises, as shown by the positive $Φ_{m}$ coefficients, while the corresponding $ϕ_{m}$ coefficients are negative, supporting the finding of a stabilizing effect of increased speculative trading.

Therefore, our findings highlight the valuable role of speculative traders in reducing volatility, particularly in crude oil futures markets, where price stability has important implications for the broader economy. While macroeconomic news tends to increase volatility across commodity markets, the damping effect of speculative trading appears strongest in energy markets. We show that this relationship is consistent across different types of announcements and remains robust when controlling for various market conditions.

5.4. Macroeconomic Surprises, Speculation, and Bid-Ask Spreads

Table 8 shows our results for the impact of macroeconomic surprises and speculative trading intensity on futures prices bid-ask spreads. This empirical analysis provides a test of informational efficiency. We first focus on energy markets. In the bid-ask spread regressions, the $γ_{m}$ coefficient denotes the effect of surprises on the spread, while the $θ_{m}$ coefficient shows the interaction effect between surprises and speculative trading intensity. The main hypothesis is whether $θ_{m} < 0$ , which would indicate that greater speculative activity improves informational efficiency through narrower spreads. Such a finding would be consistent with what we have documented above for futures returns and volatility. In the case of crude oil futures, we find that $γ_{m}$ is negative for the initial jobless claims announcement, which means that the market becomes more efficient immediately after a news release. This is consistent with the resolution of uncertainty. The $θ_{m}$ coefficient is always negative when it is significant, indicating that the bid-ask spread narrows even more (implying greater informational efficiency) after a macro announcement if crude oil futures markets benefit from greater speculative activity relative to hedging activity, as measured by the NLS proxy. The results for natural gas futures are similar to those for crude oil. The relationship between speculative trading and market efficiency is clearest during periods of higher trading volume, such as the release of storage reports and weather-related announcements.

Table 8.

Effects of Macro Announcements and Speculative Trading Intensity (NLS) on Futures Price Bid-Ask Spreads – Full Sample.

Commodities	Crude oil		Gold		Silver		Natural gas		Copper		Palladium
Announcements	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$
Macroeconomic news announcements
Initial jobless claims	−2.710***	13.698	5.425	−10.380 ***	5.850	−26.397 ***	−0.249	−4.543***	0.015	0.857	−0.191	0.537
ADP Employment	3.629	−10.942 ***	−10.542 ***	22.354	−7.399***	31.336	−0.652	−13.889 ***	0.250	2.043	0.036	−0.467
CB Consumer	1.419	−6.221***	−0.850	1.447	−1.132	3.048	0.512	0.302	0.026	0.242	−0.775	1.928
Advance retail sales	2.089	−7.055***	−2.279	4.431	−0.850	2.848	−0.227	−2.023	0.351	−3.220***	−0.419	1.513
Building permit	−0.197	1.782	−0.437	0.651	−0.774	2.561	0.301	3.765	0.030	0.368	−0.199	−0.203
Construction spending	0.177	−1.035	−0.064	0.076	−0.265	0.847	−0.187	−2.696***	0.061	−2.420***	−0.126	0.298
Consumer credit	0.371	−1.899	−0.027	0.046	−0.029	0.136	−0.041	0.047	0.055	0.359	−0.007	0.021
Consumer price index	−0.829	1.332	−2.239	4.869	−3.053	9.401	−0.042	1.982	−0.562	1.096	−1.828	3.268
Durable goods orders	1.318	−7.368***	−0.956	2.313	−1.310	5.553	−0.121	−1.497	0.203	−0.184	−0.388	1.251
Existing home sales	1.054	−5.366***	−0.398	1.067	−0.161	1.420	1.064	5.295	0.422	−1.812	0.016	−0.041
Factory orders	−0.338	1.588	−0.851	1.343	−0.769	1.833	1.549	11.321	0.014	1.088	−0.033	−0.325
GDP	0.358	−1.60	−1.891	3.738	−2.248	6.383	−0.394	−3.634***	0.121	−0.732	−0.565	0.374
Housing starts	0.190	−1.233	−0.990	1.896	−0.970	2.572	0.541	6.538	0.083	0.511	0.036	−0.563
Industrial production	0.120	−2.099	−0.195	−0.414	−0.061	−1.994	−0.203	−1.192	−0.058	−0.402	−0.181	0.282
Manufacturing capacity	−0.103	2.279	−0.612	0.922	−0.822	1.975	−0.366	−2.898***	−0.037	0.697	−0.141	0.549
New home sales	1.631	−8.343***	−0.906	1.652	−0.385	−0.407	−0.252	−0.885	0.369	−0.456	−0.010	0.057
Non-farm payroll	10.788	−38.888 ***	−29.711 ***	63.342	−10.286 ***	43.957	−2.491	71.471 ***	0.272	8.522	0.000	1.467
Pending home sales	0.825	−2.545	−0.422	0.524	−0.194	−0.157	−0.575	−9.013***	0.157	−2.360***	−0.354	0.282
Personal consumption	−0.215	−0.039	−0.826	1.629	−0.034	0.119	−0.117	3.932	0.092	2.044	−0.252	0.577
Personal income	−0.088	−0.820	−1.535	3.290	−4.400***	17.615	−0.461	−12.861 ***	−0.246	−3.838***	−0.162	0.235
Producer price index	0.714	−4.424***	−0.723	1.434	0.220	−1.894	−0.514	−2.803***	0.061	−0.121	−0.723	1.480
Treasury balance	0.046	−1.620	−0.821	1.977	−0.803	2.967	−0.126	−0.738	−0.033	−1.503	−0.314	0.842
Announcements specific to commodity markets
Weekly crude oil stock	−0.977	−3.473***
Natural Gas Weekly inventory							−1.747	−7.027***
Observations	1,193,455		1,190,001		1,138,696		1,101,836		1,180,816		749,168
$R^{2} (%)$	1.98		1.77		.56		1.85		1.28		1.51

Note. This table presents estimates of the equation $R_{SPREA D_{t}}^{t + τ} = α + \sum_{m = 1}^{22} γ_{m} D_{m, t} + δ X_{t, i} + \sum_{m = 1}^{22} θ_{m} (D_{m, t} \cdot X_{t}) + β R_{SPREA D_{t - τ}}^{t} + ϵ_{t}$ , analyzing the effects of speculative trading intensity and macroeconomic announcements on the bid-ask spread using the speculative trading intensity variable $NL S_{t}$ . The period covered is from 2007-04-01 to 2024-02-11. The $γ_{m}$ coefficients capture the instantaneous change in the bid-ask spread when a macroeconomic announcement occurs. The δ coefficient represents the effect of the speculative trading intensity variable $NL S_{t}$ . The $θ_{m}$ coefficients capture the interaction effect between macroeconomic announcements and speculative trading intensity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels (two–tailed), respectively. Standard errors are Newey–West HAC.

Comparing these results to those obtained for the other commodities in our sample, we find that the results for $θ_{m}$ (speculative intensity) are less often significant, but that they are negative when they are significant. The $θ_{m}$ coefficient being negative suggests that greater speculative trading intensity improves market efficiency, as bid-ask spreads tend to be lower when the coefficient is significant. This improvement in market efficiency appears to be most pronounced in energy markets, where accurate price discovery is particularly important given their relevance for the real economy. Overall, the results for the bid-ask spread provide additional support for our claim that greater trading activity has beneficial effects on commodity derivatives markets, with these benefits being especially notable in energy markets where efficient price discovery has important implications for both market participants and the broader economy.

5.5. Differences in Results According to Trader Type

To investigate whether differences in trader type are relevant in explaining our findings, we provide disaggregated results in this section. To this end, we estimate equations (6) to (8) for two categories of Non-Commercial traders, namely, swap dealers (SD) and money managers (MM). For each of the two, the CFTC reports the number of long and short positions in their disaggregated Commitment of Traders (COT) reports. We compute the NLS index for each trader category over time, allowing us to separately quantify the intensity of trading activity by money managers and swap dealers.

First, we examine the returns equation for money manager positions, as shown in Table 9(a). Increased trading activity by money managers has the same effect as in our baseline results. If we consider crude oil futures, for example, the $γ_{m}$ macro surprise coefficient is positive while the $θ_{m}$ coefficient for speculative trading is negative. Since $θ$ has the opposite sign to $γ$ , the implication is that increased futures trading activity by money managers lowers the impact of surprises on futures returns. This is similar to our baseline, aggregate findings. Next, Table 9(b) presents results using only swap dealer positions. Here we find a notable difference relative to money managers. This table shows that the speculative intensity coefficient $θ$ for swap dealers has the same sign as the macro surprise coefficient $γ$ . This result means that increased swap dealer trading activity seems to amplify the reaction of futures returns to macro surprises. The exception to these results is in the case of natural gas futures, where increased trading by swap dealers for some announcements appears to have the opposite effect on returns. To contrast this finding with prior research, Brunetti et al. (2016) find, using daily data, that the positions of swap dealers are not correlated with contemporaneous returns and volatility in commodity futures markets. They further show that hedge funds decrease, and hedgers increase, volatility. Our empirical analysis extends their findings using high-frequency data and the setting of macro surprises as a source of new information affecting energy and commodity futures markets.

Table 9(a).

Effects of Macro Announcements and Speculative Trading Intensity (Money Managers only) on Futures Returns.

Commodities	Crude oil		Gold		Copper		Silver		Palladium		Natural gas
Announcements	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$
Macroeconomic news announcements
Initial jobless claims	0.292**	−1.237*	−0.068	0.154	0.223***	−0.974**	−0.023***	0.150	0.010	−0.118	−0.022	−0.492
ADP Employment	0.115***	−0.654**	−0.048***	0.056	0.070***	−0.140**	0.000	0.002	−0.067***	0.115**	0.037**	0.122
CB Consumer	0.180***	−0.951***	0.006	−0.228***	0.095***	−0.523***	0.013	−0.069	−0.021	0.021	−0.005	−0.244
Advance retail sales	−0.031	0.308*	−0.014**	−0.002	0.049***	−0.122**	0.007	0.001	−0.003	−0.038	0.003	0.087
Building permit	0.047	−0.415	−0.048***	0.171***	0.034**	−0.152*	0.005	−0.126**	0.010	−0.003	−0.011	−0.093
Construction spending	−0.009	0.057	−0.010**	0.041*	−0.010	0.053	0.001	0.020	0.011	−0.013	−0.002	−0.048
Consumer credit	0.086***	−0.420**	−0.158***	0.472***	0.217***	−0.727***	−0.029***	−0.015	−0.136***	0.223***	−0.001	0.078
Consumer price index	0.093***	−0.696***	−0.058***	0.157***	0.053***	−0.119	−0.015**	−0.051	−0.062***	0.177***	0.010	−0.020
Durable goods orders	0.095***	−0.802***	−0.007	0.012	−0.012	0.079	−0.021**	−0.013	0.013	−0.050	0.013	0.053
Existing home sales	−0.024	0.219	−0.044***	0.069	0.050**	−0.145	0.003	0.012	−0.019	0.000	0.042**	0.387**
Factory orders	0.044*	−0.252	−0.083***	0.023	0.128***	−0.187**	0.006	0.027	−0.060***	0.024	−0.004	−0.282*
Gross domestic product	−0.017	0.198	−0.028***	0.008	0.046***	−0.037	0.003	0.024	0.002	−0.041*	−0.013	0.142
Housing starts	−0.003	−0.035	−0.023**	−0.020	−0.022	−0.101	−0.007	−0.034	−0.032	0.045	−0.013	0.233
Industrial production	0.040	−0.201	−0.035***	0.053	0.032**	0.061	−0.024***	−0.045	−0.012	0.032	0.004	−0.183
New home sales	0.798***	−4.073***	−0.517***	2.145***	0.771***	−4.430***	−0.015*	0.613***	0.025	−0.162	−0.005	−0.956
Non-farm employment	0.056	−0.312	−0.019*	0.007	−0.021	0.016	−0.016*	−0.034	−0.015	0.050	0.010	−0.270
Pending home sales	0.091**	−0.641***	−0.019**	0.043	0.005	−0.064	0.000	0.081	0.012	−0.049	−0.025	0.232
Personal consumption	0.058	−0.501	0.017	−0.097	0.145**	−0.834**	−0.008	−0.200	−0.017	0.009	0.004	−1.050*
Personal income	0.039*	−0.326*	−0.055***	0.133***	−0.006	−0.122	−0.003	−0.031	−0.022	0.055	−0.026	−0.112
Producer price index	0.007	−0.108	−0.031**	0.081*	−0.009	0.040	0.004	−0.118	−0.025	0.060	0.000	−0.269
Trade balance	0.292**	−1.237*	−0.068	0.154	0.223***	−0.974**	−0.023***	0.150	0.010	−0.118	−0.022	−0.492
Crude Oil Weekly inventory	−0.072***	−0.610***
Natural Gas Weekly inventory											−0.355***	−0.122
Observations	1,193,455		1,190,001		1,180,816		1,138,696		749,168		1,101,836
$R^{2} (%)$	.181		.151		.0923		.0284		.0235		.113

Note. This table presents estimates of equation (4), $R_{t}^{t + τ} = α + \sum_{m = 1}^{22} γ_{m} S_{m, t} + δ X_{t, i} + \sum_{m = 1}^{22} θ_{m} (S_{m, t} \cdot X_{t}) + β R_{t - τ}^{t} + ϵ_{t}$ using the method proposed by Kurov et al. (2019) and the money manager-only speculative trading intensity variable $NL S_{t, MM}$ , calculated with the money manager positions. The period covered is from 2007-04-01 to 2024-02-11. The $γ_{m}$ coefficients capture the instantaneous change in return when an announcement has just occurred. The coefficients $θ_{m}$ capture the instantaneous change in return when an announcement has just occurred in conjunction with the level of speculative trading intensity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels (two–tailed), respectively. Standard errors are Newey–West HAC.

Table 9(b).

Effects of Macro Announcements and Speculative Trading Intensity (Swap Dealers only) on Futures Returns.

Commodities	Crude oil		Gold		Copper		Silver		Palladium		Natural gas
Announcements	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$
Macroeconomic news announcements
Initial jobless claims	−0.186***	−0.763***	0.361***	1.023***	0.014	−0.511	0.082**	0.448**	0.006	−0.035	−0.007	0.236
ADP Employment	0.426***	1.770***	−0.732***	−1.923***	0.143***	1.908***	0.105	−0.417	−0.084*	−0.379*	−0.070***	1.981***
CB Consumer	0.086***	0.387***	−0.027***	0.061	0.047***	0.045	−0.057***	−0.252***	−0.024**	−0.137**	0.029	0.015
Advance retail sales	0.188***	0.837***	−0.221***	−0.622***	0.023*	0.330	−0.052**	−0.344**	−0.024	0.037	−0.012	0.183
Building permit	0.012	0.034	−0.028***	−0.064**	0.039***	0.196*	0.009	−0.008	−0.015**	0.062	0.067**	−0.571***
Construction spending	0.009	0.104	−0.015*	−0.040	0.016	0.154	−0.046**	−0.199**	0.013	0.067	−0.006	−0.035
Consumer credit	−0.001	0.007	−0.003	0.002	−0.004	0.005	0.007	−0.019	0.007	0.027	−0.020	0.185
Consumer price index	−0.051***	−0.123	−0.042***	−0.116**	0.109***	0.501***	−0.080***	−0.206**	−0.054***	−0.284***	0.042	−0.404
Durable goods orders	0.082***	0.408***	−0.028***	−0.028	0.023**	0.278***	−0.001	0.047	0.009	−0.090**	0.011	−0.012
Existing home sales	0.025**	0.285***	−0.007	−0.021	−0.001	0.075	−0.060***	−0.336***	−0.006	0.051	0.054	−0.336
Factory orders	−0.003	−0.038	−0.004	0.145**	0.032***	−0.194	0.015	−0.047	−0.020	−0.032	0.096***	−0.622**
Gross domestic product	0.038**	0.177**	−0.091***	−0.062	0.100***	−0.154	−0.025	0.146**	−0.052***	−0.009	−0.032	0.302
Housing starts	0.030**	0.134*	−0.055***	−0.149***	0.051***	0.218**	−0.018	0.120*	−0.010	0.023	0.019	−0.321
Industrial production	0.024	0.199*	−0.004	0.107**	0.034***	0.147	−0.030	0.098	−0.013	−0.013	−0.029	0.090
New home sales	0.072***	0.343***	−0.040***	−0.082*	0.038***	0.090	−0.038**	−0.288***	0.000	−0.026	0.016	−0.110
Non-farm employment	1.820***	8.772***	−3.079***	−8.258***	0.226***	3.881***	−0.485***	−2.661***	−0.146*	−0.684*	−0.067**	2.423**
Pending home sales	0.042***	0.275***	−0.017	0.002	−0.017	0.075	−0.033	0.199**	0.004	−0.025	−0.004	0.252
Personal consumption	−0.002	−0.005	−0.017*	−0.027	0.005	0.321**	−0.008	0.031	−0.006	0.071	0.003	−0.383
Personal income	−0.022	0.006	−0.058*	−0.154	0.082***	1.829***	−0.091	0.437	−0.023	0.039	−0.008	0.530
Producer price index	0.008	0.075	−0.024***	−0.002	0.025***	0.089	−0.032	0.111	−0.006	−0.036	−0.036	0.151
Trade balance	−0.001	0.038	−0.016	−0.062	−0.012	−0.239	−0.045	0.144	−0.014	−0.220*	0.000	0.125
Crude Oil Weekly inventory	−0.144***	0.036
Natural Gas Weekly inventory											−0.473***	0.908***
Observations	1,193,455		1,190,001		1,180,816		1,138,696		749,168		1,101,836
$R^{2} (%)$	.196		.206		.0809		.0337		.0195		.123

Note. This table presents estimates of equation (4), $R_{t}^{t + τ} = α + \sum_{m = 1}^{22} γ_{m} S_{m, t} + δ X_{t, i} + \sum_{m = 1}^{22} θ_{m} (S_{m, t} \cdot X_{t}) + β R_{t - τ}^{t} + ϵ_{t}$ using the method proposed by Kurov et al. (2019) and the swap dealer-specific speculative trading intensity variable $NL S_{t, SD}$ , calculated with the swap dealer positions. The period covered is from 2007-04-01 to 2024-02-11. The $γ_{m}$ coefficients capture the instantaneous change in return when an announcement has just occurred. The coefficients $θ_{m}$ capture the instantaneous change in return when an announcement has just occurred in conjunction with the level of speculative trading intensity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels (two–tailed), respectively. Standard errors are Newey–West HAC.

Table 10(a) and 10(b) show our disaggregated results for the variance equation using sample data for money managers and swap dealers, respectively. The money manager results are similar to what we find in the aggregate sample, namely that both good and bad surprises increase volatility (as shown by $Φ_{mm} > 0$ ), while greater money manager trading activity lowers the impact of news on volatility (as shown by $ϕ_{mm} < 0$ ). This result strengthens prior evidence in the literature about beneficial effects of speculators, which were based on daily data (Brunetti et al. 2016). In contrast, our results suggest that increased activity by swap dealers appears to increase volatility after macro news (since $ϕ_{sd} > 0$ ). Together, the results line up with our findings for the returns equation and with our main message, which is that informed traders stabilize markets by contributing new information. Swap dealers, as intermediaries, typically do not trade based on information but rather following the needs of their clients. This economic motivation can explain why our results suggest that their trading activities amplify price and volatility reactions to news. Although our discussion centers on crude oil as a benchmark commodity, the results for the other pro-cyclical commodities support our interpretation of the findings. In the case of gold futures, the disaggregated results continue to support a safe haven interpretation (Erb and Harvey 2013). Indeed, gold has features of a commodity and a currency, but earlier studies have also found that its value increases with investor risk aversion, since it is perceived as a safe haven during times of economic uncertainty and market volatility.

Table 10(a).

Effects of Macro Announcements and Speculative Trading Intensity (Money Managers only) on Futures Conditional Variance.

Commodities	Crude oil		Gold		Copper		Silver		Palladium		Natural gas
Announcements	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$
Macroeconomic news announcements
Initial jobless claims	0.086***	−0.470***	0.052***	0.011	0.104***	−0.179***	0.027***	−0.048***	0.126***	−0.140***	−0.011**	−0.048
ADP Employment	0.001	−0.032	0.040***	−0.020	0.051***	−0.025	0.025***	−0.063*	−0.028*	0.016	0.012	0.057
CB Consumer	0.112***	−0.666***	0.040***	−0.020	0.048***	0.019	0.031***	−0.047	0.062***	−0.104***	−0.008	−0.043
Advance retail sales	0.142***	−0.963***	0.081***	−0.014	0.125***	−0.085	0.033***	−0.112***	0.125***	−0.210***	0.039***	0.275**
Building permit	0.103***	−0.757***	0.021***	0.028	0.046***	−0.166***	0.006	−0.038	0.085***	−0.100***	0.028**	0.077
Construction spending	0.142***	−0.784***	0.066***	−0.050*	0.072***	−0.041	0.049***	−0.174***	0.056***	−0.013	0.007	−0.303***
Consumer credit	0.039	−0.287	0.004	0.015	0.010	0.054	0.001	0.036	−0.008	0.039	0.000	0.009
Consumer price index	0.059*	0.039	0.159***	−0.210***	0.234***	−0.520***	0.104***	−0.218***	0.182***	−0.282***	0.023	0.015
Durable goods orders	0.057**	−0.210	0.024***	0.021	0.049***	−0.025	0.009	−0.032	0.077***	−0.066*	0.002	−0.158
Existing home sales	0.115***	−0.691***	0.024***	0.040	0.041***	−0.058	0.027***	−0.115***	0.051***	−0.077**	0.032***	−0.084
Factory orders	0.162***	−1.114***	0.032***	−0.070**	0.029***	−0.138**	0.032***	−0.132***	0.042***	−0.046	0.033***	−0.460***
Gross domestic product	0.081***	−0.538**	0.055***	0.044	0.091***	−0.029	0.025***	0.008	0.122***	−0.159***	−0.022*	−0.240**
Housing starts	0.112***	−0.857***	0.021***	0.028	0.049***	−0.175***	0.011*	−0.057	0.067***	−0.074**	0.017	0.095
Industrial production	0.120***	−1.146***	0.014**	0.031	0.032***	−0.096	0.004	−0.067*	0.022	−0.080**	−0.019	−0.212*
New home sales	0.078***	−0.534**	0.049***	−0.017	0.068***	−0.033	0.025***	−0.004	0.069***	−0.121***	−0.004	−0.158
Non-farm employment	0.302***	−1.113***	0.199***	−0.243***	0.348***	0.038	0.136***	−0.173***	0.174***	−0.053	0.057***	−0.324***
Pending home sales	0.124***	−0.827***	0.022***	−0.033	0.026**	0.025	0.028***	0.002	0.061***	−0.092***	0.010	−0.336***
Personal consumption	0.032	−0.201	0.066***	−0.142***	0.087***	−0.274***	0.008	−0.030	0.120***	−0.178***	0.063***	−0.192
Personal income	0.035	−0.188	0.066***	−0.115***	0.100***	−0.306***	0.016**	−0.058	0.103***	−0.133***	0.045***	−0.219*
Producer price index	0.123***	−0.729***	0.036***	0.044	0.089***	−0.130**	0.013**	−0.039	0.127***	−0.093**	0.009	0.024
Trade balance	0.110***	−0.467*	0.017**	−0.209***	0.037***	−0.315***	0.009	0.007	0.106***	−0.096***	0.007	0.037
Crude Oil Weekly inventory	0.053*	0.553**
Natural Gas Weekly inventory											0.598***	−0.380***
Observations	1,193,455		1,190,001		1,180,816		1,138,696		749,168		1,101,836
$R^{2}$	7.869		8.237		7.398		7.119		7.50		13.643

Note. This table presents estimates of equation (7) using the speculative trading intensity variable $NL S_{t, MM}$ , calculated with the money manager positions. The equation is $σ_{t}^{2} = α_{0} + α_{1} σ_{t - 1}^{2} + α_{2} ϵ_{t}^{2} + \sum_{m = 1}^{22} Φ_{m} D_{m, t} + β X_{i, t} + \sum_{k = 1}^{n} φ_{k} I_{kt} + \sum_{h = 1}^{23} ρ_{h} D_{h}$ where $I_{kt} = D_{m, t} \cdot X_{i, t}$ and $D_{m, t}$ is a macro announcement dummy variable for release $m$ . The period covered is from 2007-04-01 to 2024-02-11. The $Φ_{m}$ coefficients capture the instantaneous change in the conditional variance when an announcement has just occurred. The $φ_{m}$ coefficients capture the conditional variance when an announcement has just occurred in conjunction with the level of speculative trading intensity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels (two–tailed), respectively. Standard errors are Newey–West HAC.

Table 10(b).

Effects of Macro Announcements and Speculative Trading Intensity (Swap Dealers only) on Futures Conditional Variance.

Commodities	Crude oil		Gold		Copper		Silver		Palladium		Natural gas
Announcements	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$	$Φ_{m}$	$φ_{m}$
Macroeconomic news announcements
Initial jobless claims	0.058***	0.233***	0.038***	0.094***	0.083***	0.264***	0.016**	0.171***	0.078***	0.212***	−0.027**	0.148*
ADP Employment	0.010	0.128	0.035***	−0.008	0.045***	−0.116	−0.004	0.132**	−0.023**	−0.062	0.019	−0.085
CB Consumer	0.065***	0.242***	0.025***	0.065**	0.052***	0.094	0.060***	0.373***	0.025**	0.163***	0.003	−0.077
Advance retail sales	0.096***	0.545***	0.075***	−0.014	0.112***	−0.050	0.070***	0.413***	0.055***	0.281***	0.048**	−0.175
Building permit	0.049***	0.250***	0.034***	0.044	0.028***	0.236***	0.068***	0.320***	0.053***	0.135***	−0.020	0.375**
Construction spending	0.086***	0.289***	0.057***	0.008	0.069***	0.200**	0.077***	0.495***	0.051***	0.030	0.009	0.084
Consumer credit	0.024*	0.154*	0.010	0.017	0.016**	−0.066	0.010	−0.033	0.004	−0.084	−0.001	0.008
Consumer price index	0.039**	0.235**	0.081***	0.205***	0.166***	0.293***	0.054**	0.177**	0.088***	0.449***	−0.022	0.360*
Durable goods orders	0.064***	0.282***	0.024***	−0.027	0.048***	0.199**	0.034**	0.179***	0.054***	0.110**	−0.043**	0.430***
Existing home sales	0.032**	−0.057	0.031***	−0.010	0.035***	0.133	−0.027	0.212***	0.024**	0.168***	0.033	0.021
Factory orders	0.060***	0.192**	0.015**	0.192***	0.045***	0.233***	−0.026	0.217***	0.025**	0.088	−0.028	0.672***
Gross domestic product	0.056***	0.345***	0.022***	0.248***	0.090***	0.167*	−0.010	0.137*	0.067***	0.245***	−0.059**	0.362**
Housing starts	0.049***	0.289***	0.027***	0.003	0.028***	0.192**	0.065***	0.317***	0.043***	0.096*	−0.015	0.244
Industrial production	0.031**	0.319***	0.033***	0.077**	0.019***	0.025	−0.011	0.058	−0.004	0.133**	−0.027	0.141
New home sales	0.048***	0.279***	0.039***	−0.041	0.066***	0.186**	−0.021	0.193***	0.028***	0.207***	−0.025	0.231
Non-farm employment	0.268***	0.842***	0.242***	−0.058*	0.361***	0.481***	0.018	0.475***	0.155***	0.187***	0.062***	0.066
Pending home sales	0.060***	0.264***	0.015**	0.004	0.030***	0.036	0.037**	0.269***	0.029***	0.150***	0.014	0.076
Personal consumption	0.005	−0.044	0.031***	−0.019	0.051***	0.262***	0.015	−0.040	0.057***	0.316***	−0.045*	0.919***
Personal income	0.008	−0.053	0.062***	0.124***	0.062***	0.455***	0.018	−0.023	0.054***	0.233***	−0.065***	0.990***
Producer price index	0.062***	0.167*	0.038***	−0.046	0.071***	0.052	−0.029	0.169**	0.095***	0.187***	0.020	−0.108
Trade balance	0.075***	0.150*	0.035***	0.155***	0.067***	0.760***	0.014	−0.022	0.073***	0.123**	0.017	−0.104
Crude Oil Weekly inventory	0.116***	0.005
Natural Gas Weekly inventory											0.484***	1.025***
Observations	1,193,455		1,190,001		1,180,816		1,138,696		749,168		1,101,836
$R^{2} (%)$	7.01		7.716		8.023		7.975		7.732		13.66

Note. This table presents estimates of equation (7) using the speculative trading intensity variable $NL S_{t, SD}$ , calculated with the swap dealer positions. The equation is $σ_{t}^{2} = α_{0} + α_{1} σ_{t - 1}^{2} + α_{2} ϵ_{t}^{2} + \sum_{m = 1}^{22} Φ_{m} D_{m, t} + β X_{i, t} + \sum_{k = 1}^{n} φ_{k} I_{kt} + \sum_{h = 1}^{23} ρ_{h} D_{h}$ where $I_{kt} = D_{m, t} \cdot X_{i, t}$ and $D_{m, t}$ is a macro announcement dummy variable for release $m$ . The period covered is from 2007-04-01 to 2024-02-11. The $Φ_{m}$ coefficients capture the instantaneous change in the conditional variance when an announcement has just occurred. The $φ_{m}$ coefficients capture the conditional variance when an announcement has just occurred in conjunction with the level of speculative trading intensity. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels (two–tailed), respectively. Standard errors are Newey–West HAC.

Lastly, we examine whether the effects on bid-ask spreads differ between trader types. Table 11(a) presents the results for money managers. As the $θ_{m}$ coefficients tend to be negative, it seems that money managers enhance market efficiency by lowering bid-ask spreads. The results for swap dealers are shown in Table 11(b). Unlike for money managers, we find that $θ_{m}$ coefficients for swap dealers tend to be positive, indicating that greater swap dealer activity increases the bid-ask spread after a macroeconomic release. The results for swap dealers therefore suggest a decline in market efficiency due to their increased trading activities. These findings are in line with our main results and further support the claim that traders in energy and commodity markets do not all have the same effect on market efficiency. The results point to the importance of information acquisition and investor attention as an economic channel.

Table 11(a).

Effects of Macro Announcements and Speculative Trading Intensity (Money Managers only) on Futures Price Bid-Ask Spreads.

Commodities	Crude oil		Gold		Copper		Silver		Palladium		Natural gas
Announcements	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$
Macroeconomic news announcements
Initial jobless claims	−2.466**	9.751	0.222	−6.107	−0.414	−2.993	−1.520**	−5.809	−7.738*	10.173	−2.069**	2.892
ADP Employment	−1.105	4.874	−0.031	0.313	−0.446	−0.606	−0.464	0.041	11.325	−20.087	−1.567	9.513
CB Consumer	−0.883	−7.590	−1.073**	−21.883 ***	−0.609	−6.322	−2.270*	−5.006	−4.894	6.322	−6.110***	29.787
Advance retail sales	−1.753	6.539	−1.848	4.412	−1.507*	−4.140	−1.112	−5.342	−8.473	16.815	−2.521	−23.726
Building permit	0.048	−5.084	0.123	−2.625	−0.438	−3.660	−0.859	−0.392	−16.807 **	−9.470	−2.481	−3.851
Construction spending	0.715	−21.505	0.411	−19.636 **	−2.036**	−6.822	−0.782	1.915	−3.651	14.688	−6.182***	14.393
Consumer credit	−0.391	−2.145	−0.232	0.923	−0.634	−0.728	0.723	4.263	2.894	0.826	−2.085	−10.933
Consumer price index	−1.794	13.111	−0.328	−2.431	−0.594	−0.574	−1.526	−7.462	−28.010 **	33.089	−2.166	17.815
Durable goods orders	−2.201	5.989	0.237	−7.012	−1.029	−5.376	−2.466*	−19.430 **	−19.847 **	−52.267 ***	−3.505	22.773
Existing home sales	−0.494	−6.379	0.152	−2.614	−0.832	−5.410	−1.458	−2.821	−5.480	6.158	−5.716***	23.659
Factory orders	−2.690	15.967	−0.517	−6.286	−1.621*	−9.048	−1.962	−1.641	−0.552	28.548	−4.095**	17.201
Gross domestic product	−3.813	16.725	−1.274	−5.901	−1.184	−6.391	−1.558	−17.412 *	−10.428	13.464	−2.793	6.617
Housing starts	−0.268	−3.673	0.022	−2.479	−0.326	−3.621	−0.123	−1.595	−14.939 *	−11.913	−2.480	−3.106
Industrial production	0.999	−10.142	−0.040	8.877	−0.268	−0.374	0.521	0.478	7.052	−13.194	−3.891*	10.562
New home sales	0.517	−13.676	−0.320	−4.684	−0.542	−2.955	−0.874	−12.355	−8.912	6.444	−4.416**	11.799
Non-farm employment	−1.439	1.498	−0.839	−7.010	−3.093***	−24.194 ***	−0.262	−5.058	−19.800 **	22.461	−2.069	19.687
Pending home sales	−1.224	4.329	0.233	−3.304	−0.660	−2.140	−2.381*	−5.643	4.603	−14.701	−5.088***	10.999
Personal consumption	−1.580	8.274	−0.382	2.582	0.286	−1.307	−0.717	−10.634	−14.710 *	−0.853	−2.604	16.070
Personal income	−1.833	4.187	0.035	−0.933	−0.548	−3.162	−1.787	−3.128	−18.449 **	−7.560	−3.686	15.017
Producer price index	1.232	−25.459	0.173	−5.424	−1.585	−8.726	0.463	−4.845	−3.853	−6.534	−0.312	7.498
Trade balance	0.780	−13.301	−0.004	0.057	0.137	−10.608	−0.445	−5.855	−11.442	39.185	−0.224	5.237
Crude Oil Weekly inventory	0.494	−12.743
Natural Gas Weekly inventory											−7.596***	−19.173
Observations	1,041,497		1,022,592		1,023,897		1,022,006		683,875		995,919
$R^{2} (%)$	.0572		.115		.12		.118		.034		.0942

Note. This table presents estimates of the equation ${R_{SPREAD}}_{t}^{t + τ} = α + \sum_{m = 1}^{22} γ_{m} D_{m, t} + δ X_{t, i} + \sum_{m = 1}^{22} θ_{m} (D_{m, t} \cdot X_{t}) + β {R_{SPREAD}}_{t - τ}^{t} + ϵ_{t}$ , analyzing the effects of speculative trading intensity and macroeconomic announcements on the bid-ask spread using the speculative trading intensity variable $NL S_{t, MM}$ , calculated with the money manager positions. The period covered is from 2007-04-01 to 2024-02-11. The $γ_{m}$ coefficients capture the instantaneous change in the bid-ask spread when a macroeconomic announcement occurs. The δ coefficient represents the effect of the speculative trading intensity variable $NL S_{t, MM}$ . The $θ_{m}$ coefficients capture the interaction effect between macroeconomic announcements and speculative trading intensity. The β coefficient represents the effect of the lagged bid-ask spread. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels (two–tailed), respectively. Standard errors are Newey–West HAC.

Table 11(b).

Effects of Macro Announcements and Speculative Trading Intensity (Swap Dealers only) on Futures Price Bid-Ask Spreads.

Commodities	Crude oil		Gold		Copper		Silver		Palladium		Natural gas
Announcements	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$	$γ_{m}$	$θ_{m}$
Macroeconomic news announcements
Initial jobless claims	−2.762***	8.993*	−2.060	−5.311	−0.635	−2.291	1.030	−14.269	−4.552	−14.417	−0.236	−20.006
ADP Employment	−1.537	−6.199	1.566	4.657	−1.004	−6.696	−0.020	−3.164	4.428	8.987	1.484	−32.340
CB Consumer	−2.100	−2.325	−8.627**	−22.901	−1.035	−2.639	1.088	−19.132	−2.840	−14.640	1.027	76.983***
Advance retail sales	−0.398	4.053	−4.037	−14.260	−2.884**	16.595*	−1.587	4.119	−3.706	−15.433	−3.912	24.528
Building permit	−0.476	0.181	−1.729	−6.980	−0.989	−5.019	−2.651	12.393	−20.045 ***	17.715	−1.270	−10.402
Construction spending	−2.171	−3.441	−3.305	5.828	−3.515**	−15.862	1.246	−12.790	1.030	−16.524	−2.314	−39.772
Consumer credit	−1.209	−3.802	−4.210	−19.234	−1.537	−10.764	−2.643	18.441	3.189	5.418	−0.788	−12.389
Consumer price index	−0.732	−2.252	−3.398	−10.564	−1.302	−8.591	0.418	−9.966	−16.993 **	−50.191	1.324	−41.288
Durable goods orders	−1.774	−1.828	−2.583	−8.903	−1.864	−8.853	−1.158	−7.402	−3.721	56.423*	−1.139	−26.706
Existing home sales	−3.868**	17.196*	−1.306	−5.419	−1.448	−5.318	1.868	−18.968	−3.195	−16.482	−0.816	−51.321 *
Factory orders	−1.794	−5.866	−7.113*	−20.339	−3.079**	−13.686	6.848**	52.381***	9.273	−49.326	1.513	62.452**
Gross domestic product	−3.160*	−8.265	−2.815	−1.405	−2.089	−8.872	−1.765	−1.475	−5.932	−23.151	−0.138	−30.615
Housing starts	−0.585	0.775	−2.085	−8.115	−0.890	−4.663	−3.395	20.134	−18.992 ***	23.191	−0.842	−15.893
Industrial production	−0.294	−1.051	5.507	13.455	−0.043	1.716	−2.711	19.603	2.817	10.151	−0.861	−33.181
New home sales	−2.890	−11.629	−0.676	2.292	−1.332	−7.428	0.002	−5.272	−6.636	−19.194	1.597	66.714**
Non-farm employment	−2.461	−7.536	−3.234	−3.808	−5.006***	12.326	0.734	−7.126	−13.547 *	−21.500	2.513	55.393*
Pending home sales	−2.620	−11.868	1.005	1.475	−1.269	−4.640	0.428	−15.443	−0.192	5.139	−2.254	−28.309
Personal consumption	−1.677	−6.903	−9.795**	50.083**	0.158	0.206	0.923	−10.787	−14.996 *	0.800	−0.649	−19.878
Personal income	−4.570**	−20.246	−4.806	−25.185	−1.160	−5.971	1.284	−17.017	−21.247 ***	17.208	−0.657	−31.763
Producer price index	−1.370	1.601	−4.059	−13.597	−3.143**	−14.842	−2.954	17.631	−5.662	0.279	0.315	−8.871
Trade balance	−1.050	−2.682	0.702	3.237	−0.238	6.589	2.776	−18.993	−0.382	−34.845	−0.464	2.483
Crude Oil Weekly inventory	0.059	7.795
Natural Gas Weekly inventory											−1.312	67.433***
Observations	1,041,497		1,022,592		1,023,897		1,022,006		683,875		995,919
$R^{2} (%)$	.071		.063		.20		.29		.031		.48

Note. This table presents estimates of the equation $R_{SPREAD, t}^{t + τ} = α + \sum_{m = 1}^{22} γ_{m} D_{m, t} + δ X_{t, i} + \sum_{m = 1}^{22} θ_{m} (D_{m, t} \cdot X_{t}) + β R_{SPREAD, t - τ}^{t} + ϵ_{t}$ , analyzing the effects of financialization and macroeconomic announcements on the bid-ask spread using the speculative trading intensity variable $NL S_{t, SD}$ , calculated with the swap dealer positions. The period covered is from 2007-04-01 to 2024-02-11. The $γ_{m}$ coefficients capture the instantaneous change in the bid-ask spread when a macroeconomic announcement occurs. The δ coefficient represents the effect of the speculative trading intensity variable $NL S_{t, SD}$ . The $θ_{m}$ coefficients capture the interaction effect between macroeconomic announcements and speculative trading intensity. The β coefficient represents the effect of the lagged bid-ask spread. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels (two–tailed), respectively. Standard errors are Newey–West HAC.

6. Discussion and Implications

Our findings contribute new insights to an unsettled literature on the impact of different types of financial participants in energy and commodity markets (Ready and Ready 2022). In addition to being an important alternative asset class, energy commodities are central to economic activity, while energy futures trading is important for price stability. Being closely related to macroeconomic risk (Cheng et al. 2015), energy commodities therefore play a key role in macro-finance. By taking a novel angle of high-frequency market reactions to macroeconomic surprises, our results contribute a more nuanced picture of the impact of speculative trading and help to reconcile previous findings in the literature.

Goldstein and Yang (2022) develop a theoretical model to examine how financial traders improve information transmission between futures and spot markets. They show how market efficiency should benefit from the presence of informed financial traders, whose actions lead to increases in pricing transparency and to lower information asymmetry. Our empirical results provide support for this model. We show that increased speculative trading activity lowers bid-ask spreads after a macro news release, in addition to reducing the magnitude of price and volatility reactions to macro surprises. This improvement in informational efficiency is especially valuable in energy markets, where price discovery has direct implications for industrial users and consumers. We find that this effect is driven by money managers. In addition, our results point to smaller, negative effects linked to swap dealers. The difference between the two sets of disaggregated empirical results can be explained by the fact that they trade for different purposes. Money managers aim to make a profit based on information, while swap dealers are intermediaries who, while perhaps informed, primarily provide services to customers such as institutional investors, in addition to hedging their swap positions. In line with this interpretation, our results also build on Fishe and Smith (2012), who show that some financial traders (e.g., money managers) are better informed than others in commodity markets.

Our findings also extend and build on the results of an earlier empirical literature that uses daily-level data. This literature includes Brunetti et al. (2016) who find that speculative traders, especially money managers and hedge funds, are helpful to energy and commodity markets, as well as Büyükşahin and Harris (2011) and Alquist and Gervais (2013) who show that the futures positions of financial firms such as hedge funds do not predict next-day changes in crude oil prices. Moreover, our results relate to Cheng et al. (2015) who show that financial investors, being better informed about markets, contribute to price discovery and liquidity. The key message is therefore that speculative trading is helpful to markets by distributing and assimilating new information into prices. The findings shown in this paper have important implications for energy markets regulation and policy. First, they suggest that attempts to limit speculative trading in energy markets could, in fact, increase price volatility and reduce informational efficiency and price discovery. Second, they indicate that different types of financial participants have distinct effects on market quality, suggesting that regulatory frameworks should pay careful attention to market composition. Third, they highlight the importance of maintaining a robust price discovery mechanism in energy markets, given their crucial role in the economy.

7. Conclusion

This paper investigates the impact of speculative trading on the real economy and energy markets through a new angle, namely high-frequency surprises in macroeconomic announcement releases, which allows for better-identified effects. We empirically test whether increased speculative trading activity amplifies or dampens the impact of macro surprises on prices and volatility in commodity futures markets. Our results suggest that increased speculative trading activity has beneficial effects for energy and commodity markets. This is accomplished by reducing volatility and improving price discovery, as indeed price stability and efficient price discovery are crucial for market participants and the broader economy. We find that a greater intensity of speculative trading does not amplify the effects of macro announcement surprises on prices or volatility. On the contrary, an increase in speculative trading in a given commodity has a damping effect: prices and volatility react less to macro surprises when speculative trading is higher. This stabilizing effect of speculation is especially valuable to energy markets, where price volatility can have significant economic consequences.

What is more, our findings are consistent with information diffusion economic arguments. Our analysis of bid-ask spreads in futures contracts further confirms that speculative trading tends to improve market efficiency. Our results show that these effects are mostly linked to the trading activities of money managers rather than swap dealers. Thus, we contribute to a literature that emphasizes how non-commercial market participants such as money managers are beneficial to commodity markets by supplying liquidity, reducing volatility, and generally improving market efficiency. This finding is particularly relevant for energy markets, which have seen substantial increases in trading volume and complexity.

Our findings have important implications for energy market regulation and policy. The damping effect on volatility shocks documented in this paper implies that speculative traders contribute to market stability, which is essential for energy security and economic planning. This stability could also facilitate investment in energy infrastructure and support the ongoing energy transition. By lowering the magnitude of volatility shocks, and thus reducing the real option value of delaying investments, our findings suggest that a greater involvement by speculative traders may also help with sustainability efforts to finance a green energy transition, alongside other instruments such as green bonds and portfolio screens for sustainable investments. Looking forward, our results suggest several promising avenues for future research in energy markets. First, the role of financial investors (speculators as well as passive investors) in facilitating the energy transition warrants further investigation. Second, the connection between speculative trading and energy market regulation remains an important area for study, given that we find different impacts for money managers and swap dealers. Finally, the impact on energy price discovery of new trading technologies and market participants is an emerging research frontier. These questions are particularly relevant given the increasing importance of energy markets in addressing climate uncertainty and in ensuring economic stability.

Supplemental Material

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Supplemental Material

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Supplemental material, sj-pdf-3-enj-10.1177_01956574251369707 for Speculative Trading in Energy Markets: Evidence from Macroeconomic Surprises by Simon-Pierre Boucher, Marie-Hélène Gagnon and Gabriel J. Power in The Energy Journal

Footnotes

Acknowledgements

The authors thank participants at seminars at Humboldt University Berlin, South Dakota State University (Ness School), University of Illinois, Urbana-Champaign (ACE), and at meetings of the Commodity & Energy Markets Association (2021),World Finance & Banking Association (2021), Société canadienne de sciences économiques (2022), 4th Ethical Finance and Sustainability (EFS) conference (2022), and Multinational Finance Society (2022). The authors also thank the editor George Filis, two anonymous referees, as well as Jocelyn Grira, Joseph Marks, Alessandro Melone (discussants) and Scott Irwin, Michel Robe, and Zhiguang Wang.

ORCID iD

Gabriel J. Power

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: For financial support, the authors thank the Social Sciences and Humanities Research Council and the Chaire Industrielle-Alliance Groupe financier. Any remaining errors are ours alone.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

1

Sources: The Financial Times, Bloomberg, S&P Global and Euronews.

2

A high-profile example is Masters (2009), who testified before the U.S. Congress in 2008 and before the CFTC in 2009 about “Ending excessive speculation in commodity markets.” While his argument is not supported by empirical evidence, as shown by , the Masters hypothesis reflects beliefs held at the time by many market participants.

3

There are a few issues with the OPEC announcements: First, they are not released at a specific time. Second, it is impossible to know precisely when a given OPEC announcement was made available to investors. Third, OPEC’s influence has weakened since the 1980s.

4

The literature argues that measuring $σ_{k}$ in this way is reasonable because the standardized surprise is not used for forecasting purposes. Using raw surprises is not recommended due to scaling issues, nor is using analyst dispersion for $σ_{k}$ because announcement coverage sometimes involves only a few analysts. For robustness, we also estimate our models using surprises where $σ_{k}$ is computed using only past observations. The main findings are unchanged. In this case, we exclude the first $M$ observations (e.g., $M = 10$ ) to get a reasonable sample size for $σ_{k}$ .

5

See the website.

6

The main findings are robust to using different window lengths by estimating using thirty-minute returns.

7

The CFTC defines commercial traders as participants in commodity markets who primarily use futures contracts to hedge their business activities (e.g., buying or selling commodities). All traders who are not classified as Commercial are automatically classified as Non-Commercial traders. To obtain the number of long positions held by Non-Commercial traders, we subtract the total long Commercial positions from the total open interest. For the number of short positions held by Non-Commercial traders, we subtract the total short Commercial Positions from the total open interest.

8

In an earlier draft, we also reported results based on two alternative proxies as well as a proxy constructed using principal component analysis. The alternative proxies are Working’s $T$ (Working 1960) and the market share of non-commercial traders (). These results, which are available upon request, are consistent with our main findings and do not change the paper’s implications.

9

See the website.

10

This category is also called “Managed money.” The CFTC writes that they are “registered commodity trading advisor (CTA); a registered commodity pool operator (CPO); or an unregistered fund identified by CFTC.” There is some overlap between Money managers and hedge funds, but they are distinct.

11

In unreported results, we run the regressions using the approach shown in . The results are similar.

12

In an earlier draft, we also reported results for different sub-periods, such as the Zero Lower Bound period, the 2008 to 2010 financial crisis and Great Recession, and the COVID-19 period. These results do not materially affect our findings or conclusions, and they are available upon request.

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