EXHIBIT 1

Redacted

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 1 of 197 PageID #:13971
UNITED STATES DISTRICT COURT 

NORTHERN DISTRICT OF ILLINOIS

MISH INTERNATIONAL 

MONETARY INC., on behalf of itself 

and all others similarly situated, 

 Plaintiff, 

 v. 

VEGA CAPITAL LONDON, LTD., 

Adrian Spires, Paul Commins, George 

Commins, Christopher Roase, Elliott 

Pickering, Aristos Demetriou, Connor 

Younger, James 

Biagioni, Henry Lunn, 

Paul Sutton, Matthew Rhys Thompson, 

and JOHN DOES 1-100, 

 Defendants.

Case No. 1:20-cv-04577 

Honorable Manish S. Shah 

EXPERT REPORT OF PROFESSOR TERRENCE HENDERSHOTT 

June 20, 2024 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 2 of 197 PageID #:13972
 Highly Confidential Page i 

Table of Contents 

I. Qualifications .......................................................................................................................1

II. Assignment ..........................................................................................................................2

III. Summary of Opinions ..........................................................................................................3

IV. Summary of Allegations and Dr. Pirrong’s Analysis ........................................................13

A. Plaintiff’s Allegations and Class Definition ......................................................... 13

B. Summary of Dr. Pirrong’s Analyses and Opinions .............................................. 14

V. Background ........................................................................................................................18

A. WTI Futures Contracts .......................................................................................... 18

1. Trading of WTI Futures Contracts .............................................................19

2. Trading at Settlement (TAS) Order Type ..................................................20

3. Delivery of Physical Crude via WTI Futures Contracts ............................22

B. Dramatic Changes in Supply and Demand Resulted in a Critical Shortage of 

Storage Space in April 2020 and Exerted Downward Pressure on WTI Prices ... 23

1. The COVID-19 Pandemic and Geopolitical Tensions Created a Large 

Imbalance between Crude Oil Supply and Demand ..................................24

2. The Resulting Price Structure Encouraged Storage over Selling, Resulting 

in Storage Space Shortages ........................................................................25

3. Storage Capacity Concerns and Declining Prices Prompted Warnings of 

Potential Negative Prices ...........................................................................30

C. WTI Futures Contract Market Dynamics on April 20, 2020, Diverged from 

Historical Penultimate Days ................................................................................. 31

1. A Larger Number of Expiring Futures Contracts Needed to Be Sold 

Compared to Past Penultimate Trading Days ............................................33

2. There Were Few Buyers Providing Liquidity in the Market and Liquidity 

Was Quickly Exhausted .............................................................................36

3. Other Oil Futures Contracts That Were Not Impacted by the Storage 

Constraints in Cushing Did Not Go Negative ............................................43

D. Negative Commodity Prices Have Occurred Due to Fundamental Factors, Such as 

Oversupply Coupled with Logistical Constraints ................................................. 44

1. Past Instances of Negative Prices Also Exhibited a Supply Glut Coupled 

with Logistical Constraints ........................................................................45

2. Contemporaneous Negative and Near-Zero Prices Appeared in Crude Oil 

Physical Markets in April 2020, Indicating Fundamental Factors Were at 

Play ............................................................................................................46

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 3 of 197 PageID #:13973
 Highly Confidential Page ii 

VI. Dr. Pirrong’s Claim That Storage Constraints Cannot Explain Negative Prices on April 

20, 2020, Is Flawed and At Odds with the Predominant Third-Party Views and Research

............................................................................................................................................47

VII. Dr. Pirrong’s TVP-VAR Model Is Conceptually Flawed, Methodologically Unsound, and 

Incapable of Establishing That Trader Defendants’ Trading on April 20, 2020, Caused 

Artificial Prices ..................................................................................................................53

A. Dr. Pirrong’s Estimation of Price Impact Is Insufficient to Establish Price 

Artificiality ............................................................................................................ 55

B. Dr. Pirrong’s Use of a TVP-VAR Model to Estimate Price Artificiality Is 

Inconsistent with and Differs from the Literature on Which He Relies ............... 63

1. Description of Dr. Pirrong’s TVP-VAR Model .........................................63

2. Dr. Pirrong’s Claim That His TVP-VAR Model Can Be Used to 

Determine the Amount of Artificiality Caused by Trader Defendants’ 

Trades Is Unsupported by the Literature on Which He Relies ..................66

C. Dr. Pirrong’s Price Impact Model Does Not Control for Confounding Factors ... 68

D. Dr. Pirrong’s TVP-VAR Model Is Highly Sensitive to Small Changes, and 

Artificiality Estimates Can Flip from Depression to Inflation .............................. 70

1. Changing the Seed of the Random Number Generator Changes Dr. 

Pirrong’s Conclusions ................................................................................71

2. Changing the Ordering of the Variables in the Regression Changes Dr. 

Pirrong’s Results, and He Provides No Basis for His Ordering ................74

E. Applying Hasbrouck’s VAR Methodology Produces Statistically Insignificant 

Price Impact Results ............................................................................................. 79

VIII. Dr. Pirrong Offers No Methodology to Establish Causation between the Trader 

Defendants’ Trades and the Purported Yet He Attributes 

 ...........................................................82

A. After , Dr. Pirrong’s TVP-VAR Model Cannot Reasonably Estimate 

Price Impact, and Dr. Pirrong Abandons the Model ............................................. 83

1. Order Flow Materially Stops Predicting Price Changes in Dr. Pirrong’s 

TVP-VAR Model after and Therefore Other Unexplained 

Factors Explain Price Changes ..................................................................84

2. Dr. Pirrong Abandons His TVP-VAR Price Artificiality Model after 

 ..............................................................................................................87

B. Dr. Pirrong’s VPIN Metric Is Not a Price Artificiality Model, Nor Does Dr. 

Pirrong Claim It Is a Price Artificiality Model ..................................................... 88

1. Description of Dr. Pirrong’s VPIN Metric Analysis .................................89

2. Dr. Pirrong’s Conclusion That Contributors to His VPIN Caused the 

Purported Is Not Supported by Academic Literature or a 

Scientific Method .......................................................................................94

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 4 of 197 PageID #:13974
 Highly Confidential Page iii 

3. Dr. Pirrong’s VPIN Metric Fails to Find That Trader Defendants’ 

Allegedly Manipulative Conduct Caused 

 .............................................98

IX. Many of the Diverse Putative Class Members Could Not Have Been Harmed, and Dr. 

Pirrong’s Methodology Is Incapable of Identifying the Number of Putative Class 

Members ..........................................................................................................................101

A. Dr. Pirrong’s Methodology Does Not, and In Fact Cannot, Assess the Number of 

Persons or Entities That Were Harmed and Belong to the Putative Class without 

Individualized Inquiry ......................................................................................... 104

1. Dr. Pirrong’s Methodology Cannot Address Multiple Accounts Owned by 

the Same Entity without Individualized and Fact-Intensive Inquiry .......105

2. Dr. Pirrong’s Methodology Ignores CME WTI Transactions That Are 

Impacted by His Alleged Price Artificiality Estimates ............................108

3. Dr. Pirrong’s Methodology Ignores ICE WTI Contract Transactions That 

Would Be Impacted by the Alleged Price Artificiality In the CME WTI 

Contract ....................................................................................................111

4. Individualized Inquiry Is Required to Assess Over-the-Counter Trading 

Positions, Including Physical Contracts ...................................................112

B. Many Putative Class Members Could Not Have Been Harmed ......................... 115

1. Putative Class Members Trading Similarly to Trader Defendants Could 

Not Have Been Harmed ...........................................................................116

2. Putative Class Members Buying and Selling the Same Amount of TAS 

Contracts Could Not Have Been Harmed ................................................117

3. Putative Class Members Trading Futures to Hedge Could Not Have Been 

Harmed .....................................................................................................117

C. Mish Is Not Representative of Putative Class Members .................................... 121

X. Each Trader Defendant’s Trading Pattern is Consistent with Speculating and Providing 

Liquidity, Not Manipulation ............................................................................................128

A. Trader Defendants’ Buying TAS and Selling Non-TAS Contracts during the 

Proposed Class Period Is Consistent with Speculating on a Price Decrease ...... 129

B. Each Trader Defendant’s Speculative Pattern Is Consistent with the Large TAS 

Order Book Imbalances ...................................................................................... 132

C. Contrary to Dr. Pirrong’s Assertions of Economically Irrational Trading, Trader 

Defendants’ Trading Patterns Were Profitable Absent the Alleged Manipulation

............................................................................................................................. 134

D. The Timing of Trader Defendants’ Sales Is Inconsistent with Settlement Price 

Manipulation ....................................................................................................... 137

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 5 of 197 PageID #:13975
 Highly Confidential Page iv 

E. Aggressive Limit Orders Are Not Themselves a Sign of Manipulation, but Rather 

Are Consistent with Trader Defendants’ Difficulty Executing in an Illiquid and 

Fast-Moving Market ........................................................................................... 139

XI. Dr. Pirrong Fails to Offer Analysis Demonstrating Plaintiff’s Claims Regarding a Trader 

Defendant–Coordinated Conspiracy to Manipulate and Depress the Prices of NYMEX 

Crude Oil Futures Contracts ............................................................................................142

A. Contrary to Dr. Pirrong’s Assertions, Trader Defendants’ Trading Patterns Varied 

Substantially ........................................................................................................ 144

B. Contrary to Dr. Pirrong’s Assertion, Trader Defendants’ Trading Behavior Was 

Not Unique .......................................................................................................... 152

C. The Correlation Analysis Presented in the Second Amended Complaint Is 

Incomplete and Methodologically Flawed and Does Not Support a Coordinated 

Conspiracy .......................................................................................................... 156

1. The Correlation Analysis Only Includes a Subset of Traders ..................157

2. The Correlation Analysis of Trader Defendants’ Position Levels Is 

Methodologically Flawed and Uninformative as to Any Purported Trading 

Conspiracy ...............................................................................................158

3. Correlations between Trader Defendants’ and Non-Defendant Traders’ 

Trading Are Not Substantially Different than Between Trader Defendants

..................................................................................................................161

D. No Analysis Is Presented by Dr. Pirrong That Supports a Coordinated Conspiracy 

by Trader Defendants .......................................................................................... 164

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 6 of 197 PageID #:13976
 Highly Confidential Page 1

I. Qualifications 

1. I am a Professor and the Willis H. Booth Chair in Banking and Finance at the Haas 

School of Business at the University of California, Berkeley. Appendix A contains my CV, a 

list of all my publications over the last ten years, and a list of my deposition and trial testimony 

over the last four years. 

2. My expertise and research focus on market microstructure, which includes how 

information is incorporated into financial asset prices and the interaction between trading, market 

structure, and prices of various financial instruments including futures contracts. Specifically, I 

have conducted research regarding how the prices of futures contracts and other derivatives, 

stocks, corporate bonds, and other over-the-counter (“OTC”) and exchange-traded products 

incorporate information. In addition, I have studied high-frequency trading, electronic 

communications networks and exchange design, regulation of financial markets, and the role of 

information technology in financial markets. My research has also addressed market efficiency 

and related topics, including market liquidity and how market prices discover and incorporate 

new information, and how trading affects prices. 

3. I have published numerous articles on the structure, design, and regulation of financial 

markets and how market participants—such as market makers, high-frequency traders, and 

institutional investors—affect price discovery and the liquidity of different financial markets in 

leading economics and finance journals, including the Journal of Finance, Journal of Financial 

Economics, Review of Financial Studies, and Review of Economic Studies. 

4. My 2005 paper, “Island Goes Dark: Transparency, Fragmentation, and Regulation,” 

published in the Review of Financial Studies, studies price discovery in the futures markets.1 

More recently, my paper “Asset Pricing: A Tale of Night and Day,” which studies returns in the 

U.S. Treasury futures market among other items, has been published in the Journal of Financial 

Economics.2 

1 Terrence Hendershott and Charles Jones, “Island Goes Dark: Transparency, Fragmentation, and Regulation,” Review of 

Financial Studies 18, no. 3 (2005): 743–793. 2 Terrence Hendershott, Dmitry Livdan, and Dominik Rosch, “Asset Pricing: A Tale of Night and Day,” Journal of Financial 

Economics 138, no. 3 (2020): 635–662. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 7 of 197 PageID #:13977
 Highly Confidential Page 2

5. I teach undergraduate- and graduate-level courses on business analytics and highfrequency finance at the Haas School of Business. I am the faculty director of the Berkeley 

Master of Financial Engineering program, which involves coursework in futures markets and 

includes examples of alleged market manipulation (among other topics). I have served on the 

editorial boards of leading finance journals, including the Journal of Finance, Journal of 

Financial Economics, and Journal of Financial Markets. In my role as editor, I have reviewed 

multiple papers concerning futures and commodity markets to assess their merits for publication. 

6. In addition to my academic work, I have served as a consultant in the office of the chief 

economist at the U.S. Commodity Futures Trading Commission (“CFTC”). I have also served as 

a member of the CFTC’s subcommittee of the Technology Advisory Committee (“TAC”). 

Furthermore, I have served as a visiting economist at the New York Stock Exchange from 2005 

to 2006, as a member of the Nasdaq Economic Advisory Board from 2004 to 2007, as chair of 

the Nasdaq Economic Advisory Board in 2007, and as a member of the Financial Industry 

Regulatory Authority (“FINRA”)’s market surveillance advisory group. 

7. Finally, I have served as an expert for the CFTC, the U.S. Securities and Exchange 

Commission (“SEC”), and the U.S. Department of Justice (“DOJ”) on multiple matters involving 

alleged market manipulation of futures contracts and other financial instruments. I have also 

consulted as an expert in commodities, FX, equities, fixed income, futures and options, and a 

range of exchange-traded and OTC instrument markets on issues related to market efficiency and 

price impact, assessing a wide variety of alleged manipulative conduct, such as trading-based 

price manipulation, benchmark manipulation, short squeeze, momentum ignition, spoofing, and 

commodities manipulation involving physical delivery. 

II. Assignment 

8. I have been asked by Dechert LLP, counsel for James Biagioni, George Commins, Paul 

Commins, Aristos Demetriou, Henry Lunn, Elliott Pickering, Christopher Roase, Paul Sutton, 

Matthew Thompson, and Connor Younger (collectively, “Trader Defendants”) to review and 

assess the opinions and analyses offered in the Second Amended Complaint and the expert report 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 8 of 197 PageID #:13978
 Highly Confidential Page 3

of Dr. Craig Pirrong (“Pirrong Report”), submitted on behalf of Plaintiff in support of class 

certification.3 

9. In undertaking this assignment, I have considered documents and data related to the 

issues in this matter. In Appendix B, I have listed the documents and data I relied on for this 

report. My opinions are also based upon my own educational and professional experience, 

including those listed under my qualifications and in my curriculum vitae. My work on this 

matter is ongoing and I reserve the right to supplement my opinions in the event that additional 

materials, including academic literature, data, documents, and depositions or other testimony that 

may subsequently come to light, are provided to me or submitted in connection with this matter. 

I am not addressing all of the merits-related points that Dr. Pirrong raises, but I reserve the right 

to do so if this matter proceeds past the class certification stage. Additionally, I reserve the right 

to comment on any opinions offered by Plaintiff’s experts at later times. 

10. The analyses and opinions expressed in this report are my own. I am being compensated 

at my standard billing rate of $1,525 per hour. I have been assisted in this matter by staff of 

Cornerstone Research, who worked under my direction. I receive compensation from 

Cornerstone Research based on its collected staff billings for its support of me in this matter. 

Neither my compensation in this matter nor my compensation from Cornerstone Research is in 

any way contingent or based on the content of my opinion or the outcome of this or any other 

matter. 

III. Summary of Opinions 

11. Based on my expertise and experience, my review of data and documents produced in 

this matter, and my assessment of the relevant literature, I find that Dr. Pirrong’s opinions suffer 

from conceptual and methodological flaws, which render his assessment and conclusions 

inappropriate and unreliable.4 

12. As a threshold matter, Dr. Pirrong’s claim that market conditions could not explain the 

price decline observed on April 20, 2020, in the May 2020 Chicago Mercantile Exchange 

3 Mish International Monetary Inc. on behalf of itself and all others similarly situated v. Vega Capital London, Ltd., et al., Second 

Amended Complaint, Corrected Copy, February 3, 2023 (“Second Amended Complaint”); Amended Class Certification Expert 

Report of Professor Craig Pirrong, May 9, 2024 (“Pirrong Report”). 

4 The bases for this conclusion are outlined in greater detail in the balance of this report. This section summarizes those bases but 

is not intended to be exhaustive. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 9 of 197 PageID #:13979
 Highly Confidential Page 4

Group’s (“CME”) West Texas Intermediate (“WTI”) Light Sweet Crude Oil futures contract 

(the “May Contract”) is flawed and contradicted by data and the predominant view.5 The 

predominant view of market participants, regulators, academics, and other informed observers 

points to the distinct market conditions on April 20, 2020, as the driving factor for the price 

decline, not the alleged manipulation by futures traders such as Trader Defendants. Distinct 

market conditions included the following (see Section V): 

a. April 20, 2020, was the penultimate day of trading in the May Contract, which 

means that any contracts remaining open had to be liquidated by the following 

day or else be closed by making or taking physical delivery of crude in Cushing, 

Oklahoma (“Cushing”). Since physical delivery was required, the conditions at 

Cushing—the contract-specified delivery location—were of critical importance. 

b. Storage at Cushing was quickly filling and was predicted to exceed full capacity, 

causing nontrivial constraints on physical delivery via futures contracts. The lack 

of available storage is consistent with long futures contract holders, who would be 

required to take physical delivery, selling futures contracts prior to expiry. 

c. “Open interest” on April 20, 2020 was much higher than on prior penultimate 

days and had an unusual composition. According to the CFTC, long position 

holders consisted of an unusually low amount of commercial trader open interest 

and an unusually high amount of financial trader open interest. Since financial 

traders do not typically handle physical crude, this implies there was an unusually 

high number of long positions that needed to sell the May Contract to exit their 

position before contract expiry. The combination of a high open interest and large 

share of long open interest held by non-commercial traders means there was an 

additional 61,049 contracts that had to be liquidated before expiry compared to 

the previous 12 penultimate days, representing a 179% increase over the average. 

13. Despite these market conditions, Dr. Pirrong’s report narrowly focuses on purported 

market manipulation as the explanation for price declines on this day while failing to consider or 

even cite the research expressing the predominant view, including a report from the CFTC 

5 The CME Group owns the New York Mercantile Exchange (NYMEX). See “NYMEX,” CME Group, 

https://www.cmegroup.com/company/nymex.html. I refer to CME and NYMEX interchangeably in this report. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 10 of 197 PageID #:13980
 Highly Confidential Page 5

supporting this view.6 Dr. Pirrong advances what he describes as “several logical problems” 

with the predominant view that price declines were caused by “fully utilized” storage and 

therefore entities that “receive delivery of oil” via futures contracts would have “no place to store 

it, thus they were willing to sell their positions at any price.”7 Yet none of Dr. Pirrong’s stated 

“logical problems” with the predominant view withstand scrutiny (see Section VI): 

a. First, Dr. Pirrong incorrectly asserts that short futures position holders could 

easily transfer ownership to long position holders in storage without moving the 

crude oil, and therefore fears of storage constraints are unfounded. Dr. Pirrong 

provides no evidence that this happened, and his logic is contradicted by the 

NYMEX delivery rules that require short position holders to agree to in-storage 

deliveries. The storage space was valuable as evidenced by oil prices in the future 

being substantially higher than current prices, meaning that short position holders 

were unlikely to give their valuable storage space away. 

b. Second, Dr. Pirrong incorrectly claims there was ample storage at Cushing, citing 

68% capacity usage, and therefore fears of storage constraints are unfounded. His 

68% capacity figure is incorrect because it refers to total storage capacity rather 

than working storage capacity, which was 84%, or “effectively full.” Dr. 

Pirrong’s capacity analysis also fails to account for the continued buildup in 

storage utilization and the storage space that was not yet full but already 

committed (and therefore unavailable for deliveries). With these factors, storage 

at Cushing was anticipated, as of April 20, 2020, to exceed capacity before the 

time delivery on the May Contract was required (i.e., by May 31, 2020). In prior 

remarks, Dr. Pirrong had stated that low 80% working storage capacity was 

“effectively full.” 

6 “Interim Staff Report: Trading in NYMEX WTI Crude Oil Futures Contract Leading Up To, On, and around April 20, 2020,” 

CFTC, November 23, 2020, https://www.cftc.gov/node/235761, p.1. The CFTC included a footnote starting that “[t]his Report 

does not analyze the propriety of trading by any particular trader or group of traders. Additionally, to the extent any trading

activity may have been abusive, manipulative, disruptive, or otherwise unlawful, an evaluation of that activity is beyond the 

scope of this Report.” However, the report “sets forth data on the geopolitical and fundamental economic drivers, as well as 

certain technical factors, preceding and coinciding with the negative settlement price of the WTI Contract on April 20.” These

analyses support the predominant view that the price declines were caused by “near capacity” storage and long position holders 

needing to exit their position prior to expiry, as they could not take delivery. I also note that the CFTC produced no subsequent 

public report on this matter. 

7 Pirrong Report, ¶¶ 338–339. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 11 of 197 PageID #:13981
 Highly Confidential Page 6

c. Third, Dr. Pirrong incorrectly claims that the price decrease prior to and rebound 

after the 1:30 PM CT settlement is proof that the negative prices were caused by 

manipulation, and therefore storage constraints were not the cause of the price 

decline.8 But Dr. Pirrong overlooks price changes that contradict his logic, 

specifically a post-settlement price drop of similar magnitude between and 

 which could not have been caused by the Trader Defendants’ alleged 

manipulative conduct. 

d. Lastly, Dr. Pirrong incorrectly claims the narrowing of calendar spreads after 

April 20, 2020, indicates alleviated storage constraint concerns. The spreads Dr. 

Pirrong’s chose to compare for this claim are misleading. He strangely compares 

different spreads (May-June and June-July) across different dates. A consistent 

comparison shows the spread widened (not narrowed) immediately after April 20, 

2020, suggesting tightening storage constraints. 

14. Based on his assumption that all Trader Defendants’ trading was manipulative, Dr. 

Pirrong proposes two distinct approaches to estimate price artificiality during the proposed class 

period. Prior to he employs a Time Varying Parameter Vector Autoregression (“TVPVAR”) model. After he abandons the TVP-VAR, and assumes that 

 Both approaches are 

flawed. 

15. There are multiple flaws with Dr. Pirrong’s TVP-VAR model that render its results 

unreliable, and the model is incapable of serving as a basis to establish class-wide harm 

attributable to the alleged conduct (see Section VII). Here, I offer an overview of why: 

a. First, Dr. Pirrong’s TVP-VAR model calculates price impact, but this is 

insufficient to estimate price artificiality, which economists define as the 

deviation between actual and hypothetical “but-for” prices absent the alleged 

manipulation. To determine a but-for price, assumptions about the but-for world 

are necessary, which Dr. Pirrong fails to provide or support. His methodology 

unreasonably implicitly assumes that sellers of Trading at Settlement (“TAS”) 

contracts that were reliant on Trader Defendants, would have had no price impact 

8 All times are in listed in central time (“CT”) unless otherwise noted. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 12 of 197 PageID #:13982
 Highly Confidential Page 7

in the but-for world without the alleged manipulative trades. This assumption is 

flawed, especially given the lack of TAS buyers on April 20, 2020. Trader 

Defendants’ TAS purchases 

 Further, Dr. Pirrong overlooks evidence that 

 Without Trader Defendants’ buying from TAS sellers in the TAS order 

books, more TAS sellers would likely have switched to the non-TAS order books 

and sold aggressively there, impacting prices in the but-for world. To the extent 

that these unsuccessful TAS sellers waited until later in the afternoon to sell in the 

non-TAS order book, the price impact may have been larger as selling more 

gradually over time tends to have a smaller price impact.9 Trader Defendants’ 

buying from TAS sellers in the TAS order books may have therefore contributed 

to a smoother and more orderly liquidation for some long position holders. Dr. 

Pirrong’s methodology fails to consider the price impact of the altered trading 

patterns of non-Defendant traders in his but-for world, undermining his assertions 

about the but-for price of the May Contract and any purported price distortion 

caused by Trader Defendants on April 20, 2020. 

b. Second, Dr. Pirrong’s use of the TVP-VAR model is inconsistent with its 

application in the academic literature. While Dr. Pirrong’s analyses are loosely 

based on a class of vector autoregression (“VAR”) models first developed in the 

market microstructure literature in the early 1990s, Dr. Pirrong eschews these 

VAR models and instead adapts a TVP-VAR model from a macroeconomic 

monetary policy article. A TVP-VAR model has never—to my knowledge—been 

9 Terrence Hendershott and Ryan Riordan, “Algorithmic trading and the market for liquidity.,” Journal of Financial and 

Quantitative Analysis 48, no. 4 (2013): 1001-1024, p. 1002 (“Institutional investors utilize ATs to trade large quantities gradually 

over time, thereby minimizing market impact and trading costs.”). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 13 of 197 PageID #:13983
 Highly Confidential Page 8

peer reviewed to study price impact in a market microstructure setting, nor is it 

generally accepted in the market microstructure literature. 

c. Third, Dr. Pirrong’s TVP-VAR model does not control for confounding factors or 

alternative explanations for the May Contract price movement because Dr. 

Pirrong’s TVP-VAR model attributes price changes to aggressive trading, 

overlooking the underlying factors that drive trading behavior. This includes 

fundamental factors, such as storage constraints, and TAS sellers that are not 

included in Dr. Pirrong’s model despite being the driving factor for the price 

decline on April 20, 2020 according to the predominant view of informed 

observers. Instead, Dr. Pirrong dismisses alternative explanations for the price 

movements, including information about storage utilization and observations 

about short commercial traders not liquidating futures positions. 

d. Fourth, the results from Dr. Pirrong’s TVP-VAR model are not robust, changing 

substantially with minor adjustments, which can even reverse the direction of the 

alleged price impact, rendering the model unreliable. 

e. Lastly, using a more standard VAR model, aligned with the market microstructure 

literature, yields 

 This finding that the price impact 

 further calls into question the reliability of Dr. Pirrong’s conclusions 

regarding Trader Defendants’ alleged manipulative impact. 

16. After Dr. Pirrong abandons the TVP-VAR model, and does not provide a 

model to establish that Trader Defendants impacted prices during the last of the 

proposed class period from During this 

 period, he simply attributes Thus, Dr. 

Pirrong has no price impact measure and no price artificiality model for the last of 

the proposed class period, during which approximately of his claimed damages occur. See 

Section VIII. 

17. Instead of using a price impact model during the last of the proposed class 

period, Dr. Pirrong uses a “diagnostic” metric he refers to as a volume-synchronized probability 

of informed trading (“VPIN”), which does not measure price impact or price artificiality. Price 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 14 of 197 PageID #:13984
 Highly Confidential Page 9

is not an input or an output to Dr. Pirrong’s VPIN metric, which is “based on the ratio of the 

absolute value of order flow imbalance to trading volume.” He claims that Trader Defendants 

 As a threshold matter, 

Dr. Pirrong has not established any scientific basis for claiming a occurred, and Dr. 

Pirrong does not cite to any third-party studies about this day that refer to the price movement as 

a I am also unaware of any regulatory reports calling this price drop a 

and there is evidence inconsistent with this period being a Setting this issue aside, 

stating that the VPIN increased sharply without providing a comparison analysis to a nonmanipulated benchmark period lacks a reliable scientific basis. Further, Dr. Pirrong’s 

 is not supportable, especially when on average 

 Lastly, similar to the pre- analysis, even if Dr. Pirrong 

had attempted to estimate the price impact of Trader Defendants during this period, 

this would be insufficient to estimate artificiality without accounting for the impact of Trader 

Defendant trading on non-Defendant patterns. In a but-for world where Trader Defendants 

 Dr. Pirrong’s methodology for the last of the proposed class 

period does not and cannot account for changes in non-Defendant trading or other confounding 

factors. See Section VIII. 

18. Setting aside the unreliable nature of Dr. Pirrong’s multiple approaches toward estimating 

price artificiality, his report also ignores that a substantial portion of the putative class either (i) 

would require individual fact-intensive inquiry to determine the existence (and extent of) any 

harm, or (ii) could not have suffered any harm as a result of his alleged price inflation. See 

Section IX. 

a. When assessing the number of purported class members, Dr. Pirrong counts 

affected accounts in the CME Audit Trail data, leading to potential inaccuracies 

when multiple accounts belong to a single entity. Analyzing May Contract 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 15 of 197 PageID #:13985
 Highly Confidential Page 10

transactions in the CME Audit Trail data by account results in a flawed 

determination of class membership and injury, with some entities potentially 

benefiting overall despite individual account losses. This issue cannot be 

addressed without individualized evidence about each putative class member that 

is not necessarily held by the CME. 

b. Dr. Pirrong’s class member identification methodology ignores CME WTI 

contract transactions in the May Contract outside CME’s electronic platform and 

therefore not captured in the Audit Trail data, such as physical deliveries, block 

trades, exchange-for-physical trades, and options exercise, which results in a 

flawed determination of class membership and harm assessment. These 

transactions that are part of the proposed class, but not part of the CME Audit 

Trail data, would involve detailed investigations into each trader to determine 

whether they are part of the putative class and whether they were harmed. 

c. Dr. Pirrong’s methodology ignores WTI futures contracts traded on the 

Intercontinental Exchange (“ICE”) that would be impacted by an alleged price 

artificiality in the CME WTI contract. The absence of account (and ownership) 

mapping between CME and ICE account numbers means that Dr. Pirrong cannot 

assess if an individual is harmed without a fact-intensive individualized inquiry. 

Since the ICE WTI contract terms specifically reference the CME WTI prices for 

cash-settlement, the necessity to assess harm in both contracts is important. Even 

if accounts could be mapped between ICE and CME, Dr. Pirrong’s artificiality for 

the CME WTI cannot be applied to the ICE WTI contract. 

d. While Dr. Pirrong admits that OTC transactions, including physical transactions 

and commodity swaps, can be impacted by the alleged manipulation, there is no 

centralized database for OTC transactions linked to CME WTI futures prices. 

Therefore, individualized inquiry would be needed to assess harm. 

e. Even when applying Dr. Pirrong’s flawed approach to counting class members, 

almost of the purported accounts meeting the class definition are 

unharmed. Furthermore, three categories of putative class members inherently 

could not be harmed: (i) those who traded similarly to the Trader Defendants, (ii) 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 16 of 197 PageID #:13986
 Highly Confidential Page 11

those who traded May Contracts intraday in the TAS order books only, and (iii) 

those who sold the May Contract to hedge OTC transactions with offsetting 

benefits. 

19. Moreover, the named plaintiff, Mish International Monetary Inc. (“Mish” or “Plaintiff”), 

is not representative of most putative class members. Mish only traded non-TAS May Contracts, 

while TAS May Contract sales account for of the alleged aggregate damages under Dr. 

Pirrong’s methodology. Only of the putative class members (or approximately 

exhibited trading activity that was analogous to Mish’s, in that they only traded non-TAS 

contracts intraday, only buying at positive prices and selling the same quantity after prices turned 

negative. Importantly, evidence suggests that Mish’s losses were due to 

 rather than the alleged manipulative conduct, indicating that Mish would 

have incurred losses regardless of the allegedly manipulative conduct. Any claim that Mish 

would not have lost as much money in the but-for world is speculative. See Section IX.

20. Dr. Pirrong’s assessment of Trader Defendants’ activity starts from the flawed premise 

that the activity was uneconomic but for the alleged manipulation and no plausible nonmanipulative rationale exists. Contrary to Dr. Pirrong’s assertion, each of the Trader 

Defendants’ trading pattern is consistent with speculative trading that profits from a decrease in 

prices on April 20, 2020. This type of trading pattern can be profitable without the need for 

manipulation. In fact, each Trader Defendant’s trading pattern would have been profitable even 

without considering the price impact that Dr. Pirrong attributes to their alleged manipulation, 

which directly contradicts Dr. Pirrong’s argument that Trader Defendants’ trading was not 

economically reasonable without the alleged manipulation. See Section X.

21. Lastly, Dr. Pirrong fails to offer reliable analysis that supports Plaintiff’s claim of a 

coordinated conspiracy by Trader Defendants to manipulate May Contract prices. See Section 

XI.

a. Plaintiff claims that the Trader Defendants traded “in unison.” Dr. Pirrong does 

not offer analysis to support this claim and a comparative analysis of each of the 

Trader Defendants’ trading reveals that 

 This lack of uniformity does not 

support Plaintiff’s argument of a coordinated conspiracy. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 17 of 197 PageID #:13987
 Highly Confidential Page 12

b. Dr. Pirrong incorrectly claims that in a trading pattern 

similar to the Trader Defendants involving 

 However, this trading pattern 

 The fact that nonDefendant traders, who are not accused of manipulation (to my knowledge), 

 undermines allegations of a 

coordinated conspiracy. 

c. Dr. Pirrong claims that 

 and that a larger share of 

aggressive trades made it more likely that they were engaged in manipulation. 

Dr. Pirrong does not provide a threshold for how large a share of aggressive 

trading a market participant must have to be considered manipulative. His 

opinion regarding Trader Defendants’ percentage of aggressive trades therefore 

lacks any basis. 

d. Plaintiff misleadingly uses a “correlation” analysis to support the alleged 

coordinated conspiracy between Trader Defendants. The correlation analysis—

which was conducted by Dr. Pirrong—is incomplete and methodologically 

flawed. It only includes a subset of Trader Defendants, which skews the results to 

find a misleadingly higher correlation than if he included all Trader Defendants. 

Further, the analysis does not actually measure the correlation of Trader 

Defendants’ trading behavior. Instead, it analyzes the correlation of their position 

levels. When all Trader Defendants are included and the correlation analysis is 

corrected to assess trading activity rather than position levels, the average 

correlation Dr. Pirrong’s correlation analysis is further 

invalidated by the fact that the correlation levels between numerous nonDefendant traders are rendering Plaintiff’s reliance on the 

correlation measure to infer a conspiracy unreliable. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 18 of 197 PageID #:13988
 Highly Confidential Page 13

e. Lastly, Dr. Pirrong falsely claims that his 

 

22. Each of these flaws, as well as those described below, provide an independent reason 

why Dr. Pirrong’s approach and methodology cannot provide a reliable basis to infer impact or 

injury, or reliably estimate damages on an aggregate or individual basis. 

IV. Summary of Allegations and Dr. Pirrong’s Analysis 

A. Plaintiff’s Allegations and Class Definition 

23. Mish, the single named plaintiff in this matter, sold May Contracts on the afternoon 

of April 20, 2020.10 Mish claims that it incurred losses when selling at an artificially low price as 

a result of “Defendants’ manipulative impact on the trading executed and the order book 

maintained for May Contracts in this District.”11

24. Mish claims that all Trader Defendants “engaged in the exact same manipulative strategy: 

purchasing May Contracts via TAS and selling May Contracts for the purpose of depressing its 

price.”12 In a TAS contract, the purchaser buys contracts at a price to be determined during the 

end-of-day settlement period, which ran from 1:28 to 1:30 PM on April 20, 2020 (the “settlement 

period”).13 Mish alleges that “in order to depress the price of the May 2020 NYMEX WTI crude 

10 Second Amended Complaint, ¶ 29. 

11 Second Amended Complaint, ¶ 29. 

12 Second Amended Complaint, ¶ 137. 

13 See “NYMEX Crude Oil,” CME Group, 

https://cmegroupclientsite.atlassian.net/wiki/spaces/EPICSANDBOX/pages/46499076/NYMEX+Crude+Oil. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 19 of 197 PageID #:13989
 Highly Confidential Page 14

oil futures contract (and thereby reap large profits by purchasing via TAS May Contracts at an 

extremely low price), the Vega Trading Defendants combined and conspired to act in unison and 

sell large volumes of May Contracts in the final hours before the settlement price of the May 

Contract.”14 Mish claims that as a result, “Plaintiff and Class members sold at prices that were 

not what they would have paid in the absence” of manipulation and were therefore harmed.15

25. The putative class is defined as: 

All persons and entities that sold a May 2020 light sweet crude oil (WTI) futures 

contract (“May contract”) traded on the New York Mercantile Exchange between 

9:00 a.m. CST and 1:30 p.m. CST (inclusive) on April 20, 2020 (including by 

trade at settlement (“TAS”)), to liquidate a long position in the May contract. 

Excluded from the Class are Defendants, their officers, directors, management, 

employees, subsidiaries, or affiliates and federal governmental entities.16

B. Summary of Dr. Pirrong’s Analyses and Opinions 

26. In this section, I briefly summarize Dr. Pirrong’s analysis and opinions. Dr. Pirrong 

claims that Trader Defendants’ trading involving buying TAS and selling outright futures 

“inherently has manipulative potential” and was manipulative.17 Dr. Pirrong claims that TAS 

purchases only have a “small upward impact on price,” and “[t]he downward price impact of the 

sales in the underlying futures contract is larger than the upward price impact of an equivalent 

volume of TAS purchases.”18 Dr. Pirrong claims that Trader Defendants profited from buying in 

the TAS order book and selling in the non-TAS order book, purportedly manipulating down the 

price and profiting due to the artificially depressed TAS settlement price.19 According to Dr. 

Pirrong “Trading Defendants traded precisely how a theoretical manipulator would trade,”20 and 

he concludes that “the absence of a non-manipulative rationale for their trading combined with 

14 Second Amended Complaint, ¶¶ 143, 254. 15 Second Amended Complaint, ¶ 343. 

16 Mish International Monetary Inc. on behalf of itself and all others similarly situated v. Vega Capital London, Ltd., et al., 

Plaintiff’s Motion for Class Certification, February 19, 2024 (“Plaintiff’s Motion for Class Certification”), ¶ 1. 17 See Deposition of Craig Pirrong, May 14, 2024 (“Pirrong Deposition”), 179:15–21 

18 Pirrong Report, ¶ 106. 

19 Pirrong Report, ¶ 106. 

20 Pirrong Report, ¶ 296. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 20 of 197 PageID #:13990
 Highly Confidential Page 15

the close correspondence between their conduct and that of a trade-based manipulator 

demonstrates that their conduct was uneconomic and manipulative.”21 

27. Dr. Pirrong claims that “Defendants’ conduct was uneconomic,” meaning that their 

strategy was inconsistent with “economically beneficial speculative, hedging, or market making 

behavior.”22 In his analysis concluding that Trader Defendants’ behavior was inconsistent with 

speculation, Dr. Pirrong makes two claims. First, he claims that 

24 I address and rebut 

this claim in Section XI. Second, Dr. Pirrong claims that Trader Defendants did not take 

advantage of an arbitrage opportunity on the ICE, which he argues is inconsistent with 

speculation.25 I address these claims in Section X. 

28. To support his claims that Trader Defendants caused the price of May Contracts to be 

artificially low, Dr. Pirrong states that price declines were not caused by fundamental factors, 

including constraints on oil storage in Cushing. I address each of these claims in Section VI. Dr. 

Pirrong makes the following arguments: 

a. He proposes that sellers “could deliver oil in store” via futures contracts and 

therefore more storage was not needed for long futures position holders taking 

delivery.26 

b. He claims that “Cushing stocks were well below storage capacity at Cushing” 

using April 24, 2020, data.27 

c. He also claims that the price of oil futures indicated that storage of crude oil was 

not constrained by pointing to a rebound in prices, suggesting that “no 

‘corrective’ information arrived during the period in which prices rose.”28 

d. He purports to perform a comparison between the price of May-June spreads on 

April 17, 2020, to the price of June-July spreads on April 24, 2020, to claim that 

21 Pirrong Report, ¶ 322. 

22 Pirrong Report, ¶ 284. 

23 See, e.g., Pirrong Report, ¶ 287 24 Pirrong Report, ¶¶ 254–255. 

25 Pirrong Report, ¶ 319 (“Another uneconomic aspect of the Trading Defendants’ trading behavior that has direct implications 

for is their failure to trade the ICE 

NYMEX lookalike contract when it was more favorably priced than the May 2020 CME Crude Oil Futures contract.”). 26 Pirrong Report, ¶ 341. 

27 Pirrong Report, ¶ 356, Appendix E at 356. 

28 Pirrong Report, ¶ 382. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 21 of 197 PageID #:13991
 Highly Confidential Page 16

the spread narrowed during the week of April 20, 2020.29 He claims this 

narrowing of spreads supports the “market assessment of the severity of any 

putative shortage of storage capacity at Cushing became less dire, not more.”30 

29. Dr. Pirrong proposes two distinct approaches to estimate price artificiality during the 

proposed class period, one approach from 9:00 AM to and a second approach from 

 to 1:30 PM. 

30. His first approach from 9:00 AM to uses the TVP-VAR model, which he 

employs to estimate the impact of Trader Defendants’ trades on prices.31 He assumes the price 

impact of Trader Defendants’ trades is manipulative, and he equates the estimated quantity of the 

Trader Defendants’ trade impact to the alleged artificiality amount. During this period, the full 

price decline is not attributed to Trader Defendants’ trading. I address this approach in Section 

VII. 

31. His second approach from to 1:30 PM assumes that 

 He claims 

32 Dr. Pirrong claims his VPIN diagnostic measures 

“order flow toxicity,” which has been shown to predict historical flash crashes.33 I discuss this 

approach in Section VIII. 

32. Figure 1 below shows Dr. Pirrong’s hypothetical “but-for” price—what the price of the 

May Contract would have been absent Trader Defendants’ purported manipulative actions—

using the cumulative price artificiality estimates from Dr. Pirrong’s two approaches. 

29 Pirrong Report, ¶¶ 353–354 (“On Friday April 17, 2020, one business day before Monday April 20, 2020, the front monthsecond month spread was ... On Friday April 24, 2020—four days after the day at issue here—the spread was 

substantially narrower: Thus, during the week-long period including April 20, 2020 the front month-second month spread 

narrowed.”). The final trading day of the May 2020 contract was April 21, 2020. On April 24, 2020, the front month was June 

and the second month was July. See “Crude Oil Futures – Contract Specs,” CME Group, 

https://www.cmegroup.com/markets/energy/crude-oil/light-sweet-crude.contractSpecs.html. 30 Pirrong Report, ¶ 354. 

31 Pirrong Report, ¶ 334 (“The TVP-VAR model and the CME audit trail data that identifies Trading Defendants’ transactions 

allows me to isolate the price impact of their uneconomic, manipulative aggressive sales.”). 

32 Pirrong Report, ¶ 257. 

33 Pirrong Report, ¶¶ 236, 239. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 22 of 197 PageID #:13992
 Highly Confidential Page 17

Figure 1

Source: Pirrong Report and Backup Materials 

Note: Midpoint refers to the midpoint of the highest bid and lowest offer prices. The but-for price for the “TVPVAR Model Through ” series is based on the Pirrong Report’s Figure 14 through . The butfor price for the series between and 1:30 PM 

is equal to the Pirrong Report’s Figure 14 artificiality at plus the cumulative change in the May Contract 

midpoint since plus the midpoint. When the midpoint is unavailable, which can happen for example 

during a market halt or a lack of buy side or sell side liquidity, the last available midpoint is used. The midpoint is 

based on Dr. Pirrong’s backup file VegaBBOCLKAll.xlsx.

33. Dr. Pirrong claims that “damages incurred by each class member because of the Trading 

Defendants’ conduct” can be calculated “in a formulaic way” and proposes an “overall 

methodology” that he claims is “quite straightforward.”34 According to Dr. Pirrong, a “class 

member suffered harm if s/he sold at a price that was artificially low due to the Trading 

Defendants’ uneconomic aggressive sales.”35 He proposes that the “damage suffered by a Class 

member on a particular sale is determined by multiplying the quantity of May Contracts by the 

34 Pirrong Report, ¶¶ 389–390. 

35 Pirrong Report, ¶ 390. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 23 of 197 PageID #:13993
 Highly Confidential Page 18

artificiality at the time of said sale.”36 He proposes to determine artificiality for the period from 

9:00 AM to using the “TVP-VAR-derived artificiality estimates,” which according to 

him, can be applied to any given transaction in this time period.37 He then proposes to calculate 

damages “attributable to the by “taking the difference between 

prices at and the price at the time of the transaction 

38 I address Dr. Pirrong’s methodology to calculate 

damages in Sections VII, VIII, and IX. 

34. Finally, Dr. Pirrong uses the CME Audit Trail data to identify members of the proposed 

class, concluding that “ accounts meet the Class Definition.”39 I address Dr. Pirrong’s 

methodology to identify putative class members in Section IX. 

V. Background 

35. In this section, I provide factual background on the CME Group’s WTI Light Sweet 

Crude Oil futures contract (“CME WTI futures”) design and specifically the functionality of the 

TAS order type. In addition, I describe the fundamentals impacting the U.S. crude oil markets in 

the weeks leading up to April 20, 2020. This factual background provides important context for 

assessing the allegations and market dynamics in this case. 

36. Dr. Pirrong contends that the futures contract design and fundamental factors outlined in 

this section, including storage constraints, could not explain the negative prices on April 20, 

2020.40 I discuss and rebut Dr. Pirrong’s arguments in Section VI. 

A. WTI Futures Contracts 

37. The allegations in this case concern trading in the CME WTI futures contract, including 

trading using the TAS order type. These core items are described in detail below. 

36 Pirrong Report, ¶ 390. 

37 Pirrong Report, ¶ 391. 

38 Pirrong Report, ¶ 394. 

39 Pirrong Report, ¶ 396. 

40 Pirrong Report, ¶¶ 338–340. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 24 of 197 PageID #:13994
 Highly Confidential Page 19

1. Trading of WTI Futures Contracts 

38. Physical crude oil is bought and sold in what is colloquially referred to as the “cash” 

market for oil. The price of crude oil is influenced by many factors. These include global 

consumption and production levels of oil, freight and logistics costs, storage costs, and the cost 

and pricing of other energy products. 

39. The CME WTI futures contract, traded on the NYMEX, is a standardized agreement to 

buy or sell 1,000 barrels of light sweet crude oil at a future date.41 In contrast to financial 

contracts executed through bilateral, OTC transactions, the price and quantity of futures contracts 

transacted are the only variables determined by the buyer and seller through the futures 

exchange’s open, anonymous, and competitive trading system. The agreement is fulfilled after 

the contract’s expiration (or “maturity”) by the delivery of physical crude oil satisfying specified 

quality and delivery criteria during the contract month. The delivery process, which I discuss in 

detail below, ensures convergence between the price of the CME WTI futures contract and the 

underlying physical crude oil. 

40. In futures trading, the buyer on the delivery date holds a “long position” while the seller 

holds a “short position.” Each contract constitutes one long (buyer) and one short (seller) 

position, and the total quantity of contracts existing at one time is referred to as the “open 

interest” in that contract. Futures trades made on the NYMEX are executed through brokers who 

accept orders on behalf of customers. These brokers are called Futures Commission Merchants 

(“FCMs”). CME clearing members provide access to the exchange’s “clearinghouses”—CME 

Clearing—and must be registered as FCMs. CME Clearing acts as the buyer to all sellers and 

the seller to all buyers.42 If the same futures contract is bought and subsequently sold by the 

same party, the position is said to be “flat,” that is, it has been offset and the obligation is 

closed.43 Futures contracts can therefore be liquidated by either entering into offsetting 

transactions prior to the contract expiration or by participating in the delivery process after 

expiration, a process I discuss in detail below. 

41 “Crude Oil Futures – Contract Specs,” CME Group, https://www.cmegroup.com/markets/energy/crude-oil/light-sweetcrude.contractSpecs.html. 

42 “Clearing Market Structure,” CME Group, https://www.cmegroup.com/education/courses/clearing/clearing-marketstructure.html. 

43 “Economic Purpose of Futures Markets and How They Work,” CFTC, 

https://www.cftc.gov/LearnAndProtect/AdvisoriesAndArticles/economicpurpose.html. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 25 of 197 PageID #:13995
 Highly Confidential Page 20

41. CME WTI futures contracts expire (or mature) on a monthly basis. The CME lists 12 

monthly contracts “for the current year and the next 10 calendar years and 2 additional contract 

months.”44 Traders pay close attention to the price relationship between different contract 

maturity months. The contract that is set to expire first is referred to as the front-month (or 

nearby) contract while other contract maturities are said to be deferred. Traders can place 

“outright” orders on a specific futures contract maturity, or calendar “spread” orders whereby 

they purchase a futures contract maturity against the sale of another. The term “spread” is also 

used to refer to the difference between the prices of each “leg” of the spread. A spread trader 

earns a profit or incurs a loss depending on the variation in the spread over time. 

42. A CME WTI futures contract’s trading terminates on the third business day before the 

25th of the month prior to the contract month. If the 25th of the month is a non-business day, 

trading terminates on the third business day before the last business day preceding the 25th of the 

month. Thus, the May Contract’s last trading day was April 21, 2020.45 

2. Trading at Settlement (TAS) Order Type 

43. In addition to placing outright or spread orders, traders have the option to place TAS 

orders. TAS orders allow traders to execute trades at the yet-to-be determined daily settlement 

price, or at a premium or discount to the settlement price. The settlement price for the CME 

WTI futures contract is determined by CME staff based on trading activity during the “settlement 

window” between 1:28 PM and 1:30 PM. If at least one non-TAS trade for a contract month 

occurs on CME’s GLOBEX electronic platform between 1:28 PM and 1:30 PM, that contract 

month settles at the volume-weighted average price (“VWAP”), rounded to the nearest tradable 

price.46 There are 21 possible prices at which TAS outright orders can trade, specifically up to 10 

44 “Crude Oil Futures – Contract Specs,” CME Group, https://www.cmegroup.com/markets/energy/crude-oil/light-sweetcrude.contractSpecs.html. 

45 “Crude Oil Futures – Contract Specs,” CME Group, https://www.cmegroup.com/markets/energy/crude-oil/light-sweetcrude.contractSpecs.html. 

46 CME GLOBEX is an electronic trading platform developed and operated by the CME for the global trading of futures and 

options. It supports the execution of a wide range of asset classes, including commodities, currencies, and financial instruments, 

facilitating nearly 24-hour trading and global access. Additionally, trades on the CME can also take place outside the GLOBEX 

platform, such as through privately negotiated transactions. See “CME Globex,” CME Group, 

https://www.cmegroup.com/globex.html. See also, “NYMEX Crude Oil,” CME Group, 

https://www.cmegroup.com/confluence/display/EPICSANDBOX/NYMEX+Crude+Oil. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 26 of 197 PageID #:13996
 Highly Confidential Page 21

cents above, 10 cents below, or at the referenced settlement price.47 The exact price differential 

(or premium/discount) to the settlement price is specified when the order is placed. 

44. TAS orders are a standard trading mechanism available across dozens of CME futures 

contracts, and are similar to various forms of reference price trading found in financial markets.48 

Some types of “Market on close” orders in equity markets and fixes in the foreign exchange 

market are additional examples where trading is based on predetermined reference points. These 

mechanisms, like TAS orders, are designed to facilitate trading at known, transparent reference 

prices. 

45. There are situations in which traders will want to lock in a price at or close to the 

settlement price, without incurring the execution risk associated with trying to trade in the 

settlement period. For example, buyers and sellers of physical oil may have referenced the WTI 

settlement price in their physical oil contracts. Traders may therefore want to hedge this physical 

price risk by executing a futures trade at or close to the settlement prices. Other traders may be 

managing an investment fund with a benchmark (against which the fund performance is 

measured) that tracks the WTI futures settlement price. 

46. Traders who have economic motivations to trade at the settlement price are incentivized 

to use TAS orders versus simply trading outright contracts close to the settlement time. Entering 

a TAS order ensures that the trade will be completed at the settlement price. These traders may 

also be willing to pay a “risk premium” by executing at a premium or discount that is a slightly 

worse price than the settlement price. This risk premium incentivizes and compensates liquidity 

47 “Crude Oil Futures – Contract Specs,” CME Group, https://www.cmegroup.com/markets/energy/crude-oil/light-sweetcrude.contractSpecs.html. In 2020, each leg of a TAS spread order could be traded at up to 10 cents above or below the 

referenced settlement price, meaning that TAS spread transactions could have a net price up to 20 cents above or below the 

referenced price. See “Market Regulation Advisory Notice, Rule 524: TAS, TAM, BTIC, and TACO Transactions,” CME 

Group, August 13, 2018, https://web.archive.org/web/20210318063322/https://www.cmegroup.com/rulebook/files/cme-groupRule-524.pdf. 

48 There are over 30 CME futures contracts with TAS orders available, across agricultural, energy, metals, and treasury markets. 

See “Trading at Settlement (TAS) Outright and Calendar Spread Availability,” CME Group, 

https://www.cmegroup.com/trading/files/tas-tam-eligibility.xlsx; “Trading at Settlement (TAS),” CME Group, 

https://www.cmegroup.com/trading/trading-at-settlement.html. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 27 of 197 PageID #:13997
 Highly Confidential Page 22

providers taking on the opposite side of the trade. The concept of a risk premium is well 

documented in the academic and market literature.49 

47. TAS orders contribute to smoothing out trading over the course of the day by allowing 

for the natural pairing of buy and sell interest at the futures settlement price throughout the day 

and thus avoiding volatile trading in the settlement window. 

3. Delivery of Physical Crude via WTI Futures Contracts 

48. The CME WTI futures contract is fulfilled at the contract maturity through delivery of 

physical crude oil satisfying specified quality and delivery criteria. This ensures that the prices 

of the nearby futures remain in line or “converge” with the price in the underlying physical 

commodity. A trader with a short position when the contract expires is obligated to deliver 1,000 

barrels of WTI light sweet crude oil to the buyer, who is obligated to take possession of them. 

Those barrels of oil must be delivered at Cushing, which is a central hub of pipelines and oil 

tanks.50 Failure to make or take delivery of the oil when obligated to do so results in hefty 

penalties and fines.51

49. Prior to expiration of the CME WTI futures contract, a trader must have secured either 

applicable pipeline capacity and/or storage to make or take delivery. The obligation to make or 

take delivery is not transferable.52 The delivery must be made between the first and last calendar 

day of the delivery month. The May Contract’s first and last delivery days were therefore May 

1, 2020, and May 31, 2020.53 

49 Jakša Cvitanić and Fernando Zapatero, Introduction to the Economics and Mathematics of Financial Markets, 2004 (“Cvitanić

and Zapatero (2004)”), p. 48 (“One way to avoid this difficulty with the theory is to assume that the investors add a risk premium 

to longer maturity rates, that is, to postulate that forward rates represent expected future spot rates plus a risk premium amount. 

The risk premium is added to compensate for the additional risk to which the long-term investor is exposed, for example, the 

inflation risk.”), p. 125 (“This quantity is called market price of risk, risk premium, or relative risk. It compares the return rates of 

the risky asset and the risk-free asset, normalized by the volatility of the risky asset.”). See also Jeffrey F. Jaffe, Randolph W. 

Westerfield, and Stephen Ross, Corporate Finance, 6th ed. (2003), p. 232; Richard A. Brealey, Stewart C. Myers, and Franklin 

Allen, Principles of Corporate Finance, 10th ed. (2011), p. 192. 50 NYMEX Rulebook, Chapter 200, Light Sweet Crude Oil Futures, 200104, as of April 28, 2020, 

https://web.archive.org/web/20200428053717/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/2/200.pdf. 51 NYMEX Rulebook, Chapter 7: Delivery Facilities and Procedures, CME Group, as of April 16, 2020, 

https://web.archive.org/web/20200416161650/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/1/7.pdf, §§ 

714 and 715. 

52 NYMEX Rulebook, Chapter 200, Light Sweet Crude Oil Futures, 200105.F, as of April 28, 2020, 

https://web.archive.org/web/20200428053717/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/2/200.pdf. 53 NYMEX Rulebook, Chapter 200, Light Sweet Crude Oil Futures, 200106, as of April 28, 2020, 

https://web.archive.org/web/20200428053717/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/2/200.pdf. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 28 of 197 PageID #:13998
 Highly Confidential Page 23

50. Prior to the last day of trading, a trader’s FCM (which, as stated above, is a broker that 

accepts orders on behalf of customers to trade and clear futures on exchanges such as NYMEX), 

is “responsible for assessing the account owner’s ability to make or take delivery for each 

account on its books with open positions in the expiring contract. Absent satisfactory 

information from the account owner, the clearing member is responsible for ensuring that the 

open positions are liquidated in an orderly manner prior to the expiration of trading.”54 Because 

many traders do not have the logistical capabilities to make or take delivery of crude oil, most 

positions must be closed prior to the expiration of trading (i.e., on or prior to April 21, 2020, in 

the case of the May Contract). Many traders opt to “roll” their position by exiting their position 

in the expiring contract and simultaneously opening a trade in the deferred contract, thereby 

maintaining a similar position in the deferred contract.55

51. In April 2020, delivery and transfer of ownership needed to take place at any pipeline or 

storage facility in Cushing with pipeline access to storage tanks owned by Enterprise or 

Enbridge.56 On the first business day after the final day of trading—April 22, 2020, in the case of 

the May Contract—the seller designates a qualified pipeline or storage facility where it will 

deliver the physical oil.57 The following day, the “Notice Day,” the buyer and seller are matched 

and the buyer instructs the seller on its preferred method of delivery.58 

B. Dramatic Changes in Supply and Demand Resulted in a Critical Shortage of 

Storage Space in April 2020 and Exerted Downward Pressure on WTI Prices 

52. In this section, I provide factual background on the U.S. crude oil markets in the weeks 

leading up to April 20, 2020, when May Contract prices went negative. This factual background 

provides important context for assessing the allegations and market dynamics in this case. 

54 FCMs are often registered clearing members with the exchange (“clearing FCMs”). Non-clearing FCMs must have their 

customers’ trades cleared by a clearing FCM. See NYMEX Rulebook, Chapter 7: Delivery Facilities and Procedures, CME 

Group, as of April 16, 2020, 

https://web.archive.org/web/20200416161650/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/1/7.pdf, § 

716. 

55 “Understanding Futures Expiration & Contract Roll,” CME Group, 

https://www.cmegroup.com/education/courses/introduction-to-futures/understanding-futures-expiration-contract-roll.html. 56 See NYMEX Rulebook, Chapter 200, Light Sweet Crude Oil Futures, 200104, as of April 28, 2020, 

https://web.archive.org/web/20200428053717/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/2/200.pdf. 57 NYMEX Rulebook, Chapter 200, Light Sweet Crude Oil Futures, 200105.B, as of April 28, 2020, 

https://web.archive.org/web/20200428053717/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/2/200.pdf. 58 NYMEX Rulebook, Chapter 200, Light Sweet Crude Oil Futures, 200105.A, 100105.E, as of April 28, 2020, 

https://web.archive.org/web/20200428053717/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/2/200.pdf. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 29 of 197 PageID #:13999
 Highly Confidential Page 24

53. In the weeks leading up to April 20, 2020, the unprecedented COVID-19 pandemic, 

among other factors, triggered a historic collapse in crude oil consumption that was not balanced 

out by a commensurate production decrease. This imbalance created a “super contango” or 

“wide carry” price structure in crude oil futures and physical markets that incentivized market 

participants to store crude oil in the U.S., and particularly in Cushing, the sole delivery location 

for CME WTI futures contracts. Consequently, storage space in Cushing became constructively 

full. 

1. The COVID-19 Pandemic and Geopolitical Tensions Created a Large 

Imbalance between Crude Oil Supply and Demand 

54. In the month preceding April 20, 2020, COVID-19 lockdowns in the U.S. caused a 

historic collapse of demand for crude oil amid a major shutdown of economic activity in large 

parts of the economy. With airplanes grounded, fewer people commuting, and businesses 

shuttered, the demand for jet fuel, gasoline, and other petroleum products plummeted. 

55. This abrupt drop in demand, coupled with uncertainty about the pandemic’s duration, 

resulted in a more rapid reduction in demand than supply.59 Producers were reluctant to bear the 

costs and risks of shutting down and restarting wells, anticipating that the decline in demand and 

prices could be temporary.60 Even when willing to cut production, producers struggled to adjust 

quickly to the sudden changes in consumption levels.61 

59 There was significant uncertainty regarding the pandemic’s duration during this period. For example, a New York Times 

Article on April 8, 2020, stated, “Without a vaccine, any progress is fragile, temporary. The social distancing measures cannot 

continue forever. And if they are relaxed without meticulous testing and the isolation of new patients, researchers say, the 

numbers of infections and fatalities are likely to soar again. Dr. Lewnard and Dr. Jha said months could pass before Americans

would be able to return to their pre-pandemic lifestyles of unrestricted movement. Even then, they said, such a return would best 

take place in stages, with the government remaining vigilant for signs of outbreak.” See “Optimism Is Less Distant as Global 

Coronavirus Battle Rages On,” The New York Times, April 8, 2020, https://www.nytimes.com/2020/04/08/us/coronavirusglobal-progress.html. 

60 “North America’s Oil Industry Is Shutting Off the Spigot,” The Wall Street Journal, April 13, 2020, 

https://www.wsj.com/articles/north-americas-oil-industry-is-shutting-off-the-spigot-11586770200?mod=article_inline; Everett 

Wheeler, Allison Good, and Gorey Paul, “No Place to Go: Oil Storage Filling up Amid Collapsing Demand, Excess Production,” 

S&P Global Market Intelligence, April 6, 2020, https://www.spglobal.com/marketintelligence/en/news-insights/latest-newsheadlines/no-place-to-go-oil-storage-filling-up-amid-collapsing-demand-excess-production-57865154. 61 It can take months for the flow from wells to taper off. See “North America’s Oil Industry Is Shutting Off the Spigot,” The 

Wall Street Journal, April 13, 2020, https://www.wsj.com/articles/north-americas-oil-industry-is-shutting-off-the-spigot11586770200?mod=article_inline. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 30 of 197 PageID #:14000
 Highly Confidential Page 25

56. From March 13, 2020, when President Trump declared a national emergency, to April 17, 

2020, consumption fell by approximately 34%, while U.S. production of crude oil—previously at 

record levels—fell by only 7% and net imports remained largely unchanged.62 

57. This oversupply imbalance was exacerbated by geopolitical tensions between major oil 

producers. On March 6, 2020, Russia rejected a call from the Organization of the Petroleum 

Exporting Countries (OPEC) members to cut production. This contributed to a 25% decrease in 

crude oil prices the following trading day.63 In retaliation, Saudi Arabia announced an increase 

in its production and Russia followed suit, which only served to further increase global oil supply 

and worsen the price fall.64 OPEC members eventually agreed on April 12, 2020, to production 

cuts, but only starting May 1, 2020.65 

2. The Resulting Price Structure Encouraged Storage over Selling, 

Resulting in Storage Space Shortages 

58. Consistent with basic economic principles, the crude oil oversupply led to a substantial 

drop in prices, including CME WTI futures. The May Contract price decreased by 70% between 

January 2, 2020, and April 17, 2020, the last closing price before April 20, 2020.66 Given the 

uncertainty around the pandemic’s duration and when demand would pick up, the decline was 

steeper for contracts closer to delivery compared to more deferred contracts, resulting in a large 

gap between the current and future prices of oil—a condition known as “super contango.”67 

Figure 2 below shows that the gap between the May 2020 and June 2020 expiring futures price 

increased precipitously after March 16, 2020. The May-June calendar spread widened from -

62 Crude oil consumption reduced from 21.456 to 14.103 million barrels per day (34%) between March 13 and April 17, 2020. 

U.S. Production of crude oil decreased from 13.1 to 12.2 million barrels per day (7)% during the same time. See “U.S. Weekly 

Product Supplied,” U.S. Energy Information Administration, https://www.eia.gov/dnav/pet/pet_cons_wpsup_k_w.htm; “Weekly 

U.S. Field Production of Crude Oil,” U.S. Energy Information Administration, 

https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=WCRFPUS2&f=W. EIA weekly data report that both U.S. 

imports and exports fell over the same period such that net imports remained flat overall. U.S. exports decreased by roughly 1.5 

million barrels a day (34%), while imports decreased by roughly 1.6 million barrels a day (24%). See “Weekly U.S. Exports of 

Crude Oil,” U.S. Energy Information Administration, 

https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=WCREXUS2&f=W; “Weekly U.S. Imports of Crude Oil,” 

Energy Information Administration, https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=WCRIMUS2&f=W. 63 See Workpaper 1. 64 “Oil Prices Dive as Saudi Arabia Takes Aim at Russian Production,” The New York Times, March 8, 2020, 

https://www.nytimes.com/2020/03/08/business/saudi-arabia-oil-prices.html. 65 Organization of the Petroleum Exporting Countries Press Release, “The 10th (Extraordinary) OPEC and non-OPEC Ministerial 

Meeting concludes,” April 12, 2020, https://www.opec.org/opec_web/en/press_room/5891.htm. 66 See Workpaper 2. 67 For example, the August 2020 WTI futures price only decreased by 47% over the same period. This resulted in the price of the 

August 2020 contract to be $12.93 above the price of the May 2020 contract. See Workpaper 2. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 31 of 197 PageID #:14001
 Highly Confidential Page 26

$0.55 to -$6.76 between March 16, 2020, and April 17, 2020.68 This was the largest gap between 

the closest expiring futures price and the futures contract expiring a month later in over ten 

years.69 This super contango condition encouraged market participants to store rather than sell 

oil, aiming to profit from the price difference. 

Figure 2

A “Super Contango” Materialized in Late March 2020 in NYMEX WTI Crude Oil 

Futures 

Source: Refinitiv

Note: A positive value indicates that the market is not in contango and a negative value indicates that the market is 

in contango. 

59. Traders with access to storage could engage in a “cash-and-carry” arbitrage, meaning 

buying and holding physical crude oil in storage while having locked in the deferred price for 

sale via a futures contract.70 Functionally, traders engaged in “cash-and-carry” arbitrage would 

68 See Figure 2 backup. 69 The largest contango over the prior ten years was -$4.59 on May 13, 2010. Refinitiv. See Workpaper 3. 70 Robert Kolb and James Overdahl, Understanding Futures Markets, 6 ed. (2006) (“Kolb and Overdahl (2006)”), pp. 108–109 

(“This is a cash-and-carry arbitrage because the trader buys the cash good and carries it to the expiration of the futures contract.”; 

“Cost-of-Carry Rule 1[:] The futures price must be less than or equal to the spot price of the commodity plus the carrying charges 

necessary to carry the spot commodity forward to delivery.... As we have seen, if prices do not conform to Cost-of-Carry Rule 

1, a trader can borrow funds, buy the spot commodity with the borrowed funds, sell the futures contract, and carry the commodity 

forward to deliver against the futures contract.”). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 32 of 197 PageID #:14002
 Highly Confidential Page 27

buy physical crude oil and sell deferred futures contracts. Then they would store the oil until the 

deferred futures expired and use the stored physical oil to deliver via futures (by fulfilling a short 

futures position obligation). These traders profit as long as the physical purchase price is less 

than the futures sale price after adjusting for the cost to store the crude oil (including financing 

and insurance costs). In Q1 2020, as the calendar spread moved into a large contango (see 

Figure 2 above), this cash-and-carry strategy became more attractive. 

60. Because a trader executing a cash-and-carry strategy uses stored physical crude oil to 

deliver via futures, storage space during a super contango typically fills up faster at or near the 

futures’ point of delivery. As described above in Section V.A.3, the CME WTI futures’ sole 

point of delivery is Cushing. 

61. A wealth of data shows that the tanks, pipelines, and other infrastructure for storing oil 

rapidly filled up across the U.S., and a storage shortage became particularly acute in Cushing. 

Indeed, according to U.S. Energy Information Administration (“EIA”) data, the weekly 

inventory buildups between mid-March 2020 and mid-April 2020 were the highest ever 

recorded. Crude oil stockpiles increased by 63 million barrels—or 14%—in the four weeks 

leading up to April 17, 2020, putting inventories at 519 million barrels, just shy of the 536 

million barrels record set in March 2017.71 In Cushing, stockpiles increased by 20 million 

barrels—or 52%—putting inventories at 60 million barrels.72 

62. As Figure 3 below illustrates, this resulted in U.S. Crude Oil Weekly Alternate Storage 

Capacity Utilization jumping from 50% to 76% in Cushing.73 Assuming the buildup continued at 

the latest rate of roughly 5 million barrels per week in Cushing, 100% capacity would have been 

reached by mid-May 2020.74 Operational constraints, however, suggest that storage could 

become effectively full well before capacity reached 100% as some of the physically unfilled 

71 See Workpaper 4; “Weekly Supply Estimates: Stocks – Crude Oil, Commercial (Excl. Lease Stock ),” U.S. Energy Information 

Administration, http://www.eia.gov/dnav/pet/pet_sum_sndw_a_epc0_sax_mbbl_w.htm. 72 See Workpaper 4. 73 See Workpaper 4. 74 See Figure 3 backup. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 33 of 197 PageID #:14003
 Highly Confidential Page 28

storage may have already been leased or otherwise committed to receive new deliveries.75 

Furthermore, some capacity had to be left available for tank-to-tank transfers, blending, and 

other routine operations.76 A Congressional Research Service report published on April 22, 

2020, stated that access and availability to the remaining storage at Cushing “is shrinking and 

that all its capacity has been leased, so it is essentially full according to traders.”77 

75 

 See “Pirrong Deposition, 141:4–6. See 

also “The 20 Minutes That Broke the U.S. Oil Market,” Bloomberg, April 25, 2020, 

https://www.bloomberg.com/news/articles/2020-04-25/the-20-minutes-that-broke-the-u-s-oil-market; “No Vacancy: Main U.S. 

Oil Storage in Cushing Is All Booked,” Reuters, April 21, 2020, https://www.reuters.com/article/us-global-oil-usa-storage/novacancy-main-u-s-oil-storage-in-cushing-is-all-booked-idUSKCN22332W; “WTI crude oil futures prices fell below zero because 

of low liquidity and limited available storage,” U.S. Energy Information Administration, April 22, 2020 (“Although Cushing has 

physically unfilled storage available, some of this physically unfilled storage is likely to have already been leased or already 

committed, limiting the uncommitted storage available for contract holders without pre-existing arrangements. In this case, those 

contract holders would likely have to pay higher rates to storage operators that have uncommitted space available”). 76 “Column: U.S. Crude Oil Storage Is Filling Rapidly,” Reuters, April 20, 2020, https://uk.reuters.com/article/uk-oil-priceskemp/column-u-s-crude-oil-storage-is-filling-rapidly-idUKKBN222111. 77 “Crude Oil Futures Prices Turn Negative,” Congressional Research Services, April 22, 2020, 

https://crsreports.congress.gov/product/pdf/IN/IN11354. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 34 of 197 PageID #:14004
 Highly Confidential Page 29

Figure 3

Cushing Weekly Storage Capacity Utilization 

Source: “Weekly Supply Estimates: Stocks – Crude Oil, Commercial (Excl. Lease Stock ),” U.S. Energy 

Information Administration, http://www.eia.gov/dnav/pet/pet_sum_sndw_a_epc0_sax_mbbl_w.htm; “Working and 

Net Available Shell Storage Capacity,” U.S. Energy Information Administration, 

https://www.eia.gov/petroleum/storagecapacity/ 

Note: Alternate Utilization Rate measures crude oil stores in tanks as well as crude oil in pipelines and in transit by 

rail in proportion to the sum of the tanks’ working storage capacity and stocks in transit. Weekly utilization rates 

were calculated from weekly crude oil stocks compared to the most recent available semiannual storage and stocks 

in transit figures released by the EIA. Forecasted Cushing Alternate Utilization Rate is calculated by assuming the 

buildup in Cushing continued at the latest rate of roughly 5 million barrels per week as of April 17, 2020. 

63. Other contemporaneous data bolster the fact that storage was running out. For example, 

on April 7, 2020, Permian WTI storage futures prices jumped from $0.12 per barrel per month to 

$3.71 per barrel per month—a 30-fold increase in a single day.78 Storage prices did not start 

subsiding until April 22, 2020. A clear sign that traditional storage was becoming unusually 

scarce and expensive can also be found in market participants resorting to storing oil on vessels 

at sea—normally a highly uneconomic proposition. The global amount of oil being stored on 

vessels at sea more than doubled between March 27, 2020, and April 17, 2020, adding almost 70 

78 ] See Workpaper 5. A Permian WTI storage future allows traders to buy and sell the right to store WTI crude oil in the 

Magellan East Houston terminal system. See “ICE Futures Europe: Permian WTI Storage Future,” Intercontinental Exchange, 

https://www.ice.com/products/71090497/Permian-WTI-Storage-Future. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 35 of 197 PageID #:14005
 Highly Confidential Page 30

million barrels in the span of three weeks, approximately the amount of storage capacity at 

Cushing.79 

64. In sum, purchasers of physical oil in Cushing, including long CME WTI futures position 

holders that had not previously secured storage space, likely had no place to store that oil by the 

time they received it on or after May 1, 2020, the May Contract’s first delivery day. 

3. Storage Capacity Concerns and Declining Prices Prompted Warnings 

of Potential Negative Prices 

65. The looming unavailability of storage capacity in Cushing for delivery of the May 

Contract was foreshadowed by many market participants, informed observers, and specialized 

news outlets in the days and weeks preceding April 20, 2020, prompting warnings of potential 

negative prices. 

66. For example, as early as March 18, 2020, Paul Sankey, managing director at Mizuho 

Securities, warned that “Oil prices can go negative.”80 

81 Negative pricing is a signal that the market does not 

need more crude oil and incentivizes producers to shut down wells. S&P Global Market 

Intelligence reported on April 6, 2020, that there was “a lot of buzz out there about prices going 

negative” due to oversupply and dwindling storage capacity.82 The article quoted an oil 

production marketing consultant who stated that “[s]torage is filling up fast. Cushing is running 

out of room. The Gulf Coast is full. The Permian is full. We may have some Permian 

producers, during the month of May, who will not be able to sell their oil at any price. Which 

79 Bloomberg. See Workpaper 6 and Figure 3 backup. 80 Jonathan Garber, “Oil Prices Could Fall below Zero: Analyst,” Fox Business, March 18, 2020, 

https://www.foxbusiness.com/markets/oil-price-could-fall-below-zero-analyst. 81

(emphasis in original). 

82 Everett Wheeler, Allison Good, and Gorey Paul, “No Place to Go: Oil Storage Filling up Amid Collapsing Demand, Excess 

Production,” S&P Global Market Intelligence, April 6, 2020, https://www.spglobal.com/marketintelligence/en/newsinsights/latest-news-headlines/no-place-to-go-oil-storage-filling-up-amid-collapsing-demand-excess-production-57865154. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 36 of 197 PageID #:14006
 Highly Confidential Page 31

has never happened in history.”83 Similarly, the International Energy Agency warned on April 

15, 2020, that physical stocks were so massive they could “overwhelm” storage capacity.84 

67. As early as April 3, 2020, the CME announced it was implementing operational changes 

to ensure CME WTI futures and options along with some other energy products could trade at 

negative prices.85 On April 8, 2020, the CME told clearing firms and clients that it was “ready to 

handle the situation of negative ... prices in major energy contracts” and had a plan in place “if 

major energy prices continue to fall toward zero in the coming months.”86 On April 15, 2020, 

CME informed its clearing members that “[r]ecent market events have raised the possibility that 

certain NYMEX energy futures contracts could trade at negative or zero trade prices or be settled 

at negative or zero values, and that options on these futures contracts could be listed with 

negative or zero strike prices.”87 On April 16, 2020, the CME also increased its margin 

requirement for the May Contract due to heightened volatility.88 Finally, on April 20, 2020, 

89

C. WTI Futures Contract Market Dynamics on April 20, 2020, Diverged from 

Historical Penultimate Days 

68. On April 20, 2020, the May Contract price went negative, falling during the 

trading day, and settled at negative $37.63 per barrel.90 The midpoint price opened at at 

5:00 PM on the evening of April 19, 2020, and declined gradually overnight, reaching by 

83 Everett Wheeler, Allison Good, and Gorey Paul, “No Place to Go: Oil Storage Filling up Amid Collapsing Demand, Excess 

Production,” S&P Global Market Intelligence, April 6, 2020, https://www.spglobal.com/marketintelligence/en/newsinsights/latest-news-headlines/no-place-to-go-oil-storage-filling-up-amid-collapsing-demand-excess-production-57865154. 84 “Oil Glut May Overwhelm Global Storage Tanks within Weeks,” Bloomberg, April 15, 2020, 

https://www.bloomberg.com/news/articles/2020-04-15/oil-glut-may-overwhelm-storage-despite-opec-cut-iea-says. 85 “Changes to Price and Strike Price Eligibility Flags for Certain Energy Products,” CME Group, April 3, 2020, 

https://www.cmegroup.com/notices/electronic-trading/2020/04/20200403.html. 86 “Advisory Notice: CME Clearing Plan to Address the Potential of a Negative Underlying in Certain Energy Options 

Contracts,” CME Group, April 8, 2020, https://www.cmegroup.com/content/dam/cmegroup/notices/clearing/2020/04/Chadv20-

152.pdf. 

87 “Advisory Notice: Testing Opportunities in CME’s ‘New Release’ Environment for Negative Prices and Strikes for Certain 

NYMEX Energy Contracts,” CME Group, April 15, 2020, https://www.cmegroup.com/notices/clearing/2020/04/Chadv20-

160.html. 

88 “Advisory Notice: Performance Bond Requirements: Energy - Effective April 17, 2020,” CME Group, April 16, 2020, 

https://cmegroup.com/notices/clearing/2020/04/Chadv20-163.html. 89 (produced by 

SOCAR Trading). 

90 See Figure 4 backup. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 37 of 197 PageID #:14007
 Highly Confidential Page 32

8:00 AM on April 20, 2020.91 The midpoint price declined more rapidly from roughly 11:00 AM 

onward and collapsed shortly after turning negative at , falling by over between 

 and 1:30 PM.92 The midpoint price rebounded to by but then 

dropped again to between and before closing at at the 

end of the trading day (see Figure 4 below). 

93 After trading negative 

on both the rest of the afternoon of April 20, 2020, and the morning of April 21, 2020, the May 

Contract ended up with a final settlement price of $10.01.94

Figure 4

Source: Pirrong Report 

Note: The price series is based on Dr. Pirrong’s midpoint series in his backup file ‘CL_BBO_420.xlsx.’

69. This section explains how the storage constraints in Cushing detailed in the previous 

section set up the conditions for this negative price event, and describes the unusual market 

91 See Figure 4 backup. 92 See Figure 4 backup. 93 For example, 

. See Workpaper 7. 94 See Figure 4 backup.

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 38 of 197 PageID #:14008
 Highly Confidential Page 33

dynamics on April 20, 2020, that contributed to the collapse of the May Contract price in a short 

period of time. 

1. A Larger Number of Expiring Futures Contracts Needed to Be Sold 

Compared to Past Penultimate Trading Days 

70. As explained above in Section V.A, the CME WTI futures contract requires the contract 

owner to take physical delivery of the crude oil in Cushing soon after the futures contract expiry 

or face large fines, penalties, and fees.95 For the May Contract expiry, market participants that 

could not take delivery of the crude oil underlying the futures contract were obligated to close 

out of their positions by April 21, 2020.96 The total number of May Contracts that remained open 

(the “open interest”) at the start of April 20, 2020, was far higher than on past penultimate 

trading days, meaning more positions needed to be liquidated than usual to avoid imminent 

delivery.97 Figure 5 below shows that open interest for the May Contract was 31% higher than 

the largest open interest and 69% higher than the average open interest for the previous 12 

contract expiry penultimate days. 

71. The CFTC has reported that a smaller share of the long side of this open interest than 

usual was held by traders that are typically involved in physical oil markets. Figure 5 below 

shows that traders classified by the CFTC as “producers and merchants” (also called 

“commercial” traders) only held 12.5% of the long open interest compared to them holding on 

average 47.1% of the long open interest in the previous 12 penultimate days, while “nonreportable” or “other reportable” traders were driving most of the higher-than-usual open 

interest.98 Other reportable traders are entities that are not classified by the CFTC as being 

Producers, Merchants, Processors, Users, Swap Dealers, or Managed Money entities and are 

therefore unlikely to make or take delivery.99 Non-reportable traders are traders that fall below 

95 NYMEX Rulebook, Chapter 7: Delivery Facilities and Procedures, CME Group, as of April 16, 2020, 

https://web.archive.org/web/20200416161650/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/1/7.pdf. 96 See ¶ 50 above. 97 See “Interim Staff Report: Trading in NYMEX WTI Crude Oil Futures Contract Leading Up To, On, and around April 20, 

2020,” CFTC, November 23, 2020, https://www.cftc.gov/node/235761, p. 23. 98 See “Interim Staff Report: Trading in NYMEX WTI Crude Oil Futures Contract Leading Up To, On, and around April 20, 

2020,” CFTC, November 23, 2020, https://www.cftc.gov/node/235761, p. 22. See also Figure 5 backup. 99 “Disaggregated Explanatory Note,” CFTC, 

https://www.cftc.gov/MarketReports/CommitmentsofTraders/DisaggregatedExplanatoryNotes/index.htm. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 39 of 197 PageID #:14009
 Highly Confidential Page 34

the reporting threshold.100 This category includes small speculators, including individual 

investors, who are unlikely to be able to make or take delivery.101 

72. The combination of a higher open interest and share of long open interest held by noncommercial traders means there were an additional 61,049 contracts (179% more) that had to be 

liquidated before expiry compared to the average of the previous 12 penultimate days.102 

Figure 5

May Contract Long Open Interest Held by Non-Commercial Traders on April 20, 

2020 Was Far Higher Than On Past Penultimate Trading Days 

Source: Refinitiv; “Interim Staff Report: Trading in NYMEX WTI Crude Oil Futures Contract Leading Up To, On, 

and around April 20, 2020,” CFTC, November 23, 2020, https://www.cftc.gov/node/235761. 

Note: Trading in NYMEX WTI Crude Oil futures contracts terminates three business days prior to the 25th calendar 

day of the month prior to the contract month. If the 25th calendar day is not a business day, trading terminates four 

business days prior to the 25th calendar day of the month prior to the contract month. 

100 “Commitment of Traders,” CME Group, https://www.cmegroup.com/tools-information/quikstrike/commitment-oftraders.html. 

101 Letter from U.S. Commodity Futures Trading Commission to U.S. Senate Committee on Agriculture and House Committee 

on Agriculture, February 8, 2022, 

https://www.agriculture.senate.gov/imo/media/doc/2022%2002%2008%20Ag%20committees%20digital%20asset%20response

%20letter.pdf (“For analytical purposes, the CFTC categorizes reportable trading above that threshold “institutional trading,” 

while nonreportable trading is generally indicative of retail participation”). 102 See Workpaper 8. 

0

20,000

40,000

60,000

80,000

100,000

120,000

May

2019

Jun

2019

Jul

2019

Aug

2019

Sep

2019

Oct

2019

Nov

2019

Dec

2019

Jan

2020

Feb

2020

Mar

2020

Apr

2020

May

2020

Jun

2020

Jul

2020

Aug

2020

Number of Contracts

Contract Expiry Month

Producer Merchant Open Interest

Non-Producer Merchant Open Interest

12 month trailing 

average non-producer 

merchant open interest

May 2020 nonproducer merchant 

open interest was 

179% higher than 

the 12 month trailing 

average

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 40 of 197 PageID #:14010
 Highly Confidential Page 35

73. Post–April 20, 2020, analyses have noted that prior to April 20, 2020, large exchangetraded funds (“ETFs”) such as the US Oil Fund piled into long positions as retail traders sought a 

way to buy cheap oil. That had the corresponding effect of increasing the quantity of shorts that 

could be taken by hedgers with abundant physical oil. Even though US Oil Fund and other ETFs 

rolled their long position from the May to the June contract between April 9, 2020, and April 13, 

2020, other non-commercial longs took their place, including retail traders and hedge funds, with 

the result being that open interest remained extraordinarily high into April 20, 2020.103 Traders 

that are not involved in physical oil markets are typically unable to take physical delivery of 

crude oil as they do not have the infrastructure to store or transport oil. In other words, there 

were more traders holding long positions on the penultimate day of the expiring contract than 

usual, and more of these traders were not in a position to take delivery and needed to sell the 

May Contract to exit their position before contract expiry.104 Consequently, a larger number of 

expiring futures contracts needed to be sold than usual, and more urgently than usual. In 

contrast, the share of the short open interest was largely composed of commercial traders. 

Because commercial traders likely “had physical oil they could deliver if need be and so could 

stay to expiration, [they] could then force the remaining longs to pay an extreme penalty” to exit 

their long positions by withholding from buying futures prior to 1:30 PM on April 20, 2020, 

effectively “squeez[ing]” the longs.105 

74. Consistent with these analyses, as explained in the next section, there were few buyers 

on April 20, 2020, and they were quickly exhausted. 

103 Philip Verleger, “Negative Prices: Never Again,” Energy Intelligence, April 23, 2020, 

https://www.energyintel.com/0000017b-a7da-de4c-a17b-e7da75620000; 

 CITI-05594–5 

. 104 CITI-05594–5 

105 Philip Verleger, “Negative Prices: Never Again,” Energy Intelligence, April 23, 2020, 

https://www.energyintel.com/0000017b-a7da-de4c-a17b-e7da75620000; “What Really Caused the Epic WTI Meltdown?” 

Energy Intelligence, April 21, 2020, https://www.energyintel.com/0000017b-a7da-de4c-a17b-e7da70fa0000; 

 CITI-05594–5 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 41 of 197 PageID #:14011
 Highly Confidential Page 36

2. There Were Few Buyers Providing Liquidity in the Market and 

Liquidity Was Quickly Exhausted 

75. Trading of the CME WTI futures contract operates on an order book. There are separate 

order books for TAS and non-TAS orders and for different contract expiry months. Market 

participants can submit different types of orders on CME that will impact the order book. For 

example, in this market, CME offers limit orders and market orders.106 A limit order allows 

market participants to specify the quantity that they would like to trade along with the highest 

price at which they are willing to buy or the lowest price at which they are willing to sell.107 Bids 

(i.e., buy orders) or asks (i.e., sell orders) can be placed at various price levels on the order 

book.108 An order is said to be “marketable” (or “aggressive”) if it can be executed immediately 

against an opposing order already in the market. For example, an ask (i.e., sell order) that is 

placed at a price level lower than the prevailing best bid (i.e., buy) is marketable. Otherwise, an 

order is “non-marketable” (or “passive”). Non-marketable orders “rest” on the order book. It is 

not guaranteed that this “resting” order will trade. For example, for a non-marketable bid to 

trade, an ask order has to be entered by another market participant at the same or a lower price 

level as the price level of the bid. In contrast, a market order allows market participants to trade 

immediately at the market’s current best available price. A market order is always marketable as 

long as there is liquidity on the other side of the orderbook. The notable difference between the 

market and limit order types is that a limit order executes if the order’s price conditions are met, 

whereas market orders execute regardless of the prevailing market prices.109 

76. The set of resting or unfilled orders that are active in the market at any given time make 

up the order book. The order book comprises two sides—one side is the set of bids to buy and 

the other side are offers to sell. Each side is further stratified by price levels.110 On the bid side 

of the order book, the most competitive (highest) price at which a participant is willing to buy 

sets the first price level (also referred to as the best bid). The next best price is the second level, 

106 “Futures Order Types,” CME Group, https://www.cmegroup.com/education/courses/things-to-know-before-trading-cmefutures/futures-order-types.html; “Iceberg Orders,” CME Group, https://www.cmegroup.com/tools-information/webhelp/ebsworkstation-quick-guide/Content/IcebergOrders.html. 

107 “Futures Order Types,” CME Group, https://www.cmegroup.com/education/courses/things-to-know-before-trading-cmefutures/futures-order-types.html. 

108 Thierry Foucault, Marco Pagano, and Ailsa Röell, Market Liquidity: Theory, Evidence, and Policy (2013), p. 18. 109 “Futures Order Types,” CME Group, https://www.cmegroup.com/education/courses/things-to-know-before-trading-cmefutures/futures-order-types.html. 

110 Larry Harris, Trading and Exchanges: Market Microstructure for Practitioners (2003) (“Harris (2003)”), p. 118. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 42 of 197 PageID #:14012
 Highly Confidential Page 37

and so forth. The offer side follows the same structure, but with the lowest price creating the 

first level (the best offer) since this is the most competitive price. The total visible quantity that 

participant(s) are willing to trade at any given price level will be shown at each level in the order 

book.111 This is referred to as the order book depth and gives insight into the available liquidity 

in the market at a given time.112 Thus, a market participant can look at the order book and 

understand the unfilled aggregate quantity and prices at which other market participants are 

willing to trade.113 A market that has a large volume of resting orders posted on the order book is 

usually described as a “liquid” or “deep” market. Liquidity (or depth) is an important feature of 

financial markets as it denotes the ability to trade large quantities as quickly as possible, at the 

lowest costs possible, and when a market participant wants to trade.114 

77. As the April 21, 2020, expiration date of the May Contract approached, with crude oil 

storage options virtually non-existent and demand suppressed, the urgency to sell off long 

positions (i.e., offer) increased dramatically. This urgency was compounded for those facing 

potential physical delivery of the oil without the means to store it. As traders without prebooked storage tried to unwind their positions to avoid delivery, they created liquidity on the ask 

(i.e., sell) side of the CME WTI futures order book and consumed bid (i.e., buy) side liquidity. 

78. By the start of the April 20, 2020, trading session at 5:00 PM on April 19, 2020, 

115 

 Figure 6 below compares 

 

111 Note that order books typically only show the visible quantity, and exclude any invisible quantity (e.g., from iceberg orders). 112 Harris (2003), p. 398. 113 Harris (2003), pp. 117, 118. 114 Harris (2003), pp. 394, 398. 115

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 43 of 197 PageID #:14013
 Highly Confidential Page 38

Figure 6

Source: CME

Note: Midpoint refers to the midpoint of the highest bid and lowest offer prices. Buy (sell) quantity is calculated as 

the total bid (offer) volume at or above (below) 2 ticks (10 ticks, 100 ticks) of the midpoint price. For example, if 

the midpoint price were $1, the buy quantity within 2 ticks of the midpoint would be calculated as the total volume 

between $0.98 and $1. Likewise, the sell quantity within 2 ticks of the midpoint would be calculated as the total 

volume between $1.02 and $1. Quantities are snapshots of depth taken at one-minute intervals. Droplines indicate 

the start of a new penultimate day. I consider the start of each penultimate day to be CME markets open 

at 5:00 PM of each trading day and close at 4:00 PM. 

79.

 Figure 7 below compares 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 44 of 197 PageID #:14014
 Highly Confidential Page 39

 

Figure 7

Source: CME

Note: Midpoint refers to the midpoint of the highest bid and lowest offer prices. Buy (sell) quantity is calculated as 

the total bid (offer) volume at or above (below) 2 ticks (10 ticks, 100 ticks) of the midpoint price. For example, if 

the midpoint price were $1, the buy quantity within 2 ticks of the midpoint would be calculated as the total volume 

between $0.98 and $1. Likewise, the sell quantity within 2 ticks of the midpoint would be calculated as the total 

volume between $1.02 and $1. Quantities are snapshots of depth taken at one-minute intervals. Droplines indicate 

the start of a new penultimate day. I consider the start of each penultimate day to be CME markets open 

at 5:00 PM of each trading day and close at 4:00 PM. 

80. Figures 8 and 9 below 

 Figure 8 displays 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 45 of 197 PageID #:14015
 Highly Confidential Page 40

 

81. Figure 9 conveys 

 

 As I describe in more detail in Section VIII.A 

below, 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 46 of 197 PageID #:14016
 Highly Confidential Page 41

Figure 8

Source: CME 

Note: Midpoint refers to the midpoint of the highest bid and lowest offer prices. Buy (sell) quantity is calculated as 

the total bid (offer) volume at or above (below) 2 ticks (10 ticks, 100 ticks) of the midpoint price. For example, if 

the midpoint price were $1, the buy quantity within 2 ticks of the midpoint would be calculated as the total volume 

between $0.98 and $1. Likewise, the sell quantity within 2 ticks of the midpoint would be calculated as the total 

volume between $1.02 and $1. Quantities are snapshots of depth taken at one-minute intervals. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 47 of 197 PageID #:14017
 Highly Confidential Page 42

Figure 9

Source: CME

Note: Midpoint refers to the midpoint of the highest bid and lowest offer prices. Buy (sell) order quantity is 

calculated as the total bid (offer) volume at each price level. Quantities are snapshots of depth taken at one-minute 

intervals. Midpoints are snapshots taken at one-minute intervals. Within each one-minute time interval, the latest 

available timestamp is considered. Prices correspond to the TAS premium or discount in cents per barrel relative to 

the TAS settlement price. 

82. In lay terms, under normal market circumstances, the unwinding of a long position by 

traders without the means to store oil would have been bought by users of oil; however, with 

storage being close to capacity, no one wanted to buy the oil. Sellers of May Contracts were 

therefore forced to sell these contracts at negative prices—essentially paying someone to take the 

oil off their hands. This is consistent with the view of numerous market participants, academics, 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 48 of 197 PageID #:14018
 Highly Confidential Page 43

and other informed observers who attributed the order book imbalance and negative prices on 

April 20, 2020, to the lack of storage space at Cushing. 

83. For example, 

 

117 Other self-regulatory bodies, U.S. 

government agencies, the CFTC Chairman, market participants, and academics reached similar 

conclusions.118 

3. Other Oil Futures Contracts That Were Not Impacted by the Storage 

Constraints in Cushing Did Not Go Negative 

84. While the May Contract went negative on April 20, 2020, due to the confluence of 

demand shock from the COVID-19 pandemic and the lack of storage capacity in Cushing, the 

more deferred CME WTI futures contracts and other oil futures contracts such as Brent futures 

did not. On April 20, 2020, while the May Contract plunged, the June 2020 CME WTI futures 

contract remained positive despite an 18% decline.119 Similarly, June 2020 Brent futures also 

remained positive with a smaller price decline, settling at $25.57.120 

85. The significant divergence in prices between different oil futures contracts in April 2020 

can be attributed to factors specific to the storage constraints at Cushing and the nature of the 

contracts themselves. Other oil futures prices did not fall as much because they were not 

exposed to the short-term storage constraints in Cushing. 

116 117

 Defendants did not own any storage space at Cushing. 118 See Appendix C. 119 See Workpaper 2. 120 See Workpaper 2. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 49 of 197 PageID #:14019
 Highly Confidential Page 44

86. Importantly, Brent futures contracts are settled financially rather than physically. Thus, 

holders of May 2020 Brent futures contracts unable to take physical delivery of crude oil were 

not required to sell off positions. Moreover, Brent futures contracts are based on production 

from multiple oil fields in Europe’s North Sea and are therefore not exposed to the logistical 

challenges associated with the landlocked Cushing hub. Lastly, Brent oil is produced at or closer 

to sea, making it easier to quickly store and move on vessels at sea. 

87. Similarly, deferred CME WTI futures contracts were not immediately exposed to the 

storage constraints in Cushing. Holders of June 2020 CME WTI futures contracts were not 

obligated to receive delivery before June 1, 2020, reducing the urgency for traders that were 

unable to take delivery to sell off their positions. As discussed above, the uncertainty 

surrounding the pandemic’s duration, whether production would fall and if so by how much, and 

when demand would pick up also translated into uncertainty as to when storage constraints in 

Cushing would ease. 

88. In sum, other oil futures contracts such as Brent futures and deferred contracts were 

shielded from the specific storage issues at Cushing, allowing them to better absorb the demand 

shocks caused by the pandemic. 

D. Negative Commodity Prices Have Occurred Due to Fundamental Factors, 

Such as Oversupply Coupled with Logistical Constraints 

89. An oversupply of a commodity, when coupled with logistical constraints that prevent its 

efficient disposal or necessitate holding it at costs exceeding its anticipated future sale value, can 

compel traders to sell at negative prices. Although rare, such negative price phenomena have 

occurred in several energy commodities markets. Tellingly, in April 2020, negative prices 

occurred not only in futures markets but also in at least some physical crude oil markets in the 

Permian basin close to Cushing, indicating fundamental factors were at play. These markets, 

much like the WTI crude oil market on April 20, 2020, exhibited an extreme supply glut coupled 

with logistical incapacities. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 50 of 197 PageID #:14020
 Highly Confidential Page 45

1. Past Instances of Negative Prices Also Exhibited a Supply Glut 

Coupled with Logistical Constraints 

90. While April 2020 was the first time in history that CME WTI futures prices became 

negative, several other commodity prices, including natural gas, propane, and electricity have 

previously traded negatively.121 In each of these events, fundamental factors linked to oversupply 

coupled with storage and logistics issues were at play. 

91. For example, in April and May 2019, next-day natural gas prices at the Waha hub in the 

Permian basin in West Texas fell below zero. A Reuters article reported that this was caused by 

storage and logistics constraints: 

The problem is construction of new oil and gas pipelines in the Permian has not 

kept up with output, which has more than doubled over the past three years as the 

United States has risen to become the world’s largest oil producer. So producers 

are forced to burn or flare the gas, pay others with space on existing pipelines to 

take it or stop drilling.122 

Because “Texas rules [only] allow firms to flare gas for up to six months,” producers are willing 

to pay others to take the gas once they can no longer burn it.123

92. Similarly, electricity is not easily storable and “many of the facilities [generating 

electricity] aren’t designed to switch on and off quickly.”124 The electricity market therefore 

sometimes prices negatively when “the amount of electricity being generated is outstripping 

demand during certain hours of the day.”125 

121 “Crude Oil Futures Prices Turn Negative,” Congressional Research Services, April 22, 2020, 

https://crsreports.congress.gov/product/pdf/IN/IN11354 (“[O]ther commodity prices, including natural gas and propane, have 

previously traded negatively”). See also CME-NegativeCrude_0000048 

122 “U.S. Natural Gas Prices Turn Negative in Texas Permian Shale Again,” Reuters, May 22, 2019, 

https://www.reuters.com/article/idUSKCN1SS1GB/. 123 “U.S. Natural Gas Prices Turn Negative in Texas Permian Shale Again,” Reuters, May 22, 2019, 

https://www.reuters.com/article/idUSKCN1SS1GB/. 124 “Power Worth Less than Zero Spreads as Green Energy Floods the Grid,” Bloomberg, August 6, 2018, 

https://www.bnnbloomberg.ca/power-worth-less-than-zero-spreads-as-green-energy-floods-the-grid-1.1119243; Michael Sewalt 

and Cyriel De Jong, “Negative Prices in Electricity Markets,” Commodities Now, June 2023, https://www.kyos.jp/wpcontent/uploads/sites/2/2016/10/Commodities-Now-Negative-prices-in-Electricity-Markets.pdf. 125 “Power Worth Less than Zero Spreads as Green Energy Floods the Grid,” Bloomberg, August 6, 2018, 

https://www.bnnbloomberg.ca/power-worth-less-than-zero-spreads-as-green-energy-floods-the-grid-1.1119243; Michael Sewalt 

and Cyriel De Jong, “Negative Prices in Electricity Markets,” Commodities Now, June 2023, https://www.kyos.jp/wpcontent/uploads/sites/2/2016/10/Commodities-Now-Negative-prices-in-Electricity-Markets.pdf. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 51 of 197 PageID #:14021
 Highly Confidential Page 46

2. Contemporaneous Negative and Near-Zero Prices Appeared in Crude 

Oil Physical Markets in April 2020, Indicating Fundamental Factors 

Were at Play 

93. As storage capacity in the U.S. started to fill up, some physical crude oils started to price 

at negative values as early as mid-March 2020. Mercuria Energy Group Ltd., a trading house, 

bid negative 19 cents per barrel for Wyoming Asphalt Sour, a dense oil used mostly to produce 

paving bitumen, “effectively asking producers to pay for the luxury of getting rid of their 

output.”126 Energy market analysts and traders at the time warned that “other crude streams are 

likely to see negative prices soon” in “areas where storage is filling up quickly.”127

94. In the Permian basin, closer to Cushing, reports emerged in the early morning of April 

20, 2020, that “[b]uyers in Texas are offering as little as $2 a barrel for some oil streams, raising 

the possibility that producers may soon have to pay to have crude taken off their hands.”128 A 

hedge fund manager specializing in energy derivatives posted at 3:22 AM that “there is no limit 

to the downside to prices when inventories and pipelines are full,” that “[n]egative prices are 

possible,” and that some landlocked physical oil prices were already negative.129

95. Physical prices did end up negative in the Permian basin during the week of April 20, 

2020, when “producers of crude streams such as South Texas Sour and Eastern Kansas Common 

had to pay more than $50 a barrel to offload their output.”130

126 “One Corner of U.S. Oil Market Has Already Seen Negative Prices,” Bloomberg, March 27, 2020, 

https://www.bloomberg.com/news/articles/2020-03-27/one-corner-of-u-s-oil-market-has-already-seen-negative-prices. 127 “One Corner of U.S. Oil Market Has Already Seen Negative Prices,” Bloomberg, March 27, 2020, 

https://www.bloomberg.com/news/articles/2020-03-27/one-corner-of-u-s-oil-market-has-already-seen-negative-prices. 128 “Oil Plunges below $12 as Storage Rapidly Fills,” Bloomberg, April 20, 2020, https://www.mrt.com/business/oil/article/Oilplunges-below-12-as-storage-rapidly-fills-15212436.php. 

129 Pierre Andurand [@AnduranPierre], Twitter Post, X, April 20, 2020, 3:22 AM, 

https://twitter.com/AndurandPierre/status/1252150610167771136; Pierre Andurand [@AnduranPierre], Twitter Post, X, April 20, 

2020, 3:27 AM, https://twitter.com/AndurandPierre/status/1252151840474529793. 130 “The Next Chapter of the Oil Crisis: The Industry Shuts Down,” Bloomberg, April 27, 2020, 

https://www.bloomberg.com/news/articles/2020-04-26/the-next-chapter-of-the-oil-crisis-the-industry-shuts-down. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 52 of 197 PageID #:14022
 Highly Confidential Page 47

VI. Dr. Pirrong’s Claim That Storage Constraints Cannot Explain Negative Prices on 

April 20, 2020, Is Flawed and At Odds with the Predominant Third-Party Views 

and Research 

96. Dr. Pirrong claims that fundamental factors, including storage constraints, cannot explain 

the negative prices on April 20, 2020.131 However, this claim is flawed and inconsistent with the 

views and research of many third-parties described in Section V above. Dr. Pirrong advances 

what he describes as “several logical problems” with fundamental factors explaining negative 

prices.132 Each of these “problems” fails to withstand critical scrutiny. 

97. First, Dr. Pirrong claims short futures position holders could pass ownership to long 

futures positions holders taking delivery without the crude leaving the existing storage facility 

(i.e., “deliver oil in store”).133 However, this argument ignores an important aspect of the 

NYMEX delivery rules, specifically that long position holders can opt to take delivery “in store” 

but that short position holders must also agree to make delivery in storage.134 Short position 

holders were unlikely to agree, and long position holders could not know whether they would 

prior to the end of trading on April 21, 2020. 

98. To understand why short position holders were unlikely to agree, consider that, on April 

20, 2020, the prevailing super contango price structure discouraged short position holders from 

giving their valuable in-tank storage space to others, including long position holders. As 

explained in Section V above, the super contango made storage space valuable because future 

crude oil prices were substantially higher than current prices. Storing crude oil and selling it in 

the future was therefore a more attractive strategy. By giving up storage space, the short position 

holder would forego a valuable profit opportunity. Indeed, Dr. Pirrong acknowledges this 

disincentive to give up storage space elsewhere in his report, stating “there was no reason for 

those holding inventories of oil at Cushing to deliver it at negative prices rather than to continue 

to hold it for sale later at positive prices.”135 Instead of the short position holder giving up its 

131 Pirrong Report, ¶¶ 338–340. See also Pirrong Report, ¶ 349 (emphasis added) (“[T]he rebound in prices after 1:30 p.m. on 

April 20, 2020 is most plausibly explained by a recognition by potential takers and makers of delivery that the negative price did 

not reflect supply and demand fundamentals.”). 132 Pirrong Report, ¶ 339. 133 Pirrong Report, ¶ 340. 134 NYMEX Rulebook, Chapter 200, Light Sweet Crude Oil Futures, 200104, as of April 28, 2020, 

https://web.archive.org/web/20200428053717/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/2/200.pdf 

(emphasis added) (“[I]f the seller agrees to such transfer and if the facility used by the seller allows for such transfer, without 

physical movement of product, by in-tank transfer of title to the buyer”). 135 Pirrong Report, ¶ 347. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 53 of 197 PageID #:14023
 Highly Confidential Page 48

storage space, the short position holder could deliver oil that was not already in storage at 

Cushing.136

99. Second, Dr. Pirrong claims that “Cushing stocks were well below storage capacity at 

Cushing” and “occupied 68 percent of capacity.”137 However, Dr. Pirrong uses total storage 

capacity rather than working storage capacity. Cushing stocks, according to Dr. Pirrong’s own 

figures, were 84% of “working storage capacity.”138 His claim that there was plenty of storage 

capacity available is contradicted by his own statements on April 20, 2020, that Cushing was 

probably over 80% and therefore effectively full.139 Moreover, Dr. Pirrong uses April 24, 2020, 

storage data. As explained in Section V.B. above, the May Contract’s first and last delivery days 

were May 1, 2020, and May 31, 2020, so assuming the buildup continued at the latest rate of 

roughly 5 million barrels per week, prior to production cuts made after April 20, 2020, May 

deliveries would have occurred after storage capacity utilization in Cushing had reached 100%.140 

Because this analysis does not account for already committed storage space, it therefore also 

appears unlikely that long futures position holders would have been able to secure storage for an 

early May delivery.141 In fact, contemporaneous analysis from market participants reveals they 

came to the realization around April 20, 2020, that storage in Cushing would reach full capacity 

as early as May. For example, Figure 10 shows that 

 

136 The short position holder can always elect to deliver oil to a point of connection between their incoming pipeline and the 

outgoing pipeline or storage facility designated by the buyer, without giving up storage space. See NYMEX Rulebook, Chapter 

200, Light Sweet Crude Oil Futures, 200104, as of April 28, 2020, 

https://web.archive.org/web/20200428053717/https://www.cmegroup.com/content/dam/cmegroup/rulebook/NYMEX/2/200.pdf. 137 Pirrong Report, ¶ 356. 138 (63,378,000 barrels of Cushing stocks) / (75,835,000 barrels of tank working storage capacity) = 84%. See Pirrong Report, ¶ 

356. 

 See Pirrong Deposition, 140:24–25 . 139 Craig Pirrong, “WTI-WTF? Part II (of How Many???),” Streetwise Professor, April 21, 2020, 

https://streetwiseprofessor.com/wti-wtf-part-ii-of-how-many/ (“The most recent data from the EIA indicates about 55 mm bbl of 

oil storage at Cushing. That’s about 80 percent of nameplate capacity (also per EIA.). Due to operational constraints (e.g., need 

working space to move barrels in and out; can’t mix different grades in the same tank) that’s probably effectively full”). 140 “Weekly Supply Estimates: Stocks – Crude Oil, Commercial (Excl. Lease Stock),” U.S. Energy Information Administration, 

buildup week of April 17, 2020, http://www.eia.gov/dnav/pet/pet_sum_sndw_a_epc0_sax_mbbl_w.htm. Supply conditions 

changed following April 20, 2020, as the negative prices on that day sent a strong signal to producers, which prompted some of 

them to shut down production. As a result, the number of active wells dropped 23% from 529 to 408 between April 17, 2020, 

and May 1, 2020. See Workpaper 9; “North America Rotary Rig Count (Jan 2000-current),” Baker Hugues, 

https://bakerhughesrigcount.gcs-web.com/na-rig-count. 

141 The delivery period for the May 2020 CME WTI contract starts on May 1, 2020, and shorts can deliver oil throughout the 

month of May; therefore, the uncertainty surrounding the longs’ ability to secure storage in May mattered. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 54 of 197 PageID #:14024
 Highly Confidential Page 49

 Dr. Pirrong’s view that storage at Cushing was adequate is also notably at odds with the 

predominant view, which aligns with the findings of the CFTC report.142 This predominant view 

points to the distinct market conditions on April 20, 2020, as driving factors for the sell-off. 

These conditions included delivery constraints due to the anticipation that storage at Cushing 

would soon be at capacity, an unusually high open interest in the expiring May Contract, and a 

unique mix of market participants comprising an unusually low percentage of commercial traders 

and an unusually high percentage of financial traders.143 

Figure 10

Source: Chevron__Vega_0000023, Chevron__Vega_0000251, Chevron__Vega_0000220 

100. Third, Dr. Pirrong’s report fails to cite or address the key findings of the CFTC report. 

142 See “Interim Staff Report: Trading in NYMEX WTI Crude Oil Futures Contract Leading Up To, On, and around April 20, 

2020,” CFTC, November 23, 2020, https://www.cftc.gov/node/235761, pp. 12–13 (“The buildup of oil in storage facilities 

significantly impacted available storage capacity. ... [D]ata from the EIA shows that storage facilities at Cushing had increased

from about 50% of capacity in mid-March to approximately 76% of capacity by April 17. Anecdotal press reports during the 

week leading up to the April 20 trade date suggested that crude oil storage was in particularly short supply. Some reports 

indicated that most, if not all, of the empty storage space had already been committed. ... As the May Contract approached 

expiration on April 21, spreads between the May and June Contracts widened, consistent with an increase in storage capacity 

concerns”), p. 14 (“The dissolution of the OPEC Plus agreement in March, coupled with the delay in resuming the production 

cuts until May, further exacerbated crude oil oversupply concerns, ultimately raising fears about the market’s ability to store

excess production. These concerns were particularly pressing at the Cushing storage facilities that serve as the delivery point for 

the physically-settled WTI Contract.”) 

143 Pirrong Deposition, 118:7–12 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 55 of 197 PageID #:14025
 Highly Confidential Page 50

144 Dr. Pirrong’s report fails to adequately consider these 

alternative explanations for the observed price movements. Shortly after the proposed class 

period ended, Dr. Pirrong speculated that strategic trading—potentially rising to the level of 

manipulation—could have contributed to the price drop, but all of the examples he provided 

involved manipulation by traders in the physical market.145 His report in this matter does not 

consider the possibility of strategic trading (including potential manipulation) by physical 

traders. 

101. Fourth, Dr. Pirrong claims that the price rebound after 1:30 PM is proof that the negative 

prices were not caused by fundamental factors, such as storage issues, but rather were caused by 

Trader Defendants’ alleged manipulation.146 However, Dr. Pirrong fails to acknowledge that, 

after rebounding to by , prices dropped again to between 

 and , more than an hour after the settlement, before closing for the day at a price 

of (see Figure 4 above).147 This second major price decline could not have been caused 

by Trader Defendants’ alleged manipulative trading, since none of them were selling after the 

1:30 P.M. CT settlement. 

144 Pirrong Deposition, 99:18–100:4 

 60:4–62:11 

145 Craig Pirrong, “WTI-WTF?,” Streetwise Professor, April 20, 2020, https://streetwiseprofessor.com/wti-wtf/ (“The proximate 

cause of these wild gyrations, and unprecedented negative prices is, of course, the collapse of demand and the looming 

exhaustion of storage space, including at the delivery point of Cushing, OK. But although this is a necessary condition for today’s 

events, it is not a full explanation.... The likely cause is the difficulty of liquidating about 100,000 open contracts (100 million 

barrels!) in such extreme technical conditions. It is plausible, and indeed likely, that strategic behavior–perhaps rising to the level 

of manipulation–is the major cause of how prices moved today against the background of conditions that were widely known on 

Friday.... In these conditions, a trader, or a group of traders with modest-sized short positions can exercise market power by 

delivering even a small amount of oil over and above the quantity that should flow to Cushing. This drives down the price and 

allows the trader or traders to cover his (their) position(s) at artificially low prices.”); Craig Pirrong, “WTI-WTF? Part II (of How 

Many???),” Streetwise Professor, April 21, 2020, https://streetwiseprofessor.com/wti-wtf-part-ii-of-how-many/ (“But there is 

another market power play possible here. A firm controlling storage could crash prices (and spreads) by withholding that capacity 

from the market.”).

146 Pirrong Report, ¶ 349. 147 See Figure 4 backup. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 56 of 197 PageID #:14026
 Highly Confidential Page 51

102. Finally, Dr. Pirrong claims that the narrowing of calendar spreads between Friday, April 

17, 2020, and Friday, April 24, 2020, is proof that the storage capacity shortage at Cushing was 

becoming less dire the week of April 20, 2020.148 However, Dr. Pirrong’s claim is grossly 

misleading in that he compares two different calendar spreads across those dates, namely the 

May-June calendar spread on April 17, 2020, and the June-July calendar spread on April 24, 

2020.149 

 Dr. Pirrong’s spread analysis compares apples to oranges.150 Had Dr. Pirrong used the 

June-July calendar spread consistently on both dates, he would have found that the spread was 

very similar (-$4.39 versus -$4.28) between those two dates. Moreover, the choice of these two 

dates is arbitrary.151 As Figure 11 shows, on April 17, 20, and 21, 2020, the June-July calendar 

spread widened from -$4.39 to -$5.85 to -$7.12, respectively. Following Dr. Pirrong’s logic, the 

“market assessment of the severity of any putative shortage of storage” therefore increased on 

and in the days immediately after April 20, 2020. 

148 Pirrong Report, ¶¶ 353–354. 149 Pirrong backup production CL2020KM.xlsx. 150 Pirrong Deposition, 142:2–4. 151 While the EIA inventory reports are based on Friday data, they are only released the following Wednesday. See “Weekly 

Petroleum Status Report Schedule,” U.S. Energy Information Administration, 

https://www.eia.gov/petroleum/supply/weekly/schedule.php. The EIA storage data for Friday, April 17, 2020, and April 24, 

2020, were only released on Wednesday, April 22, 2020, and Wednesday, April 29, 2020, respectively. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 57 of 197 PageID #:14027
 Highly Confidential Page 52

Figure 11

Price Spreads in CME WTI June-July Futures Widened Immediately After April 20, 

2020 

Source: Refinitiv

Note: A positive value indicates that the market is in backwardation and a negative value indicates that the market is 

in contango. 

103. Dr. Pirrong also claims that “there was no public information on April 20, 2020—and 

particularly during the period -1:30p.m.—that would explain a massively greater 

deviation from the empirical relationship on that day.”152 However, he did not in fact conduct a 

“systematic” news article search for this matter nor does he know when critical storage and crude 

oil consumption data he cites to were released.153

104. In sum, none of Dr. Pirrong’s “logical problems” with fundamental factors explaining 

negative prices are valid. Furthermore, Dr. Pirrong’s opinion is at odds with the predominant 

152 Pirrong Report, ¶ 378. 153 Pirrong Deposition, 519:16–23 

 610:21–611:15 

 

 444:4–

446:21 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 58 of 197 PageID #:14028
 Highly Confidential Page 53

view and research of market participants, academics, and other informed observers I described in 

Section V.C.2 above.154

VII. Dr. Pirrong’s TVP-VAR Model Is Conceptually Flawed, Methodologically Unsound, 

and Incapable of Establishing That Trader Defendants’ Trading on April 20, 2020, 

Caused Artificial Prices 

105. Dr. Pirrong’s TVP-VAR model is his method for estimating the purported price impact of 

Trader Defendants’ trading and the associated alleged artificial depression in the period before 

 Upon review of the data and evidence, I find that Dr. Pirrong’s TVP-VAR model, 

described below, suffers from multiple conceptual and methodological flaws and, as a result, 

does not provide a reliable basis upon which to form his opinions. 

106. First, Section VII.A below explains that Dr. Pirrong’s TVP-VAR model at most 

calculates price impact, which is not sufficient to estimate price artificiality. Economists define 

the price artificiality of market manipulation as the deviation between the actual price and the 

hypothetical “but-for” price absent the manipulation (i.e., the difference between the actual and 

but-for worlds).155 Estimating a price impact is insufficient to determine a but-for price. In order 

to determine a but-for price, an economist needs to make reasonable assumptions about what 

would happen in the but-for world. Dr. Pirrong fails to lay out and support these necessary 

assumptions. Moreover, Dr. Pirrong’s methodology implicitly assumes the TAS sellers that 

relied on Trader Defendants buying TAS would have had no price impact in the but-for world 

absent Trader Defendants’ alleged manipulative trades.156 

107. This assumption is economically nonsensical in this matter given 

 Because Trader Defendants’ 

 (see Section X below describing Trader Defendants’ trading patterns), Trader 

Defendants’ 

154 See ¶ 83 above. See also Appendix C. 155 Pirrong Deposition, 173:23–174:3 

 156 Pirrong Deposition, 180:7–16 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 59 of 197 PageID #:14029
 Highly Confidential Page 54

 

108. Dr. Pirrong’s methodology assumes that either (i) TAS sellers’ trading would have been 

unchanged and they would have sold to other TAS buyers, which unlike Defendants would have 

had no price impact in the non-TAS market, or (ii) TAS sellers would have instead sold in the 

non-TAS market with no price impact. Dr. Pirrong ignores evidence that 

 Absent Trader Defendants’ trading, an even greater number of TAS sellers would 

have been unable to execute their TAS sale orders and would have likely had to sell in the nonTAS order books, likely aggressively. Such aggressive selling in the but-for world would have 

had a price impact that would replace the Trader Defendants’ actual world trading price impact. 

109. In other words, Dr. Pirrong’s methodology cannot measure the impact that market 

participants who sold TAS contracts to Trader Defendants would have had absent Trader 

Defendants’ trading (i.e., in the “but-for” world). This fundamental flaw alone precludes Dr. 

Pirrong from making credible assertions regarding the but-for price of the May Contract and any 

price distortion allegedly caused by the Trader Defendants’ trading on April 20, 2020. 

110. Second, Section VII.B below explains how Dr. Pirrong’s use of the TVP-VAR model to 

estimate price impact allegedly caused by Trader Defendants’ trades is inconsistent with the 

academic literature’s application of the model. Dr. Pirrong’s analyses are loosely based on a 

VAR model first developed in the market microstructure literature in the early 1990s. However, 

Dr. Pirrong eschews this market microstructure VAR model and instead adapts a TVP-VAR 

model from a macroeconomics article. A TVP-VAR model has never—to my knowledge—been 

peer reviewed to study price impact in a market microstructure setting, nor is it generally 

accepted in the market microstructure literature. 

111. Third, Section VII.C below explains that the inability of Dr. Pirrong’s model in 

estimating a but-for price is closely related to its failure to control for confounding factors or 

alternative explanations for the May Contract price movements such as storage constraints or 

genuine market supply and demand changes. As such, Dr. Pirrong’s opinion that Trader 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 60 of 197 PageID #:14030
 Highly Confidential Page 55

Defendants artificially depressed the May Contract and that, but for their manipulative trading, 

the contract would not have traded as low as it did is unfounded. 

112. Fourth, as I explain in Section VII.D below, Dr. Pirrong’s TVP-VAR model does not 

generate robust results—namely, his results change substantially when the model is 

implemented with minor changes while keeping Dr. Pirrong’s stated assumptions in place.157 

These small alterations should not result in substantially changed outcomes in a robust model. 

113. Finally, Section VII.E below explains that applying a more standard price impact VAR 

model, in-line with the one used in the market microstructure paper cited by Dr. Pirrong, 

produces indicating that the estimated price impact of the 

Trader Defendants’ trading activity is effectively The fact that a 

standard VAR model produces further calls into question the 

reliability of Dr. Pirrong’s conclusions regarding Trader Defendants’ alleged manipulative 

impact. 

114. Together, these limitations render Dr. Pirrong’s model unsound as a basis for his 

opinions. In other words, the limitations of his framework alone preclude Dr. Pirrong from 

reliably reaching his conclusions. 

A. Dr. Pirrong’s Estimation of Price Impact Is Insufficient to Establish Price 

Artificiality 

115. Dr. Pirrong purports to estimate whether Trader Defendants’ conduct depressed prices of 

May Contracts on April 20, 2020, by estimating the price impact caused by Trader Defendants’ 

aggressive trading. However, as Dr. Pirrong acknowledged during his deposition, his estimation 

of price impact is not sufficient to estimate artificiality since “you don’t read the artificiality out 

of the model directly.”158 

116. Economists define the price artificiality of market manipulation as the deviation between 

the actual price and the hypothetical “but-for” price absent the manipulation (i.e., the difference 

157 When Dr. Pirrong estimates his model and constructs IRFs, he imposes a set of “identifying restrictions” by assuming that 

“trades can cause contemporaneous price movements, but price movements do not contemporaneously cause trades.” See

Pirrong Report, ¶¶ 222–223. These assumptions impose a set of restrictions to estimate his model, but there is not a unique way 

of estimating the model. See Section VIII.D.2. 158 Pirrong Deposition, 174:9–25 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 61 of 197 PageID #:14031
 Highly Confidential Page 56

between the actual and but-for worlds).159 Estimating a price impact is insufficient to determine a 

but-for price. In order to determine a but-for price, an economist needs to make reasonable 

assumptions about what would happen in the but-for world. Dr. Pirrong fails to lay out and 

support these necessary assumptions. 

117. Implicitly, Dr. Pirrong’s methodology relies on the economically nonsensical assumption 

that the trading patterns of others, including TAS sellers that relied on Trader Defendants buying 

TAS, would remain unchanged absent Trader Defendants’ alleged manipulative trades. During 

his deposition, Dr. Pirrong 

160 Dr. Pirrong therefore also assumes that in the but-for world where Trader Defendants 

would not be buying TAS, actual-world TAS sellers would have been able to sell similar 

quantities in the TAS order books (but not to Trader Defendants).161

118. This assumption is illogical given Dr. Pirrong 

ignores evidence that TAS sellers in the real world were 

 In the absence of Trader 

Defendants’ trading, a greater number of TAS sellers would have been unable to execute their 

TAS sale orders and would have had to sell in the non-TAS order books. Because only of 

non-aggressive (or passive) orders executed during the proposed class period, these non-TAS 

sales would likely have required aggressive orders to execute.162 Such aggressive selling would 

have had a price impact that would replace some or all of the Trader Defendants’ actual world 

159 Pirrong Deposition, 173:23–174:3 

160 Pirrong Deposition, 180:7–16 

161

 See Pirrong 

Deposition, 176:6–10 

 178:13–23 

285:25–286:6 

162 See Workpaper 10. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 62 of 197 PageID #:14032
 Highly Confidential Page 57

trading price impact. Because Dr. Pirrong’s purported price impact methodology cannot account 

for this but-for price impact, Dr. Pirrong’s analysis is unreliable and incapable of establishing 

whether putative class members were harmed. 

119. As I explained in Section V.C.2 above, 

 (see Figure 12 below). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 63 of 197 PageID #:14033
 Highly Confidential Page 58

Figure 12

Source: CME 

Note: Midpoint refers to the midpoint of the highest bid and lowest offer prices. Buy (sell) quantity is calculated as 

the total bid (offer) volume at or above (below) 1 tick (2 ticks, 5 ticks) of the midpoint price. For example, if the 

midpoint price were $1, the buy quantity within 2 ticks of the midpoint would be calculated as the total volume 

between $0.98 and $1. Likewise the sell quantity within 2 ticks of the midpoint would be calculated as the total 

volume between $1.02 and $1. The midpoint in outright trading fell below zero at 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 64 of 197 PageID #:14034
 Highly Confidential Page 59

120. Dr. Pirrong’s analysis ignores the inevitable selling pressure in the non-TAS outright and 

spread order books that would still exist absent Trader Defendants’ trading. This pressure, 

stemming from sellers with long positions who could not take delivery, would replace Trader 

Defendants’ non-TAS outright and spread selling in a but-for world where Trader Defendants 

did not purchase TAS contracts. In essence, because Trader Defendants’ 

 (see Section X below describing Trader Defendants’ 

trading patterns), 

121. While not stated explicitly, Dr. Pirrong’s methodology assumes that the TAS sellers that 

traded with Trader Defendants and wanted or needed to exit long positions would not sell in the 

non-TAS order books.163 Dr. Pirrong offers no support for this assumption. For this to be true, 

these traders would either need to remain long or find other traders willing to buy TAS to 

effectively replace Trader Defendants. The former solution was not feasible as many holders of 

long positions on April 20, 2020, were not in a position to take delivery and were obligated to 

exit their long May Contract position prior to the expiration of trading on April 21, 2020.164 

122. The alternative solution of seeking replacement traders to purchase TAS contracts was 

unlikely to succeed given 

 During his deposition testimony, 

165 Any such replacement TAS buyers 

may have hedged their TAS purchases by selling in the outright or spread order books, not unlike 

163 Pirrong Deposition, 284:24–285:11 

164 See Section V.A.3 ¶28. See also “Interim Staff Report: Trading in NYMEX WTI Crude Oil Futures Contract Leading Up To, 

On, and around April 20, 2020,” CFTC, November 23, 2020, https://www.cftc.gov/node/235761. At the opening of the April 20, 

2020, trading session, only 46% of the long open interest was held by commercial traders. See Table 2 and Table 3: 49,430 / 

108,593 = 46%. 

165 Pirrong Deposition, 285:18–24 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 65 of 197 PageID #:14035
 Highly Confidential Page 60

Trader Defendants’ trading. Dr. Pirrong’s implicit assumption that replacement traders would 

not impact the price by selling in the outright or spread order books is therefore nonsensical and 

unsupported. 

123. Moreover, in the actual world, 

 For instance, consider , whose activity is displayed in 

Figure 13 below. 

Figure 13

Source: CME 

Note: 

[1] Order book TAS buy quantity represents the total bid quantity, in contracts, in the May TAS order book across all 

order book levels (that is, buy-side liquidity). Quantities are snapshots of depth taken at one-minute intervals. 

[2] Cumulative cancelled TAS sell orders is the cumulative sum of 

124.

 

166 See Workpaper 11. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 66 of 197 PageID #:14036
 Highly Confidential Page 61

 

 

 

 

 

 

 

125. This account’s behavior was not unique. 

 

 

 Dr. Pirrong’s implicit assumptions that replacement TAS buyers would have 

materialized absent Trader Defendants’ TAS buying is inconsistent with the economic realities 

of the May Contract on April 20, 2020. 

126. In the absence of Trader Defendants’ trading, the volume of net May TAS buyers during 

the proposed class period would have been reduced by contracts.174 Without replacement 

TAS buyers, a greater number of TAS sellers would have been unable to execute their TAS sale 

167 See Workpapers 11 and 12. 168 See Figure 13 backup. 169 See Figure 13 backup; Workpaper 11. 170 See Workpaper 11. 171 See Figure 13 backup; Workpaper 11. 172 See Workpaper 13. 173 See Workpaper 13. 174 See Workpaper 14. Defendants purchased and sold TAS contracts between 9:00 AM and 1:30 PM. Defendants 

provided further buy side liquidity prior to 9:00 AM, purchasing an additional TAS contracts. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 67 of 197 PageID #:14037
 Highly Confidential Page 62

orders and would have had to sell in the non-TAS order books, sometimes aggressively. While 

the available data do not allow for a definitive determination of whether TAS sellers who sold to 

Trader Defendants held a long position without individualized inquiry, Dr. Pirrong’s flawed 

methodology estimates that May TAS contracts were sold to Trader Defendants by 

traders that were long at those times.175 These traders were likely unable to take delivery and 

would have needed to liquidate their positions by selling in the outright or spread order books, 

similar to the actions taken by Trader Defendants in the real world. To contextualize the volume 

of the TAS contracts purchased by Trader Defendants, this volume was roughly 

 greater than the maximum buy side TAS liquidity of available during the proposed 

class period.176 To the extent that these unsuccessful TAS sellers waited until later in the 

afternoon to sell in the non-TAS order book, the price impact may have been larger because 

selling more gradually over time tends to have a smaller price impact.177 Trader Defendants’ 

buying from TAS sellers in the TAS order book may have therefore contributed to a smoother 

and more orderly liquidation for some long position holders. 

127. In sum, Trader Defendants provided substantial liquidity to the TAS sellers. In the butfor world, to avoid taking delivery, market participants who placed TAS sale orders would have 

had to sell a similar number of contracts in the outright or spread order books to what Trader 

Defendants sold in the real world. In the unlikely scenario where new buyers would have 

materialized in the TAS order book, similar to Trader Defendants, these TAS buyers would 

likely have sold in the non-TAS outright and spread order books. These non-TAS sales would 

have had a price impact under Dr. Pirrong’s approach that would replace the Trader Defendants’ 

actual world trading price impact under Dr. Pirrong’s approach. This supports the idea that, in a 

but-for world where the Trader Defendants did not purchase TAS contracts, prices may have 

reached similar levels as they did in the actual world, and Dr. Pirrong’s price artificiality 

estimates are thus unreliable and incapable of determining whether putative class members were 

harmed. 

175 See Section VII.B.1; Workpaper 12. 176 See Workpaper 15. 177 Terrence Hendershott and Ryan Riordan, “Algorithmic Trading and the Market for Liquidity,” Journal of Financial and 

Quantitative Analysis 48, no. 4 (2013): 1001-1024, p. 1002 (“Institutional investors utilize ATs to trade large quantities gradually 

over time, thereby minimizing market impact and trading costs.”). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 68 of 197 PageID #:14038
 Highly Confidential Page 63

B. Dr. Pirrong’s Use of a TVP-VAR Model to Estimate Price Artificiality Is 

Inconsistent with and Differs from the Literature on Which He Relies 

128. While Dr. Pirrong’s analyses are loosely based on a class of “vector autoregression 

(‘VAR’) models first developed by Hasbrouck in the early 1990s,” as I explain below, his 

adaptation of the Hasbrouck framework in the context of his analysis has not been used in peerreviewed academic articles estimating price impact.178 This, in part, makes Dr. Pirrong’s use of 

the TVP-VAR model unreliable for estimating the price impact purportedly caused by Trader 

Defendants. 

1. Description of Dr. Pirrong’s TVP-VAR Model 

129. Dr. Pirrong’s TVP-VAR model is based on a methodology first used in Hasbrouck (1991) 

to “[m]easur[e] the information content of stock trades,” and models the relationship between 

price changes and transactions for a particular stock.179 The model used in Hasbrouck (1991) 

estimates the dynamic relationship between buyer-initiated (i.e., the buyer placed an aggressive 

order) or seller-initiated (i.e., the seller placed an aggressive order) trades and price changes, that 

is, how past price changes and trades can predict future trades, and how an unpredicted trade is 

associated with further price movements. 

130. To attempt to capture intraday variations in price impact, Dr. Pirrong eschews 

Hasbrouck’s model and adapts a TVP-VAR model from an article on macroeconomics and 

monetary policy.180 Dr. Pirrong claims that by using such a TVP-VAR model rather than 

Hasbrouck’s VAR model, his implementation of the Hasbrouck (1991) framework controls for 

178 Pirrong Report, ¶ 164 (“In particular, vector autoregression (‘VAR’) models first developed by Hasbrouck in the 1990s and 

widely employed to quantify how trades move prices and take into account the possibility that price movements can also cause 

trades.”). See also Pirrong Report, Appendix C, pp. 1, 6 (After stating that “[a] Hasbrouck vector autoregression (VAR) model of 

order flow and price movements is the standard market microstructure method for quantifying the impact of trades on prices[,]” 

Dr. Pirrong lists as a reference used in Appendix C: “Hasbrouck, J. Measuring the Information Content of Stock Trades. 46 

Journal of Finance (1991): 179-207.”). 

179 Joel Hasbrouck, “Measuring the Information Content of Stock Trades,” The Journal of Finance 46, no. 1 (1991): 179–207 

(“Hasbrouck (1991)”), p. 181 (“The primary statistical technique employed here is a vector autoregression applied to quote and 

trade data.”). 

180 Pirrong Report, ¶¶ 174, 181. See also Giorgio E. Primiceri, “Time Varying Structural Vector Autoregressions and Monetary 

Policy,” The Review of Economic Studies 72, no. 3 (2005): 821–852 (“Primiceri (2005)”). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 69 of 197 PageID #:14039
 Highly Confidential Page 64

possible intraday changes in the coefficients and in the volatility and correlation of the shocks 

that affect the variables of the model.181 

131. In his implementation of the Hasbrouck (1991) framework, Dr. Pirrong purports to 

estimate the cumulative price impact caused by Trader Defendants’ trading during the 9:00 AM–

1:30 PM period on April 20, 2020.182 According to Dr. Pirrong’s results, 

183 Dr. Pirrong then claims 

that these results allow him to compute damages associated with the Trader Defendants’ actions 

for each putative class member.184 

132. As I explain in more detail in Section VII.D below, Dr. Pirrong’s TVP-VAR relies on a 

“Bayesian” estimation algorithm that draws from a set of random distributions to estimate the 

parameters of his model. Dr. Pirrong sets the number of iterations at 10,000. The draws from 

the random distributions are computed using a random number generator. A random number 

generator is a computer algorithm that generates sequences of numbers that appear to be random, 

simulating draws from a predefined statistical distribution function, such as a normal distribution 

or a uniform distribution.185 To replicate results that involve draws from random number 

generators, researchers usually fix the starting point, or “seed,” used by these generators. Fixing 

the seed ensures that the sequence of random numbers remains identical every time the code is 

rerun. Dr. Pirrong’s methodology uses 

186

133. Dr. Pirrong designed his TVP-VAR model to quantify the relationship (i.e., coefficients) 

between (i) the current and lagged values of the change in the price of May Contract, June 2020 

futures, and the May-June 2020 spread over a time interval of 15 seconds; and (ii) the current 

and lagged values of the net aggressor volume in the May Contract, June 2020 futures, and the 

181 See, e.g., Pirrong Report, ¶ 174 (“The standard implementation of the VAR approach assumes that all the coefficients in the 

model, and the variance-covariance matrix of the error terms, are constant during the estimation period. However, price impact 

can vary throughout the day.”), ¶ 181 (“In this [TVP-VAR ] approach, the model coefficients ... vary randomly over time.... 

Further, they can differ for every interval. Moreover, in this approach the variances and covariances of the error terms ... can also 

vary randomly over time.”). 

182 Pirrong Report, Section VI.D. 183 Pirrong Report, ¶ 231. 184 Pirrong Report, ¶¶ 391–396. 185 Cvitanić and Zapatero (2004), p. 363. 186 Pirrong Deposition, 208:23–209:5. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 70 of 197 PageID #:14040
 Highly Confidential Page 65

May-June 2020 spread defined during the same 15-second interval.187 Dr. Pirrong uses the 

midpoint (i.e., the average between the best bid and best offer) for his price datapoints.188 The 

resulting TVP-VAR model’s estimated coefficients purport to represent the predicted price 

impact per traded contract of aggressive orders. 

134. Using the estimated coefficients of his model, Dr. Pirrong computes the price impact of 

aggressive trades by constructing impulse response functions (“IRFs”), which measure how a 

shock to a given variable of the model (e.g., an aggressive trade) “impacts” the other variables of 

the model (e.g., the price change in the May Contract). For his model to measure “impact,” or 

causation—as opposed to mere correlation—Dr. Pirrong assumes “trades can cause 

contemporaneous price movements, but price movements do not contemporaneously cause 

trades.”189 Dr. Pirrong then computes the “cumulative impact of order imbalances on prices” 

using the estimated coefficients of his model and the net aggressor volume for each 15-second 

interval.190 Applying this methodology to the Trader Defendants’ aggressive trades, Dr. Pirrong 

concludes “[t]he running total (cumulative sum) of these price movements measures the total 

(cumulative) impact of Trader Defendants’ aggressive trades and prices for each interval.”191 

Finally, Dr. Pirrong claims that “the TVP-VAR-derived artificiality estimates ... can be used to 

determine the amount of artificiality in a transaction at the price [sic] time the transaction took 

place.”192 

135. As I explain next, Dr. Pirrong’s interpretation of his price impact model to estimate 

artificial prices allegedly caused by Trader Defendants’ trades is inconsistent with the academic 

literature’s application of both the VAR and TVP-VAR models. 

187 Pirrong Report, ¶ 168. 188 Pirrong Report, ¶ 169. 189 Pirrong Report, ¶ 223. 190 Pirrong Report, ¶ 222. 191 Pirrong Report, ¶ 229, Figure 14. 192 Pirrong Report, ¶ 391. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 71 of 197 PageID #:14041
 Highly Confidential Page 66

2. Dr. Pirrong’s Claim That His TVP-VAR Model Can Be Used to 

Determine the Amount of Artificiality Caused by Trader Defendants’ 

Trades Is Unsupported by the Literature on Which He Relies 

136. Dr. Pirrong’s claim that his TVP-VAR model can be used to determine the price impact 

or the amount of artificiality caused by Trader Defendants’ trades is inconsistent with the 

application of the model from the academic literature. 

137. First, to my knowledge, a TVP-VAR model has never been peer reviewed to study price 

impact in a market microstructure setting, nor is it generally accepted in the market 

microstructure literature.193 The TVP-VAR model he adapts was used to measure time variation 

over decades in a macroeconomic and monetary policy model, not variations over a few hours in 

a market microstructure model. Dr. Pirrong testified that 

.

194 Inconsistent with 

academic literature, Dr. Pirrong uses the TVP-VAR model in a market microstructure setting. 

138. Second, in contrast to Dr. Pirrong’s conclusions, the Hasbrouck (1991) model simply 

estimates a lead-lag relationship between aggressive orders and price changes and does not 

purport to demonstrate that this price impact was artificial. As the title of his study indicates, the 

model described by Hasbrouck (1991) was designed to “[m]easur[e] the information content of 

stock trades,” which refers to the extent to which trades can incorporate information into the 

prices and predict the direction of imminent market moves.195 As the article suggests, “[w]ithin 

this framework, a trade’s information effect [on security prices] may be meaningfully measured 

as the ultimate price impact of the trade innovation.”196 Put differently, the trade itself is not the 

original source of information or the primary cause of the price move. Instead, the trade can act 

as a conduit, conveying information known by market participants. 

193 To my knowledge, VAR models with time-varying parameters have only been used in the field of macroeconomics to 

estimate macroeconomic policy changes over time and implied shifts in private sector behavior. See, e.g., Primiceri (2005), p. 

822. 

194 During his deposition testimony, 

 See Pirrong Deposition, 196:6–19 

 See Pirrong Deposition, 195:11–24. 195 See Hasbrouck (1991). 196 See Hasbrouck (1991). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 72 of 197 PageID #:14042
 Highly Confidential Page 67

139. Third, Dr. Pirrong’s model assumes the price impact associated with Trader Defendants’ 

trading is the same as the price impact associated with non-Defendants trading. Indeed, by 

grouping all aggressive trades of a specific contract during a 15-second window together without 

differentiating Trader Defendants’ trading from other trading, Dr. Pirrong’s TVP-VAR model 

effectively assumes that the price impact of an aggressive trade is the same for all market 

participants. This means that the estimated price impact attributed to Trading Defendants is 

partly driven by the trading of non-Defendant traders. If at some point in time Trader 

Defendants’ trading had a lower price impact relative to the trading from other market 

participants, Dr. Pirrong would not capture this occurrence and would overestimate the alleged 

price artificiality. 

140. Fourth, the Hasbrouck (1991) methodology does not contemplate the question of whether 

the price would have moved more or less if trading had not occurred, which by extension means 

the Hasbrouck model does not estimate a “but-for” price. By attempting to estimate price 

artificiality, Dr. Pirrong draws an inference about the but-for price of May Contracts and MayJune spreads based on the output of his TVP-VAR model. However, Dr. Pirrong relies on a 

model that was not designed to establish an asset’s but-for price. This reliance reflects his 

fundamental misuse of the relevant academic literature and the methodology he purports to 

employ.197 The Hasbrouck (1991) methodology, 

 is designed to describe the actual world, not to estimate a but-for world.198

141. Put differently, suppose a piece of exogenous, value-relevant information arrives in the 

market. According to Hasbrouck, both the changes in prices set by passive and aggressive trades 

work together to make this information part of the stock’s price. This process helps the stock 

price “arrive at the current efficient price,” meaning it accurately reflects all available 

information.199 The impact of trading on how quickly and how much the price changes is 

measured by the IRF, but Hasbrouck’s approach does not address whether less trading would 

have led to a smaller change in price. Instead, in Hasbrouck’s framework, the amount of trading 

197 See, e.g., Pirrong Report, ¶ 228 (emphasis in original) (“Since I will show infra that [Trader Defendants’] aggressive trades 

were uneconomic, the resulting price impacts are measures of price artificiality—i.e., the price movement that occurred as the 

results of Defendants’ uneconomic conduct up through ”). 198 Pirrong Deposition, 192:5–12 

199 Hasbrouck (1991), p. 183. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 73 of 197 PageID #:14043
 Highly Confidential Page 68

affects the channel (i.e., aggressive or passive trades) through which the efficient price is 

discovered but does not necessarily change the “efficient price.” 

142. To understand why Dr. Pirrong’s model cannot estimate a but-for price or distinguish 

legitimate market reactions from alleged manipulative activities, consider the following 

hypothetical example. A company trading at $10 a share announces it will be acquired at $30 a 

share (with no chance the acquisition will not be completed). Trader Z quickly places aggressive 

orders to purchase shares at prices below $30. The expected swift price increase that follows the 

announcement could be solely attributed to Trader Z’s aggressive purchases. In this situation, 

Dr. Pirrong’s model would find a relationship between Trader Z’s aggressive purchases and the 

subsequent price increase. Dr. Pirrong’s model would incorrectly conclude that Trader Z caused

the price increase, and that the price increase would not have happened absent Trader Z’s 

purchase. In reality, the stock price increase would have occurred absent Trader Z’s aggressive 

trades because the true value of the stock is above the current value of $10. 

143. Therefore, while not stated explicitly, Dr. Pirrong’s TVP-VAR model interpretation 

inherently assumes that the trading patterns of others, including TAS sellers that relied on Trader 

Defendants buying TAS, would remain unchanged absent Trader Defendants’ alleged 

manipulative trades. However, as I explain in Section VII.A, this is an economically nonsensical 

assumption when considering the CME WTI futures market dynamics on April 20, 2020, and the 

fact that without Trader Defendants’ trading, TAS sellers would have faced an acute lack of 

liquidity. 

C. Dr. Pirrong’s Price Impact Model Does Not Control for Confounding 

Factors 

144. The inability of Dr. Pirrong’s TVP-VAR model to estimate a but-for price is related to 

the model’s failure to control for confounding factors or alternative explanations for the May 

Contract price movements. This occurs because Dr. Pirrong’s TVP-VAR model attributes price 

changes to aggressive trading, overlooking the underlying factors that drive trading behavior.200 

This means his model is biased towards attributing price changes to trading, when in fact the 

changes (and the trades) may be attributable to some other cause that he ignores. This is 

200 Pirrong Deposition, 242:14–17. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 74 of 197 PageID #:14044
 Highly Confidential Page 69

commonly called omitted-variable bias, which means the impact of variables absent from the 

model are incorrectly attributed to variables included in the model. 

145. For example, his TVP-VAR model does not include factors such as storage and logistical 

constraints, genuine market supply and demand changes, TAS sellers needing to exit their long 

positions, or short commercial traders “forc[ing] the remaining longs to pay an extreme penalty” 

to exit their long positions by withholding futures purchases, effectively “squeez[ing]” the 

longs.201 Withholding of trading would not be accounted for in Dr. Pirrong’s model as the TVPVAR only accounts for aggressive trading, not the lack of trading. Without controlling for such 

confounding factors or alternative explanations, Dr. Pirrong’s methodology is biased to 

overestimate the price impact associated with Trader Defendants’ trades. 

146. The impact of confounding factors is demonstrated by Dr. Pirrong’s model ceasing to 

explain price changes. The Rଶ of Dr. Pirrong’s TVP-VAR model measures the fraction of the 

variance of price changes explained by his trading variables, and during the last of 

the proposed class period his Rଶ substantially declines.202 The Rଶ for the May midpoint price 

change and the May-June midpoint spread change fell to levels close to zero well before the last 

 of the proposed class period.203 Therefore, a decrease in Rଶ is consistent with 

confounding factors or alternative explanations not being included in his model, yet being the 

drivers of price changes. 

147. In addition to not accounting for confounding factors, Dr. Pirrong constructs his model in 

a way that assume trades cause prices, but prices cannot cause trades. 204 Functionally, in a TVPVAR the variables placed at the top of the vector can cause contemporaneous changes to the 

variables below, but the variables at the top cannot contemporaneously be affected by the 

201 Philip Verleger, “Negative Prices: Never Again,” Energy Intelligence, April 23, 2020, 

https://www.energyintel.com/0000017b-a7da-de4c-a17b-e7da75620000 (“The USO effect was still there because it pulled in 

more shorts, that is it allowed more producers to hedge, leaving the market more exposed to the risk of a squeeze by the shorts—

which is what happened.”); “What Really Caused the Epic WTI Meltdown?” Energy Intelligence, April 21, 2020, 

https://www.energyintel.com/0000017b-a7da-de4c-a17b-e7da70fa0000; 

 CITI-05594–5 202 Pirrong Report, ¶ 205. 

203 In fact, the Rଶ for the May midpoint price change fell to at while the Rଶ for the May-June 

midpoint spread change fell to around at . See Workpaper 16. This means that the fraction of 

the variability in prices explained by the order flow variations was almost nil during that time. According to Dr. 

Pirrong’s own logic, the low Rଶ values produced by the TVP-VAR model before the last is symptomatic 

of a change in market dynamics and cast a doubt on the use of the TVP-VAR model during that period. Pirrong 

Report, ¶¶ 205–207. 

204 Pirrong Report, ¶¶ 222–223 (“[T]rades can cause contemporaneous price movements, but price movements do not 

contemporaneously cause trades.”). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 75 of 197 PageID #:14045
 Highly Confidential Page 70

variables below. Therefore in his code Dr. Pirrong placed the trade variables on top of the price 

variables.205 

148. The importance of the unilateral causation restriction put in place by Dr. Pirrong does not 

allow for price movements to cause the need to aggressively trade. Dr. Pirrong’s model assumes 

that aggressive trading always causes price movements. This assumption is not realistic, 

especially in the unusual price movements on April 20, 2020. Further, Dr. Pirrong uses 15-

second time windows, which is longer than it takes for orders to be processed and trades 

executed. Within a 15-second time window, traders may have aggressively traded as a reaction 

to rapid price movements earlier in the same 15-second time window. Put differently, aggressive 

trading, including Trader Defendants’, may be caused by price movements that are themselves 

caused by underlying confounding factors. Dr. Pirrong’s model does not allow this direction of 

causality. In his deposition, 

206 

 

149. In sum, Dr. Pirrong’s methodology does not offer a reliable framework for distinguishing 

price impacts arising from confounding factors or alternative explanations that have been 

advanced by numerous observers (see Section V.C above). His methodology therefore fails to 

establish that any putative class member was harmed by his purported price artificially. 

D. Dr. Pirrong’s TVP-VAR Model Is Highly Sensitive to Small Changes, and 

Artificiality Estimates Can Flip from Depression to Inflation 

150. In addition to the flaws and limitations discussed in Sections VII.A through VII.C, Dr. 

Pirrong’s price artificiality conclusions rely on a model that does not produce robust results. 

Even setting aside that Dr. Pirrong’s price impact model is insufficient to estimate price 

artificiality, basic diagnostic analyses reveal that Dr. Pirrong’s TVP-VAR model is inherently 

unreliable and yields substantially different results when implemented with minor changes. 

205 Pirrong Deposition, 215:20– 24 

206 See Pirrong Deposition, 212:16–23 

 213:22–214:4 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 76 of 197 PageID #:14046
 Highly Confidential Page 71

Estimating his results with his own code and data, maintaining all of his stated assumptions, but 

changing the starting seed used for the random number generator in his estimation or the 

arbitrary ordering of the variables in his TVP-VAR model drastically alters the results of his 

model. 

151. Overall, the results showing that Dr. Pirrong’s model is not robust to minor changes are 

not surprising given the large number of parameters in his model. In Dr. Pirrong’s deposition, 

207 This is 

worrisome because, as explained in Section VII.B.1, the Bayesian estimation algorithm used by 

Dr. Pirrong to estimate his model draws from a set of random distributions over 10,000 iterations 

to estimate the parameters of his model. Given the large number of parameters, implementing 

Dr. Pirrong’s model even with minor changes can affect the draws from the random distributions 

and therefore the estimated parameters, leading to substantially different results. 

1. Changing the Seed of the Random Number Generator Changes Dr. 

Pirrong’s Conclusions 

152. As explained in Section VII.B.1 above, Dr. Pirrong’s methodology relies on a Bayesian 

estimation algorithm that uses draws from a random number generator with a prefixed seed to 

estimate the parameters of his model. Changing the seed of a random number generator changes 

the sequence of values drawn from a predefined distribution function, but it keeps the statistical 

properties of the predefined distribution function constant. In his deposition testimony, 

208

153. For example, the two sequences {0.9,-1.2, 0.6} and {-0.5, -1.3, 0.9} were generated by 

the computer software MATLAB using the seeds 1020 and 2001 and a standard normal 

207 Pirrong Deposition, 196:20–197:7 

 208 Pirrong Deposition, 207:6–13 

 208:9–15 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 77 of 197 PageID #:14047
 Highly Confidential Page 72

distribution function.209 As shown, different seeds generate different sequences of numbers. The 

different seeds, however, do not change the fact that both sequences represent draws from the 

same standard normal distribution function. As the number of draws increases, the mean of each 

sequence should converge to 0 while the variance of each sequence should converge to 1, 

reflecting the fact that both sequences were generated by the same standard normal distribution 

function. 

154. The Bayesian estimation used by Dr. Pirrong relies on 10,000 draws from different 

distribution functions. At a basic level, Bayesian estimation treats parameters as random 

variables with an unknown distribution. The objective of such models is to estimate the 

distribution of the parameters based on prior beliefs of the distribution (the “prior distribution”) 

and conditional on the data sample observed. To achieve this, Bayesian estimation takes draws 

of the parameters from the “posterior distribution”—that is, the distribution of the parameters 

conditional on the data observed and conditional on the prior distribution.210 These draws 

depend, among other things, on the specific seed of the random number generator chosen by the 

researcher. 

155. As explained above, setting different values for the seed of a random number generator 

changes the sequence of the random numbers drawn by the algorithm, but it does not change the 

underlying statistical properties of the function the numbers are drawn from. In certain cases, 

Bayesian estimators can be sensitive to the particular sequence of numbers drawn by the random 

numbers generator.211 

156. Dr. Pirrong’s model suffers from this very problem. To illustrate, I replicate his results 

by changing nothing except for the seed used in his estimation algorithm. To keep the 

comparison simple, I focus on Dr. Pirrong’s estimated cumulative price artificiality at 1:30 PM 

and replicate Dr. Pirrong’s results using three different seeds: 606368, 4, and 774239. Figure 14 

below shows the results. In the case of the May outright contract, varying the seed generates 

cumulative artificiality results ranging between /bbl and /bbl compared to the 

/bbl estimated when using Dr. Pirrong’s same seed. Similar results can be observed in the 

209 The numbers are rounded to one decimal place and the random number generator algorithm used is known as “Marsenne 

Twister.” 

210 Andrew Gelman, et al., Bayesian Data Analysis, 3rd ed. (2021), p. 3. 211 See Christiane Baumeister and James Hamilton, “Inference in Structural Vector Autoregressions when the Identifying 

Assumptions are not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations,” Journal of Monetary 

Economics 100, (2018): 48–65, p. 21. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 78 of 197 PageID #:14048
 Highly Confidential Page 73

case of the May-June spread. The TVP-VAR model’s cumulative price impact using these three 

seeds and the 1020 seed used by Dr. Pirrong are depicted in Figure 15 below. It shows that the 

alleged price impact for the outright contract diverges throughout the proposed class period for 

each of these different seeds. 

157. I note that two of the three seeds shown in Figures 14 and 15 produce 

12 

Figure 14

Source: Pirrong Report and Backup Materials 

Note: Artificiality measures are based on Dr. Pirrong’s TVP-VAR model’s cumulative price impact estimates as of 

1:30 PM. Seed sensitivities are run using Dr. Pirrong’s produced code with 24-core computing power. Results are 

slightly sensitive to computing power. When running seed 1020 with 22 cores, May outright and May-June spread 

Artificiality are and , respectively. Following Dr. Pirrong’s methodology, I ran 12,500 iterations and 

discarded the first 2,500. 

212 During his deposition testimony, 

 See Pirrong Deposition, 208:16–209:2. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 79 of 197 PageID #:14049
 Highly Confidential Page 74

Figure 15

Source: Pirrong Report and Backup Materials 

Note: Artificiality measures are based on Dr. Pirrong’s TVP-VAR model’s cumulative price impact estimates for the 

May outright contract. Seed sensitivities are run using Dr. Pirrong’s produced code with 24-core computing power. 

Results are slightly sensitive to computing power. Dr. Pirrong’s results use 12,500 iterations and discard the results 

of the first 2,500, and seed sensitivities were run using 12,500 iterations and discarding the first 2,500. 

2. Changing the Ordering of the Variables in the Regression Changes 

Dr. Pirrong’s Results, and He Provides No Basis for His Ordering 

158. An additional known issue with the type of Bayesian model estimated by Dr. Pirrong is 

that the arbitrary order of the model’s variables affects the results, yet Dr. Pirrong provides no 

support to choose the particular order he uses in his estimation. In fact, the same academic 

article that develops the model Dr. Pirrong uses in his report states that “it is important to 

highlight a potential drawback of the modelling assumptions [in the model equations] in 

combination with the priors.... The drawback is the fact that, in principle, the order of the 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 80 of 197 PageID #:14050
 Highly Confidential Page 75

variables matters in [the VAR equation].”213 This is a well-known issue in this literature and has 

been acknowledge by various researchers.214

159. In a TVP-VAR like the one Dr. Pirrong estimates, the variables of the model are ordered 

in a vector, which is an ordered list of elements usually sorted in a row or a column. The order 

of the variables is related to the causality assumptions imposed by the researcher, which are 

necessary to understand how a shock to one variable (e.g., an aggressive purchase of the May 

Contract) can affect the response of the other variables (e.g., the midpoint price of the May 

Contract). 

160. As explained in Section VII.B.1, when Dr. Pirrong estimates his model and constructs 

IRFs, he imposes a set of “identifying restrictions” that require him to place the trade variables 

on top of the price variables, as he effectively does in his code and confirmed in his deposition.215 

161. In fact, Dr. Pirrong orders the variables in the vector, from top to bottom, as follows: 

May outright trade imbalance, May-June spread trade imbalance, June outright trade imbalance, 

May outright midpoint price change, May-June midpoint spread change, June outright midpoint 

price change. This order is consistent with Dr. Pirrong’s identifying restrictions since the three 

trade variables are placed on top of the three price variables, but this ordering combination is not 

the only one that satisfies his identifying restrictions.216 For example, Dr. Pirrong could have 

ordered the variables as follows, while still satisfying his identifying restrictions: June outright 

trade imbalance, May-June spread trade imbalance, May outright trade imbalance, June outright 

midpoint price change, May-June midpoint spread change, May outright midpoint price change. 

To determine a unique ordering of the variables, Dr. Pirrong would have to make and 

213 Primiceri (2005), p. 827. 214 Mark Bognanni, “A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification,” 

Federal Reserve Bank of Cleveland Working Paper 18-11 (2018) (“Bognanni (2018)”), p. 2 (“[I]nference in the model of 

Primiceri (2005) depends on the ordering of variables in the VAR. Hence, an n-variable dynamic system admits n! distinct 

estimates of the time-varying parameters in the first stage, which form the key input into the set identification procedure.”). See 

also Timothy Cogley and Thomas J. Sargent, “Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US,” 

Review of Economic Dynamics 8, no. 2 (2005): 262–302 (“Cogley and Sargent (2005)”); Emily B. Fox and Mike West, 

“Autoregressive Models for Variance Matrices: Stationary Inverse Wishart Processes,” arXiv, arXiv:1107.5239 (2011) (“Fox and 

West (2011)”). 

215 See Pirrong Report, ¶¶ 222–223. See also Pirrong Deposition, 215:20–24 

216 Pirrong Deposition, 216:13–217:10 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 81 of 197 PageID #:14051
 Highly Confidential Page 76

substantiate specific assumptions on how trade variables affect each other—and the same would 

be necessary for price variables. 

162. This implies that, absent these specific assumptions, Dr. Pirrong’s model can be 

estimated in multiple ways by permuting the order of the variables as long as the identifying 

restrictions are satisfied.217 However, the academic literature I discussed at the beginning of this 

section shows that Bayesian estimation is not always robust to the arbitrary order of the variables 

of the model. At a minimum, researchers using these types of models should always check the 

robustness of their results to alternative orderings of the variables, something Dr. Pirrong fails to 

do in this case.218

163. To test how the ordering of the variables in the TVP-VAR model affect Dr. Pirrong’s 

results, I estimated his model for each of the 36 different possible permutations of the trade and 

price variables that satisfy his identifying restrictions. In this exercise, I used Dr. Pirrong’s 

original seed and I focused on the estimated cumulative price impact at 1:30 PM. Figure 16 

below shows the range of estimated cumulative price impact at 1:30 PM across the 36 different 

possible permutations. 

 The TVP-VAR model’s cumulative price 

impact using this permutation over the proposed class period is depicted in Figure 17 below. 

 This finding alone demonstrates that Dr. Pirrong’s 

model is fundamentally flawed and incapable of reliably and accurately estimating price impact, 

let alone artificiality. 

217 Since there are three trade variables and three price variables in Dr. Pirrong’s model, these variables can be sorted in 3! × 3! = 

36 different permutations. 

218 See, e.g., Primiceri (2005) p. 827 (After recognizing the issue of the ordering of the variables affecting the results of the TVPVAR estimation, the author notes that in his particular empirical application the results are robust to alternative ordering of the 

variables: “It is worth pointing out that, in the context of the empirical application of this paper, the results obtained with

different orders of the variables are very similar.”). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 82 of 197 PageID #:14052
 Highly Confidential Page 77

Figure 16

Source: Pirrong Report and Backup Materials 

Note: 

[1] Except for the ordering of the variables, Dr. Pirrong’s seed and methodology are replicated exactly to estimate 

the model and artificiality. 

[2] The ordering of variables to produce the minimum alleged price impact in the May outright and May-June 

spread are (QM, QKM, QK, DPM, DPKM, DPK) and (QM, QKM, QK, DPKM, DPM, DPK), respectively, where QM is the net 

imbalance of June outright trades, QKM is the net imbalance of May-June spread trades, QK is the net imbalance of 

May outright trades, DPKM is the change in midpoint price of May-June spread, DPM is the change in midpoint price 

of June outright, and DPK is the change in midpoint price of May outright. 

[3] The ordering of variables to produce the maximum alleged price impact in both the May outright and the MayJune spread is (QK, QM, QKM, DPKM, DPM, DPK). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 83 of 197 PageID #:14053
 Highly Confidential Page 78

Figure 17

Source: Pirrong Report and Backup Materials 

Note: 

[1] Except for the ordering of the variables, Dr. Pirrong’s seed and methodology are replicated exactly to estimate 

the model and artificiality. 

[2] The ordering of variables to produce the plotted “reordered” price impact is (QK, QM, QKM, DPKM, DPM, DPK) 

where QK is the net imbalance of May outright trades, QM is the net imbalance of June outright trades, QKM is the net 

imbalance of May-June spread trades, DPKM is the change in midpoint price of May-June spread, DPM is the change 

in midpoint price of June outright, and DPK is the change in midpoint price of May outright. 

164. Finally, as mentioned in Section VII.B above, Dr. Pirrong’s TVP-VAR model conflates 

the price impact associated with Trader Defendants trading and the price impact associated with 

other market participants trading. Differentiating the price impact of Trader Defendants would 

require splitting the trade variables into two groups: Trader Defendants’ aggressive trades and 

other aggressive trades. However, this would increase the number of possible permutations (i.e., 

variable ordering) to sort the variables of the TVP-VAR model from 36 to 4,320, and the number 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 84 of 197 PageID #:14054
 Highly Confidential Page 79

of parameters to be estimated from approximately 150,000 to 350,000.219 Dr. Pirrong’s model 

would therefore likely present even more serious robustness issues.220 

165. In sum, the type of model used by Dr. Pirrong deviates from the price impact literature, 

and basic diagnostic analyses reveal that Dr. Pirrong’s model is inherently unreliable. TVPVAR models have, to my knowledge, not been used in peer reviewed articles to study price 

impact in a market microstructure setting, are not generally accepted in the market 

microstructure literature, and are subject to criticism even in the field of macroeconomics from 

which they are drawn.221 Moreover, Dr. Pirrong does not assess the statistical error rates 

associated with his analysis and, leaving all of his stated assumptions in place, even minor 

changes to how it is implemented can yield completely different results. 222

E. Applying Hasbrouck’s VAR Methodology Produces Statistically 

Insignificant Price Impact Results 

166. The reliability of Dr. Pirrong’s idiosyncratic TVP-VAR price impact model is further 

called into question by the fact that applying Hasbrouck’s (1991) standard VAR methodology of 

price impact produces 

167. Unlike Dr. Pirrong’s novel TVP-VAR implementation, the Hasbrouck VAR 

implementation to measure price impact assumes that all parameters are constant and estimates 

parameters by either ordinary least squares (“OLS”) or maximum likelihood. I estimate Dr. 

Pirrong’s model under these conditions. Specifically, I apply the VAR equations defined in the 

Pirrong Report and I compute cumulative IRFs under Dr. Pirrong’s own identifying restrictions 

using the Hasbrouck OLS method.223

168. Figure 18 below shows that the results of this exercise produce 

 to an aggressive purchase of a May Contract. The figure presents cumulative IRFs of an 

aggressive purchase of one May Contract for the May outright midpoint price and the May-June 

219 See Workpaper 17. 220 With six trade variables and three price variables in Dr. Pirrong’s model, these variables can be sorted in 6! × 3! = 4,320 

different permutations. See Workpaper 17. 221 See, e.g., Bognanni (2018); Cogley and Sargent (2005); Fox and West (2011). 222 When economists make estimations, they usually report the degree of uncertainty associated with them. One way of reporting 

this uncertainty in Bayesian statistics is with a “credible interval,” that is, a range of values the estimated parameter or estimated 

function can take with a prespecified probability, conditional on the observed data sample. By analyzing the credible intervals, 

one can determine whether or not the price impact estimation is statistically different from zero. Dr. Pirrong fails to provide 

credible intervals associated with his cumulative IRFs (Figure 9 and Figure 10) and with his measures of artificiality (Figure 13 

and Figure 14). 

223 See Pirrong Report, ¶¶ 179, 222. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 85 of 197 PageID #:14055
 Highly Confidential Page 80

midpoint spread. The cumulative IRFs take into account the responses 20 periods (five minutes) 

after the aggressive purchase is made, and are therefore directly comparable to the IRFs 

presented in Figure 9 of the Pirrong Report.224 

Figure 18

Source: Pirrong Report and Backup Materials 

Note: Values plotted are the cumulative price impact of an aggresive purchase of a single May outright contract five 

minutes after the purchase. This figure shows 95% confidence intervals calculated by using 10,000 iterations of a 

statistical method known as bootstrap, whereby in each iteration impulse response functions are estimated based on 

a random draw of the fitted errors of the model constructed with the parameters estimated in the prior iteration. 

169. Figure 18 shows that 

224 Pirrong Report, ¶ 197 (“Figure 9 presents the IRF for the May order imbalance variable. The depicted IRF is for 20 periods 

after the imbalance occurs (i.e., 5 minutes in the model with 15 second time intervals.)”). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 86 of 197 PageID #:14056
 Highly Confidential Page 81

225

170. Figure 18 not only contains point estimates of the cumulative IRFs but it also shows 95% 

confidence intervals. 

 

227 

This means that the estimated price impact of each of the Trader Defendants’ trading activity on 

the May Contract is effectively 

171. In sum, unlike Dr. Pirrong’s untested TVP-VAR model, a Hasbrouck VAR model 

produces IRFs that are indistinguishable from zero. This calls into question the reliability of Dr. 

Pirrong’s idiosyncratic TVP-VAR model, especially since he has not tested if his TVP-VAR 

results are statistically different from zero. 

225 See Pirrong Report, ¶ 199 (“The mean impact of an aggressive trade in the May contract on the May midpoint 

and May-June spread midpoints is approximately cent/contract [sic] in the direction of the trade (i.e., with aggressive sales 

causing price declines).”). See Workpaper 18. 226 See Workpaper 18. 227 When economists make estimations they usually report the degree of uncertainty associated with them. Far and away, the 

most commonly accepted significance threshold is 5%, which is also often referred to as the 95% “confidence level.” See, e.g., 

David A. Freedman and David H. Kaye, “Reference Guide on Statistics,” in Reference Manual on Scientific Evidence, ed. 

National Research Council et al., 3rd ed. (2011), pp. 211–302, at p. 251 (internal citations omitted) (“In practice, statistical 

analysts typically use levels of 5% and 1%. The 5% level is the most common in social science, and an analyst who speaks of 

significant results without specifying the threshold probably is using this figure.”), p. 284 (“[C]onfidence interval.... For 

estimates such as averages or rates computed from large samples, a 95% confidence interval is the range from about two standard

errors below to two standard errors above the estimate.”), p. 285 (“[C]onfidence level. See confidence interval.”).

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 87 of 197 PageID #:14057
 Highly Confidential Page 82

VIII. Dr. Pirrong Offers No Methodology to Establish Causation between the Trader 

Defendants’ Trades and the Purported , Yet He Attributes 

172. Dr. Pirrong does not provide a methodology to establish that Trader Defendants’ trading 

impacted prices after during the purported 228 Yet, Dr. Pirrong attributes 

173. In Section VIII.A, I explain how Dr. Pirrong abandons his TVP-VAR model after 

 Dr. Pirrong explains that order flow materially stops explaining price changes in his 

model, meaning some other factors not identified or explained by Dr. Pirrong contributed to 

price changes during the purported Therefore, Dr. Pirrong has no price artificiality 

model during the last of the proposed class period.229 Approximately of Dr. 

Pirrong’s damages are associated with the artificiality measure during the last of the 

proposed class period,230 making this period critical to putative class members.231

174. In Section VIII.B, I explain how Dr. Pirrong pivots to an entirely different approach, his 

VPIN metric. Specifically, during the purported he calculates two VPIN metrics that 

he claims measure order flow toxicity: one for May outrights and one for May-June spreads. 

228 Unless noted otherwise, here and throughout this report I use the expression “last of the proposed class period,” 

the “period after ,” “during the purported ,” or the “post- ” to refer to the period between the start of 

the purported and 1:30 PM, as Dr. Pirrong does in his report. However, according to Dr. Pirrong, the purported 

 starts at ; see, e.g., Pirrong Report, ¶ 394 (emphasis added) (“the price artificiality attributable to any transaction 

occurring during the can be calculated formulaically by taking the difference between prices at 

 and the price at the time of the transaction ). Throughout the 

Pirrong Report, Dr. Pirrong is inconsistent regarding the starting time of the purported and he repeatedly uses the 

expression “post ” and as reference to the purported period; see, e.g., Pirrong Report, ¶ 381 

(“Therefore, the post price movements are instead best understood as ‘endogenous volatility’ characteristic of a 

”); see also Pirrong Report, ¶ 199 (emphasis in original) (“This erratic behavior during the last of trading is 

consistent with the analysis presented supra.”). 229 Previously I described why Dr. Pirrong’s TVP-VAR model is not suitable to estimate a but-for price and by extension price 

artificiality. When I refer to Dr. Pirrong’s TVP-VAR model as a price artificiality model, I am referring to how Dr. Pirrong uses 

it, and I am not agreeing the use is correct. 

230 See Figure 28 backup. 231 Dr. Pirrong states that “the price artificiality attributable to any transaction occurring during the can be calculated 

formulaically by taking the difference between prices at ) and the price at the time of the 

transaction .” See Pirrong Report, ¶ 394. This implies that he switches his artificiality 

estimation approach from the TVP-VAR model to 

 See Pirrong Deposition 229:8–230:13. Even by Dr. Pirrong’s logic, 

 

Throughout this report, for all alleged artificiality and damages calculations, I therefore apply his TVP-VAR through 

, which produces an artificiality estimate for the 15 second interval through 

 I also note that Dr. Pirrong is not clear about how to calculate artificiality post-

 Dr. Pirrong’s statement in ¶ 394 of his report may be interpreted as only taking into account the price difference between

 and the time of the transaction, and ignoring the artificiality estimated by his TVP-VAR prior to Given

the lack of clarity, I assume Dr. Pirrong adds his TVP-VAR estimated cumulative artificiality to the full price decline after 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 88 of 197 PageID #:14058
 Highly Confidential Page 83

His VPIN metrics do not calculate price artificiality, nor does he claim they calculate price 

artificiality. In fact, unlike the TVP-VAR model, Dr. Pirrong’s VPIN metrics do not even 

estimate price impact. Price is not an input or an output to Dr. Pirrong’s VPIN metrics. His 

VPIN metrics are simply “based on a ratio of the absolute value of order flow imbalance to 

trading volume.”232 He claims contributors to his VPIN metric for the May-June spreads (but not 

the May outrights) caused the purported , and that Trader Defendants collectively 

made up a sufficient portion of his VPIN metric, and therefore 

.

233 Dr. Pirrong’s assumption that 

contributors to his VPIN metric caused the purported is unsupported by academic 

literature and not subject to a scientific method. Further, his assumption to attribute 

 is not supported, deeply flawed, and does not control for 

potential confounding factors. Finally, the same conceptual flaws I outlined in Section VII.A 

above apply in the last of the proposed class period, whereby estimating the price 

impact of Trader Defendants’ trading is not sufficient to estimate artificiality. In a but-for world 

where the Trader Defendants did not purchase TAS contracts, prices may have reached similar 

levels as they did in the actual world, including during Dr. Pirrong’s purported 

A. After Dr. Pirrong’s TVP-VAR Model Cannot Reasonably Estimate 

Price Impact, and Dr. Pirrong Abandons the Model 

175. The ability of order flow to explain price changes in Dr. Pirrong’s TVP-VAR model 

materially declines after , and he therefore abandons the TVP-VAR model.234 If the 

TVP-VAR model does not materially explain price changes, then it cannot reasonably estimate 

232 Pirrong Report, ¶ 235. 233 Dr. Pirrong claims that the VPIN measures order flow toxicity and that “the academic literature on flash crashes identifies 

order flow toxicity as a cause of such crashes.” He then claims that 

 See Pirrong Report, ¶¶ 26, 235, 239, 257. 234 Dr. Pirrong is not explicit about abandoning his TVP-VAR model; however, the change in the method to estimate price 

artificiality is evident from his methodology to compute damages. See Pirrong Report, ¶ 391 (“For the period 9 a.m.-1:30 p.m 

[sic], the TVP-VAR-derived artificiality estimates ... can be used to determine the amount of artificiality in a transaction at the 

price time the transaction took place”), ¶ 394 (“[T]he price artificiality attributable to any transaction occurring during the 

 can be calculated formulaically by taking the difference between prices at and the 

price at the time of the transaction Although these two statements are contradictory, the 

code produced by Dr. Pirrong to estimate artificiality is consistent with using the TVP-VAR price artificiality only until 

so I assume the time period Dr. Pirrong refers to in ¶ 391 is a mistake not corrected in his Corrected Report. During his 

deposition testimony, See Pirrong Deposition 229:6–20 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 89 of 197 PageID #:14059
 Highly Confidential Page 84

price impact. Dr. Pirrong’s reasoning for his abandonment of the TVP-VAR model is as 

follows: “[T]he TVP-VAR model exhibits different market dynamics before and after about 

 and “order flow explains little of the period price movements, and the 

magnitudes of the non-order flow price movements during this period were substantially greater 

than prior to ” Dr. Pirrong calls this period a 235

176. Since order flow cannot explain price changes in Dr. Pirrong’s model in the last 

 of the proposed class period, this means some other factors not identified or explained 

by Dr. Pirrong contributed to price changes during the purported Dr. Pirrong 

abandons his TVP-VAR after , effectively leaving Dr. Pirrong without a price 

artificiality model during his purported period. 

1. Order Flow Materially Stops Predicting Price Changes in Dr. 

Pirrong’s TVP-VAR Model after and Therefore Other 

Unexplained Factors Explain Price Changes 

177. Dr. Pirrong’s TVP-VAR model relies on order flow predicting price changes, and if order 

flow materially stops predicting price changes, then some other factors are explaining price 

changes that are not captured by his model. Dr. Pirrong does not explain what these other factors 

are, and abandons his TVP-VAR model during the purported Below I explain in 

more detail how his TVP-VAR model materially ceases to explain price changes. 

178. For any of the three price change variables, the difference between the observed price 

change and the price change predicted by Dr. Pirrong’s TVP-VAR model corresponds to the 

error term that measures the magnitude of a price change not explained by the variables in his 

model.236 The standard deviations of these error terms increase dramatically after approximately 

 as volatility increased dramatically, as shown in Dr. Pirrong’s Figure 11, which I 

have reproduced below in Figure 19 and limited to May and May-June spread futures 

contracts.237 The fact that the error term standard deviation increased after indicates 

235 Pirrong Report, ¶ 205. See also Pirrong Deposition, 235:1–13 

236 I am referring to the error terms of the price equations within Dr. Pirrong’s TVP-VAR model. 237 Pirrong Report, ¶ 203 (“Figure 11 presents the standard deviations of the price equation error terms which quantify the 

magnitude of price variations not explained by contemporaneous order flow and lagged price changes and order flow. These 

standard deviations ... rise steeply around ). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 90 of 197 PageID #:14060
 Highly Confidential Page 85

that, after that time, variables within Dr. Pirrong’s TVP-VAR model, such as order flow, could 

no longer materially explain price changes. Factors not captured or explained by the model 

became more important.238 For example, during his deposition testimony, 

 Therefore, 

it would be possible for public information on fundamental factors, such as storage availability, 

to explain the error terms during the purported period.239 This type of public 

information is simply ignored under Dr. Pirrong’s TVP-VAR model because, as explained in 

Section VII.C, the TVP-VAR model does not control for confounding factors that may have 

contributed to price declines on April 20, 2020. 

238 Pirrong Deposition, 236:5–11 

239 Pirrong Deposition, 236:12–25 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 91 of 197 PageID #:14061
 Highly Confidential Page 86

Figure 19

Source: Corrected Pirrong Report and Backup Materials 

Note: Data are derived from Dr. Pirrong’s backup file ‘TVP_VAR_Output_15Sec_BBO_9_1330_Graphs.xlsx.’ 

179. To gauge the importance of the factors not captured by Dr. Pirrong’s model during the 

purported period, I compare the mean standard deviation of the error term—

calculated by Dr. Pirrong’s TVP-VAR model every 15 seconds—before and after the start of the 

purported For the May-June midpoint spread change, the mean of the standard 

deviation of the error term increased by nearly , increasing from /bbl to bbl. 

For the May outright midpoint change, the mean of the standard deviation of the error term 

increased over , from bbl to /bbl.240 During the purported period, 

the standard deviations of the error terms in the May-June midpoint spread change and the May 

outright midpoint change equations reached maximums of bbl and /bbl, 

respectively, further illustrating the large impact of variables not captured by Dr. Pirrong’s 

240 See Workpaper 16. In these calculations and below, the period prior to the purported corresponds to the period 

from 9:00 AM to , while the purported period corresponds to the period from to 1:30 PM. I 

chose as the cutoff because 

 (see Pirrong Deposition 229:8–230:13). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 92 of 197 PageID #:14062
 Highly Confidential Page 87

model. To put this into context, the value of the standard deviation of the unexplained error term 

bbl) is nearly as large as the price artificiality calculated by Dr. Pirrong at the start of the 

purported period ( /bbl).241

180. An additional indicator of the inadequate performance of Dr. Pirrong’s model during the 

purported period is the drop of the Rଶ associated with each price equation. As 

explained in Section VII.C.1, the Rଶ measures the fraction of the variance of the dependent 

variable of a model (Dr. Pirrong’s variable for the variance of the May midpoint price change) 

explained by the variance of the control variables of the model (Dr. Pirrong’s order flow 

variables). 

181. As Dr. Pirrong points out in his report, “the fraction of the variability in prices explained 

by order flow variations declined substantially in the last of trading.”242 This is 

illustrated by Figure 12 in the Pirrong Report, which shows that the Rଶ for the May midpoint 

price changes and the May-June midpoint spread changes reached values close to zero after 

 When comparing the period from prior to the purported with the purported 

 period, the mean Rଶ declined from to for the May outright midpoint price 

change and from to for the May-June midpoint spread change.243 

182. In sum, the evidence discussed above highlights the inadequate performance of Dr. 

Pirrong’s TVP-VAR model during the last of the proposed class period, when Dr. 

Pirrong abandoned this model, and renders the TVP-VAR model unreliable for calculating 

damages during this period. 

2. Dr. Pirrong Abandons His TVP-VAR Price Artificiality Model after 

183. The previous discussion explains how Dr. Pirrong’s TVP-VAR model materially stops 

explaining price changes and is not used to calculate price artificiality after Dr. 

Pirrong devotes a whole section of his report to explain that there was a change in market 

241 See Workpaper 16. 242 Pirrong Report, ¶ 205. 243 I note that, according to Figure 12 in the Pirrong Report, the Rଶ for the May midpoint price change and the May-June 

midpoint spread change also fell to levels close to 0 well before In fact, the Rଶ for the May midpoint price change fell 

to at , while the Rଶ for the May-June midpoint spread change fell to around at This 

indicates that the inadequate performance of Dr. Pirrong’s model is not restricted to the purported period. See 

TVP_VAR_Output_15Sec_BBO_9_1330_Graphs.xlsx in Backup Materials for Pirrong Report. See Workpaper 16. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 93 of 197 PageID #:14063
 Highly Confidential Page 88

dynamics (see Section VI.B in the Pirrong Report) after and then turns to an analysis of 

order flow toxicity for this period (see Section VII in the Pirrong Report). 

184. Despite pivoting to an order flow toxicity analysis to compute price artificiality during 

the post- period, Dr. Pirrong claimed in his deposition that 

244 However, the results discussed in the previous 

section undermine this assertion since his TVP-VAR ceases to operate as 

intended after and thus cannot form any support, much less adequate support, for 

estimating price impact and price artificiality after that time.245 

185. Application of Dr. Pirrong’s TVP-VAR model in the last of the proposed 

class period is undermined by the market dynamics during the period. The sharp increase in 

volatility in the last of the proposed class period leads to standard deviations of the 

error terms in the price equations of Dr. Pirrong’s TVP-VAR model that are large relative to Dr. 

Pirrong’s estimated price artificiality. For the May-June spreads, the maximum standard 

deviation of the error is bbl and the mean during the purported is bbl, 

which is and respectively, of Dr. Pirrong’s artificiality as of .246 

186. In sum, in the last of the proposed class period, Dr. Pirrong’s TVP-VAR 

model is inadequate to estimate price impact and price artificiality. 

B. Dr. Pirrong’s VPIN Metric Is Not a Price Artificiality Model, Nor Does Dr. 

Pirrong Claim It Is a Price Artificiality Model 

187. As discussed in the previous section, Dr. Pirrong abandons his TVP-VAR price 

artificiality model during the proposed class period after and changes his focus to a 

metric he calls VPIN, which purports to measure “order flow toxicity.” In effect, Dr. Pirrong 

does not have a single method to estimate price artificiality for the full proposed class period, 

and he has no method to measure price artificiality after During his deposition, Dr. 

Pirrong 

244 Pirrong Deposition, 231:12–19 

245 Pirrong Deposition, 236:5–11 

246 See Workpaper 16. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 94 of 197 PageID #:14064
 Highly Confidential Page 89

247 

248 Instead, Dr. Pirrong claims that 

order flow toxicity, which his VPIN metric purportedly measures, caused the purported 

 Trader Defendants collectively made up a sufficient portion of his VPIN metric, and 

therefore 

 Dr. Pirrong’s assumption that contributors to his VPIN caused the purported 

 is unsupported by academic literature and not subject to a scientific method. Further, his 

assumption to attribute is not supported by 

his analysis or the academic literature and does not control for potential confounding factors. At 

best, I find this full decline assumption puzzling since the assumption simply ignores actual 

trading. 

1. Description of Dr. Pirrong’s VPIN Metric Analysis 

188. Before getting into the specific problems with Dr. Pirrong’s use of the VPIN metric, it is 

first useful to explain this metric at a high level. His VPIN “is based on a ratio of the absolute 

value of order flow imbalance to trading volume,” which he also refers to as a measure of “order 

flow toxicity.”249 Price is not an input or an output to Dr. Pirrong’s VPIN metric.250 Since his 

VPIN is based on absolute values, it is directionally agnostic and does not predict price changes 

up or down. Connecting the VPIN metric to this matter, Dr. Pirrong cites to an academic paper 

that states “the VPIN metric[] could be used by market makers to anticipate a rise in volatility 

and estimate the risk of a liquidity-induced crash.”251 Dr. Pirrong argues that the increase in the 

VPIN metric in the May-June spread market in this matter anticipated the rise in volatility and 

the purported Notably, a high VPIN metric is connected to anticipating volatility, 

247 Pirrong Deposition, 254:11–17 

248 Pirrong Deposition, 267:21–268:6 

249 Pirrong Report, ¶ 235. 250 David Easley, Marcos M. López de Prado, and Maureen O’Hara, “Flow Toxicity and Liquidity in a High-Frequency World,” 

The Review of Financial Studies 25, no. 5 (2012): 1457–1493 (“Easley et al. (2012)”), p. 1457. The VPIN represents a 

“procedure to estimate flow toxicity based on volume imbalance and trade intensity.” 

251 David Easley, Marcos López de Prado, and Maureen O’Hara, “The Microstructure of the ‘Flash Crash’: Flow Toxicity, 

Liquidity Crashes and the Probability of Informed Trading,” The Journal of Portfolio Management 37, no. 2 (2011): 118–128 

(“Easley et al. (2011)”), p. 127. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 95 of 197 PageID #:14065
 Highly Confidential Page 90

not the direction of price movements. Dr. Pirrong does not claim his VPIN model results in a 

price impact or price artificiality measure for the purported 252

189. At a high level, Dr. Pirrong uses a two-part argument. First, he claims that the total 

VPIN for all traders in the market caused the purported Second, he claims that 

Trader Defendants 

 I describe each in turn. 

190. For his total market VPIN, Dr. Pirrong calculates his metric for the full proposed class 

period for the May outright and May-June spread. In Figures 20 and 21 below, I have 

reproduced Dr. Pirrong’s VPIN calculations for the proposed class period, with the addition of a 

drop line for the purported period.253 These calculations are what Dr. Pirrong uses to 

draw the conclusion that his total VPIN metric (blue line) was high and therefore is attributed to 

the purported 

252 Pirrong Deposition, 267:21–268:6 

253 Pirrong Report, Figures 15, 16. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 96 of 197 PageID #:14066
 Highly Confidential Page 91

Figure 20

Source: Corrected Pirrong Report and Backup Materials 

Note: Figure 15 in the Pirrong Report erroneously depicts VPIN assuming equal time intervals for each observation. 

In contrast, Dr. Pirrong’s VPIN analysis defines intervals by a fixed volume traded, thus the time between 

observations varies with market conditions. To correct this charting error, I plot observations according to the 

timestamp when each fixed volume amount is filled, resulting in differing intervals. Consequently, this figure does 

not match the Pirrong Report Figure 15 exactly. Data are derived from Dr. Pirrong’s backup file ‘Vega VPIN 

Analysis.xlsx.’ 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 97 of 197 PageID #:14067
 Highly Confidential Page 92

Figure 21

Source: Corrected Pirrong Report and Backup Materials 

Note: Figure 16 in the Pirrong Report erroneously depicts VPIN assuming equal time intervals for each observation. 

In contrast, Dr. Pirrong’s VPIN analysis defines intervals by a fixed volume traded, thus the time between 

observations varies with market conditions. To correct this charting error, I plot observations according to the 

timestamp when each fixed volume amount is filled, resulting in differing intervals. Consequently, this figure does 

not match the Pirrong Report Figure 16 exactly. Data are derived from Dr. Pirrong’s backup file ‘Vega VPIN 

Analysis.xlsx.’ 

191. Dr. Pirrong’s analysis 

.254 

192. For the May outright in Figure 20 above, Dr. Pirrong’s results show that 

.

255 Since Dr. Pirrong does not provide a comparison of what is a 

normal level for VPIN, there is no conclusion as to whether the VPIN is “high.” 

254 Pirrong Deposition, 260:16–21 

. 255 Pirrong Deposition, 274:3–12 

 See Workpaper 19. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 98 of 197 PageID #:14068
 Highly Confidential Page 93

193. For the May-June futures contract in Figure 21, Dr. Pirrong states 

256 

 Again, since 

Dr. Pirrong does not provide a comparison of what is a normal level for VPIN, there is no 

conclusion as to whether the VPIN is “high.” Moreover, in his deposition testimony 

257

194. For the Trader Defendants–only VPIN (red line), Dr. Pirrong calculates his VPIN 

measures for the full proposed class period for the May outright and May-June spread, 

258 

 

 

195. In sum, 

 Next I will describe the problems with Dr. 

Pirrong’s use and conclusions drawn from his VPIN metric. 

256 Pirrong Report, ¶ 239. See also Pirrong Deposition, 260:3–11 

257 Pirrong Deposition, 269:25–270:3 

258 Pirrong Report, ¶¶ 240–241. 259 Pirrong Report, ¶¶ 242, 257. 260 Pirrong Deposition, 282:5–10 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 99 of 197 PageID #:14069
 Highly Confidential Page 94

2. Dr. Pirrong’s Conclusion That Contributors to His VPIN Caused the 

Purported Is Not Supported by Academic Literature or a 

Scientific Method 

196. Dr. Pirrong concludes that the purported was caused by contributors to his 

total market VPIN metric in the May-June spread. Below I will describe some of the flaws with 

this conclusion. First, Dr. Pirrong’s assumption that his VPIN metric predicts is 

controversial in the literature. Second, Dr. Pirrong fails to follow a scientific method in reaching 

his conclusions. Third, Dr. Pirrong fails to demonstrate that his VPIN is persistently high prior 

to his purported which is an expected condition according to the Easley et al. 

(2012) paper he relies on.261

197. In the description of Dr. Pirrong’s VPIN metric, I described his total VPIN for all traders 

together (blue line in Figures 20 and 21) and separately for Trader Defendants only (red line in 

Figures 20 and 21). In this section, I am referring to Dr. Pirrong’s total VPIN reflected by the 

blue line. 

198. First, the academic literature does not support that Dr. Pirrong’s VPIN measure predicts 

 Dr. Pirrong relies on an academic article by David Easley, Mario Lopez de Prado, 

and Maureen O’Hara, titled “Flow Toxicity and Liquidity in a High-Frequency World” (Easley 

et al. (2012)), to attempt to establish a link between the VPIN metric 262 Dr. 

Pirrong’s VPIN metric differs from the Easley et al. (2012) academic paper measure in that Dr. 

Pirrong does not need to imperfectly estimate what trades are aggressive using an algorithm, 

since the data he uses In a 2015 study, Andersen and Bondarenko 

found that when a VPIN measure does not use the imperfect algorithm, but rather accurate trade 

categories, “the VPIN metric based on the actual order imbalance is negatively correlated with 

future volatility.”263 In other words, increases in a VPIN metric constructed with the actual trade 

classification, like the one Dr. Pirrong uses, would be indicative of lower volatility, not higher 

volatility as Dr. Pirrong claims. This is therefore directly opposite to what Dr. Pirrong claims. 

261 Easley et al. (2012). 262 Easley et al. (2012), p. 1459 (“[O]rder flow as captured by the VPIN metric was becoming increasingly toxic in the hours 

before the May 6, 2010, ‘flash crash,’ and that this toxicity contributed to the withdrawal of many liquidity providers from the 

market.”). In his deposition testimony, See Pirrong 

Deposition, 264:8–14 

263 Torben G. Andersen and Oleg Bondarenko, “Assessing Measures of Order Flow Toxicity and Early Warning Signals for 

Market Turbulence,” Review of Finance 19, no. 1 (2015): 1–54 (“Andersen and Bondarenko (2015)”), p. 5 (emphasis added). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 100 of 197 PageID #:14070
 Highly Confidential Page 95

199. There are additional academic papers specifically addressing the numerous shortcomings 

of the Easley et al. (2012) VPIN measure of predicting flash crashes. Among these 

shortcomings, the academic literature has pointed to the issue that, by construction, VPIN 

measures like the one proposed by Easley et al. (2012) are correlated with trading volume and 

volatility, which can be persistent over time. The reason is that these VPIN metrics are 

constructed with trade classification algorithms that produce errors correlated with volatility and 

trading volume. Since volatility is persistent over time, high volatility predicts high future 

volatility, and therefore a high VPIN metric will be associated with high volatility episodes by 

construction. Thus, the fact that a VPIN was high before a particular high-volatility episode does 

not mean contributors to the VPIN caused the high-volatility episode, but rather it shows that the 

VPIN is constructed with variables that are correlated with volatility and trading volume.264 

Since Dr. Pirrong does not use the Easley et al. (2012) trade classification algorithm, I will not 

address all the critiques in further detail. However, it is important to note that because of these 

methodology differences, the academic literature does not support the purported relationship 

between Dr. Pirrong’s VPIN metric .265

200. Second, Dr. Pirrong fails to follow a scientific method in reaching his analysis and 

provides no falsifiable hypotheses and statistical hypothesis testing. Dr. Pirrong concludes that 

because the blue VPIN line increased sharply, it caused the purported . But, Dr. 

Pirrong offers no benchmark or threshold to create a hypothesis to test whether his VPIN metric 

on April 20, 2020, is high enough or persists long enough to have triggered the alleged 

 after because, according to his deposition testimony, 

264 See, e.g., Torben G. Andersen and Oleg Bondarenko, “VPIN and the Flash Crash,” Journal of Financial Markets 17 (2014): 

1–46, at p. 1 (“[The VPIN] is a poor predictor of short run volatility, [] it did not reach an all-time high prior, but rather after, the 

flash crash, and [] its predictive content is due primarily to a mechanical relation with the underlying trading intensity.”); Torben 

G. Andersen and Oleg Bondarenko, “Reflecting on the VPIN Dispute,” Journal of Financial Markets 17 (2014): 53–64, at p. 53 

(“[I]t is crucial for the interpretation of VPIN as a harbinger of market turbulence or as a predictor of short-term volatility to 

control for current volume and volatility. Doing so, we find no evidence of incremental predictive power of VPIN for future 

volatility.”); Andersen and Bondarenko (2015), p. 1 (“VPIN is unsuitable for capturing order flow toxicity or signaling ensuing

market turbulence.”). 

265 During his deposition testimony, 

 See Pirrong Deposition, 270:9–15 

 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 101 of 197 PageID #:14071
 Highly Confidential Page 96

266 Stating that the VPIN increased sharply but not providing an analysis of 

a non-manipulated comparison benchmark period lacks a reliable scientific basis. 

201. Key characteristics of the process of seeking knowledge through the scientific method are 

the use of falsifiable hypotheses and statistical hypothesis testing.267 A hypothesis is said to be 

falsifiable when it can be proven false.268 Statistical hypothesis testing allows one to define 

quantifiable and objective criteria (conditional on the model’s assumptions) to reject a hypothesis 

or not.269 Furthermore, statistical hypothesis testing allows one to measure the power of a test, 

which is the “chance that a statistical test will declare an effect when there is an effect to be 

declared.”270 

202. Despite their flaws and their inadequacy to compute price artificiality, VAR models 

allow for falsifiable hypotheses and statistical hypothesis testing. For example, as shown in 

Section VII.E above, in standard VAR models one could test whether or not the price impact 

associated with each trade variable is statistically different from zero. In contrast, in Dr. 

Pirrong’s VPIN metric does not allow for falsifiable hypotheses and statistical hypothesis testing. 

203. Dr. Pirrong also does not follow the benchmark method put forth by the papers he cites. 

Specifically, Easley et al. (2011) and Easley et al. (2012) compared their specific VPIN 

calculation leading up to the E-mini S&P 500 flash crash on May 6, 2010, to a benchmark period 

to assess whether the VPIN metric was abnormally high. 271 For benchmarking levels, Easley et 

al. (2012) states, “[f]or predicting toxicity-induced volatility, what matters is whether the level of 

VPIN at any time is unusual relative to its distribution for the asset in question. The actual level 

of the VPIN ... is not our primary concern.”272 Dr. Pirrong does not perform benchmark analysis 

on his VPIN metric levels, and thus he fails to follow the method he purports to implement. 

266 Pirrong Deposition, 269:1–11 

267 Karl Popper, The Logic of Scientific Discovery (2005), p. 18. 268 Karl Popper, The Logic of Scientific Discovery (2005), p. 18. 269 Frederick Gravetter and Larry Wallnau, Statistics for the Behavioral Sciences, 10 ed. (2015), pp. 228–232. 270 National Research Council et al., Reference Manual on Scientific Evidence, 3rd ed. (2011), p. 254. 271 Easley et al. (2011), p. 122 (“Our first observation is that the VPIN metric for E-mini S&P 500 futures was abnormally high at

least one week before the flash crash.... Our second observation is that this situation worsened (from the point of view of 

liquidity providers) several hours before the crash. ...[B]y 11:55 a.m. on May 6 the realized value of the VPIN metric was in the 

10% tail of the distribution (it exceeded a 90% CDF(VPIN) critical value). By 1:08 p.m., the realized value of the VPIN metric 

was in the 5% tail of the distribution (over a 95% CDF(VPIN)). By 2:30 p.m., the VPIN metric reached its highest level in the 

history of the E-mini S&P 500. At 2:32 p.m., the crash began, according to the CFTC-SEC Report time line.”). 272 Easley et al. (2012), p. 1472 (emphasis added). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 102 of 197 PageID #:14072
 Highly Confidential Page 97

204. Dr. Pirrong limits his analysis to depicting the values his VPIN metrics take on April 20, 

2020, only.273 This is a critical flaw in his analysis because VPIN results change based on the 

choices of parameters used (e.g., bucket size).274 Dr. Pirrong provides no apples-to-apples 

comparison of whether or not the VPIN metric on April 20, 2020, is high, low, or normal. This 

is a crucial step he skips in his analysis. Further, without a comparison to a typical VPIN 

measure, there is no ability to conduct hypothesis testing and statistical analysis on what 

constitutes a significantly high VPIN metric or long duration. 

205. Dr. Pirrong’s lack of statistical rigor is reflected in the vagueness of his conclusions that 

the Trader Defendants 275 or 

276 During his 

deposition, Dr. Pirrong 

277

206. Third, even according to the Easley et al. (2012) paper Dr. Pirrong relies on, showing 

sharp increases or high levels of the VPIN metric is not sufficient to predict a high-volatility 

event or flash crash. It takes continuously high levels of VPIN to reliably predict a highvolatility event: “[E]ven for high levels of VPIN, absolute returns over the next volume bucket 

are most often not large. ...[I]t takes persistently high levels of VPIN to reliably generate large 

absolute returns.”278 Dr. Pirrong’s analysis does not show continuously high levels of VPIN, and 

therefore is not analogous with the results found by Easley et al. (2012). 

207. In sum, academic literature does not support that Dr. Pirrong’s VPIN metric predicts 

 and his VPIN metric is not compared to any benchmark, making it essentially 

useless for hypothesis testing. 

273 See Pirrong Report, Figures 15, 16. 274 Pirrong Deposition 270:9–15 

275 Pirrong Report, ¶ 256 (emphasis added). See also Pirrong Deposition, 279:6–12 (emphasis added) 

276 Pirrong Report, ¶ 257 (emphasis added). 277 See also Pirrong Deposition, 281:22–282:3 (emphasis added) 

278 Easley et al. (2012), pp. 1485–1486. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 103 of 197 PageID #:14073
 Highly Confidential Page 98

3. Dr. Pirrong’s VPIN Metric Fails to Find That Trader Defendants’ 

Allegedly Manipulative Conduct Caused 

 

208. In the prior section, I explained the fatal flaws in Dr. Pirrong’s total VPIN metric (blue 

line in Figures 19 and 20). In this section, I discuss the flaws in Dr. Pirrong’s Trader Defendant–

only VPIN metric (red line in Figures 19 and 20). 

209. Even assuming, for the sake of argument, that contributors to Dr. Pirrong’s total VPIN 

metric caused the purported Dr. Pirrong still has no model to properly determine the 

price artificiality attributable to Trader Defendants during the alleged Dr. Pirrong’s 

VPIN metric does not explain how the cause of the purported can be attributed to a 

particular individual trader or group of traders.279 Dr. Pirrong’s VPIN metric is simply an 

estimated measure of order flow toxicity with no model to estimate the price impact associated 

with any particular trade.280 

210. Dr. Pirrong argues that the nature of flash crashes is non-linear, meaning “that small 

changes in market conditions trigger extremely large price movements.”281 However, his VPIN 

metric fails to explain his apparent “straw that broke the camel’s back” theory, whereby 

 For example, consider the following 

hypothetical example whereby a threshold of 100 contracts sold aggressively would cause a flash 

crash, other traders sold 81 contracts, and Trader Defendants sold 20. Dr. Pirrong’s VPIN 

measure would lead him to claim that the 20 contracts sold by Trader Defendants’ “broke the 

camel’s back”—but it is hard to believe that 20 contracts caused the hypothetical flash crash. 

Without a model able to clarify these issues, any effort to attribute 

 

211. In the absence of a model to provide a measure of price artificiality during the purported 

 Dr. Pirrong assumes 

279 Pirrong Deposition, 264:15–20 

280 Pirrong Deposition, 267:21–268:6 

281 Pirrong Report, ¶ 257. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 104 of 197 PageID #:14074
 Highly Confidential Page 99

 Even assuming the VPIN metric causes flash crashes, Dr. 

Pirrong’s 

212. Based on Dr. Pirrong’s VPIN metric calculations, 

 

213. Since Dr. Pirrong’s VPIN metric is directly impacted by “aggressive” trades (since 

aggressive trades remove resting orders from the order book), I also examine the percentage of 

aggressive trades attributed to Trader Defendants. In the at-issue period, aggressive sell trades 

primarily increase the order book imbalance, and therefore increase Dr. Pirrong’s VPIN measure. 

To the extent the Trader Defendants were acting as a conduit for TAS sellers (see Section 

VII.A), then none of the VPIN is directly attributable to Trader Defendants. 

214. Between 9:00 AM and 1:30 PM, by volume, Trader Defendants only accounted for 

of aggressive sales of non-TAS contracts, meaning that of aggressive sales were generated 

by other traders.283 This suggests that non-Defendant traders contributed the majority of 

aggressive sales volume and to the “high” VPIN measured during the proposed class period—

especially in the May outright futures contracts—contradicting the idea of 

 

215. Moreover, even though Dr. Pirrong calls the period between and 1:30 PM a 

I note that Dr. Pirrong has not 

established any scientific basis for claiming a occurred, and Dr. Pirrong does not cite 

to any third-party study about this day that refers to the price movement as a I am 

also unaware of any regulatory reports calling this price drop a and there is evidence 

that this period may not be consistent with a . Dr. Pirrong states that flash crashes are 

associated with large price drops, followed by pronounced price rebounds.284 In contrast, the 

price pattern of the May Contract depicted in Figure 4 above shows that while prices fell 

282 The average VPIN share attributed to non-Defendant traders is calculated as one minus the average of Trader Defendants’ 

VPIN share over the proposed class period. See Workpaper 19. 283 See Workpaper 20. 284 Pirrong Report, ¶¶ 107, 118. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 105 of 197 PageID #:14075
 Highly Confidential Page 100

between and 1:30 PM and rebounded by , they then dropped again between 

 and such that the price level was close to the same at 1:30 PM as at 

Such a pattern is not typically associated with a flash crash, in a flash crash prices plummet once 

and then bounce back without a subsequent drop of comparable magnitude. For example one of 

the most notable market events described by the CFTC as a flash crash occurred on May 6, 2010, 

when the U.S. stock market experienced a rapid and severe drop in prices before rebounding 

within minutes (see Figure 22 below). 

Figure 22

The Flash Crash in U. S. Equity Indices on May 6, 2010 Exhibited a Rapid and 

Severe Drop in Prices before Rebounding within Minutes 

 

Source: Andrei Kirilenko, et al., “The Flash Crash: The Impact of High Frequency Trading on an Electronic 

Market,” CFTC (2014). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 106 of 197 PageID #:14076
 Highly Confidential Page 101

216. Even assuming for the sake of argument that

 his price impact estimations during the purported are 

not sufficient to estimate artificiality. His conclusions suffer from the same flawed but-for world 

assumption as the one he relies on for his TVP-VAR model (see Section VII.A) above. Dr. 

Pirrong’s analysis during the purported implicitly assumes that the trading patterns of 

other traders would remain unchanged absent Trader Defendants’ alleged manipulative trades. 

This includes TAS sellers that relied on Trader Defendants buying TAS to exit their long 

positions. Dr. Pirrong’s analysis does not account for this situation, effectively discarding the 

possibility that the alleged triggering of the purported from aggressive selling would 

have occurred even without the actions of Trader Defendants. In fact, Dr. Pirrong explicitly 

claims that, without Trader Defendants’ trades, the purported would have been less 

likely to happen.285 In sum, Dr. Pirrong’s VPIN metric does not support that 

IX. Many of the Diverse Putative Class Members Could Not Have Been Harmed, and 

Dr. Pirrong’s Methodology Is Incapable of Identifying the Number of Putative Class 

Members 

217. Dr. Pirrong does not explain how or why, in light of the extreme macroeconomic 

environment of COVID-19 and unprecedented price volatility of April 20, 2020, his TVP-VAR 

model or his post- approach “can be applied on a class-wide basis to quantify the 

damages Trading Defendants caused for the members of the class” or whether each putative class 

member suffered any harm.286 

218. As explained in Section IX.A below, Dr. Pirrong’s methodology is incapable of assessing 

the number of putative class members and whether they were harmed without fact-intensive 

individualized inquiry. Dr. Pirrong claims that unique accounts meet the “class definition” 

and that a damages “calculation can be performed in an identical way for each Class Member 

based on their trading records.”287 However, his calculations and claim of common method of 

proof are incorrect for at least three reasons. 

285 Pirrong Report, ¶ 257. 286 Pirrong Report, ¶ 190. 287 Pirrong Report, ¶ 396. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 107 of 197 PageID #:14077
 Highly Confidential Page 102

a. First, Dr. Pirrong’s methodology assesses the number and alleged harm to 

accounts instead of persons or entities. The proposed class definition comprises 

“[a]ll persons and entities that sold a May 2020 [] WTI[] futures contract [during 

the putative class period] to liquidate a long position in the May contract.”288 

Multiple accounts can roll up to a single entity, and an account with a long 

position during the putative class period may belong to an entity that was not, in 

fact, net long when aggregating positions across all of its accounts. Furthermore, 

an entity whose damages on sales in some accounts are equal or lower than the 

benefits on purchases in other accounts would not be harmed overall. Dr. 

Pirrong’s methodology cannot address the effects of the alleged manipulation 

across accounts. Therefore, Dr. Pirrong cannot determine whether an entity was 

harmed and is part of the putative class without individualized inquiry. 

b. Second, Dr. Pirrong’s methodology uses the CME Audit Trail data, which do not 

reflect CME WTI trades that took place outside CME’s GLOBEX electronic 

platform.289 This includes contracts fulfilled at the contract maturity through 

delivery of physical crude and “ex-pit” transactions such as block trades or 

exchange-for-physical (“EFP”) transactions. Produced third-party data from a 

very limited set of trading entities 

 An entity 

whose damages on sales recorded in the CME Audit Trail data were lower than 

the benefits on purchases not captured in the CME Audit Trail data would not be 

harmed overall. Furthermore, Dr. Pirrong relies on transactions between the start 

of the proposed class period and the expiry of the May Contract to determine 

whether an entity sold to “liquidate a long position in the May contract” during 

the proposed class period. Inasmuch as it ignores non-GLOBEX transactions, 

options exercise, and May Contract deliveries, Dr. Pirrong’s “determin[ation] that 

 accounts meet the Class Definition” is flawed because he cannot know 

288 Plaintiff’s Motion for Class Certification, ¶ 1. 289 CME GLOBEX is an electronic trading platform developed and operated by the CME for the global trading of futures and 

options. It supports the execution of a wide range of asset classes, including commodities, currencies, and financial instruments, 

facilitating nearly 24-hour trading and global access. Additionally, trades on the CME can also take place outside the GLOBEX 

platform, such as through privately negotiated transactions. See “CME Globex,” CME Group, 

https://www.cmegroup.com/globex.html; “CME GLOBEX REFERENCE GUIDE,” CME Group, https://bit.ly/3Khp8iw, p. 2. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 108 of 197 PageID #:14078
 Highly Confidential Page 103

whether the owner of the account was actually “long” when it sold the May 

Contract.290 Again, Dr. Pirrong cannot determine whether an entity was harmed 

and is part of the putative class without individualized inquiry. 

c. Third, Dr. Pirrong’s methodology ignores ICE WTI contract transactions that 

would be impacted by an alleged price artificiality in the CME WTI contract. The 

absence of account (and ownership) mapping between CME and ICE account 

numbers means that Dr. Pirrong cannot assess if an individual is harmed without a 

fact-intensive individualized inquiry. Since the ICE WTI contract terms 

specifically reference the CME WTI prices for cash-settlement, the necessity to 

assess harm in both contracts is important. Even if accounts could be mapped 

between ICE and CME, Dr. Pirrong’s artificiality for the CME WTI cannot be 

applied to the ICE WTI contract. 

d. Fourth, there is no centralized database containing information on entities’ OTC 

transactions, including physical transactions, whose price is directly linked to the 

CME WTI futures price. Traders whose damages on sales of futures were equal 

to or higher than their benefits on OTC purchases of the same contract could not 

be harmed. It is not possible to assess each putative class member’s OTC 

benefits, including cash market benefits, without a fact-intensive individualized 

inquiry into the history of each OTC contract for each putative class member. 

219. Moreover, Section IX.B below explains that while Dr. Pirrong makes no assessment of 

how many putative class members actually suffered harm under his proposed analysis, even 

applying his flawed methodology results in almost of unharmed accounts among his 

purported unique accounts meeting the proposed class definition.291 Even more 

fundamentally, I identify three categories of putative class members who could not be harmed by 

Dr. Pirrong’s alleged price artificiality, meaning trading entities belonging to the putative class 

and for whom harm is impossible as a conceptual matter: 

a. Putative class members who traded like Trader Defendants, that is, purchasing 

TAS May Contracts and selling non-TAS May Contracts during the alleged class 

period. 

290 Pirrong Report, ¶ 396. 291 See Figure 28 backup. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 109 of 197 PageID #:14079
 Highly Confidential Page 104

b. Putative class members who only traded TAS May Contracts intraday, meaning 

they purchased and sold the same number of TAS May Contracts on April 20, 

2020. 

c. Putative class members who used CME WTI futures contracts to hedge OTC 

transactions, including physical transactions, and whose benefits on OTC 

purchases were equal to or exceeded their damages on sales of futures. 

220. Finally, as explained in Section IX.C below, my analysis of the trading and alleged 

damages for the accounts Dr. Pirrong claims are part of the alleged class reveals that 

named plaintiff Mish is not representative of the overwhelming majority of putative class 

members. For example, while TAS May Contract sales represent of alleged aggregate 

damages when applying Dr. Pirrong’s methodology, Mish only traded non-TAS May 

Contracts.292 Furthermore, available contemporaneous records and deposition testimony indicate 

that 

A. Dr. Pirrong’s Methodology Does Not, and In Fact Cannot, Assess the 

Number of Persons or Entities That Were Harmed and Belong to the 

Putative Class without Individualized Inquiry 

221. Dr. Pirrong’s methodology cannot determine, without individualized inquiry, whether a 

trading entity was harmed by the alleged price artificiality estimates or whether it belongs to the 

putative class. Dr. Pirrong claims that unique accounts meet the proposed class definition 

and that a damages “calculation can be performed in an identical way for each Class Member 

based on their trading records.”293 However, even if Dr. Pirrong’s artificiality estimates were 

reliable—and they are not as I explain in Sections VII and VIII above—his calculations and 

claim of a common method of proof are incorrect for at least three reasons. 

292 See Figure 28. 293 Pirrong Report, ¶ 396. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 110 of 197 PageID #:14080
 Highly Confidential Page 105

1. Dr. Pirrong’s Methodology Cannot Address Multiple Accounts 

Owned by the Same Entity without Individualized and Fact-Intensive 

Inquiry 

222. Dr. Pirrong’s analysis relies on CME Audit Trail data that 

 Yet Plaintiff acknowledges that the 

same trading entity may have multiple trading accounts, such that all accounts for the same 

entity would have to be aggregated in order to assess both (i) whether any individual trader is a 

member of the proposed class, and (ii) whether such a trader would have been harmed by 

allegedly artificial prices.

294 Combining account numbers by entity requires a fact-intensive 

process that varies across entities. Treating individual accounts separately is problematic for 

several reasons. 

223. First, the same economic entity (e.g., trading firm) may buy and sell CME WTI futures 

contracts across different accounts that may follow different strategies and leave the owner 

unharmed overall. While Dr. Pirrong’s view is that price artificiality can provide economic 

benefit to purchasers of affected contracts, because his methodology does not aggregate accounts 

by owners, it does not allow for the potentially beneficial effects of transactions across accounts 

and therefore cannot determine whether an entity was harmed. 

295 In 

order to definitively determine if the same trading entity holds multiple accounts within a 

particular FCM or across multiple FCMs, one would need to conduct an individualized and factintensive inquiry with each FCM. 

224. Second, the same economic entity (e.g., trading firm) may hold simultaneous long and 

short positions in the same contract across different accounts. An account with a long position 

during the alleged class period may belong to an entity that was not, in fact, long when 

considering all of its accounts. Dr. Pirrong therefore (i) likely greatly overstates the number of 

294 Mish International Monetary Inc., on behalf of itself and all others similarly situated v. Vega Capital London, LTD., et al., 

Plaintiff’s Memorandum of Law in Support of Its Motion for Class Certification Corrected Copy, February 23, 2024, p. 11 

(“[A]n individual Class member could have traded May contracts on April 20 through more than one account such that the 

number of unique accounts may not equal the number of individual Class members”). Indeed, third-party productions 

 See Workpaper 21. 

A single account may also reflect trading on behalf of multiple customers, further requiring the need for individualized inquiry. 

See, e.g., Pirrong Deposition, 296:2–6 

295 See footnote 305 below. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 111 of 197 PageID #:14081
 Highly Confidential Page 106

putative class members, and (ii) cannot identify whether a trading individual or entity meets 

Plaintiff’s class definition. 

225. To illustrate the flaw in Dr. Pirrong’s approach, consider the following hypothetical: 

trading firm A owns two trading accounts. Suppose that trading account 1A is long 10 contracts 

while trading account 2A is short 10 Contracts 9:00 AM. Further, suppose trading firm A exits 

its positions in both accounts by selling 10 May Contracts in account 1A and buying 10 May 

Contracts in account 2A at 1:08 PM when the price reached $0. In the aggregate, trading firm A 

does not fit the proposed class definition as it always had an overall flat position and was never 

net long the May Contract during the proposed class period. Furthermore, the trading firm is 

also unharmed in the aggregate as the damages in its account 1A are equal to the benefits in its 

account 2A. However, Dr. Pirrong’s methodology would consider account 1A as within the 

putative class and would attribute damages to trading firm A while ignoring the benefits from 

account 2A. This is because Dr. Pirrong’s methodology does not, and in fact cannot, address this 

issue of aggregating benefits and damages across different accounts. 

226. The multiple account issue outlined in the above hypothetical example is not merely 

theoretical; it actually manifests in the data. By conducting individualized mapping of the CME 

Audit Trail data to produced third-party trading data, 

227. For example, 

 

228. Similarly, trade data produced by 

296 See

; Workpaper 22. 297 See Backup Materials for Pirrong Report: TotalQtySalesAccts.xlsx; ClassMembersNegativeZeroStartAccts.xlsx. 298 See Workpaper 23. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 112 of 197 PageID #:14082
 Highly Confidential Page 107

 

300 This may be because, as discussed in the next section, some 

transactions do not show up in the CME Audit Trail data. These missing transactions can have 

an impact on whether a trading entity is harmed or not and whether it meets the proposed class 

definition and point to the need for even further individualized inquiry. 

 

 

229. These examples found using individualized analysis demonstrate the inability of Dr. 

Pirrong’s method to assess putative class members with more than one account, and, further, his 

method cannot tell which putative class members have more than one account. 

 

 Confirming the identity of putative 

class members would require additional data 4 

299 See Workpaper 24. 300 See Workpaper 24. 301 PSXVEG0000001.xlsx 302 Pirrong Deposition, 324:4–14 

303 Pirrong Deposition, 295:19–296:6 

304 Pirrong Deposition, 324:15–25 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 113 of 197 PageID #:14083
 Highly Confidential Page 108

This is because obtaining and compiling these data would require individualized and factintensive inquiry with purported class members, FCMs, clearing members, and foreign brokers. 

As a recent CFTC rule proposal explains, exchanges such as the CME usually “identify traders 

by account numbers” but “do not routinely collect detailed trader-identifying data.” Even the 

CFTC obtains such trader-identifying data directly from FCMs, clearing members, and foreign 

brokers as part of the data collected under the CFTC’s Large Trader Reporting Program.305 

6

2. Dr. Pirrong’s Methodology Ignores CME WTI Transactions That Are 

Impacted by His Alleged Price Artificiality Estimates 

230. Dr. Pirrong’s methodology only focuses on a subset of transactions in the putative class. 

Specifically, Dr. Pirrong relies on CME Audit Trail data that do not reflect futures trades outside 

CME’s GLOBEX electronic platform, but which are part of the proposed class definition.307 

These include “ex-pit” transactions such as block trades or EFP transactions, certain transactions 

executed through brokers, contracts fulfilled at the contract maturity through delivery of physical 

305 A recent CFTC rule proposal explains that Designated Contract Markets (or “DCMs”) such as the CME “identify traders by 

account numbers, but certain DCMs do not routinely collect detailed trader-identifying data” and that the CFTC “generally 

obtains such trader-identifying data from FCMs, clearing members, and foreign brokers through § 17.01.” In addition, “Section 

17.01 requires, separately, that reporting firms submit information, via Form 102, identifying the traders behind special accounts 

by name, address, and occupation, once an account accrues a reportable position. 17 CFR 17.01. Reporting firms, as appropriate,

submit Form 102 to the Commission for each account when that account becomes reportable as a special account. By aggregating 

information from § 17.00(a) large trader reports and Form 102, the Commission can determine the size of each reportable trader’s 

overall positions across special accounts held with multiple FCMs, clearing members, or foreign brokers.” See “17 CFR Part 17: 

Large Trader Reporting Requirements,” CFTC, May 1, 2024, https://www.cftc.gov/media/10606. 306 Pirrong Deposition, 325:1–15 

307 Pirrong Deposition, 312:17–23 

, 315:4–18

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 114 of 197 PageID #:14084
 Highly Confidential Page 109

crude, and options exercise.308 Such transactions can have an impact on whether a trading entity 

is harmed or not and whether it meets the proposed class definition. 

231. An entity that incurred damages on sales recorded in the CME Audit Trail data but 

incurred greater benefits on purchases not captured in the CME Audit Trail data would not be 

harmed overall. For example, block trades are privately negotiated trades that are executed away 

from CME’s order book but at prices that are “fair and reasonable,” meaning the price must be 

consistent with the prices of other transactions in the same contract at the relevant time.309 Block 

trades, by transacting at or close to the prevalent market price, would therefore be subject to Dr. 

Pirrong’s alleged artificiality. Because it ignores block trades and other ex-pit transactions that 

were executed during the alleged class period and would be impacted by Dr. Pirrong’s alleged 

price artificiality estimates, Dr. Pirrong’s methodology cannot accurately determine whether an 

entity was actually harmed. 

232. Furthermore, to determine whether an account sold to “liquidate a long position in the 

May contract” during the alleged class period, Dr. Pirrong needs futures positions that are not 

directly reflected in the CME Audit Trail data he uses.310 If Dr. Pirrong does not have the full set 

of transactions, then his position calculation is wrong and his assessment of which accounts are 

in the putative class is wrong. I find Dr. Pirrong does not have the full set of transactions 

because he ignores non-GLOBEX transactions such as ex-pit trades, options exercise, and May 

Contract deliveries. Therefore, Dr. Pirrong’s “determin[ation] that accounts meet the Class 

Definition” is not correct.311 

233. To begin with, Dr. Pirrong calculates positions by assuming a position of zero at the 

expiration of the May Contract on April 21, 2020, and building back the account position until 

308 “Ex-pit” transactions refer to trades executed outside the traditional open outcry trading pits or the GLOBEX electronic 

platform, including block trades and EFP transactions. Block trades are privately negotiated, large-volume trades between parties 

that are reported to the exchange and meet minimum size requirements. EFP transactions involve swapping a futures position for

a physical position or vice versa. Additionally, as explained in Section V.A.3, CME WTI futures contracts may be fulfilled at 

maturity by the delivery of the physical commodity. Options on futures contracts can be exercised, resulting in the establishment 

of a position in the underlying futures contract according to the terms of the option. See “Daily Bulletin Glossary,” CME Group, 

2012, https://www.cmegroup.com/tools-information/files/CME-Group-Daily-Bulletin-Glossary.pdf; “Understanding Block 

Trades,” CME Group, November 1, 2017, https://www.cmegroup.com/education/articles-and-reports/understanding-blocktrades.html; “Exchange for Related Positions (EFRPs)” CME Group, https://www.cmegroup.com/clearing/operations-anddeliveries/accepted-trade-types/efp-efr-eoo-trades.html. 

309 “Market Regulation Advisory Notice, Rule 526: Block Trades,” CME Group, https://www.cmegroup.com/rulebook/files/cmegroup-Rule-526.pdf. 

310 Plaintiff’s Motion for Class Certification, ¶1. 311 Pirrong Report, ¶ 396. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 115 of 197 PageID #:14085
 Highly Confidential Page 110

the start of the proposed class period on April 20, 2020, using transactions.312 But CME public 

reports indicate that 2,427 May Contracts were delivered, meaning that—contrary to Dr. 

Pirrong’s assumption—not all entities had a position of zero at the expiration of the May 

Contract.313

234. These types of investigations require individualized inquiry, and Dr. Pirrong’s 

methodology cannot be modified to include these transactions absent individualized inquiries. 

For example, while the CME reports which FCMs made or took delivery, the data do not indicate 

which underlying trading entity made or took delivery and for how many contracts, unless the 

FCM did so for its own “house” account. The CME positions data, which provide end-of-day 

positions and would include open positions at the close of trading on April 21, 2020, only cover 

reportable positions for large traders that hold a position equal to or higher than 350 contracts in 

CME WTI contracts.314 These data would therefore also be insufficient to establish the position 

at contract expiry of accounts with smaller positions. Only individualized inquiry enables the 

identification of non-reportable traders that made or took delivery, and such information resides 

solely with the trading entities’ FCM. 

235. Concrete examples found in non-party productions in this case 

312 Dr. Pirrong’s methodology for reconstructing an account’s position during the proposed class period assumes that the account 

position was zero at the expiration of the May 2020 futures contract and works backwards. In other words, Dr. Pirrong 

reconstructs the position by going backwards from zero at expiration to the start of the proposed class period by summing all 

May 2020 futures contract transactions within a specific account. See Pirrong Deposition, 296:7–297:5 

. 313 “DVL662-T Report,” CME Group, December 30, 2020. See Pirrong 

Deposition, 298:2–4 

314 Reportable and non-reportable positions refer to data collected under the CFTC’s Large Trader Reporting Program. In April 

2020, any trader with a position of 350 CME WTI contracts or larger in any single futures or option expiration month had to 

report all their positions to the CFTC. Such traders are referred to as “reportable” or “large” traders, and traders below the 350-

contract threshold are “non-reportable.” See “Large Trader Reporting Program,” CFTC, 

https://www.cftc.gov/IndustryOversight/MarketSurveillance/LargeTraderReportingProgram/ltrp.html. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 116 of 197 PageID #:14086
 Highly Confidential Page 111

315 

236. Produced third-party trading data from a very limited set of trading entities 

.

316 

317 

318 

319 Dr. Pirrong’s methodology of calculating positions and alleged harm is unable to 

account for these transactions without individualized inquiry. 

3. Dr. Pirrong’s Methodology Ignores ICE WTI Contract Transactions 

That Would Be Impacted by the Alleged Price Artificiality In the 

CME WTI Contract 

237. Dr. Pirrong’s methodology exclusively examines CME WTI contract transactions, 

ignoring trades in ICE’s cash-settled Crude Oil Futures contract (“ICE WTI contract”). The ICE 

WTI contract is explicitly linked to the CME WTI contract, because the ICE WTI contract is 

cash-settled based on the CME WTI contract price.320 Although trades involving the ICE WTI 

contract are not included in Plaintiff’s class definition, Dr. Pirrong asserts that ICE WTI contract 

settlement prices at expiration would be “artificially depressed by the same amount” as the CME 

315 VEGA_MISH_000043277; 

VEGA_MISH_000042708.1.xlsx. 316 . 317 Non-partyBP024467 

318 Non-partyBP029546 See 

also “Learn About Exercise and Assignment,” CME Group, https://www.cmegroup.com/education/courses/introduction-tooptions/learn-about-exercise-and-assignment.html 

319 Non-partyBP029555 320 “The West Texas Intermediate Light Sweet Crude Oil futures contract is cash settled against the prevailing market price for 

US light sweet crude. It is a price in USD per barrel equal to the penultimate settlement price for WTI crude futures as made 

public by NYMEX for the month of production per 2005 ISDA Commodity Definitions.” “WTI Crude Futures,” ICE, 

https://www.ice.com/products/213/WTI-Crude-Futures. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 117 of 197 PageID #:14087
 Highly Confidential Page 112

WTI contract, and that prior to expiration, the ICE WTI contact could be impacted by his 

artificiality because the two prices “[converge] to the same price,” 321 

238. The same economic entity (e.g., trading firm) may have bought (sold) CME WTI 

contracts and sold (bought) ICE WTI contracts such that the entity is unharmed overall. Because 

Dr. Pirrong’s methodology ignores ICE transactions executed during the alleged class period, he 

cannot accurately determine whether an entity was actually harmed. An attempt to include ICE 

transactions would require a fact intensive individualized inquiry. The account numbers (and 

ownership) in ICE data do not correspond to CME Audit Trail data account numbers, and 

therefore Dr. Pirrong cannot link the ICE accounts to his CME Audit Trail data accounts 

that purport to meet Plaintiff’s class definition. An individualized review and time-intensive 

analysis of each putative class member’s account statements, FCM inquiries, and in-house 

trading data would enable Dr. Pirrong to assess whether an entity is harmed. 

239. Even if the putative class members could be linked across the CME and ICE data, Dr. 

Pirrong’s CME May Contract artificiality estimates and methodology cannot be applied to ICE 

transactions because, he asserts there was a “breakdown of the normal relationship between 

CME and ICE prices” on April 20, 2020.322 

240. In sum, the absence of account (and ownership) mapping between CME and ICE account 

numbers means that Dr. Pirrong cannot assess if an individual is harmed without a fact-intensive 

inquiry. Since the ICE WTI contract terms specifically reference the CME WTI prices for cashsettlement, the necessity to assess harm in both contracts is important. Even if accounts could be 

mapped between ICE and CME, Dr. Pirrong’s artificiality for the CME WTI cannot be applied to 

the ICE WTI contract. 

4. Individualized Inquiry Is Required to Assess Over-the-Counter 

Trading Positions, Including Physical Contracts 

241. Similarly, it is not possible to assess 

each putative class member’s OTC benefits, including cash market benefits, without a fact321 Pirrong Report, ¶¶ 53, 262. 322 Pirrong Report, Section VIII. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 118 of 197 PageID #:14088
 Highly Confidential Page 113

intensive individualized inquiry into the history of each OTC contract for each putative class 

member.323 

242. A primary use of futures is to hedge against the risk of price movements in OTC markets. 

Therefore, many participants in the WTI crude oil market have OTC positions, including 

physical positions, that are hedged using futures contracts. 

his methodology is incapable of 

assessing if putative class members transacting in physical (i.e., cash) crude oil benefited or were 

harmed by Trader Defendants’ alleged manipulation.324 This is a significant flaw 

325 As I explain in more detail in Section 

IX.B.3 below, if they are hedging exactly, any damages that these putative class members incur 

in the futures market due to the alleged manipulation equals benefits in the OTC market. For 

example, buyers and sellers of physical oil may reference the CME WTI futures price in their 

physical oil contracts.326 The final price of such physical oil is determined by the WTI price plus 

a premium or discount. The price of other OTC financial instruments such as commodity swaps 

may also rely on the CME WTI futures price.327

243. Produced third-party futures trading data 

323 Pirrong Deposition, 317:15–22 324 Pirrong Deposition, 317:23–318:5 

 317:6–

22 

325 Pirrong Deposition, 316:12–17 

326

See Pirrong 

Deposition 483:22–484:4. 

327 See Pirrong Deposition, 314:2–6 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 119 of 197 PageID #:14089
 Highly Confidential Page 114

328

244. The production of third-party OTC transactions data would be required to determine, 

through fact-intensive individualized inquiry, whether any trading entity benefited or was 

harmed according to Dr. Pirrong’s price artificiality estimate on such transactions. Because no 

centralized record of all OTC transactions exists, it is not possible to assess each putative class 

member’s benefits or damages on each physical or other OTC transaction without this factintensive individualized inquiry into each cash market and OTC contract for each putative class 

member. 

245. A further complication is that each contract and each party to the contract may have 

different internal processes for recording and managing cash transactions. Establishing an 

alleged impact will depend on the individualized contractual and pricing documents for each 

transaction. Dr. Pirrong has not explained how the many different processes can be generalized 

into a class-wide approach because each putative class member’s physical transactions will need 

to be assessed separately. Absent a thorough detailed analysis of these OTC contracts from each 

putative class member, an economist cannot make a reliable assessment of whether a commercial 

market participant suffered any harm from a deflated futures price, much less measure the 

amount of damages (or benefits). 

246. In at least one other pending case, Dr. Pirrong acknowledged the need to net benefits 

related to cash positions linked to futures positions hedges. In In Re: Term Commodities Cotton 

Futures Litigation, Dr. Pirrong “made an adjustment to reflect hedging” as “[t]heory predicts that 

manipulation can cause the prices of some non-deliverable cotton to increase” and “[h]olders of 

this cotton would have realized a gain as a result of the manipulation.”329 

330 Unlike the U.S. cotton market, 

328 PSXVEG0000001.xlsx. 

 See Workpaper 25. 329 In Re: Term Commodities Cotton Futures Litigation, Pirrong Report (Redacted), March 18, 2022, footnote 260. 330 Pirrong Deposition, 308:24–309:2 

 299:16–18 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 120 of 197 PageID #:14090
 Highly Confidential Page 115

there are no centralized data on physical transactions in crude oil markets.331 Given the CME 

data limitations and the lack of centralized data on physical crude oil and other OTC transactions 

such as commodity swaps, a fact-intensive individualized inquiry would be required to account 

for any benefits putative class members might have experienced from the alleged price deflation. 

332 However, Dr. Pirrong does not explain 

how such an exercise would avoid fact-intensive individualized inquiry, 

333

B. Many Putative Class Members Could Not Have Been Harmed 

247. As stated previously, Dr. Pirrong does not identify the number of putative class members, 

but instead he identifies accounts that are purportedly in the proposed class. Dr. Pirrong 

identifies accounts belonging to the putative class but makes no assessment of the number 

of putative class members these accounts belong to or whether these accounts were harmed 

according to his alleged price artificiality estimates. 

248. Assuming for the sake of the argument that Dr. Pirrong’s methodology of counting 

accounts is correct and his price artificiality estimates are correct, I find that of the 

accounts identified by Dr. Pirrong as meeting the proposed class definition—or almost are 

unharmed.334 These accounts both purchased and sold the affected May Contracts in a way 

where the “economic loss” suffered from sales at “deflated” prices was equal to or smaller than 

331 The U.S. Cotton market has a CFTC “Cotton-On-Call” report and a centralized Electronic Warehouse Receipt database that 

contains information on the quantity of physical cotton that has been bought or sold. “Cotton On-Call,” CFTC, 

https://www.cftc.gov/MarketReports/CottonOnCall/index.htm; “eCotton’s Electronic Warehouse receipt Providership System,” 

Intelligent Decisions, Inc., http://www.ecotton.com/documents/EWR/EwrProviderOverview.pdf 332 Pirrong Deposition, 320:7–321:5 

 321:6–19 

333 Pirrong Deposition, 325:1–9 

334 Pirrong Report, ¶¶ 390–396; Figure 28 backup. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 121 of 197 PageID #:14091
 Highly Confidential Page 116

the “economic gain” from purchases. Such accounts were not harmed and may in fact have 

benefitted from the allegedly artificial prices. 

249. Even more fundamentally, I identify three categories of putative class members who 

could not be harmed by Plaintiff’s alleged price artificiality, irrespective of Dr. Pirrong’s 

methodology, meaning trading entities belonging to the putative class for whom harm is 

impossible as a conceptual matter. I discuss these in the next sections. 

1. Putative Class Members Trading Similarly to Trader Defendants 

Could Not Have Been Harmed 

250. The first category of putative class members for whom harm is impossible as a 

conceptual matter is entities that traded like Trader Defendants. Dr. Pirrong argues that the 

Trader Defendants benefitted from artificially depressing prices. But at the risk of stating the 

obvious, any putative class member who traded like Trader Defendants—purchasing TAS 

contracts and selling non-TAS contracts in similar quantities during the proposed class period—

also benefited and could not have been harmed. 

251. As I demonstrate in Section XI.B below, contrary to Dr. Pirrong’s claim, Trader 

Defendants’ trading behavior was not unique. Other non-Defendant traders 

 

252. Given the CME Audit Trail data limitations discussed in Sections IX.A.1 and IX.A.2 

above, it is not possible to accurately assess the number of putative class members that traded 

like Trader Defendants without individualized inquiry. However, the CME Audit Trail data 

provide a reference point to estimate the number of accounts whose purchases of TAS contracts 

and sales of non-TAS contracts were analogous to Trader Defendants. Specifically, I identify 

335

335 See Workpaper 26. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 122 of 197 PageID #:14092
 Highly Confidential Page 117

2. Putative Class Members Buying and Selling the Same Amount of TAS 

Contracts Could Not Have Been Harmed 

253. The second category of putative class members for whom harm is impossible as a 

conceptual matter are individuals or trading entities that bought and sold the same number of 

TAS contracts on April 20, 2020, and did not engage in any other trades. As explained in 

Section V.A.2 above, TAS contracts execute at a premium or discount to the yet-to-bedetermined daily settlement price. Because no artificiality is alleged on the magnitude of the 

premium or discount, the alleged price deflation on all TAS contract prices is the same. One 

TAS contract purchase at the allegedly depressed settlement price would benefit by the same 

amount as one TAS contract sale’s damages. Putative class members who only purchased and 

sold the same number of TAS contracts on April 20, 2020, could therefore not have been 

harmed. 

254. Using the CME Audit Trail data, I estimate the number of accounts Dr. Pirrong identifies 

as putative class members that could not have been harmed because they bought and sold the 

same amount of TAS contracts on April 20, 2020. While this is not a substitute for the type of 

individualized inquiry discussed above, it provides a reference point. 

336

3. Putative Class Members Trading Futures to Hedge Could Not Have 

Been Harmed 

255. The third category of putative class members for whom harm is impossible as a 

conceptual matter are individuals or trading entities that used May Contracts to hedge. 

Specifically, entities allegedly damaged on futures contracts but with equal or greater benefits on 

non-futures OTC positions could not be harmed. I discussed this concept in Section IX.A.3 

above in the context of the lack of centralized data on non-futures OTC transactions and the need 

for individualized inquiry, but the concept is discussed again in this section in the context of 

putative class members that could not be harmed. 

256. As discussed in Section IX.A.3 above, a primary use of futures is to hedge against the 

risk of price movements, and many participants in the WTI crude oil market have OTC positions, 

336 See Workpaper 27. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 123 of 197 PageID #:14093
 Highly Confidential Page 118

including physical positions, that are hedged using futures contracts. For example, buyers and 

sellers of physical oil may reference the WTI settlement price in their physical oil contracts. 

Such traders may hedge the price risk associated with these OTC contracts by executing futures 

trades that involve taking an offsetting combination of a non-TAS and TAS futures position to 

hedge against the price risk of the yet-to-be-determined settlement price. If they are hedging 

exactly, any damages that these putative class members incur in the futures market due to the 

alleged manipulation equal benefits in the OTC market. 

257. To illustrate this hedging strategy, consider the following hypothetical example (depicted 

in Figure 23 below): an energy company buys 1,000 barrels of physical oil for spot delivery on 

April 19, 2020, at a 5 cent premium to the yet-to-be-determined April 20, 2020, May Contract 

settlement price.337 A futures price decline between the time of the physical purchase and the 

time of settlement would cause the company to buy the physical oil at a cheaper price (it would 

realize a gain) while an increase in futures price would cause it to buy the physical oil at a higher 

price (it would incur a loss). In order to hedge this physical purchase price risk, it immediately 

buys one May non-TAS futures contract (corresponding to 1,000 barrels). On April 20, 2020, it 

also sells one TAS contract in order to ensure it exits the non-TAS futures hedge at the 

settlement price referenced in the physical contract. The long non-TAS futures contract 

position—exited with the TAS—would incur a loss from a decrease in the futures price between 

the time of the physical transaction and the time of settlement, while it would incur a gain if the 

futures price increased. In effect, the gain (loss) the energy company would accrue on its 

physical contract sale from a futures price decrease (increase) would equal the loss (gain) on its 

short non-TAS futures position. 

337 Spot delivery in physical crude oil markets refers to the transaction process where crude oil is bought or sold for immediate 

delivery, rather than for delivery at a future date. In this context, “spot” implies that the transaction is settled promptly, usually 

within a few days of the trade agreement. The exact timing can vary based on logistical factors and the specific terms agreed 

upon by the trading parties. See John Hull, Options, Futures, and Other Derivatives, 9th ed. (2015), p. 6. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 124 of 197 PageID #:14094
 Highly Confidential Page 119

Figure 23

A Hypothetical Hedger Is Unharmed by Price Movements 

258. Similarly, Figure 24 below illustrates using the same energy company example while 

overlaying Dr. Pirrong’s alleged artificiality amount, Dr. Pirrong’s damages calculation 

methodology only considers the impact of the alleged artificiality on the TAS sale, not the 

benefit of the physical oil sale. In this example, the energy company would be unharmed 

overall.338 

338 The non-TAS future purchase on April 19, 2020, would be unaffected by Dr. Pirrong’s artificiality estimates given the 

purchase price was set outside the proposed class period. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 125 of 197 PageID #:14095
 Highly Confidential Page 120

Figure 24

Source: Pirrong Report and Backup Materials 

Note: Artificiality is calculated attributing Only the first 

observation in each minute is used to plot artificiality. 

259. For these hedgers, in order to determine if they suffered economic harm, any alleged 

damages they suffered on futures positions due to the alleged deflation should equal benefits they 

received on cash positions. Dr. Pirrong completely ignores these basic economic facts in his 

proposed methodology, which likely leads him to include putative class members who could not 

have suffered economic harm in his analysis. 

260. Critically, and as discussed above, it is not possible to assess each putative class 

member’s benefits on OTC transactions, including in the physical cash market, without factintensive individualized inquiry into the history of each contract for each putative class member. 

For example, the cash market is characterized by individualized negotiations and nuanced 

structure, rendering such an analysis extremely challenging. Each firm’s process for establishing 

an alleged impact will depend largely on the individualized contractual and pricing documents 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 126 of 197 PageID #:14096
 Highly Confidential Page 121

from the firm. This cannot easily be generalized into a class-wide or even a firm-wide approach. 

Each individualized contract and individualized hedging transaction would need to be assessed 

separately. 

261. This issue likely applies to a large fraction of putative class members. According to the 

CFTC, “[a]ll of a trader’s reported futures positions in a commodity are classified as commercial 

if the trader uses futures contracts in that particular commodity for hedging.”339 At the opening 

of the April 20, 2020, trading session, 46% of the long open interest was held by commercial 

traders.340 Furthermore, non-commercial traders could also hedge OTC financial instruments 

such as commodity swaps that rely on the CME WTI futures price. 

C. Mish Is Not Representative of Putative Class Members 

262. The circumstances and trading activity of named plaintiff Mish diverge substantially 

from those of the overwhelming majority of the accounts identified by Dr. Pirrong as putative 

class members. An analysis of the CME Audit Trail data for putative class members’ 

accounts, as well as an evaluation of the alleged damages of these accounts based on Dr. 

Pirrong’s price artificiality estimates, reveals that Mish’s experience is not representative of the 

alleged damages caused to accounts identified by Dr. Pirrong as putative class members. 

Moreover, Mish’s trading sequence and stated trading rationale reflects trading decisions that do 

not align with the majority of putative class members. This in turn suggests that Mish still would 

have incurred losses even absent Trader Defendants’ allegedly manipulative trading. 

263. While Dr. Pirrong’s price artificiality estimates are flawed (as detailed in Sections VII 

and VIII above), and any accurate assessment of class membership, injury, and calculation of 

damages would require individualized inquiry (as explained in Section IX.A above), his 

methodology can still be instructive for the purposes of testing whether Mish is a suitable class 

representative. Applying Dr. Pirrong’s methodology to the CME Audit Trail data, as shown in 

339 “Commitment of Traders, Explanatory Notes,” CFTC, 

https://www.cftc.gov/MarketReports/CommitmentsofTraders/ExplanatoryNotes/index.htm (“All of a trader’s reported futures 

positions in a commodity are classified as commercial if the trader uses futures contracts in that particular commodity for 

hedging as defined in CFTC Regulation 1.3, 17 CFR 1.3(z).”). 

 See Pirrong Deposition, 111:21–112:4. 340 49,430 / 108,593 = 46%. See “Interim Staff Report: Trading in NYMEX WTI Crude Oil Futures Contract Leading Up To, 

On, and around April 20, 2020,” CFTC, November 23, 2020, https://www.cftc.gov/node/235761, Tables 2, 3. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 127 of 197 PageID #:14097
 Highly Confidential Page 122

Figure 25 below, demonstrates that TAS transactions account for roughly of Dr. Pirrong’s 

alleged aggregate damages. 

Figure 25

Source: CME; Pirrong Report and Backup Materials 

Note: Alleged damages (benefits) are calculated as the quantity sold (bought) multiplied by the alleged artificiality 

of the instrument at the time of the transaction based on Figure 14 of the Pirrong Report until After 

 artificiality is calculated as the difference between the “but-for” price as of and the 

transaction price. The “but for” price is the midpoint based on Dr. Pirrong’s best bid and offer (BBO) data 

production less artificiality. Alleged damages and benefits are aggregated within an account. Alleged TAS damages 

are calculated based on the artificiality as of 1:30 PM. Artificiality of May-July 2020 WTI futures spread and MayJuly 2020 WTI futures TAS spread are calculated based on the artificiality of the non-TAS May Contract and TAS 

May Contract respectively. 

264. Furthermore, according to available contemporaneous records and deposition testimony, 

341

Despite the abundance of public sources warning of potential negative WTI futures prices, 

including five notices from the CME, 

342 

265. As depicted in Figure 26 below, at Mish placed a buy order for nonTAS May Contracts. The market filled this order within at a price of less 

than before the price went negative.343 Mish’s stated trading rationale for its purchase 

of May Contracts was that 

341 Plaintiff_Mish_0000948–55 at 48; 

Plaintiff_Mish_0001040–44 at 40 

Deposition of Robert Mish, December 20, 2023 (“Mish Deposition”), 175:14–15 

342

343 Plaintiff_Mish_0000638.xlsx. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 128 of 197 PageID #:14098
 Highly Confidential Page 123

344 Consistent with this belief, 

approximately after its purchase, at Mish placed an offer to sell these 

 May Contracts at a price of 345 However, the price continued to decline and at 

the price fell below zero, causing 

346 Approximately eight minutes later, at Mish 

liquidated its May Contracts at 347 

344

345 Plaintiff_Mish_0000638.xlsx. 

 346 Deposition of Justin Boshnack, December 18, 2023 (“Boshnack Deposition”), 186:8–12; Mish Deposition, 374:14–15. 347 Plaintiff_Mish_0000638.xlsx; CME Audit Trail data. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 129 of 197 PageID #:14099
 Highly Confidential Page 124

Figure 26

Source: CME; Pirrong Report and Backup Materials 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement period is 

1:28 PM to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades executed 

during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. Mish sold contracts at 

 exiting its position. The but-for price for the “TVP-VAR Model Through series is based on 

Pirrong Report’s Figure 14 through The but-for price for the 

 series between and 1:30 PM is equal to Pirrong’s Figure 14 artificiality at 

 plus the cumulative change in the May Contract midpoint since plus the midpoint. When the 

midpoint is unavailable, which can happen for example duringa market halt or a lack of buy side or sell side 

liquidity, the last available midpoint is used. The midpoint is based on Dr. Pirrong’s backup file 

VegaBBOCLKAll.xlsx.

266. Assuming Mish’s trading rationale remained unchanged, it would have incurred losses 

even absent Trader Defendants’ alleged manipulative actions. Any claim that Mish would not 

have lost as much money in the but-for world is speculative. Dr. Pirrong testified that 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 130 of 197 PageID #:14100
 Highly Confidential Page 125

348 Figure 27 below shows Dr. Pirrong’s hypothetical “but-for” price—what the 

price of the May Contract would have been absent Trader Defendants’ purported manipulative 

actions—using Dr. Pirrong’s cumulative price artificiality estimates. Even without Trader 

Defendants’ alleged price impact, the price of the May Contract would have dropped below $0 at 

approximately and would have remained negative through the end of the proposed class 

period. If Mish traded according to the strategy it outlined at deposition, 

 This 

would still have resulted in losses as prices went negative. Mish’s losses are therefore 

attributable to rather than to 

Trader Defendants’ alleged conduct. These same types of issues need to be addressed for each 

putative class member, because prices would have still gone negative in Dr. Pirrong’s but-for 

world. 

348 Pirrong Deposition, 242:1–17 

 303:12–21 

 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 131 of 197 PageID #:14101
 Highly Confidential Page 126

Figure 27

Source: Pirrong Report and Backup Materials 

Note: The but-for price for the “TVP-VAR Model Through ” series is based on Pirrong Report’s Figure 

14 through The but-for price for the series between and 1:30 PM is equal to Pirrong 

Report’s Figure 14 artificiality at plus the cumulative change in May 2020 CME WTI futures midpoint 

since plus the midpoint. 

267. The CME Audit Trail data suggest that Mish’s trading decisions do not align with the 

majority of accounts Dr. Pirrong claims are part of the alleged class. Figure 28 shows that only 

 of the putative class members (or approximately , including Mish, exhibited trading 

activity that was analogous to Mish’s, in that they only traded non-TAS contracts intraday, 

buying at both positive prices and selling equal quantities shortly after prices turned negative. 

On the other hand, of the purportedly harmed putative class members’ accounts, 

accounts—representing of Dr. Pirrong’s alleged aggregate damages—traded TAS contracts. 

Similarly, accounts—or over of accounts Dr. Pirrong claims are part of the alleged 

class—only traded prior to 10:00 AM, before prices went negative.349 

349 There are purported class members who are unharmed according to Dr. Pirrong’s methodology of applying his price 

artificiality estimates to the CME Audit Trail Data. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 132 of 197 PageID #:14102
Highly Confidential Page 127

Figure 28

Source: CME; Pirrong Report and Backup Materials 

Note: Putative class member accounts are pulled from Pirrong’s backup files 

ClassMembersNegativeZeroStartAccts.xlsx and TotalQtySalesAccts.xlsx. The category “Trading behavior analogous 

to Mish” includes traders who bought while the WTI May-20 price was positive, sold while WTI May-20 prices 

were negative, did not transact during the purported and bought and sold in equal amounts during the 

proposed class period. The category “No TAS, Other” includes traders who did not trade TAS during the proposed 

class period and who did not meet the criteria for any other category. 

268. In sum, the overwhelming majority—almost of damages claims are predicated on 

the impacts of trading TAS contracts, while Mish only traded non-TAS contracts. Contrary to 

public information at the time, Mish’s trading decisions, 

 do not align with the vast majority of putative class members and 

suggest that Mish would have incurred losses even absent Trader Defendants’ alleged 

manipulative actions. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 133 of 197 PageID #:14103
 Highly Confidential Page 128

X. Each Trader Defendant’s Trading Pattern is Consistent with Speculating and 

Providing Liquidity, Not Manipulation 

269. Plaintiff asserts that Trader Defendants manipulated the market for May Contracts by 

“[exerting] significant cumulative net selling pressure on [this] futures contract over a specific 

time period.”350 In particular, Plaintiff alleges that “[Trader] Defendants agreed to and did use 

their sales as manipulative ‘ammo’ to depress May Contract prices.”351 Dr. Pirrong purports to 

find supportive evidence of this alleged manipulative scheme and contends that “Trading 

Defendants’ conduct was uneconomic,” “coincided exactly with trade-based manipulative 

conduct,” and that “there is no plausible non-manipulative rationale for their trading behavior.”352 

He claims that “[Trader Defendants’] trading conduct was not consistent with economically 

beneficial speculative, hedging, or market making behavior.”353 However, as shown in this 

section, these claims are systematically rebutted using calculations, market conditions, and 

market operation facts.354

270. In Section X.A, I explain that Plaintiff and Dr. Pirrong ignore that each of the Trader 

Defendants’ trading patterns is consistent with speculative trading that profits from a decrease in 

prices on April 20, 2020. The trading pattern is profitable if selling non-TAS contracts prior to 

settlement occurs at higher prices than purchases at settlement via the TAS contracts. This type 

of trading pattern can be profitable without the need to involve any type of manipulation. 

271. In Section X.B, I explain that 

 are consistent with decreasing prices over the course of the day. Each Trader 

Defendant’s speculative trading pattern is consistent 

350 Second Amended Complaint, ¶ 2.a. 351 Second Amended Complaint, ¶ 2.b. 352 Pirrong Report, ¶¶ 284, 322. 353 Pirrong Report, ¶ 284. 354 Furthermore, 

 See Pirrong Deposition, 224:23–

225:9 

 Not only is Dr. Pirrong’s methodology incapable of distinguishing Trader Defendants’ manipulative from 

non-manipulative trading but adapting his model to distinguish the price impact associated with manipulative trades would 

require differentiating manipulative from non-manipulative trades in the vector of his TVP-VAR model. This would further 

increase the number of parameters to be estimated and Dr. Pirrong’s model would therefore likely present even more serious 

robustness issues (See discussion in Section VII.D). 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 134 of 197 PageID #:14104
 Highly Confidential Page 129

272. In Section X.C, I show that each Trader Defendant’s trading pattern was profitable absent 

the alleged manipulative scheme, which directly contradicts Dr. Pirrong’s argument that Trader 

Defendants’ trading was not economically reasonable without the alleged manipulation. 

273. In Section X.D, I discuss Dr. Pirrong’s prior academic work on TAS manipulation. Dr. 

Pirrong’s prior writings about TAS-related manipulative trading patterns state that the 

manipulating party should execute the majority of non-TAS contracts during the settlement 

window. 

355 

274. In Section X.E, I explain that it is not manipulative per se to use aggressive orders, yet in 

this matter Dr. Pirrong describes “aggressive” selling at any time during the day by Trader 

Defendants as manipulative. Dr. Pirrong fails to explain that all executed trades in the futures 

market consist of an “aggressive” and a “passive” order. Further, using aggressive orders during 

the extreme market conditions on April 20, 2020 is consistent with attempting to ensure 

execution in a rapidly changing market, which is particularly true when Trader Defendants are 

immediately hedging using TAS purchases.356

A. Trader Defendants’ Buying TAS and Selling Non-TAS Contracts during the 

Proposed Class Period Is Consistent with Speculating on a Price Decrease 

275. As explained in more detail in Section XI below, Trader Defendants 

357 This type of trading pattern aims to profit from a 

decrease in prices over the course of the day.358 Put differently, a trader that pursues this type of 

trading pattern profits if they sell at a higher price in the non-TAS market than the settlement 

355 See Workpaper 28. 356 As mentioned in Section VII.A above, only of non-aggressive (or passive) orders executed during the proposed class 

period. 

357 Trader Defendants I refer to Trader Defendants as “selling nonTAS contracts.” 

358 I note that there is one Trader Defendant

 is pointed out by Dr. Pirrong in ¶ 351 of his report, in which he refers to Trader Defendants 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 135 of 197 PageID #:14105
 Highly Confidential Page 130

price they will purchase at using TAS contracts. The trader is therefore speculating that prices 

will decrease between the time they sell the asset and the time they purchase it at settlement. 

This type of trading pattern can be profitable without the need to involve any type of 

manipulation (including “trade-based manipulation” alleged by Dr. Pirrong).359 This type of 

trading pattern is not without risk.360 Prices can quickly increase, and traders can incur losses 

rather than gains. 

276. Figure 29 shows an example of the trading of one Trader Defendant (Aristos Demetriou) 

on April 20, 2020. 

 

 (see Exhibits 1 to 6 in Appendix D). 

359 In his deposition, 

 See Pirrong Deposition, 378:7–17 

 355:14–24 

 373:11–374:1. 360 Pirrong Deposition, 392:7–17 

361 This is an example chart. The charts for the other Trader Defendants can be found in Exhibits 1 to 6 in Appendix D; they 

confirm that 

See VEGA_MISH_000042650–73 at 61. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 136 of 197 PageID #:14106
 Highly Confidential Page 131

Figure 29

Source: CME 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement 

period is 1:28 PM to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades 

executed during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. 

277. In sum, Trader Defendants’ speculative trading patterns differ but each of the patterns 

profits if prices decrease. This profit occurs in scenarios without any alleged market 

manipulation. If prices had increased over the course of the day, each Trader Defendant would 

have experienced losses. The binary outcome of profiting when prices decrease and losing when 

prices increase is consistent with speculation.362 

362 I note that prices on this day were volatile throughout the trading day. That means that Trader Defendants might have lost on

some trades and gained on others. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 137 of 197 PageID #:14107
 Highly Confidential Page 132

B. Each Trader Defendant’s Speculative Pattern Is Consistent with the Large TAS 

Order Book Imbalances 

278. In addition to all the factors contributing to decreasing crude oil prices described in 

Section V, 

 were consistent with decreasing prices. 

279. As Dr. Pirrong acknowledges, 

363 Market participants, like Trader Defendants, gain information from 

order book imbalances and liquidity when deciding how to trade, and a sell side imbalance is 

consistent with more market participants expecting prices to fall. These imbalances were 

therefore consistent with each Trader Defendant’s trading pattern. 

280. Below are exhibits depicting 

 This provides a visual to see what the traders were seeing. As 

explained above in Section V.C.2, a market participant can look at the order book and understand 

the unfilled aggregate quantity and prices at which other market participants are willing to 

trade.364 If, for whatever reason, one side of the order book (e.g., the buy side) is showing less 

order book depth than the other side (e.g., the sell side), there is said to be an “order book 

imbalance.” Market participants gain information from order book imbalances and order book 

liquidity when deciding on how to trade.365 For example, when an order book shows a sell side 

imbalance—meaning more orders have been submitted to the sell side than the buy side—traders 

typically infer that prices in the relevant instrument are about to decrease as there are more 

sellers than buyers in the market. The opposite holds true if a market shows an imbalance on the 

buy side.366

281. Dr. Pirrong correctly observes that the day at issue experienced 

363 Pirrong Report, ¶ 125. 364 Harris (2003), pp. 117, 118. 365 Jonathan Brogaard, Terrence Hendershott, and Ryan Riordan, “Price Discovery without Trading: Evidence from Limit 

Orders,” The Journal of Finance 74, no. 4 (2019): 1621–1658. 366 For examples of how order imbalances predict future price movements, see Rama Cont, Arseniy Kukanov, and Sasha Stoikov, 

“The Price Impact of Order Book Events,” Journal of Financial Econometrics 12, no. 1 (2014): 47–88. See also Charles Cao, 

Oliver Hansch, and Xiaoxin Wang, “The Information Content of an Open Limit‐Order Book,” Journal of Futures Markets: 

Futures, Options, and Other Derivative Products 29, no. 1 (2009): 16–41. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 138 of 197 PageID #:14108
 Highly Confidential Page 133

 

 

282. Figure 30 shows the daily order book imbalance on each day leading up to April 20, 

2020, with orange shades reflecting sell interest and blue shades reflecting buy interest. 

 

283. Moreover, as discussed in Section IX.C, even Dr. Pirrong’s TVP-VAR model suggests 

that prices would have declined over the course of the day and dropped below zero without any 

purported manipulation by the Trader Defendants. Assuming for the sake of argument that Dr. 

Pirrong’s TVP-VAR model accurately reflects price dynamics during the proposed class period 

(which I disagree with), this supports that each Trader Defendant’s trading pattern was consistent 

with decreasing prices during the relevant period. 

367 Pirrong Report, ¶ 125. 368 I note that order book imbalance is another factor that is not included in Dr. Pirrong’s TVP-VAR model. 369 See Workpaper 29. 370 See Workpaper 30. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 139 of 197 PageID #:14109
 Highly Confidential Page 134

Figure 30

Source: CME 

Note: Midpoint refers to the midpoint of the highest bid and lowest offer prices. Buy (sell) quantity is calculated as 

the total bid (offer) volume at or above (below) 1 tick (2 ticks, 5 ticks) of the midpoint price. For example, if the 

midpoint price were $1, the buy quantity within 1 tick of the midpoint would be calculated as the total volume 

between $0.99 and $1. Likewise, the sell quantity within 1 tick of the midpoint would be calculated as the total 

volume between $1.01 and $1. CME TAS markets are closed between 1:30 PM and 5:00 PM every weekday. Data 

are not plotted between Friday at 4:00 PM and Sunday at 4:00 PM while CME markets are closed for the weekend. 

Markets open at 5:00 PM Sunday through Thursday. Quantities are snapshots of depth taken at one-minute 

intervals. 

C. Contrary to Dr. Pirrong’s Assertions of Economically Irrational Trading, 

Trader Defendants’ Trading Patterns Were Profitable Absent the Alleged 

Manipulation 

284. When calculating Trader Defendants’ profits and using Dr. Pirrong’s but-for price that 

removes alleged artificiality (instead of the actual transaction prices for Trader Defendants’ 

trades), each of the Trader Defendants still earned a profit. This directly contradicts Dr. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 140 of 197 PageID #:14110
 Highly Confidential Page 135

Pirrong’s allegations that Trader Defendants’ actions would not have been economic absent the 

alleged manipulation. 

285. Dr. Pirrong supports Plaintiff’s claims that “[Trader] Defendants intentionally caused the 

price of May 2020 Crude Oil Futures to be artificially low by making sales of May 2020 Crude 

Oil Futures and May 2020 Crude Oil Futures Spreads on April 20, 2020 that would have been 

uneconomic but for Trader Defendants’ ability to profit on purchases of TAS contracts.”371 In 

addition, Dr. Pirrong purports that “selling the ICE contract was more economically attractive 

than selling the CME contract: in essence, the Trading Defendants were selling low, rather than 

selling high, which competitive, non-manipulative traders would not do.”372 He also asserts that 

“if the Trading Defendants truly wanted to offset the exposures they had assumed by buying 

CME Crude Oil Futures TAS contracts, it would have been more profitable to do so by selling 

ICE contracts instead of CME contracts.”373 

286. However, Dr. Pirrong fails to acknowledge that Trader Defendants’ trading pattern was 

profitable absent the alleged manipulation. Applying Dr. Pirrong’s but-for price—which I 

assume to be accurate for the sake of argument—I find that each of the Trader Defendant’s 

profits were still positive (see Figure 31). In fact, using Dr. Pirrong’s but-for price, I find that 

Trader Defendants’ aggregated profits would have been between and 

(see Figure 31). For robustness I use two different scenarios regarding Dr. Pirrong’s but-for 

price during his purported 

 In 

both scenarios, each Trader Defendant’s trading remains profitable. Dr. Pirrong is therefore not 

correct when he claims that Trader Defendants’ trading was “uneconomic” and therefore must be 

manipulative. 

371 Pirrong Report, ¶¶ 76–77. See also, Pirrong Report, ¶¶ 6–7, 228. 372 Pirrong Report, ¶ 319. 373 Pirrong Report, ¶ 319. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 141 of 197 PageID #:14111
 Highly Confidential Page 136

Figure 31

Source: Pirrong Report and Backup Materials 

Note: But-for profits are calculated as the quantity traded times the but-for price under each scenario. The but-for 

price for the “Using Dr. Pirrong’s Figure 14 Artificiality Through 1:30 PM” series is based on the Pirrong Report’s 

Figure 14 through 1:30 PM. The but-for price for the 

 series before is based on Pirrong Report’s Figure 14. Between and 1:30 PM, 

the but-for price is equal to the May Contract midpoint, minus Dr. Pirrong’s Figure 14 artificiality at 

plus the cumulative change in the in May Contract midpoint since When the midpoint is unavailable, 

which can happen for example during a market halt or a lack of buy side or sell side liquidity, the last available 

midpoint is used. The midpoint is based on Pirrong’s backup files VegaBBOCLKAll.xlsx, VegaBBOCLMAll.xlsx, 

VegaBBOCLNAll.xlsx. 

287. Separately, Dr. Pirrong’s claim that “it would have been profitable for the Trading 

Defendants to buy CME contracts and sell ICE contracts”374 is incorrect for at least two reasons. 

First, functionally trading on ICE does not offset a CME TAS position. ICE and CME are 

different exchanges, and trading on one exchange does not offset positions on the other. This is 

true for Trader Defendants as well as all other market participants. Second, even if somehow 

374 Pirrong Report, ¶ 319. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 142 of 197 PageID #:14112
 Highly Confidential Page 137

ICE trading offset CME positions, on April 20, 2020, ICE had “virtually stopped trading” and 

had insufficient liquidity. 

288. Trading in the ICE contract may not have been feasible given a lack of liquidity. 

 

289. In sum, Trader Defendants’ trading patterns were profitable absent the alleged 

manipulation, which directly contradicts Plaintiff’s and Dr. Pirrong’s assertion that Trader 

Defendants’ trading was manipulative because it was uneconomic. Further, Dr. Pirrong’s claim 

that Trader Defendants should have traded on ICE to offset positions on CME is wrong and 

functionally cannot occur because these are two separate exchanges. 

D. The Timing of Trader Defendants’ Sales Is Inconsistent with Settlement Price 

Manipulation 

290. Based on Dr. Pirrong’s own academic work about settlement price manipulation and 

related prior legal matters, the timing of Trader Defendants’ sales is not consistent with 

settlement manipulation. 

291. In his report, Dr. Pirrong cites to a working paper he wrote on “Derived Pricing: 

Fragmentation, Efficiency, and Manipulation” in 2019.377 He claims to show “how the existence 

of TAS markets create[s] manipulative opportunities” and “presents an example of manipulation 

of TAS contracts by the trading firm Optiver in 2008” whereby Optiver sells the majority of its 

375 

376 Pirrong Deposition, 277:15–278:5 

377 Pirrong Report, footnote 39. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 143 of 197 PageID #:14113
 Highly Confidential Page 138

non-TAS contracts during the settlement period to enact manipulation.378 Dr. Pirrong 

summarizes the Optiver matter as follows:379

Optiver would usually accumulate the bulk of its TAS position during the 

morning of the settlement day. If it obtained a sufficiently large TAS position, 

starting about 2:25 (three minutes prior to the beginning of the settlement 

window) Optiver began to trade heavily in the underlying ... in the opposite 

direction as its TAS position through market orders. Optiver’s volume during the 

2:25-2:30 period was roughly equal to the size of the TAS position it had 

accumulated. Optiver traded so that approximately 25 percent of its volume was 

executed from 2:25-2:27:59, and the remainder during the settlement period. It 

intended that its volume during the 2:28-2:29:59 settlement window would 

represent such a large fraction of total volume during this period that its average 

execution price during this period would approximately equal the VWAP, and 

thus the settlement price. 

292. In other words, both Dr. Pirrong’s theoretical model and the Optiver matter suggest that if 

an entity was trying to depress the TAS settlement price, the most impactful way would be to 

concentrate sales during the settlement window. As I describe in detail in Section XI.A and as 

Dr. Pirrong confirmed in his deposition,380

81 Further, in contrast to the TAS trade-based 

manipulation described in Dr. Pirrong’s 2019 article, Trader Defendants 

382 

378 Craig Pirrong, “Derived Pricing: Fragmentation, Efficiency, and Manipulation,” Working Paper (2019), pp. 2, 16–17. 379 Craig Pirrong, “Derived Pricing: Fragmentation, Efficiency, and Manipulation,” Working Paper (2019), pp. 16–17 (emphasis 

added). 

380 Pirrong Deposition, 85:6–12 

381 See Workpaper 28. 382 Craig Pirrong, “Derived Pricing: Fragmentation, Efficiency, and Manipulation,” Working Paper (2019), pp. 13 – 14. See also

Pirrong Deposition, 85:6–12 

 is pointed out by Dr. Pirrong in ¶ 351 of his report, in which he refers to Trader Defendants 

See Workpaper 31. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 144 of 197 PageID #:14114
 Highly Confidential Page 139

293. Instead, Trader Defendants 

383 Specifically, Trader Defendants 



387

294. Dr. Pirrong has no explanation for why Trader Defendants, who were supposedly trying 

to drive the settlement price lower, would 

295. Further, in the Optiver case, Dr. Pirrong attributed a lack of price reversal to “permanent 

price impact” due to the manipulative conduct.388 Contrary to the Optiver matter in which prices 

did not reverse after the settlement window, Dr. Pirrong claims that prices reversed after the 

settlement window on April 20, 2020.389 

296. In sum, Plaintiff’s and Dr. Pirrong’s claims regarding Trader Defendants’ allegedly 

manipulative trading behavior are inconsistent with how Dr. Pirrong himself has outlined, in 

theory and through examples, the ways in which traders can profit from trading TAS 

manipulatively. 

E. Aggressive Limit Orders Are Not Themselves a Sign of Manipulation, but 

Rather Are Consistent with Trader Defendants’ Difficulty Executing in an 

Illiquid and Fast-Moving Market 

297. Setting aside Dr. Pirrong’s prior paper describing how selling during the settlement 

period is a method for manipulation, in this matter Dr. Pirrong describes “aggressive” selling at 

any time during the day by Trader Defendants as manipulative. In his report, Dr. Pirrong 

describes Trader Defendants’ trading 

383 See Workpaper 28. 384 See Workpaper 32. 385 See Workpaper 32. 386 See Workpaper 32. 387 See Workpaper 32. 388 Craig Pirrong, “Derived Pricing: Fragmentation, Efficiency, and Manipulation,” Working Paper (2019) pp. 19, 21–22. 389 Pirrong Deposition, 87:10–18 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 145 of 197 PageID #:14115
 Highly Confidential Page 140

390 Dr. Pirrong contends that 

391 Dr. Pirrong also contends that Trader Defendants’ 

 sales on April 20 had an increasingly depressive effect on May Contract prices and 

increasingly exacerbated the order imbalance in the May Contract.”392 He claims that Trader 

Defendants 

393 From his observations of Trader 

Defendants’ trading patterns, he concludes that their “actions ... mirrored exactly the conduct of 

an entity implementing what economists define as a trade-based manipulation strategy utilizing 

TAS contracts.”394

298. As a basic matter, Dr. Pirrong does not provide a threshold for how large a share a market 

participant has to have in terms of (aggressive) trading volume to be considered indicative of 

manipulative trading. 

 

396 Yet, as discussed above, Dr. Pirrong does not offer any 

methodology to establish which trading was or was not manipulative. 

397 His opinion regarding Trader Defendants’ percentage of aggressive 

trades therefore lacks any basis. 

299. In addition to this fundamental flaw in his approach, Dr. Pirrong does not explain that all

trades in the futures market—that is, every single trade—consists of an “aggressive” and 

“passive” order. Just because the word “aggressive” is used to describe an order, it does not 

390 Pirrong Report, ¶ 314. 391 Pirrong Report, ¶ 288. 392 Pirrong Report, ¶ 13.c. 393 Pirrong Report, ¶ 16. 394 Pirrong Report, ¶ 321. 395 Pirrong Deposition, 330:24–331:10. 396 Pirrong Deposition, 330:24–331:10. 397 Pirrong Deposition, 331:11–332:13. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 146 of 197 PageID #:14116
 Highly Confidential Page 141

mean it is nefarious. Without an “aggressive” order, no trades would ever occur since together 

aggressive and passive orders make a trade. 

300. Orders that can immediately execute are called “aggressive.” This means all market 

orders are aggressive, and limit orders that can immediately trade are aggressive. Orders that do 

not immediately trade are called “passive,” which are limit orders that make up the resting 

liquidity of the order book.398 

301. Without the use of “aggressive” orders, certain Trader Defendants may not have been 

able to offset their TAS purchases and may not have returned to an overall neutral position by 

the end of the trading day. Put simply, aggressive trading can be necessitated by market 

conditions, rather than being a tell-tale sign of manipulative intent, as Dr. Pirrong tries to 

claim.399 As explained in more detail in Section XI below, 

 

302. Dr. Pirrong also ignores that Trader Defendants’ speculative trading patterns were 

dependent on the successful execution of (i) orders to purchase TAS contracts, and (ii) orders to 

sell non-TAS contracts. The speculative trading patterns would have been jeopardized if any of 

these types of orders did not execute. For example, if a Trader Defendant had bought a TAS 

contract but had not managed to sell an outright contract prior to settlement, they would have 

faced post-settlement price risk—that is, the risk of prices moving adversely to Trader 

Defendants’ position while they are trying to exit out of their position. There are two additional 

benefits of Trader Defendants ending the day without a position. The first benefit is to not incur 

the risk of carrying a position overnight, which might incur a loss if there are potential price 

fluctuations that occur after the market closes and before it opens the next day. The second 

benefit stems from the at-issue day being one day before the expiration of the contract maturity. 

If Trader Defendants had ended the day with an open position, they would have needed to exit 

the position on the next day since Trader Defendants were not set up to make or take delivery of 

crude oil. Any market participants holding positions at the close of trading on April 21, 2020, 

398 Harris (2003), p. 75. 399

 See Pirrong Deposition, 389:21–390:5 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 147 of 197 PageID #:14117
 Highly Confidential Page 142

were making or taking delivery. The last day of trading in a contract tends to have limited 

liquidity, making the risk of adverse price movements a possibility for Trader Defendants. 

303. Setting aside that aggressive trades are not inherently manipulative, I find that 

Dr. Pirrong incorrectly claims that Trader Defendants 

400 

401 

402

304. Setting aside the incorrect representations made by Dr. Pirrong, it is important to 

recognize the market environment in which the disputed activity took place. April 20, 2020, is 

an example of an extremely volatile market environment with rapidly declining prices in the 

middle of a global pandemic. In a volatile market environment that is characterized by (rapidly) 

declining prices, these declining prices often lead to more aggressive selling as traders are 

pressured to sell quickly to avoid further losses or guarantee execution. This urgency to sell can 

be particularly acute when the market is subject to numerous trading halts. The fact that Trader 

Defendants used aggressive orders does not automatically mean they did so with the objective of 

maximizing price impact for manipulative purposes. Instead, using aggressive orders is 

consistent with an attempt to execute in a rapidly changing market. Dr. Pirrong fails to 

acknowledge this, and instead attributes all of Trader Defendants’ trades to his construed theory 

of manipulation. 

XI. Dr. Pirrong Fails to Offer Analysis Demonstrating Plaintiff’s Claims Regarding a 

Trader Defendant–Coordinated Conspiracy to Manipulate and Depress the Prices 

of NYMEX Crude Oil Futures Contracts 

305. Both Plaintiff and Dr. Pirrong allege that Trader Defendants engaged in a coordinated 

conspiracy to manipulate and depress prices, and they attempt to support their claims by 

400 Pirrong Report, ¶¶ 19, 21. 401 See Workpaper 33. 402 See Workpaper 32. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 148 of 197 PageID #:14118
 Highly Confidential Page 143

presenting a “correlation”403 and 404 analysis. Plaintiff alleges that 

from “at least as early as April 2020, the Vega [Trader] Defendants[] combined, conspired, and 

agreed to depress on April 20, 2020 prices of the May 2020 [WTI] contract.”405 Dr. Pirrong 

contends that Trader Defendants’ alleged manipulative conduct followed the pattern of “[f]irst, 

the purchase of TAS contracts. Second, the sale of the instruments underlying the TAS 

contracts. To maximize price impact, sell the underlying instruments aggressively, that is, by 

taking liquidity.”406 Dr. Pirrong further claims that 407 

306. Below I discuss substantial problems with Plaintiff’s and Dr. Pirrong’s claims of a 

coordinated trading conspiracy based on my review of their analyses and the produced data. To 

summarize: 

 Contrary to Plaintiff’s and Dr. Pirrong’s claim, Trader Defendants 

 Contrary to Plaintiff’s and Dr. Pirrong’s claim, Trader Defendants’ 

 Plaintiff’s “correlation” analysis is incomplete and methodologically flawed. 

o First, the analysis presented in the Second Amended Complaint only includes a 

subset of the Trader Defendants and is therefore incomplete.408

o Second, Plaintiff’s analysis is methodologically flawed because Plaintiff claims to 

assess if “Trader Defendants engaged in coordinated trading”409 but instead of 

assessing “coordinated trading,” Plaintiff analyzes correlation in coordinated 

position levels. Revising Plaintiff’s correlation analysis to be conducted on 

trading instead of position levels for the six Trader Defendants specified in the 

403 Second Amended Complaint, ¶ 141; Pirrong Report, ¶¶ 294–295. 404 Pirrong Report, ¶¶ 294–295. 405 Second Amended Complaint, ¶ 1. 406 Pirrong Report, ¶¶ 284–285. 407 Pirrong Report, ¶ 287. 408 See Plaintiff_Mish_0000028. I note that the file includes 

409 Second Amended Complaint, heading C.2, “The Vega Trading Defendants Engaged in Coordinated Trading In WTI Crude 

Oil Futures Contracts A Month Prior to Engaging in Substantially Similar But More Extreme Conduct on April 20, 2020.” See 

also Second Amended Complaint, ¶ 286. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 149 of 197 PageID #:14119
 Highly Confidential Page 144

Second Amended Complaint, the average correlation falls from to 410 

Correcting the analysis to include all Trader Defendants (not just a subset like 

Plaintiff does) reduces the average correlation from to 411

o Third, interpretation of the correlation analysis is rendered meaningless 



 

 Moreover, Dr. Pirrong does not offer any hypothesis about Trader Defendants’ 

trading patterns that he purports to test statistically. By lacking testable hypotheses, Dr. 

Pirrong’s analysis becomes unscientific and his opinions arbitrary. 

307. Each of these findings are described in more detail below. 

A. Contrary to Dr. Pirrong’s Assertions, Trader Defendants’ Trading 

Patterns Varied Substantially 

308. Based on my review of Trader Defendants’ trading data, 

 as I discuss in Section XI.B. 

410 I note that it is not entirely clear how Plaintiff arrived at its Trader Defendant minute-by-minute positions. I have therefore 

run my own analyses, which may differ slightly from Plaintiff’s analysis. See Workpaper 34. 411 See Workpaper 34. 412 Pirrong Report, ¶¶ 294–295. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 150 of 197 PageID #:14120
 Highly Confidential Page 145

 

309. Figure 32 below plots each of the Trader Defendants’ net May Contract positions on 

April 20, 2020. 

413 See Workpaper 35. See also Figures 33 to 36; Exhibits 1 to 6 in Appendix D. 414

 See Pirrong Deposition, 465:8–468:25. 415 The only exception to this is which is 

consistent with the use of TAS contracts to exit their open positions. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 151 of 197 PageID #:14121
 Highly Confidential Page 146

Figure 32

Source: CME; VEGA_MISH_000042708.1 (Vega Produced Trade Data) 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement 

period is 1:28 PM to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades 

executed during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. Connor 

Younger’s purchase of contracts after 1:30 PM does not appear in the CME Audit Trail data and is pulled from 

the VEGA produced trade data. 

310. Figures 33 through 36 show a subset of Trader Defendants’ trading on CME in the 

products at issue on April 20, 2020. Below I describe these Trader Defendants’ trading on the 

day and, by way of example, illustrate how 

 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 152 of 197 PageID #:14122
 Highly Confidential Page 147

311. Figure 33 shows the cumulative position in barrels and number of contracts of Aristos 

Demetriou on April 20, 2020, 

 

 

312. Figure 34 shows the cumulative position in the same contracts for Connor Younger on 

April 20, 2020. 

 

 

 

 

313. Figure 35 shows the cumulative position in the same contracts for Elliott Pickering on 

April 20, 2020. 

 

416 See Workpaper 36. 417 See Workpaper 35. 418 See Workpaper 35. 419 See Workpaper 36. 420 See Workpaper 36. 421 See Workpaper 36. 422 See Workpaper 35. 423 See Workpaper 36. 424 See Workpaper 35. 425 See Workpaper 36. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 153 of 197 PageID #:14123
 Highly Confidential Page 148

 

 

 

 

 

 

314. Figure 36 shows the cumulative position in the same contracts for Matthew Thompson on 

April 20, 2020. 

 

 

 

 

315. None of Trader Defendants’ trading was 

 which undermines Plaintiff’s and Dr. Pirrong’s claim that 

they engaged in a conspiracy to depress prices. 

426 See Workpaper 36. 427 See Workpaper 36. 428 See Workpaper 36. 429 See Workpaper 36. 430 See Workpaper 35. 431 See Workpaper 35. 432 See Workpaper 36. 433 See Workpaper 36. 434 See Workpaper 36. 435 See Workpaper 35. 436 See Workpaper 35. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 154 of 197 PageID #:14124
 Highly Confidential Page 149

Figure 33

Source: CME 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement 

period is 1:28 PM to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades 

executed during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 155 of 197 PageID #:14125
 Highly Confidential Page 150

Figure 34

Source: CME; VEGA_MISH_000042708.1 (Vega Produced Trade Data) 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement 

period is 1:28 PM to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades 

executed during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. Connor 

Younger’s purchase of contracts after 1:30 PM does not appear in the CME Audit Trail data and is pulled from 

the VEGA produced trade data. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 156 of 197 PageID #:14126
 Highly Confidential Page 151

Figure 35

Source: CME 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement 

period is 1:28 PM to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades 

executed during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 157 of 197 PageID #:14127
 Highly Confidential Page 152

Figure 36

Source: CME 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement 

period is 1:28 PM to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades 

executed during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. 

B. Contrary to Dr. Pirrong’s Assertion, Trader Defendants’ Trading 

Behavior Was Not Unique 

316. Dr. Pirrong further claims that 437 to buy TAS 

contracts and sell non-TAS contracts. He contends that 

 Based on my 

review of the produced data, Dr. Pirrong’s claims are incorrect. The data show other traders with 

what Dr. Pirrong calls his “elements” of “trade-based manipulation,” which is “[f]irst, the 

437 Pirrong Report, ¶ 287. 438 Pirrong Report, ¶ 291. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 158 of 197 PageID #:14128
 Highly Confidential Page 153

purchase of TAS contracts. Second, the sale of the instruments underlying the TAS contracts,” 

and third, “sell[ing] the underlying instruments aggressively.”439 

317. Dr. Pirrong does not offer any methodology for determining if a trade is manipulative; he 

simply assumes it for Trader Defendants’ trading.440 

 The fact that non-Defendant traders, who are not accused of manipulation (to 

my knowledge), undermines Dr. Pirrong’s 

attempt to portray Trader Defendants’ trading as different or manipulative. 

318. Figures 37 through 39 show 

319. Figure 37 shows that 

 

439 Pirrong Report, ¶ 285. 440 Dr. Pirrong relies on his claim that Trader Defendants’ behavior was uneconomic without proposing a test to determine 

whether individual trades were manipulative. See Pirrong Report, ¶ 322 (“The absence of a non-manipulative rationale for their 

trading combined with the close correspondence between their conduct and that of a trade-based manipulator demonstrates that 

their conduct was uneconomic and manipulative”). Dr. Pirrong then proceeds to assume that all trades conducted by Trader 

Defendants were manipulative (Pirrong Report, ¶ 334 (“The TVP-VAR model and the CME audit trail data that identifies 

Trading Defendants’ transactions allows me to isolate the price impact of their uneconomic, manipulative aggressive sales”)). 441 See Workpaper 37. I restrict to accounts that had offsetting TAS and non-TAS positions as of 1:30 PM. When including 

accounts with TAS purchases and non-TAS sales quantities within 10% of each other at 1:30 PM, there are 42 such accounts. 442 See Workpaper 35. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 159 of 197 PageID #:14129
 Highly Confidential Page 154

Figure 37

Source: CME 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement 

period is 1:28 to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades 

executed during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. 

320. Figure 38 shows that 

443 

443 See Workpaper 35. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 160 of 197 PageID #:14130
 Highly Confidential Page 155

Figure 38

Source: CME 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement 

period is 1:28 to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades 

executed during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. 

321. Figure 39 shows that 

444 

444 See Workpaper 35. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 161 of 197 PageID #:14131
 Highly Confidential Page 156

Figure 39

 

Source: CME 

Note: The price of the May Contract first traded below zero at on the CME. The daily settlement 

period is 1:28 to 1:30 PM. The settlement price is based on the VWAP of the outright CME GLOBEX trades 

executed during this period. Trading halts at 4:00 PM each business day and resumes at 5:00 PM. 

C. The Correlation Analysis Presented in the Second Amended Complaint Is 

Incomplete and Methodologically Flawed and Does Not Support a 

Coordinated Conspiracy 

322. As detailed below, the correlation analysis presented in the Second Amended Complaint 

is based on only a subset of Trader Defendants, the analysis does not actually measure the 

correlation of Trader Defendants’ trading behavior, and the analysis has no meaningful 

interpretation since non-Defendant traders also have similar correlations. When considering all 

the methodological flaws, the correlation analysis is not reliable or useful for evaluating the 

allegations and does not support a coordinated conspiracy. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 162 of 197 PageID #:14132
 Highly Confidential Page 157

1. The Correlation Analysis Only Includes a Subset of Traders 

323. Setting aside methodological flaws, Plaintiff’s conspiracy correlation analysis is based on 

a subset of traders, and contains material errors that reverse Plaintiff’s high correlation 

conclusions. 

324. Plaintiff uses a “correlation” analysis to ostensibly support an allegation that a conspiracy 

between Trader Defendants existed to decrease prices because “the five Vega Trading 

Defendants who build up the largest May Contract short positions, did so in a manner that is 

extremely highly correlated on a minute-by-minute basis with one another as well as with 

Defendant Paul Commins’ buildup of his May Contract short position.”445 Specifically the 

Trader Defendants included in this analysis are Paul Commins, Mr. Younger, Mr. Demetriou, 

Mr. Pickering, Mr. Roase, and Mr. Lunn.446 

325. Plaintiff alleges that correlations for this subset of Trader Defendants ranged from 

 According to Plaintiff, this is “extremely unlikely to happen by 

independent decision making or freak occurrence,”448 and “highly consistent with a very carefully 

executed agreement to build cumulative net selling pressure on the market in a manner which 

will succeed in depressing prices.”449 

326. Plaintiff’s correlation analysis is based on calculating correlations only among six Trader 

Defendants. If all traders are included, Plaintiff’s average correlation decreases from to 

450 But as shown below, even this analysis remains flawed. 

445 Second Amended Complaint, ¶ 5. 446 Second Amended Complaint, ¶¶ 140–142. I include Paul Commins in this subset because Plaintiff also alleges that “each of 

the [five Vega Trading] Defendants additions to its positions was highly correlated with those of Defendant Paul Commins.” See

Second Amended Complaint, ¶ 142. 

447 Second Amended Complaint, ¶ 141. Note this range does not include Paul Commins. 448 Second Amended Complaint, ¶ 5. 449 Second Amended Complaint, ¶ 5. 450 See Workpaper 38. I note that Dr. Pirrong 

 See Pirrong Deposition, 488:10–

490:7 

 The average correlation excluding and is 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 163 of 197 PageID #:14133
 Highly Confidential Page 158

2. The Correlation Analysis of Trader Defendants’ Position Levels Is 

Methodologically Flawed and Uninformative as to Any Purported 

Trading Conspiracy 

327. Plaintiff claims to be assessing if “Trading Defendants engaged in coordinated trading”451

and purports that Trader Defendants “combined and conspired to sell large volumes of May 

Contracts in the final hours before the settlement price of the May Contract was to be 

determined.”452 Yet, the purported correlation analysis does not measure correlation in trading as 

Plaintiff has set out to analyze. Instead, it measures correlation in position levels.453 Correlating 

position levels provides information on the direction in which a position was accumulated. To 

understand if trading activity is similar across market participants, one must analyze the actual 

trading patterns of these market participants—which is better captured by correlating changes in 

positions (i.e., trade executions). 

454 Yet, despite the fact that Dr. 

Pirrong was involved in Plaintiff’s correlation analysis,455 his report analysis does not cover the 

correlation of Trader Defendants’ trading. 

328. This methodological flaw results in vastly inflated correlation figures. If Plaintiff had 

actually correlated trading instead of position levels, the analysis would show much lower levels 

of correlation among Trader Defendants’ trading. The average correlation across the six Trader 

Defendants’ specified in the Second Amended Complaint falls from to 456 If trading 

correlations for all Trader Defendants are considered, the correlation drops even further to 

457 

329. Figures 40 and 41 show that correlations across Trader Defendants’ trading instead of 

position levels are much lower than for both non-TAS and TAS May Contracts. 

451 Second Amended Complaint, heading C.2, “The Vega Trading Defendants Engaged in Coordinated Trading In WTI Crude 

Oil Futures Contracts A Month Prior to Engaging in Substantially Similar But More Extreme Conduct on April 20, 2020.” 452 Second Amended Complaint, ¶ 143. 453 Pirrong Deposition, 491:11–

454 Pirrong Deposition, 458:12–23, 498:8–15. 455 Pirrong Deposition, 488:10–20 456 See Workpaper 34. 457 See Workpaper 34. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 164 of 197 PageID #:14134
 Highly Confidential Page 159

 The 

average correlation across Trader Defendants is for May non-TAS and for May TAS 

(see Figure 42 below). 

Figure 40

Source: CME 

Note: Correlations are calculated minute-to-minute from 12:00:07 AM to 1:54:07 PM on April 20, 2020. May legs 

of May-June and May-July spread contracts are included. 

Figure 41

Source: CME 

Note: Correlations are calculated minute-to-minute from 12:00:07 AM to 1:54:07 PM on April 20, 2020. May legs 

of May-June and May-July spread contracts are included. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 165 of 197 PageID #:14135
 Highly Confidential Page 160

330. The analysis above demonstrating that correlation across Trader Defendants is 

substantially lower using trading instead of position levels is also true for different periods of 

time. Figures 40 and 41 use the time period in the Second Amended Complaint spanning from 

12:00:07 AM to 1:54:07 PM on April 20, 2020. It is not clear why this time period was chosen. 

If I conduct the same calculations for (i) the proposed class period (9:00 AM–1:30 PM), and (ii) 

the last hour of trading during the proposed class period (12:30 PM–1:30 PM), the conclusions 

are unchanged. The findings for the three different time periods are summarized in Figure 42, 

which reports the average Trader Defendant correlations for position levels and trading for both 

non-TAS and TAS May Contracts.458

Figure 42

Source: CME 

Note: 

[1] Correlations are calculated minute-to-minute on April 20, 2020, for each account before calculating group 

averages. May legs of May-June and May-July spread contracts are included. 

[2] The full period covers the time period used in the Second Amended Complaint analysis: 12:00:07 AM to 

1:54:07 PM. 

[3] The proposed class period covers 9:00 AM to 1:30 PM. 

[4] The last hour of proposed class period covers 12:30 PM to 1:30 PM. 

331. Overall, these findings, regardless of the time period shown, demonstrate that Trader 

Defendants show smaller correlations on trading compared to position levels. 

458 See Figure 45 backup for detailed trader tables for each of the different time periods. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 166 of 197 PageID #:14136
 Highly Confidential Page 161

3. Correlations between Trader Defendants’ and Non-Defendant 

Traders’ Trading Are Not Substantially Different than Between 

Trader Defendants 

332. As I discuss in Section XI.B, other non-Defendant traders 

 This renders reliance on the correlation analysis meaningless for purposes of 

assessing the alleged conspiracy among Trader Defendants. The results contradict Plaintiff’s 

statement that correlations among Trader Defendants are “extremely unlikely to happen by 

independent decision making” since I identified 

 and do not involve allegations of a “carefully executed 

agreement” to coordinate trading to depress prices.459

333. Figures 43 and 44 show correlations between non-Defendant traders, between Trader 

Defendants and between non-Defendant traders and Trader Defendants for position levels and 

trades, respectively. 

334. In Figure 43 on position levels, for example, the correlation between non-Defendant 

trader The correlation between nonDefendant trader These correlations 

are correlation that Plaintiff asserts exists between Trader 

Defendants Henry Lunn and Paul Commins.460 

459 Second Amended Complaint, ¶ 5. 460 Second Amended Complaint, ¶ 142. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 167 of 197 PageID #:14137
 Highly Confidential Page 162

Figure 43

Source: CME 

Note: Correlations are calculated minute-to-minute from 12:00:07 AM to 1:54:07 PM on April 20, 2020. May legs 

of May-June and May-July spread contracts are included. 

335. In Figure 44 on trades, for example, the correlation between non-Defendant trader 

 The correlation between nonDefendant trader These correlations 

are correlation between Trader Defendants Henry Lunn and 

George Commins. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 168 of 197 PageID #:14138
 Highly Confidential Page 163

Figure 44

Source: CME 

Note: Correlations are calculated minute-to-minute from 12:00:07 AM to 1:54:07 PM on April 20, 2020. May legs 

of May-June and May-July spread contracts are included. 

336. The many correlations in Figures 43 and 44 can be summarized by the average 

correlation of three different groupings: non-Defendant traders, Trader Defendants and nonDefendant traders, and Trader Defendants (see Figure 45 below). Across these groupings, 

 

Figure 45

Source: CME 

Note: Correlations are calculated minute-to-minute from 12:00:07 AM to 1:54:07 PM on April 20, 2020, for each 

account before calculating group averages. May legs of May-June and May-July spread contracts are included. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 169 of 197 PageID #:14139
 Highly Confidential Page 164

337. Figure 45 along with Figures 43 and 44 show that Plaintiff’s correlation analysis is 

meaningless for purposes of assessing the purported conspiracy.461 The examples I provide show 

correlations between traders not alleged to have engaged in a conspiracy that to 

correlations between Trader Defendants alleged to have engaged in a conspiracy. Overall, 

reliance on the correlation measure put forth by Plaintiff to infer a conspiracy is improper and 

unreliable. 

D. No Analysis Is Presented by Dr. Pirrong That Supports a Coordinated 

Conspiracy by Trader Defendants 

338. Dr. Pirrong does not appear to present analysis to support a coordinated conspiracy 

among Trader Defendants. Dr. Pirrong’s 

 

 In fact, Dr. Pirrong was not able to 

point to any academic literature that supports this type of analysis or this type of analysis being 

used for trading patterns.464 

 

339. Dr. Pirrong contends that Trader Defendants’ 

 

461 The conclusions of the analysis are confirmed when using different time periods. The additional time periods I analyzed are 

the proposed class period of 9:00 AM through 1:30 PM and the last hour of trading during the proposed class period (12:30 PM– 

1:30 PM). See Figure 45 backup. 462 Pirrong Report, ¶¶ 294–295. 463 Pirrong Deposition, 384:10–21, 464 Pirrong Deposition, 382:15–24 

383:25–384:9 

465 Pirrong Report, ¶ 294. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 170 of 197 PageID #:14140
 Highly Confidential Page 165

 

 

340. When analyzing Dr. Pirrong’s Figure 22, I find that 

 468 

341. In fact, 

 Figure 

46 shows in Dr. Pirrong’s Figure 22, 

469 Dr. Pirrong conveniently ignores this fact, which is not consistent 

with the conclusions he makes from his chart. 

466 Pirrong Report, ¶ 295. 467 Pirrong Report, ¶ 295. 468 Pirrong Report, ¶ 295 469 Pirrong Report, ¶ 295. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 171 of 197 PageID #:14141
 Highly Confidential Page 166

Figure 46

Source: CME 

Note: White stars indicate the net May TAS transactions and net aggressive May non-TAS transactions of Trader 

Defendants between 5:00 PM on April 19, 2020, and 4:00 PM on April 20, 2020. 

 May TAS transactions and May non-TAS transactions include 

May outrights, May-June spreads, and May-July spreads. 

 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 172 of 197 PageID #:14142
 Highly Confidential Page 167

342. Dr. Pirrong uses the word “correlation” in reference to Trader Defendants’ trading 

patterns only once in the body of his report.470 Dr. Pirrong stated that he analyzed 

471 These correlations are not correlations across Trader Defendants or across nonDefendant traders. Rather, this is simply a correlation between 

 

343. Finally, Dr. Pirrong’s analysis fails to meet the most basic scientific requirement: Dr. 

Pirrong does not offer any hypothesis about Trader Defendants’ trading patterns that he purports 

to test statistically. By lacking testable hypotheses, Dr. Pirrong’s analysis becomes unscientific. 

344. In sum, and correlation analysis put forth by Dr. Pirrong do not 

measure coordination among Trader Defendants, and the conclusions he draws are incorrect. 

Executed this 20th of June, 2024 

___________________________________ 

 Terrence Hendershott, Ph.D. 

470

 See Pirrong Deposition, 465:18–468:8 

 

 See Pirrong Deposition, 607:13–608:11. This 

contradicts an article by Bloomberg—which I understand prompted Plaintiff’s filing of the complaint—which claimed that 

Defendants traded in “unison.” See “London Traders Hit $500 Million Jackpot When Oil Went Negative,” Bloomberg, August 3, 

2020, https://www.bloomberg.com/news/articles/2020-08-04/oil-s-plunge-below-zero-was-500-million-jackpot-for-a-fewlondon-traders?sref=aHrd9Iuo&embedded-checkout=true. 

471 Pirrong Report, ¶ 295. 

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 173 of 197 PageID #:14143
Terrence Hendershott 

June 2024

Professor 

Willis H. Booth Chair in Banking and Finance 

Faculty Director, Master of Financial Engineering 

Haas School of Business 

University of California 

Berkeley, CA 94720-1900 

phone: (510) 643-0619 

fax: (510) 643-1412 

hender AT haas.berkeley.edu 

Education 

Ph.D., Operations, Information, and Technology, Graduate School of Business, Stanford University, 1999. 

B.S., Mathematics and Statistics, Miami University, 1989.

Publications 

 When failure is an option: Fragile liquidity in over-the-counter markets (with Dmitry Livdan, Dan Li, and

Norman Schürhoff), Journal of Financial Economics 157 (July 2024).

 Transparency in Fragmented Markets: Experimental Evidence (with Marvin Wee and Yuanji Wen),

Journal of Financial Markets 59 (June 2022), 1-20.

 Asset Price Dynamics with Limited Attention (with Albert Menkveld, Remy Praz, and Mark Seasholes),

Review of Financial Studies 35 (February 2022), 962-1008.

 Does Financial Market Structure Impact the Cost of Raising Capital? (with James Brugler and Carole

Comerton-Forde), Journal of Financial and Quantitative Analysis 56 (August 2021), 1771-1808.

 FinTech as a Game Changer: Overview of Research Frontiers (with Michael Zhang, Leon Zhao, and Eric

Zheng), Information Systems Research 32 (March 2021), 1-17.

 Asset Pricing: A Tale of Night and Day (with Dmitry Livdan and Dominik Rösch), Journal of Financial

Economics 138 (December 2020), 635-662.

 Relationship Trading in OTC Markets (with Dan Li, Dmitry Livdan, and Norman Schürhoff), Journal of

Finance 75 (April 2020), 683-734.

 Short Selling and Price Discovery in Corporate Bonds (with Roman Kozhan and Vikas Raman), Journal of

Financial and Quantitative Analysis 54 (January 2020), 77-115.

 Price Discovery without Trading: Evidence from Limit Orders (with Jonathan Brogaard and Ryan

Riordan), Journal of Finance 75 (August 2019), 1621-1658.

 High Frequency Trading and the 2008 Short Sale Ban (with Jonathan Brogaard and Ryan Riordan),

Journal of Financial Economics 124 (April 2017), 22-42.

 Are Institutions Informed about News? (with Dmitry Livdan and Norman Schürhoff) Journal of Financial

Economics 117 (August 2015), 249-287.

 Click or Call? Auction versus Search in the Over-the-Counter Market (with Ananth Madhavan), Journal of

Finance 70 (February 2015), 419-447.

 Price Pressures (with Albert Menkveld), Journal of Financial Economics, 114 (December 2014), 405-423.

 High-Frequency Trading and Price Discovery (with Jonathan Brogaard and Ryan Riordan), Review of

Financial Studies 27 (August 2014), 2267-2306. Won Michael J. Brennan Best Paper Award for best paper

published in Review of Financial Studies in 2015.

 Liquidity provision and stock return predictability (with Mark Seasholes), Journal of Banking & Finance

45 (August 2014), 140-151.

 How Slow is the NBBO? A Comparison with Direct Exchange Feeds (with Shengwei Ding and John

Hanna), Financial Review 49 (May 2014), 313-332.

 High-Frequency Trading and the Execution Costs of Institutional Investors (with Jonathan Brogaard,

Stefan Hunt, and Carla Ysusi), Financial Review 49 (May 2014), 345-369. Won Financial Review

Outstanding Publication Award for 2014.

 Levelling the Trading Field (with David Easley and Tarun Ramadorai), Journal of Financial Markets 17

(January 2014), 65-93.

 The Intended and Collateral Effects of Short-Sale Bans as a Regulatory Tool (with Ethan Namvar and

Blake Phillips), Journal of Investment Management 11 (2013), 5-13.

Appendix A Highly Confidential Page 1

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 174 of 197 PageID #:14144
• Algorithmic Trading and the Market for Liquidity (with Ryan Riordan), Journal of Financial and

Quantitative Analysis 48 (August 2013), 1001-1024. Won 2013 Philip Brown Prize.

• Informed Trading and Portfolio Returns (with Alex Boulatov and Dmitry Livdan), Review of Economic

Studies 80 (January 2013), 35-72.

• Automation, Speed, and Stock Market Quality: The NYSE’s Hybrid (with Pam Moulton), Journal of

Financial Markets 14 (November 2011), 568-604.

• Does Algorithmic Trading Increase Liquidity? (with Charles Jones and Albert Menkveld), Journal of

Finance 66 (February 2011), 1-33. Won New York Stock Exchange Euronext Award for best paper on

equity trading, Western Finance Association (2008). Finalist for the Smith-Breeden Prize for best paper

published in the Journal of Finance.

• Time Variation in Liquidity: The Role of Market Maker Inventories and Revenues (with Carole ComertonForde, Charles Jones, Pam Moulton, and Mark Seasholes), Journal of Finance 65 (February 2010), 295-

331.Won Nasdaq Award for best paper on market microstructure, Financial Management Association

(2007).

• The NFL Should Auction Possession in Overtime Games (with Yeon-Koo Che), Economists’ Voice 9

(October 2009), http://www.bepress.com/ev/vol6/iss9/art5/.

• A Comparison of Trading and Non-Trading Mechanisms for Price Discovery (with Michael Barclay),

Journal of Empirical Finance 15 (December 2008), 839-849.

• How to Divide the Possession of a Football? (with Yeon-Koo Che), Economics Letters 99 (June 2008),

561-565.

• Order Consolidation, Price Efficiency, and Extreme Liquidity Shocks (with Michael Barclay and Charles

Jones), Journal of Financial and Quantitative Analysis 43 (March 2008), 93-121.

• Market Maker Inventories and Stock Prices (with Mark Seasholes), American Economic Review (P&P) 97

(May 2007), 210-214.

• Automation versus Intermediation: Evidence from Treasuries Going Off the Run (with Michael Barclay

and Kenneth Kotz), Journal of Finance 61 (October 2006), 2395-2414.

• A Model of Direct and Intermediated Sales (with Jie Zhang), Journal of Economics & Management

Strategy 15 (Summer 2006), 279-316.

• Island Goes Dark: Transparency, Fragmentation, and Regulation (with Charles Jones), Review of Financial

Studies 18 (Fall 2005), 743-793.

• Trade-through Prohibitions and Market Quality (with Charles Jones), Journal of Financial Markets 8

(February 2005), 1-23.

• Liquidity Externalities and Adverse Selection: Evidence from Trading After Hours (with Michael Barclay), 

Journal of Finance 59 (April 2004), 681-710.

• Competition Among Trading Venues: Information and Trading on Electronic Communications Networks

(with Michael Barclay and Tim McCormick), Journal of Finance 58 (December 2003), 2637-2666. Won

New York Stock Exchange Award for best paper on equity trading, Western Finance Association (2001).

Nominated for the Smith-Breeden Prize for best paper published in the Journal of Finance.

• Price Discovery and Trading After Hours (with Michael Barclay), Review of Financial Studies 16 (Winter

2003), 1041-1073.

• Electronic Trading Systems in Financial Markets, IEEE-IT Professional 5 (Jul/Aug 2003), 10-14.

• The Future of Virtual Malls (with Patric Hendershott and Robert Hendershott), Real Estate Finance 18

(Spring 2001), 25-32.

• Crossing Networks and Dealer Markets: Competition and Performance (with Haim Mendelson), Journal of

Finance 55 (October 2000), 2071-2115. Nominated for the Smith-Breeden Prize for best paper published in 

the Journal of Finance.

• Bundling and Optimal Auctions of Multiple Products (with Christopher Avery), Review of Economic

Studies 67 (July 2000), 483-497.

• Will the Internet Reduce the Demand for Mall Space? (with Patric Hendershott and Robert Hendershott),

Real Estate Finance 17 (Spring 2000), 41-46.

Working Papers 

 All-to-All Liquidity in Corporate Bonds (with Dmitry Livdan and Norman Schürhoff)

Appendix A Highly Confidential Page 2

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 175 of 197 PageID #:14145
 Quote Competition in Corporate Bonds (with Dmitry Livdan, Dan Li, Norman Schürhoff, and Kumar 

Venkataraman) 

 Option Auctions (with Saad Khan and Ryan Riordan) 

 Stock Exchanges as Platforms for Data and Trading (with Marc Rysman and Rainer Schwabe) 

 Order Exposure in High Frequency Markets (with Bidisha Chakrabarty, Samarpan Nawn, and Roberto 

Pascual) 

 Public and Private Information in Quotes and Trades (with James Brugler) 

 Market Predictability and Non-Informational Trading (with Mark Seasholes) 

Books, Reviews, and Chapters 

 Handbook of Economics and Information Systems (Editor), Elsevier, ISBN 0444517715. 

 Implementation Shortfall with Transitory Price Effects (with Charles Jones and Albert Menkveld), chapter 

in High Frequency Trading: A Survival Guide, Eds. David Easley, Marcos Lopez de Prado, and Maureen 

O’Hara, Risk Books. 

 Book Review of Econometrics of Financial High-Frequency Data, by Nikolaus Hautsch, Quantitative 

Finance (2013). 

Other Publications 

 Call for Papers—Special Issue of Information Systems Research Fintech – Innovating the Financial 

Industry Through Emerging Information Technologies (with Michael Zhang, Leon Zhao, and Eric Zheng), 

Information Systems Research (December 2017), 885-886. 

 Automated Trading, Encyclopedia of Quantitative Finance. 

 Preface to the Focus Theme Section: ‘Financial Market Engineering’ (with Dirk Neumann, Robert 

Schwartz, Bruce Weber, and Christof Weinhardt), Electronic Markets 16 (May 2006), 98-100. 

 An Economic View of Information Systems (with Krishnan Anand), Introduction to Special Issue on 

Information Systems and Economics, Decision Support Systems 41(May 2006), 683-687. 

 Wall St’s appeal for new rules is not altruistic, Financial Times, comment/op-ed, 7/21/2004, p. 13. 

 Should the Outcome of a Coin Flip Mean So Much in NFL Overtime? Bid for the Ball (with Jonathan 

Berk), Wall Street Journal Online, 12/22/2003. 

Honors, Awards, Miscellaneous 

 Advisory Committee, Consolidated Audit Trail (2022-) 

 Faculty Director, Master of Financial Engineering, UC Berkeley (2020-) 

 University of California Retirement System Advisory Board (2020-24; Chair 2023-24) 

 Willis H. Booth Chair in Banking and Finance, Haas School of Business, UC Berkeley (2017-) 

 Norwegian Finance Initiative Grant to study the effect of technological and regulatory changes on market 

structure and transparency across equity and fixed-income markets in the US and Europe (2018-2022) 

 Digging into Data Round 4; Digging Into High Frequency Data: Present And Future Risks And 

Opportunities (2016-2020) 

 Market Surveillance Advisory Group, Financial Industry Regulatory Authority (2016-2018, 2021-2022) 

 Cheryl and Christian Valentine Chair, Haas School of Business, UC Berkeley (2012-2017) 

 Consultant, Office of the Chief Economist, Commodity Futures Trading Commission (2016-2017) 

 Distinguished Visiting Scholar, Securities and Exchange Commission (2015). 

 Michael J. Brennan Best Paper Award for best paper published in Review of Financial Studies in 2014. 

 Financial Review Outstanding Publication Award for 2014. 

 2013 Philip Brown Prize. 

 High Frequency Trading Subcommittee of the Technology Advisory Committee, Commodity Futures 

Trading Commission (2012-2013) 

 Barbara and Gerson Bakar Faculty Fellow, Haas School of Business, UC Berkeley (2011-2012) 

 Consultant, Office of the Chief Economist, Commodity Futures Trading Commission (2009-2011) 

 Visiting Scholar, University of Sydney (2010) 

 Net Institute Grant (2009) 

 Kauffman Foundation Entrepreneurship & Innovation Research Grant (2008-2009) 

Appendix A Highly Confidential Page 3

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 176 of 197 PageID #:14146
 New York Stock Exchange Euronext Award for best paper on equity trading, Western Finance Association 

(2008) 

 Visiting Fellow, The Paul Woolley Centre for the Study of Capital Market Dysfunctionality, London 

School of Economics (2008) 

 Nasdaq Award for best paper on market microstructure, Financial Management Association (2007) 

 Visiting Professor, Université Paris-Dauphine (2007, 2008, 2009, 2010, 2012, 2013) 

 Nasdaq Economic Advisory Board, (2004-7; Chair 2007) 

 Visiting Economist, New York Stock Exchange (2005-2006) 

 National Science Foundation Grant #0133848, CAREER: Electronic Trading Systems (2002-2006) 

 Schwabacher Fellow (outstanding teaching and research), University of California, Berkeley (2005-2006) 

 Junior Faculty Research Grant, Committee on Research, University of California, Berkeley (2001, 2003) 

 New York Stock Exchange Award for best paper on equity trading, Western Finance Association (2001) 

 Simon School Teaching Honor Roll, University of Rochester (2000, 2001) 

 Xerox Assistant Professor, University of Rochester (1999-2001) 

 Frye Fellowship, Stanford University (1992) 

 Chiles Fellowship, Stanford University (1991) 

Teaching Experience 

 High-Frequency Finance (MFE 230X), UC Berkeley. 

 Introduction to Business Analytics (UGBA 104), UC, Berkeley. 

 Information Technology Strategy (MBA 247B, ENGIN 298A, INFOSYS 290, UGBA 196), UC, Berkeley. 

 Operations Management (MBA and EWMBA 204), UC, Berkeley. 

 Financial Information Systems (CIS 446/Finance 446), University of Rochester. 

 Investment Management and Trading Strategies (Finance 434), Simon School, University of Rochester. 

Professional Service 

Editorial: 

 Associate Editor, Journal of Finance, 2022- 

 Associate Editor, Journal of Financial Economics, 2021- 

 Associate Editor, Review of Asset Pricing Studies, 2021- 

 Associate Editor, Journal of Financial Markets, 2012- 

 Associate Editor, Management Science, 2010-2018 

 Associate Editor, Journal of Banking & Finance, 2015-2016 

 Co-Editor, Journal of Economics and Management Strategy, 2006-2013 

 Associate Editor, Information Systems Research, 2004-2005 

 Associate Editor, Decision Support Systems, 2003-2016 

 Advisory Editor, Handbooks in Information Systems, Elsevier 

 Guest Editor, Special Issue of Information Systems Research Fintech – Innovating the Financial Industry 

Through Emerging Information Technologies (with Michael Zhang, Leon Zhao, and Eric Zheng), 

Information Systems Research

 Guest Editor, Special Issue on Behavioral Finance and Recent Developments in Capital Markets, PacificBasin Finance Journal 

 Guest Editor, Focus Theme Section: ‘Financial Market Engineering’, Electronic Markets 

 Guest Editor, Special Issue on Information Systems and Economics, Decision Support Systems

Conferences: 

 Western Finance Association, program committee, 2011- 

 SAFE Market Microstructure Conference, program committee, 2017- 

 International FinTech, InsurTech & Blockchain Forum, program committee, 2018-2021 

 Napa Conference on Financial Markets, program committee, 2009-2017 

 European Finance Association, program committee, 2001-2004, 2012-2016 

 French Finance Association Paris December Conference, 2013-2019 

Appendix A Highly Confidential Page 4

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 177 of 197 PageID #:14147
 Finance Down Under Conference, 2013-2016 

 Society for Financial Econometrics and Tinbergen University (Amsterdam) Conference on Measuring and 

Understanding Asset Price Changes: The Price of Liquidity, and the Liquidity of Price, program 

committee, 2011 

 NYSE-Euronext/Dauphine University, 3rd Workshop on Financial Market Quality, organizer, 2010 

 NYSE Euronext & Tinbergen Institute Workshop on Liquidity and Volatility, program committee, 2009 

 National Institute of Securities Markets Conference on Structure, Microstructure and Regulation of 

Securities Markets, Mumbai, India, program committee, 2008 

 NYSE-Euronext/Dauphine University, 2nd Workshop on Financial Market Quality, organizer, 2008 

 INFORMS Conference on Information Systems and Technology, program committee, 2000-6 

 Microstructure of International Financial Markets, Hyderabad, India, program committee, 2006 

 FinanceCom (International Workshop on Finance Industry Enterprise, Applications & Services), program 

committee, 2005-2012 

Appendix A Highly Confidential Page 5

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 178 of 197 PageID #:14148
Prior Testimony 

Last 4 Years 

Deposition and Trial Testimony (2019, 2020), Emily and Malcom Fairbairn v. Fidelity Investments 

Charitable Gift Fund, Case No. 3:18-cv-04881 (U.S. District Court Northern District of California). 

Deposition and Hearing Testimony (2020, 2021), In re Global Brokerage, Inc f/k/a FXCM Inc. 

Securities Litigation, Case No. 1:17-cv-00916-RA-BCM (U.S. District Court Southern District of 

New York). 

Deposition Testimony (2021), Budicak, Inc., et al. v. Lansing Trade Group, LLC, et al., Case No. 2:19-

cv-02449 (U.S. District Court District of Kansas). 

Deposition Testimony (2021), City of Providence v. BATS Global Markets Inc., et al., Case No. 2:19-

cv-02449 (U.S. District Court Southern District of New York). 

Deposition Testimony (2021), Iowa Public Employees' Retirement System, et al. v. Bank of America 

Corporation, et al., Case No. 1:17-cv-06221-KPF (U.S. District Court Southern District of New York). 

Deposition Testimony (2022), In re QuantumScape Securities Litigation, Case No. 3:21-cv-00058-

WHO (U.S. District Court Northern District of California). 

Deposition Testimony (2022), Set Capital LLC v. Credit Suisse AG, Case No. 1:18-cv-02268-AT-SN 

(U.S. District Court Southern District of New York). 

Deposition Testimony (2022), In re CBL & Associates Properties, Inc. Securities Litigation Case No. 

1:19-cv-00181-JRG-CHS (U.S. District Court Eastern District of Tennessee). 

Deposition, Hearing, and Trial Testimony (2023, 2024), SEC vs Terraform Labs PTE LTD and Do 

Hyeong Kwon, Case No. 1:23-cv-1346 (U.S. District Court Southern District of New York). 

Deposition Testimony (2024), Tang Capital Partners, LP v. BRC Inc., Case No. 1:22-cv-03476 (U.S. 

District Court Southern District of New York). 

Deposition and Trial Testimony (2024), In re Rocket Companies, Inc. Stockholder Derivative 

Litigation, Civil Action No. 2021-1021-KSJM (Court of Chancery of the State of Delaware). 

Deposition Testimony (2024), Shupe v. Rocket Companies, Inc., Case No. 21-cv-11528 (U.S. 

District Court Eastern District of Michigan Northern Division). 

Appendix A Highly Confidential Page 6

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 179 of 197 PageID #:14149
Documents Relied Upon 

Academic Articles 

 Andrei Kirilenko, et al., “The Flash Crash: The Impact of High Frequency Trading on an 

Electronic Market,” CFTC (2014) 

 Charles Cao, Oliver Hansch, and Xiaoxin Wang, “The Information Content of an Open 

Limit‐Order Book,” Journal of Futures Markets: Futures, Options, and Other Derivative 

Products 29, no. 1 (2009): pp. 16–41 

 Christiane Baumeister and James Hamilton, “Inference in Structural Vector 

Autoregressions When the Identifying Assumptions Are Not Fully Believed: ReEvaluating the Role of Monetary Policy in Economic Fluctuations,” Journal of Monetary 

Economics 100, no. C (2018): pp. 48–65 

 Craig Pirrong, “Derived Pricing: Fragmentation, Efficiency, and Manipulation,” 

University of Houston, Working Paper (2019), 

https://www.bauer.uh.edu/spirrong/TAS_Manipulation.pdf 

 David Easley, Marcos López de Prado, and Maureen O’Hara, “The Microstructure of the 

‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed 

Trading,” The Journal of Portfolio Management 37, no. 2 (2011): pp. 118–128 

 David Easley, Marcos M. López de Prado, and Maureen O’Hara, “Flow Toxicity and 

Liquidity in a High-Frequency World,” The Review of Financial Studies 25, no. 5 (2012): 

pp. 1457–1493 

 Emily B. Fox and Mike West, “Autoregressive Models for Variance Matrices: Stationary 

Inverse Wishart Processes,” arXiv, arXiv:1107.5239 (2011) 

 Giorgio E. Primiceri, “Time Varying Structural Vector Autoregressions and Monetary 

Policy,” The Review of Economic Studies 72, no. 3 (2005): pp. 821–852 

 Joel Hasbrouck, “Measuring the Information Content of Stock Trades,” The Journal of 

Finance 46, no. 1 (1991): pp. 179–207 

 Jonathan Brogaard, Terrence Hendershott, and Ryan Riordan, “Price Discovery without 

Trading: Evidence from Limit Orders,” The Journal of Finance 74, no. 4 (2019): pp. 

1621–1658 

 Mark Bognanni, “A Class of Time-Varying Parameter Structural Vars for Inference 

under Exact or Set Identification,” Federal Reserve Bank of Cleveland, Working Paper 

18-11 (2018), https://www.clevelandfed.org/publications/working-paper/2018/wp-1811-

a-class-of-time-varying-parameter-structural-vars 

 Rama Cont, Arseniy Kukanov, and Sasha Stoikov, “The Price Impact of Order Book 

Events,” Journal of Financial Econometrics 12, no. 1 (2014): pp. 47–88 

 Terrence Hendershott and Charles M. Jones, “Island Goes Dark: Transparency, 

Fragmentation, and Regulation,” The Review of Financial Studies 18, no. 3 (2005): pp. 

743–793 

Appendix B Highly Confidential Page 1

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 180 of 197 PageID #:14150
 Terrence Hendershott and Ryan Riordan, “Algorithmic Trading and the Market for 

Liquidity,” Journal of Financial and Quantitative Analysis 48, no. 4 (2013): pp. 1001–

1024 

 Terrence Hendershott, Dmitry Livdan, and Dominik Rosch, “Asset Pricing: A Tale of 

Night and Day,” Journal of Financial Economics 138, no. 3 (2020): pp. 635–662 

 Timothy Cogley and Thomas J. Sargent, “Drifts and Volatilities: Monetary Policies and 

Outcomes in the Post WWII US,” Review of Economic Dynamics 8, no. 2 (2005): pp. 

262–302 

 Torben G. Andersen and Oleg Bondarenko, “Assessing Measures of Order Flow Toxicity 

and Early Warning Signals for Market Turbulence,” Review of Finance 19, no. 1 (2015): 

pp. 1–54 

 Torben G. Andersen and Oleg Bondarenko, “Reflecting on the VPIN Dispute,” Journal 

of Financial Markets 17, no. 1 (2014): pp. 53–64 

 Torben G. Andersen and Oleg Bondarenko, “VPIN and the Flash Crash,” Journal of 

Financial Markets 17, no. C (2014): pp. 1–46 

Books and Book Chapters 

 Andrew Gelman, et al., Bayesian Data Analysis, 3rd ed. (2021) 

 David A. Freedman and David H. Kaye, “Reference Guide on Statistics,” in Reference 

Manual on Scientific Evidence, ed. National Research Council et al., 3rd ed. (2011), pp. 

211–302 

 Frederick Gravetter and Larry Wallnau, Statistics for the Behavioral Sciences, 10 ed. 

(2015) 

 Jakša Cvitanić and Fernando Zapatero, Introduction to the Economics and Mathematics 

of Financial Markets (2004) 

 Jeffrey F. Jaffe, Randolph W. Westerfield, and Stephen Ross, Corporate Finance, 6th ed. 

(2003) 

 John Hull, Options, Futures, and Other Derivatives, 9th ed. (2015) 

 Karl Popper, The Logic of Scientific Discovery (2005) 

 Larry Harris, Trading and Exchanges: Market Microstructure for Practitioners (2003) 

 National Research Council et al., Reference Manual on Scientific Evidence, 3rd ed. 

(2011) 

 Richard A. Brealey, Stewart C. Myers, and Franklin Allen, Principles of Corporate 

Finance, 10th ed. (2011) 

 Robert Kolb and James Overdahl, Understanding Futures Markets, 6th ed. (2006) 

 Thierry Foucault, Marco Pagano, and Ailsa Röell, Market Liquidity: Theory, Evidence, 

and Policy (2013) 

Appendix B Highly Confidential Page 2

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 181 of 197 PageID #:14151
Expert Reports 

 Amended Class Certification Report of Professor Craig Pirrong, May 9, 2024, with 

Backup Materials 

 In Re: Term Commodities Cotton Futures Litigation, Amended Expert Report of Dr. 

Craig Pirrong (Redacted), March 18, 2022 

Depositions 

 Deposition of Justin Boshnack, December 18, 2023 

 Deposition of Robert Mish, December 20, 2023, with Exhibits 

 Deposition of Craig Pirrong, May 14, 2024, with Exhibits 

Legal Documents 

 Mish International Monetary Inc. on behalf of itself and all other similarly situated v. 

Vega Capital London, Ltd., et al., Plaintiff’s Memorandum of Law in Support of Its 

Motion for Class Certification, Corrected Copy, February 23, 2024 

 Mish International Monetary Inc. on behalf of itself and all other similarly situated v. 

Vega Capital London, Ltd., et al., Plaintiff’s Motion for Class Certification, February 19, 

2024 

 Mish International Monetary Inc. on behalf of itself and all other similarly situated v. 

Vega Capital London, Ltd., et al., Second Amended Complaint, Corrected Copy, 

February 3, 2023

Rules and Regulatory Documents 

 “17 CFR Part 17: Large Trader Reporting Requirements,” Commodity Futures Trading 

Commission, May 1, 2024, https://www.cftc.gov/media/10606 

 “CME GLOBEX Reference Guide,” CME Group, https://bit.ly/3Khp8iw 

 “Large Trader Reporting Program,” Commodity Futures Trading Commission, 

https://www.cftc.gov/IndustryOversight/MarketSurveillance/LargeTraderReportingProgr

am/ltrp.html 

 Letter from U.S. Commodity Futures Trading Commission to U.S Senate Committee on 

Agriculture and House Committee on Agriculture, February 8, 2022, 

https://www.agriculture.senate.gov/imo/media/doc/2022%2002%2008%20Ag%20commi

ttees%20digital%20asset%20response%20letter.pdf 

 “NYMEX Rulebook, Chapter 7: Delivery Facilities and Procedures,” CME Group, as of 

April 16, 2020, 

https://web.archive.org/web/20200416161650/https://www.cmegroup.com/content/dam/c

megroup/rulebook/NYMEX/1/7.pdf 

Appendix B Highly Confidential Page 3

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 182 of 197 PageID #:14152
 “NYMEX Rulebook, Chapter 200, Light Sweet Crude Oil Futures,” as of April 28, 2020, 

https://web.archive.org/web/20200428053717/https://www.cmegroup.com/content/dam/c

megroup/rulebook/NYMEX/2/200.pdf 

Publicly Available Reports 

 Commodity Futures Trading Commission, “Interim Staff Report: Trading in NYMEX 

WTI Crude Oil Futures Contract Leading Up To, On, and around April 20, 2020,” 

November 23, 2020, https://www.cftc.gov/node/235761 

 Everett Wheeler, Allison Good, and Gorey Paul, “No Place to Go: Oil Storage Filling up 

Amid Collapsing Demand, Excess Production,” S&P Global Market Intelligence, April 6, 

2020, https://www.spglobal.com/marketintelligence/en/news-insights/latest-newsheadlines/no-place-to-go-oil-storage-filling-up-amid-collapsing-demand-excessproduction-57865154 

 Jeffrey Currie et al., “Oil Views: An Industry Game-Changer,” Goldman Sachs 

Commodities Research, March 30, 2020, https://valori.it/wpcontent/uploads/2020/04/Oil-Views_-An-industry-game-changer-1.pdf 

 Michael Sewalt and Cyriel De Jong, “Negative Prices in Electricity Markets,” 

Commodities Now, June 2023, https://www.kyos.jp/wpcontent/uploads/sites/2/2016/10/Commodities-Now-Negative-prices-in-ElectricityMarkets.pdf 

 Philip Verleger, “Negative Prices: Never Again,” Energy Intelligence, April 23, 2020, 

https://www.energyintel.com/0000017b-a7da-de4c-a17b-e7da75620000 

 U.S. Energy Information Administration, “WTI Crude Oil Futures Prices Fell below Zero 

Because of Low Liquidity and Limited Available Storage,” April 22, 2020, 

https://www.eia.gov/petroleum/weekly/archive/2020/200422/includes/analysis_print.php 

Notices and Press Releases 

 “Advisory Notice: CME Clearing Plan to Address the Potential of a Negative Underlying 

in Certain Energy Options Contracts,” CME Group, April 8, 2020, 

https://www.cmegroup.com/notices/clearing/2020/04/Chadv20-152.html 

 “Advisory Notice: Performance Bond Requirements: Energy - Effective April 17, 2020,” 

CME Group, April 16, 2020, https://cmegroup.com/notices/clearing/2020/04/Chadv20-

163.html 

 “Advisory Notice: Testing Opportunities in CME’s ‘New Release’ Environment for 

Negative Prices and Strikes for Certain NYMEX Energy Contracts,” CME Group, April 

15, 2020, https://www.cmegroup.com/notices/clearing/2020/04/Chadv20-160.html 

 “Changes to Price and Strike Price Eligibility Flags for Certain Energy Products,” CME 

Group, April 3, 2020, https://www.cmegroup.com/notices/electronictrading/2020/04/20200403.html 

Appendix B Highly Confidential Page 4

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 183 of 197 PageID #:14153
 “Market Regulation Advisory Notice, Rule 524: TAS, TAM, BTIC, and TACO 

Transactions,” CME Group, August 13, 2018, 

https://web.archive.org/web/20210318063322/https://www.cmegroup.com/rulebook/files/

cme-group-Rule-524.pdf 

 “Market Regulation Advisory Notice, Rule 526: Block Trades,” CME Group, 

https://www.cmegroup.com/rulebook/files/cme-group-Rule-526.pdf 

 Organization of the Petroleum Exporting Countries Press Release, “The 10th 

(Extraordinary) OPEC and non-OPEC Ministerial Meeting concludes,” April 12, 2020, 

https://www.opec.org/opec_web/en/press_room/5891.htm 

Public Press Articles and Other Web Content 

 “Clearing Market Structure,” CME Group, 

https://www.cmegroup.com/education/courses/clearing/clearing-market-structure.html 

 “CME Globex,” CME Group, https://www.cmegroup.com/globex.html 

 “Column: U.S. Crude Oil Storage Is Filling Rapidly,” Reuters, April 20, 2020, 

https://www.reuters.com/article/idUSKBN222110 

 “Commitment of Traders,” CME Group, https://www.cmegroup.com/toolsinformation/quikstrike/commitment-of-traders.html

 “Commitment of Traders, Explanatory Notes,” Commodity Futures Trading 

Commission, 

https://www.cftc.gov/MarketReports/CommitmentsofTraders/ExplanatoryNotes/index.ht

m 

 “Crude Oil Futures – Contract Specs,” CME Group, 

https://www.cmegroup.com/markets/energy/crude-oil/light-sweetcrude.contractSpecs.html 

 “Crude Oil Futures Prices Turn Negative,” Congressional Research Services, April 22, 

2020, https://crsreports.congress.gov/product/pdf/IN/IN11354 

 “Daily Bulletin Glossary,” CME Group, 2012, https://www.cmegroup.com/toolsinformation/files/CME-Group-Daily-Bulletin-Glossary.pdf 

 “Disaggregated Explanatory Note,” Commodity Futures Trading Commission, 

https://www.cftc.gov/MarketReports/CommitmentsofTraders/DisaggregatedExplanatory

Notes/index.htm 

 “Economic Purpose of Futures Markets and How They Work,” Commodity Futures 

Trading Commission, 

https://www.cftc.gov/LearnAndProtect/AdvisoriesAndArticles/economicpurpose.html 

 “eCotton’s Electronic Warehouse Receipt Providership System,” EWR, Inc., 

http://www.ecotton.com/documents/EWR/EwrProviderOverview.pdf 

Appendix B Highly Confidential Page 5

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 184 of 197 PageID #:14154
 “Exchange for Related Positions (EFRPs)” CME Group, 

https://www.cmegroup.com/clearing/operations-and-deliveries/accepted-trade-types/efpefr-eoo-trades.html 

 “Futures Order Types,” CME Group, 

https://www.cmegroup.com/education/courses/things-to-know-before-trading-cmefutures/futures-order-types.html 

 “ICE Futures Europe: Permian WTI Storage Future,” Intercontinental Exchange, 

https://www.ice.com/products/71090497/Permian-WTI-Storage-Future 

 “Iceberg Orders,” CME Group, https://www.cmegroup.com/toolsinformation/webhelp/ebs-workstation-quick-guide/Content/IcebergOrders.html 

 “Learn About Exercise and Assignment,” CME Group, 

https://www.cmegroup.com/education/courses/introduction-to-options/learn-aboutexercise-and-assignment.html 

 “London Traders Hit $500 Million Jackpot When Oil Went Negative,” Bloomberg, 

August 3, 2020, https://www.bloomberg.com/news/articles/2020-08-04/oil-s-plungebelow-zero-was-500-million-jackpot-for-a-few-londontraders?sref=aHrd9Iuo&embedded-checkout=true 

 “No Vacancy: Main U.S. Oil Storage in Cushing Is All Booked,” Reuters, April 21, 2020, 

https://www.reuters.com/article/us-global-oil-usa-storage/no-vacancy-main-u-s-oilstorage-in-cushing-is-all-booked-idUSKCN22332W 

 “North America Rotary Rig Count (Jan 2000-current),” Baker Hugues, 

https://bakerhughesrigcount.gcs-web.com/na-rig-count 

 “North America’s Oil Industry Is Shutting Off the Spigot,” The Wall Street Journal, April 

13, 2020, https://www.wsj.com/articles/north-americas-oil-industry-is-shutting-off-thespigot-11586770200?mod=article_inline 

 “NYMEX Crude Oil,” CME Group, April 26, 2024, 

https://www.cmegroup.com/confluence/display/EPICSANDBOX/NYMEX+Crude+Oil 

 “NYMEX,” CME Group, https://www.cmegroup.com/company/nymex.html 

 “Oil Glut May Overwhelm Global Storage Tanks within Weeks,” Bloomberg, April 15, 

2020, https://www.bloomberg.com/news/articles/2020-04-15/oil-glut-may-overwhelmstorage-despite-opec-cut-iea-says 

 “Oil Plunges below $12 as Storage Rapidly Fills,” Midland Reporter-Telegram, April 20, 

2020, https://www.mrt.com/business/oil/article/Oil-plunges-below-12-as-storage-rapidlyfills-15212436.php 

 “Oil Prices Could Fall below Zero: Analyst,” Fox Business, March 18, 2020, 

https://www.foxbusiness.com/markets/oil-price-could-fall-below-zero-analyst 

 “Oil Prices Dive as Saudi Arabia Takes Aim at Russian Production,” The New York 

Times, March 8, 2020, https://www.nytimes.com/2020/03/08/business/saudi-arabia-oilprices.html 

Appendix B Highly Confidential Page 6

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 185 of 197 PageID #:14155
 “One Corner of U.S. Oil Market Has Already Seen Negative Prices,” Bloomberg, March 

27, 2020, https://www.bloomberg.com/news/articles/2020-03-27/one-corner-of-u-s-oilmarket-has-already-seen-negative-prices 

 “Optimism Is Less Distant as Global Coronavirus Battle Rages On,” The New York 

Times, April 8, 2020, https://www.nytimes.com/2020/04/08/us/coronavirus-globalprogress.html 

 Pierre Andurand [@AnduranPierre], Twitter Post, X, April 20, 2020, 3:22 AM, 

https://twitter.com/AndurandPierre/status/1252150610167771136 

 Pierre Andurand [@AnduranPierre], Twitter Post, X, April 20, 2020, 3:27 AM, 

https://twitter.com/AndurandPierre/status/1252151840474529793 

 “Power Worth Less than Zero Spreads as Green Energy Floods the Grid,” Bloomberg, 

August 6, 2018, https://www.bnnbloomberg.ca/power-worth-less-than-zero-spreads-asgreen-energy-floods-the-grid-1.1119243 

 “The 20 Minutes That Broke the U.S. Oil Market,” Bloomberg, April 25, 2020, 

https://www.bloomberg.com/news/articles/2020-04-25/the-20-minutes-that-broke-the-us-oil-market 

 “The Next Chapter of the Oil Crisis: The Industry Shuts Down,” Bloomberg, April 27, 

2020, https://www.bloomberg.com/news/articles/2020-04-26/the-next-chapter-of-the-oilcrisis-the-industry-shuts-down 

 “Trading at Settlement (TAS),” CME Group, 

https://www.cmegroup.com/trading/trading-at-settlement.html 

 “U.S. Natural Gas Prices Turn Negative in Texas Permian Shale Again,” Reuters, May 

22, 2019, https://www.reuters.com/article/idUSKCN1SS1GB/ 

 “Understanding Block Trades,” CME Group, November 1, 2017, 

https://www.cmegroup.com/education/articles-and-reports/understanding-blocktrades.html 

 “Understanding Futures Expiration & Contract Roll,” CME Group, 

https://www.cmegroup.com/education/courses/introduction-to-futures/understandingfutures-expiration-contract-roll.html 

 “What Really Caused the Epic WTI Meltdown?” Energy Intelligence, April 21, 2020, 

https://www.energyintel.com/0000017b-a7da-de4c-a17b-e7da70fa0000 

 “WTI Crude Futures,” ICE, https://www.ice.com/products/213/WTI-Crude-Futures

 “WTI-WTF? Part II (of How Many???),” Streetwise Professor, April 21, 2020, 

https://streetwiseprofessor.com/wti-wtf-part-ii-of-how-many/ 

 “WTI-WTF?” Streetwise Professor, April 20, 2020, https://streetwiseprofessor.com/wtiwtf/ 

Data 

 Bloomberg 

Appendix B Highly Confidential Page 7

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 186 of 197 PageID #:14156
 CME Audit Trail Data 

 CME DVL662-T “DVL662-T Report,” CME Group, December 30, 2020

 CME Order Entry Data 

 “Cotton On-Call,” Commodity Futures Trading Commission, June 13, 2024, 

https://www.cftc.gov/MarketReports/CottonOnCall/index.htm 

 “Working and Net Available Shell Storage Capacity,” U.S. Energy Information 

Administration, https://www.eia.gov/petroleum/storagecapacity/ 



xlsx 

 Refinitiv 

 “Trading at Settlement (TAS) Outright and Calendar Spread Availability,” CME Group, 

https://www.cmegroup.com/trading/files/tas-tam-eligibility.xlsx 

 “U.S. Weekly Product Supplied,” U.S. Energy Information Administration, 

https://www.eia.gov/dnav/pet/pet_cons_wpsup_k_w.htm 

 “Weekly Petroleum Status Report Schedule,” U.S. Energy Information Administration, 

https://www.eia.gov/petroleum/supply/weekly/schedule.php 

 “Weekly Supply Estimates: Stocks – Crude Oil, Commercial (Excl. Lease Stock),” U.S. 

Energy Information Administration, 

http://www.eia.gov/dnav/pet/pet_sum_sndw_a_epc0_sax_mbbl_w.htm 

 “Weekly U.S. Exports of Crude Oil,” U.S. Energy Information Administration, 

https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=WCREXUS2&f=W 

 “Weekly U.S. Field Production of Crude Oil,” U.S. Energy Information Administration, 

https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=WCRFPUS2&f=W 

 “Weekly U.S. Imports of Crude Oil,” Energy Information Administration, 

https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=WCRIMUS2&f=W 

Bates Stamped Documents 

 Chevron__Vega_0000023–35 

 Chevron__Vega_0000220–34 

 Chevron__Vega_0000251–66 

 CITI-05594–5 

 CME-Negative-Crude_0000048 







 (produced by SOCAR Trading) 

Appendix B Highly Confidential Page 8

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 187 of 197 PageID #:14157
 IFEU-000007–20 

 Non-partyBP024467 

 Non-partyBP029546 

 Non-partyBP029555 

 Plaintiff_Mish_0000028 

 Plaintiff_Mish_0000638

 Plaintiff_Mish_0000948–55 

 Plaintiff_Mish_0001040–44 

 Plaintiff_Mish_0001571–72 

 PSXVEG0000001 

 VEGA_MISH_000042650–73 

 VEGA_MISH_000042708.1 

 VEGA_MISH_000043277 

Note: In addition to the documents on this list, I considered all documents cited in my 

report and my exhibits to form my opinions. 

Appendix B Highly Confidential Page 9

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 188 of 197 PageID #:14158
Other Self-Regulatory Bodies, U.S. Government Agencies, the CFTC Chairman, Market 

Participants, and Academics Attributed the Order Book Imbalance and Negative Prices on 

April 20, 2020, to the Lack of Storage Space at Cushing

Self-Regulatory Bodies 

• The Futures Industry Association (FIA) attributed WTI futures prices becoming negative

on April 20, 2020, to some traders that failed to anticipate this shortage of storage

capacity at Cushing by noting that “one day before expiration, they realized that they had

to get out of their positions before expiration—or take delivery of barrels of physical oil

with limited or no options for storage. This led to a wave of selling, and this is what

caused the futures price to fall to a negative $37.63 per barrel on April 20 as the market

looked for a clearing price.”

o “FAQs on Negative Pricing in Exchange-Traded Futures and Options,” FIA, May

6, 2020, https://www.fia.org/fia/articles/faqs-negative-pricing-exchange-tradedfutures-and-options.

U.S. Government Agencies

• The U.S. Energy Information Administration (EIA) attributed negative WTI futures

prices on April 20, 2020, to “several factors, including the inability of traders who had

purchased futures to find other market participants to sell futures contracts to. In

addition, in this case, the scarcity and high cost of available crude oil storage meant

several market participants were heavily incentivized to close their positions before

having to physically settle their contracts, with some contract holders resorting to selling

their futures contracts at negative prices, in effect paying a counterparty to allow them to

exit their positions.”

o “Low liquidity and limited available storage pushed WTI crude oil futures prices

below zero,” U.S. Energy Information Administration (EIA), April 27, 2020,

https://www.eia.gov/todayinenergy/detail.php?id=43495.

CFTC Chairman

• When discussing the reasons for negative WTI futures prices on April 20, 2020, CFTC

Chairman Tarbert pointed to a “fundamental supply and demand issue ...[I]t’s not a

financial markets issue at this point. Obviously we’re looking into it to make sure we

understand all the factors, but basically it can be explained by what’s actually going on in

the real markets, which obviously is a tremendous amount of dislocation with respect to

storage, supply, capacity, and dramatically decreased demand. So, the markets are just

simply reflecting at this point what’s going on in the real economy, which obviously is a

lot of volatility ... [I]n fact, it was somewhat expected because as many of the viewers

know, futures contract ultimately relies on convergence. At the end of the day, at the end

of trading, the futures price needs to be the same as the cash. So, for weeks now, we’ve

seen spot physical cash prices of crude approach zero and go negative. So, market

participants as well as the CFTC have actually been preparing for some time to make sure

that our trading systems could handle negative prices.”

o Commodity Futures Trading Commission Press Release, “ICYMI: Chairman

Tarbert Discusses Crude Oil Futures on CNBC’s Squawk Alley,” April 21, 2020,

https://www.cftc.gov/PressRoom/PressReleases/8155-20.

Appendix C Highly Confidential Page 1

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 189 of 197 PageID #:14159
Market Participants

• Public press summarized reactions from market participants regarding the reason for why 

WTI futures prices went negative. Market participants generally agreed that storage 

constraints in Cushing led to negative WTI futures prices on April 20, 2020. Below are 

examples of such reactions. 

• “[The] realization among traders and funds managers” that “[t]he terminals [in Cushing] 

have already contracted their storage 100 per cent ... sparked a panic on Monday that 

caused the expiring May oil contract to crash to minus-$37.63 a barrel.” 

o “No Vacancy: Main U.S. Oil Storage in Cushing Is All Booked,” Reuters, April 

21, 2020, https://www.reuters.com/article/us-global-oil-usa-storage/no-vacancymain-u-s-oil-storage-in-cushing-is-all-booked-idUSKCN22332W. 

• “That threat of having to take physical delivery is why the May contract plunged on 

Monday, said Jeff Carter, a venture capitalist and former Chicago Mercantile Exchange 

board member” and “U.S. crude reflected global oversupply, high levels in U.S. storage, 

and convergence of futures and cash prices ahead of the May West Texas Intermediate 

(WTI) oil contract’s expiration on Tuesday, CME Group said.” 

o “CME Group, as Oil Contract Plunges Negative, Says Markets Working Fine,” 

Reuters, April 20, 2020, https://www.reuters.com/article/idUSL1N2C82MD/. 

• “CME Chairman and Chief Executive Terrence Duffy said in an interview that WTI 

futures worked as designed, and their foray into negative territory was a signal of real 

market forces at work. ‘It’s not a price that makes you feel good,’ he said. ‘But the 

reality is, there is oversupply, there is under-demand that’s virus driven, and there is 

nowhere to put the stuff.’” 

o “Negative Oil Prices Pose Headache for Futures Giant CME,” The Wall Street 

Journal, April 22, 2020, https://www.wsj.com/articles/negative-oil-prices-poseheadache-for-futures-giant-cme-11587547802. 

• “The negative pricing in the front-month May WTI futures contract was driven by three 

fundamental factors impacting the global oil market: 1) Significant oversupply of crude 

oil in the US, 2) Significantly reduced demand for oil due to COVID-19 impacts, 3) 

Concerns about increasingly full US storage for WTI.”; “So our WTI contract did exactly 

what it was designed to do, which is reflect the underlying supply and demand 

dynamics.” 

o CME internal email, “Ag Advisory Committee Meeting Summary – UPDATE,” 

April 23, 2020, CME-Negative-Crude_0000046. 

Academics

• In an article by Adam S. Nagy and Nobel Memorial Prize Winner Professor Robert C. 

Merton, the authors attribute the negative WTI futures prices to storage constraints. The 

authors note that “[u]nder normal market conditions, at some discount the natural users of 

oil would buy these contracts from the speculators providing liquidity on the buy side of 

the [order book]. Since the hedgers have the infrastructure to handle and store the 

commodity, if the spot, near and further dated futures prices are not in equilibrium, they 

can buy the oil (at spot or leading future prices) and sell it at a later date via a further 

dated futures contract. Therefore, under normal market conditions this arbitrage 

opportunity would allow speculators to liquidate their positions at some discount. 

However, as storage facilities are already operating near capacity the cash and carry trade 

Appendix C Highly Confidential Page 2

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 190 of 197 PageID #:14160
is more costly to implement and liquidity is impaired. Given this impaired liquidity, 

speculators are forced to sell oil at negative prices, essentially paying someone for taking 

the oil off their hand[s].”

o Adam S. Nagy and Robert C. Merton, “Negative WTI Crude Futures Prices Event 

Study,” Massachusetts Institute of Technology, April 26, 2020, 

https://mitsloan.mit.edu/shared/ods/documents?PublicationDocumentID=7455. 

Appendix C Highly Confidential Page 3

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 191 of 197 PageID #:14161
Appendix D Exhibit 1 Highly Confidential Page 1

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 192 of 197 PageID #:14162
Appendix D Exhibit 2 Highly Confidential Page 2

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 193 of 197 PageID #:14163
Appendix D Exhibit 3 Highly Confidential Page 3

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 194 of 197 PageID #:14164
Appendix D Exhibit 4 Highly Confidential Page 4

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 195 of 197 PageID #:14165
Appendix D Exhibit 5 Highly Confidential Page 5

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 196 of 197 PageID #:14166
Appendix D Exhibit 6 Highly Confidential Page 6

Case: 1:20-cv-04577 Document #: 371-1 Filed: 07/26/24 Page 197 of 197 PageID #:14167