U.S. Department of the Treasury

Board of Governors of the Federal Reserve System

Federal Reserve Bank of New York

U.S. Securities and Exchange Commission

U.S. Commodity Futures Trading Commission

Joint Staff Report:

The U.S. Treasury Market

on October 15, 2014

THE D

EPARTMENT OF THE TREASURY

1789

July 13, 2015

Joint Staff Report: 

The U.S. Treasury Market on October 15, 2014

This is a report of staff findings from the U.S. Department of the Treasury, the Board of 

Governors of the Federal Reserve System, the Federal Reserve Bank of New York, the U.S. 

Securities and Exchange Commission, and the U.S. Commodity Futures Trading Commission. 

The report represents only the views of staff, and the organizations listed above have expressed

no view regarding the analysis, findings, or conclusions contained herein.

Executive Summary 

The U.S. Treasury market is the deepest and most liquid government securities market in 

the world. It plays a critical and unique role in the global economy, serving as the primary means 

of financing the U.S. federal government, a significant investment instrument and hedging 

vehicle for global investors, a risk-free benchmark for other financial instruments, and an 

important market for the Federal Reserve’s implementation of monetary policy. 

On October 15, 2014 (“October 15”), the market for U.S. Treasury securities, futures, and 

other closely related financial markets experienced an unusually high level of volatility and a 

very rapid round-trip in prices. Although trading volumes were high and the market continued to 

function, liquidity conditions became significantly strained. The yield on the benchmark 10-year 

Treasury security, a useful gauge for the price moves in other, related instruments that day, 

experienced a 37-basis-point trading range, only to close 6 basis points below its opening level. 

Intraday changes of greater magnitude have been seen on only three occasions since 1998 and, 

unlike October 15, all were driven by significant policy announcements. Moreover, in the narrow 

window between 9:33 and 9:45 a.m. ET, yields exhibited a significant round-trip without a clear 

cause, with the 10-year Treasury yield experiencing a 16-basis-point drop and then rebound. For 

such significant volatility and a large round-trip in prices to occur in so short a time with no 

obvious catalyst is unprecedented in the recent history of the Treasury market. 

The abrupt occurrence of such significant and unexplained volatility—particularly in the 

narrow “event window” starting at 9:33 a,m, ET—calls for a deeper analysis of the conditions 

that contributed to the events of October 15 and the structure of this important market. This 

report has been prepared by the staff of the U.S. Department of the Treasury (Treasury), the 

Board of Governors of the Federal Reserve System (Board), the Federal Reserve Bank of New 

York (FRBNY), the U.S. Securities and Exchange Commission (SEC) and the U.S. Commodity 

1
Futures Trading Commission (CFTC).1

 It summarizes a set of preliminary findings on October 

15, which are based in part on transaction-level, non-public data that staff obtained from the 

primary locations for price discovery in the Treasury market.2

 It also describes important 

characteristics of the current structure of the Treasury market and proposes a series of next steps, 

including continued analysis of the events of October 15. Because analysis is ongoing and the 

data are an incomplete snapshot of the U.S. interest rate complex, the findings presented are 

necessarily preliminary and limited in scope. Nonetheless, the analysis provides information 

useful in understanding the market conditions and the movements in prices on October 15, and it 

will serve as a foundation for future work in the study of Treasury market structure and 

functioning. 

Section 1 of the report provides an overview of the U.S. Treasury market, liquidity, 

applicable regulations, and the data used in the report. For the purpose of this report, the U.S. 

Treasury market comprises the secondary market trading of cash Treasury securities as well as 

the futures and options on Treasury securities. Prices are tightly linked across these markets, and 

linked as well to activity in related markets such as short-term U.S. interest rate futures and U.S. 

interest rate swaps. Treasury securities are traded over the counter, and trades are executed by 

voice or on electronic trading platforms within the regulatory framework established by the 

Government Securities Act (GSA) of 1986, as amended.3

 Futures are traded on regulated futures 

exchanges and are transacted within the regulatory framework established by the Commodity 

Exchange Act. The report relies on participant-level transaction data from the most liquid parts 

of the Treasury market, including that for benchmark securities (the “cash” market) and futures 

(the “futures” market). 

1 The Treasury, SEC, and federal bank regulators, including the Comptroller of the Currency, Board, and the Federal 

Deposit Insurance Corporation, regulate different aspects of the cash Treasury market and many of its participants, 

while the CFTC regulates the futures markets, including the Treasury futures markets, and many of its participants. 

2 In accordance with Section 8 of the Commodity Exchange Act, codified at 7 U.S.C. § 12, this report does not 

publish data or information that would separately disclose individual business transactions or market positions, trade 

secrets, or names of customers. All non-public data and information presented in this report has been anonymized 

and aggregated.

3 See Public Law 99-571, October 28, 1986 and Public Law 103-202, December 17, 1993.

2
Section 2 of the report explores the events of October 15, including the two defining 

traits of the day—the unusually high volatility and round-trip in prices despite the lack of an 

obvious driver, and the strains in liquidity conditions especially during the event window. As 

described further in the report, the 37-basis-point trading range in the 10-year Treasury security 

on October 15 was both unusual and of historic size. On the three occasions when intraday 

moves were greater than 37 basis points, important news was released that significantly 

influenced the public’s expectations for monetary policy. By contrast, the only notable news on 

October 15 was the release of somewhat weaker-than-expected U.S. retail sales data at 8:30 a.m. 

ET. While the data appeared to prompt the initial decline in interest rates, the reaction was 

significantly larger than would have been expected given the modest surprise in the data. 

Moreover, the retail sales data do little to explain the fact that large price movements occurred 

more than an hour after the release. 

Liquidity conditions in the Treasury market were also atypical on October 15. While the 

term “liquidity” may be subject to various uses and interpretations, for the purpose of this report, 

it might be most simply defined as the cost associated with executing a trade. Academics and 

practitioners have often used simple price and quantity metrics to describe this cost, but they 

have also combined that information to construct more sophisticated and comprehensive ways of 

measuring the cost of trading. On October 15, both simple and complex measures showed signs 

of significant deterioration. For example, the dollar amount of standing quotes in the central limit 

order books (CLOBs) on cash and futures trading platforms—a measure of the quantity of 

liquidity that is commonly referred to as “market depth”—fell dramatically in the hour before the 

event window. Measures of transaction costs also showed signs of significant deterioration. 

Despite these changes, trading volumes reached record highs, trading took place in a continuous 

manner during the event window and throughout the day, and prices for U.S. Treasury securities 

and futures remained closely linked. 

Section 2 also discusses the market environment in the days and weeks preceding 

October 15, which may have contributed to the general level of volatility on the day but is an 

unlikely explanation for the dynamics observed in the event window. More specifically, growth 

and deflation risks in the Eurozone, and the fact that the European Central Bank’s response was 

as yet unclear, had generated considerable uncertainty among of investors going into the day—a 

3
sentiment reportedly exacerbated by the alleged tone of the annual IMF/World Bank meetings 

the prior weekend. Additionally, there was an unwind of “short” positions on U.S. interest rates 

ahead of and on October 15. Such “short” positions were predicated on an anticipated rise in 

interest rates, and had become particularly popular among investors employing leverage, 

particularly in shorter-term interest rate futures contracts. But as interest rates began to move 

lower in September and early October, leveraged funds unwound these short positions by taking 

on offsetting long positions. While much of the unwind took place in the two weeks ahead of 

October 15, the moves on October 15 were among the most significant in terms of their day-today change. These position changes were most notable in shorter-duration rate instruments, but 

likely had the effect of putting further downward pressure on yields across the Treasury curve. 

Section 3 of the report discusses the key findings from the analysis of participant-level 

transaction data, with a particular focus on the period leading up to and including the most 

volatile period of the day, the 9:33 to 9:45 a.m. ET event window. While no single cause is 

apparent in the data, the analysis thus far does point to a number of findings which, in aggregate, 

help explain the conditions that likely contributed to the volatility. 

• An analysis of transactions shows that, on average, the types of firms participating in

trading on October 15 did so in similar proportions to other days in the sample data.

Principal trading firms (PTFs) represented more than half of traded volume, followed by

bank-dealers. Both bank-dealers and PTFs continued to transact during the event

window, and the share of PTF trading increased significantly.

• The trading volume of PTFs and bank-dealers in the cash and futures markets is highly

concentrated in the most active firms. In the cash market, for instance, the 10 most active

PTFs conducted more than 90 percent of the trading activity of all PTFs on October 15,

while the 10 most active bank-dealers accounted for nearly 80 percent of the trading

activity of all banks. The concentration findings were generally similar for the futures

market.

• A review of position changes shows sizable changes in net positions by different types of

participants following the retail sales data release. However, during the event window,

only modest changes in net positions occurred, suggesting that changes in global risk

sentiment and associated investor positions may help to explain a portion of the price

4
movements during the day, but do not appear to explain the round-trip in prices during 

the event window itself. 

• During the event window, an imbalance between the volume of buyer-initiated trades and 

the volume of seller-initiated trades is observed, with more buyer-initiated trades as 

prices rise in the first part of the window, and more seller-initiated trades as prices fall in 

the second part of the event window. Such imbalances are common during periods of 

significant directional market moves. Both bank-dealers and PTFs initiate these liquidityremoving trades, though PTFs account for the largest share. At the same time, strong 

evidence suggests that PTFs, as a group, also remained engaged as liquidity providers 

throughout the event window, implying that more than one type of PTF strategy was at 

work. 

• Several large transactions—though not unusual in size relative to other sample days—

occurred between the retail sales release and the start of the event window. Some 

coincided with a significant reduction in market bid and offer depth—both during this 

interval and at the start of the event window itself. But during the event window, the 

analysis does not suggest a direct causal relationship between the volatility and one or 

more large transactions, orders, or substantial position change. 

• The significant reduction in market depth following the retail sales data release appears to 

be the result of both the high volume of transactions and bank-dealers and PTFs changing 

their participation in the cash and futures order books. During the event window, bankdealers tended to widen their bid-ask spreads, and for a period of time provided no, or 

very few, offers in the order book in the cash Treasury market. At the same time, PTFs 

tended to reduce the quantity of orders they supplied, and account for the largest share of 

the order book reduction, but maintained tight bid-ask spreads. Both sets of actions 

prompted the visible depth in the cash and futures order books to decline at the top price 

levels. 

• The time required by the futures exchange to process incoming orders, or “latency,” 

increased just prior to the start of the event window. This latency was associated with a 

significant increase in message traffic—in this case elevated due to order cancellations. 

Transaction data also show a higher incidence of “self-trading” during the event window. 

For the purpose of this report, self-trading is defined as a transaction in which the same 

5
entity takes both sides of the trade so that no change in beneficial ownership results. 

Although self-trading represented a non-trivial portion of volume, this activity also 

appears on days other than October 15 in the sample. Any causal connection between the 

unusually high level of cancellations or the self-trading and the event window at this time 

remains unknown. 

In sum, record trade volumes, a decline in order book depth, changes in order flow and 

liquidity provision, and notable and unusual market activity together provide important insight 

into the factors that may have contributed to the heightened volatility, decreased liquidity, and 

round-trip in prices on October 15. 

To better understand the context for the conditions, the report in Section 4 reviews broad 

changes to the structure of the Treasury market over the past two decades. In particular, the 

growth in high-speed electronic trading has contributed to the growing presence of PTFs in 

Treasury markets, with these firms now accounting for the majority of trading and providing the 

vast majority of market depth. By contrast, bank-dealer activity in the “interdealer” market now 

accounts for well under half of the trading and quoting activity, a significantly smaller share of 

market intermediation than in the past, perhaps reflecting increasing costs and competitive 

pressures associated with market-making activities in the Treasury market. These changes in 

intermediation and the provision of liquidity have coincided with significant growth in the U.S. 

fixed-income market and potential changes in the demand for liquidity by many investors. 

By many metrics, the liquidity and efficiency of trading in the Treasury market are as 

robust as they have ever been. For example, bid-ask spreads have remained steady at very low 

historical levels. But the changes in market structure also raise questions about evolving risks, 

such as whether an improvement in average liquidity conditions may come at the cost of rare but 

severe bouts of volatility that coincide with significant strains in liquidity. The changing nature 

of liquidity also suggests that the way it is measured may need to be enhanced in order to obtain 

a more meaningful understanding of the state of the market. 

 Finally, as explained in Section 5, the events of October 15 underscore the importance of 

efforts by the official and private sectors to understand more fully the implications of the 

6
evolving Treasury market structure for liquidity, trading and risk management practices, data 

access, and monitoring and surveillance. To further such efforts, the report suggests next steps in 

four areas: 

• further study of the evolution of the U.S. Treasury market and its implications for market 

structure and liquidity, 

• continued monitoring of trading and risk management practices across the U.S. Treasury 

market and a review of the current regulatory requirements applicable to the government 

securities market and its participants, 

• an assessment of the data available to the public and to the official sectors on U.S. 

Treasury cash securities markets, and 

• continued efforts to strengthen monitoring and surveillance and to promote interagency 

coordination related to the trading across the U.S. Treasury market. 

7
Section 1: Background on Treasury Market Liquidity, Regulation, and Data 

Liquidity and the Treasury Market

The U.S. Treasury market is the deepest and most liquid government securities market in 

the world. This superior liquidity is important for a number of reasons: it accrues lower cost of 

borrowing to Treasury thus benefitting taxpayers, it allows U.S. Treasury securities to act as a 

reliable interest rate benchmark for a wide range of private market transactions, it provides a 

reliable means for market participants to transfer interest rate risk on a substantial scale, and it is 

supportive of the implementation of U.S. monetary policy. 

While the term “liquidity” can be subject to various uses and interpretations, for the 

purposes of this report it might be most simply defined as the cost associated with executing a 

trade. Academics and practitioners have used both simple price and quantity metrics to describe 

this cost, along with more sophisticated methods that combine price and quantity information to 

measure the cost of trading more comprehensively. Another manner in which liquidity might be 

viewed is across an immediacy spectrum. Through that lens, liquid markets are those where 

participants are able to continuously transact even if there is little market depth and prices are 

very responsive to incoming orders to buy or sell securities. In this case, there might be a high 

cost to transact, but still a continuous ability to change positions. 

Either definition—whether centered on cost or immediacy—might be viewed as a 

relatively narrow form of liquidity on its own. A broader form of market liquidity might require 

the conditions from both definitions be met: participants can continuously transact, and 

relatively large transactions have a limited cost associated with them. That is, markets are most 

liquid when they are both continuous and deep. That said, price volatility and liquidity certainly 

interact and can be co-dependent. 

The U.S. Treasury market enjoys liquidity defined more broadly, with continuous trading 

and substantial market depth. However, on October 15, specifically in the 12 minute event 

window, the U.S. Treasury market—while in one sense remaining liquid as participants were 

able to continuously transact—experienced uncharacteristically shallow market depth. 

Moreover, the continuous trading in these 12 minutes seemed unrelated to any new information, 

8
leading to questions about the efficiency of price formation in the Treasury market during that 

time. A higher incidence of such strains in market liquidity could prove harmful to the many 

critical functions this market enables and serves. 

Regulation and the Treasury Market

Several agencies under a range of authorities are responsible for regulating various 

components of the Treasury market and its participants. The GSA established the regulatory 

scheme for the regulation of brokers and dealers in the government securities market. Congress, 

in enacting the GSA, largely relied on the existing federal regulatory structure when assigning 

registration, examination, reporting, and enforcement responsibilities.4

 The GSA authorized 

Treasury to promulgate rules governing transactions in government securities by government 

securities brokers and dealers. In consultation with the Treasury, the SEC, federal bank 

regulators, and the Financial Industry Regulatory Authority (FINRA) also have the authority to 

issue sales practice rules for U.S. government securities secondary market. 

Non-bank-affiliated brokers or dealers that solely conduct a business in government 

securities are required to register with the SEC, but are subject to Treasury rulemaking. General 

purpose securities brokers or dealers, and financial institutions that conduct a government 

securities business, are required to file a written notice with their appropriate regulatory agency. 

The enforcement and examination authorities in the government securities market reside 

with the SEC, FINRA or the appropriate bank regulator. In addition, the GSA applies the antifraud and anti-manipulation provisions of the federal securities laws to government securities 

brokers and dealers.5

 

4 The history of the GSA made clear that it was intended to address identified weaknesses in the market without 

creating duplicative requirements, unnecessarily impairing the operation of the market, increasing the costs of 

financing the public debt or compromising the execution of monetary policy.

5 There are, however, several differences in the current regulatory requirements applicable to the government 

securities market as compared to other U.S. securities, commodities and derivatives markets. For example, FINRA 

Rule 2010, Standards of Commercial Honor and Principles of Trade, apply to government securities. However, more

granular FINRA Rules, such as FINRA Rule 2121 Fair Prices and Commissions, currently does not apply to 

transactions in government securities. SEC rules applicable to alternative trading systems do not apply to alternative 

9
Treasury and Eurodollar futures (and options on these futures) are regulated by the 

CFTC, created by Congress in 1974 as an independent agency with the mandate to regulate 

commodity futures and option markets in the United States. The CFTC’s mandate has been 

renewed and/or expanded multiple times in subsequent years. The CFTC and its predecessor 

agencies were established to protect market participants and the public from fraud, manipulation, 

and other abusive practices in the commodity futures and options markets. After the 2008 

financial crisis and the subsequent enactment of the Dodd-Frank Wall Street Reform and 

Consumer Protection Act, the CFTC’s mission expanded to include oversight of the swaps 

markets. The CFTC administers the Commodity Exchange Act (CEA), 7 U.S.C. section 1, et 

seq. The CEA establishes a comprehensive regulatory structure to oversee futures and swaps 

trading, including surveillance of the futures and options markets. Surveillance and enforcement 

authority for these rules sits with the CFTC. 

 Through regulatory or private sector efforts, securities and futures trading venues have 

expanded risk management practices over recent years. These risk management tools address 

concerns or risks related to both automated and manual trading, and aim to mitigate the 

possibility of activity, or price movement, which may not accurately represent fundamental 

forces of supply and demand. In the futures markets, these tools include order price and quantity 

controls (to avoid “fat finger” errors), circuit breakers which trigger during extremely rapid price 

movements, kill switches, message and order throttles, self-trade prevention tools and post-trade 

drop copy. Many of these controls can be customized at the level of a firm, desk or trader. Cash 

market venues have some similar risk mitigation devices, although they currently do not have 

circuit breaker protocols in place. 

Background on Treasury Cash and Futures Markets 

																																																																																																																																																																																																			

trading systems through which only government securities are traded (although such venues may voluntarily adopt 

such standards). Real time public reporting rules applicable to transactions in other securities and derivatives do not 

apply to transactions in Treasury securities. Large non-broker and non-dealer participants in the government 

securities market are not required to register (unlike large swap market participants). 

10
This report analyzes trading activity in the U.S. Treasury market, focusing on order and 

trade data from venues trading either cash Treasury securities or Treasury futures. 

In the cash market, the Treasury issues bills, nominal fixed-rate coupon securities, 

nominal floating rate securities (FRNs) and inflation indexed securities (TIPS).6

 The nominal 

coupon curve is the most active location for secondary market trading, and a large portion of 

activity takes place in the most recently issued nominal coupon securities.7 These securities are 

referred to as “benchmark” issues, as the yields of these securities are used as a reference to price 

a number of private market transactions. Treasury securities are transacted across multiple 

secondary market venues: interdealer trading of the benchmark securities occurs mainly on 

centralized electronic trading platforms utilizing a CLOB protocol such as Broker Tec and 

eSpeed.8 In contrast, dealer-to-customer trading is usually done on a bilateral basis—either 

through voice or a variety of electronic means. Section 4 of the report contains a more detailed 

discussion of the structure of the cash market. 

Treasury futures across a broad set of maturities are traded at the Chicago Board of Trade 

(CBOT), a regulated futures exchange, and settle against an underlying Treasury security.9

Eurodollar futures, a related short-term USD interest rate futures contract, are listed on the 

Chicago Mercantile Exchange (CME), and settle against the 3-month LIBOR rate. Both 

6 Treasury bills are issued at a discount and mature in one year or less. FRNs are issued at an original maturity 

(currently of 2 years) and have a coupon that adjusts based on 13-week bill auction rates. Nominal coupon 

securities pay a fixed semi-annual coupon and are currently issued at original maturities of 2-, 3-, 5-, 7-, 10- and 30-

years. Finally, TIPS pay a fixed real rate of interest, undergo principal accrual based on realized inflation, and are 

currently issued at original maturities of 5-, 10- and 30-years. 7 Once another security is issued at a given original maturity point it becomes the new benchmark security (or new 

“on-the-run” security) for that maturity point and the former benchmark security is then said to trade in the “off-therun” market. Trading of off-the-run securities has always been less active and is not the focus here as price 

discovery in Treasury markets primarily occurs in the benchmark issues in the cash market and in the futures 

markets.

8 BrokerTec offers a “workup” mechanism that is initiated by a regular order book trade. The workup provides an 

opportunity for all traders to transact additional quantity on either side of the initial trade at the price of the initiating 

trade. The workup mechanism on BrokerTec is widely used and represents a majority of BrokerTec trading volume 

during the event window.

9

 Product maturities for futures include 2-year, 3-year, 5-year, and 10-year Treasury notes, the 30-year Treasury 

bond (for underlying bonds of 15 to 25 years maturity), and the ultra-long bond (maturity greater than 25 years). 

11
exchanges are owned by the CME Group, with the vast majority of futures trades occurring on 

an anonymous CLOB, though larger or more complex trades may happen in the futures pit or 

off-exchange as blocks. Futures regulations mandate that all trading in a futures contract occur 

on, or, in the case of blocks, get reported to, the trading platform operated by the futures 

exchange where the contract is listed. All trades are then reported, on a post-trade basis, through 

a real-time public ticker. The CFTC, as the futures regulator, receives a transaction audit trail 

with participant identifiers which aids in ongoing market surveillance and enforcement. 

Data and Firm Classification Methods 

To analyze the events during and around the event window on October 15, staff obtained 

access to participant-level transaction data from the major cash trading platforms—BrokerTec 

and eSpeed—and from the CME.10 Staff also had access to participant-level order book 

information. In addition to data on October 15, data for 16 control days were obtained, including 

four days characterized by considerable volatility, and twelve days of unremarkable volatility. 

The volatile control days were May 22, 2013, June 19-20, 2013, and June 5, 2014, and the nonvolatile control days were April 2-17, 2014. Data from both the cash and futures markets 

provide price, quantity and timing information, and allow for full reconstructions of trade and 

order book activity within the focal contracts and benchmark maturities of at least millisecond 

granularity. 

Participant identifiers, at varying levels of granularity, are included in trade and order 

audit trails in the cash and futures markets. Using these, participants were grouped into several 

broad categories based on their business model and corporate structure. The firm categories used 

for futures and cash classification are bank dealer, non-bank dealer, hedge fund, asset manager, 

and PTFs. Categorizing the firms requires some judgment, particularly given that they sometimes 

share certain characteristics or may act in multiple capacities. However, the presence of legal 

																																																												

10 Cash market trade data presented here are from Broker Tec, though eSpeed data was also analyzed. Results for 

market concentration and participation levels (discussed later in this report) were similar across both platforms.

12
name identifiers allows for the classification of participants ex-ante by legal status in 

combination with existing information about trading objectives/motivations, investment horizon 

and balance sheet capacity.11 Appendix A provides more detail on the classification framework 

used, and describes some distinct characteristics of the five different types of firms. Importantly, 

each type of firm employs some level of automated trading, with varied degrees of sensitivity to 

market speed, and functions as both a liquidity provider and taker, with varied holding periods 

depending on strategy. Bank dealers, for example, utilize manual trading strategies to a greater 

extent than other categories, especially in the cash market and when taking liquidity, have higher 

fill ratios, and transact as agents on behalf of customers. PTFs are uniquely characterized by 

their almost exclusive use of automated trading, lower fill ratios, and primarily principal trading 

activity.12 Nevertheless, the tables in Appendix A provide statistics indicating that activity within 

a category varies considerably by specific firm. In particular, some bank-dealer and hedge fund 

trading patterns exhibit characteristics of PTFs, while many smaller PTFs clearly are not trading 

rapidly. 

There are several aspects of the U.S. Treasury and broader U.S. fixed income market that 

are not represented in this data. For example, cash Treasury market data do not include the large 

dealer-to-customer market, in which dealers transact—either through voice or electronic 

means—with their customers. Additionally, data for similarly liquid U.S. interest rate products, 

such as plain-vanilla interest rate swaps, are not incorporated into this report, nor is data on 

interest rate options. Nonetheless, the data do capture the most liquid interest rate products 

traded over a CLOB (namely, benchmark cash securities trading and interest rate futures), which 

																																																												

11 Due to the differing nature and granularity of the available data within the futures and cash markets, it was also 

necessary to exogenously identify, and match, firms across trading in both markets. Categorizing firms purely by 

trading behavior, an endogenous classification that is often done in absence of legal name identifiers, can be difficult

because it requires the establishment of measurement thresholds in order to separate activity between categories.

The entity-level classification scheme employed in the report is in broad agreement with the observed trading 

activity patterns in the sample and, by implication, is also largely consistent with such endogenous and activitybased classification. 

12 In the report, the term high-frequency trading (HFT) firm is not used given the difficulty in drawing an effective 

dividing line between those firms that should be included in the category and those that should not.

13
arguably serve as the primary locations for price discovery in U.S. interest rates.13 Certain offexchange transactions for futures, as well as end-of-day futures positions data, are referenced at 

specific parts of this analysis. 

																																																												

13 A review of the data shows very tight linkages between activity in cash and futures prices at a lead-lag of about 5

milliseconds, strongly suggesting that price discovery and liquidity provision in both markets are tightly linked by 

automated trading activity. Appendix B provides additional detail on the analysis of such cross-market activity

14
Section 2: The Events of October 15, 2014 

On October 15, 2014, U.S. Treasury cash and futures markets experienced significant 

volatility amidst record trading volumes, including a rapid round-trip in prices that occurred with 

no new exogenous information (Figure 2.1). While yields drifted slightly lower in the early 

morning, more notable price action began at 8:30 am ET with the release of the U.S. retail sales 

report for the month of September. The data printed slightly weaker than expected: advance sales 

excluding automobiles declined 0.2 percent, month-over-month, while the median expectation 

from economists surveyed by Bloomberg suggested a gain of 0.2 percent. As is typically the case 

with the release of weaker-than-expected economic data, interest rates across the U.S. fixed 

income complex declined on the news, but the response this time was unusually sharp. Market 

participants widely noted that the 11 basis point decline in the benchmark 10-year Treasury 

security yield in the 25 minutes following the release was significantly larger than would have 

been expected based on the surprise (or unexpected) component of the data alone.14 Further, 

trading conditions in fixed income markets began to change. Trading volumes on the futures 

exchange and electronic cash platforms surged, and the dollar amount of standing quotes in the 

CLOBs—an important measure of liquidity commonly referred to as “market depth”—fell 

sharply in the hour after the release (Figures 2.2 and 2.3).15 

Yields continued to trend somewhat lower over the next hour, when they suddenly moved 

sharply lower just after 9:30am, despite the apparent absence of any news. In the six minutes 

between 9:33 am ET and 9:39 am ET, the 10-year yield decreased 16 basis points. Between 9:39 

am ET and 9:45 am ET, the 10-year yield then abruptly reversed course and nearly retraced the 

latter move, again with no apparent trigger. These sharp moves between 9:33 and 9:45 am ET 

																																																												

14 The expected yield response to a surprise in retail sales (excluding automobiles) was calculated using yield 

changes corresponding to the retail sales excluding autos data releases over the preceding two years. The surprise on 

October 15 was 1.4 standard deviations from the mean, which would be associated with a 2 basis point decline. 

15 Market depth is defined as the volume of orders available for execution in the CLOB, which is the set of standing 

orders at established prices available for execution at a given point in time. 

15
represent the October 15 event window.16 Price volatility in the Treasury market declined 

noticeably thereafter, though the market was still more volatile than on an average day. Between 

9:30 and 9:45 a.m., market depth in the 10-year security was about 20 percent of its year-to-date 

average for that 15-minute period. It recovered somewhat shortly thereafter, though remained 

lower than an average day. 

For such significant price movements to rapidly occur without a clear catalyst in one of 

the world’s most liquid markets in such a short period of time is highly unusual. Trading 

volumes in the Treasury market on the day posted record highs, and reached 6 to 10 times their 

average levels during the event window. Volumes in other electronic fixed income markets for 

which data is available, such as plain-vanilla interest rate swaps traded over Swap Execution 

Facilities (SEFs), were also high, though not to the same extent as in the Treasury market. 

Despite the significant changes in many measures of liquidity, trading took place in a 

continuous fashion. No trades executed on the interdealer cash and futures platforms analyzed 

were broken or adjusted, nor was price “gapping”—or significant jumps from one price point to 

another with no transactions in between—a feature of trading on October 15. The magnitude of 

trading volumes and continuity of pricing showed that the ability to transact remained in place 

even at the most volatile times of the day, although individual trade sizes tended to be smaller 

than average. 

While trading activity continued during this period, numerous market participants 

reported significant liquidity concerns. Some participants temporarily disengaged their 

automated price quoting systems and instead relied on manual or voice trading to reduce their 

risk. In the Treasury market, the market impact of transacting in large but not unusual size (for 

example, a quantity of $100 million at the 10-year maturity point) would have required a trader 

																																																												

16 Specifically, during the event window, the 10-year Treasury yield fell from 2.02 percent at 9:33:19 ET to 1.86

percent at 9:39:39, before retracing to 1.99 percent at 9:44:35. Given the available data, no clear link has been 

identified between the event window and the open of the U.S. equity market at 9:30 ET.

16
to execute standing orders at price points far from the best bid or offer in the CLOB (Figure 

2.4). 

By the end of the U.S. trading session on October 15, the yield on the 10-year Treasury 

note was 2.14 percent, only six basis points below the closing level on the previous day, despite 

trading in an intraday range of 37 basis points. Intraday moves of this size are highly unusual; 

since 1998, larger intraday trading ranges have only been observed on three occasions.17 

Moreover, in contrast to October 15, each of these other outsized intraday moves followed 

significant new fundamental information being received by markets. Further, two of the three 

instances resulted in a notably larger net change on the day as the market incorporated the new 

information. (Figures 2.5 and 2.6) 

While the most pronounced moves on October 15 occurred in the Treasury market, other 

markets, especially those that are closely tied to U.S. interest rates, also experienced volatility. 

Intraday movements in interest rate swaps mirrored those in the Treasury market, and implied 

volatility on options on interest rate swaps rose markedly at all tenors. Implied volatility on 

options at some maturities rose by the largest amount in a single day since the financial crisis. 

Other asset classes, including foreign exchange, equities, and commodities also experienced 

some volatility on October 15, although moves in those markets were comparatively more 

modest (Figure 2.7). 

The Market Environment 

Several factors are important to understanding the market environment in the weeks and 

days preceding October 15, most notably increasing risks to the global economic outlook and the 

																																																												

17 The announcement of an increase in the large-scale agency asset purchase program and the introduction of 

Treasury security purchases on March 18, 2009, resulted in a 55 basis point intraday trading range and a net change 

of 47.5 basis points. The announcement of a coordinated cut in interest rates across major global central banks on 

October 8, 2008, resulted in a 43 basis point intraday trading range and a net change of 13.5 basis points. The 

introduction of calendar-based guidance into the FOMC statement and the downgrade of the United States’ credit 

rating on August 9, 2011, resulted in a 40 basis point intraday trading range and a net change of 7 basis points. 

17
unwind of substantial positions designed to profit from a rise in U.S. interest rates. A number of 

factors reportedly weighed on the global growth outlook. In the first two weeks of October, 

growth and deflation risks in the Eurozone, accompanied by uncertainty around the European 

Central Bank’s response, generated considerable uncertainty on the part of investors. Many 

market participants reportedly viewed the tone of the annual IMF/World Bank meetings the 

weekend prior to October 15 as having exacerbated these sentiments. In addition, the minutes 

from the September meeting of the Federal Open Market Committee were widely interpreted by 

market participants as suggesting a somewhat weaker outlook for the U.S. economy than 

previously expected. Several investors also reported increasing concerns over the risks associated 

with a widespread Ebola outbreak. In response to these risks to the outlook, many investors 

turned to safe haven assets, driving up prices and lowering yields on U.S. Treasury securities and 

related instruments. 

The moves in early October proved costly for investors who remained positioned for a 

rise in U.S. interest rates, rather than a fall. Based on data from the futures market, “short” 

positions on interest rates had become particularly popular among investors employing leverage, 

and were largely predicated upon an improvement in U.S. economic growth that would lead to a 

rise in short-term interest rates. Levered short positions in shorter-term interest rate futures 

contracts, such as Eurodollar futures, had reached a record level by the end of September. But as 

interest rates began to move lower in September and early October, leveraged funds unwound 

these short positions by offsetting their short exposure (Figure 2.8). Much of this unwind took 

place in the two weeks ahead of October 15, but the change was also significant on October 15 

itself. These position changes were most notable in shorter-term rate instruments, but given 

cross-market linkages, likely had the effect of putting further downward pressure on yields 

across the Treasury curve. 

In addition, market participants reported that some large asset managers had maintained 

positions structured to profit from a continuation of the low-volatility environment that 

characterized much of 2014, though data to validate such claims are limited. Some market 

participants have speculated that a change in the distribution of certain options-specific risk 

factors among certain firms could have been a contributing factor. In particular, anecdotal 

commentary suggested that some dealers had absorbed a portion of the sizable “short volatility” 

18
position believed to have been previously maintained by large asset managers. As volatility 

spiked on October 15, those positions would have prompted some dealers to dynamically hedge 

this exposure, exacerbating the downward move in yields. 

 As a result of both the changing views and losses on interest rate positions (including 

exposure in related derivatives such as options), investors may have been particularly sensitive to 

new information that might confirm perceptions that the global growth outlook had dimmed. 

These factors may explain the abnormally large reaction to the modestly worse-than-expected 

retail sales data the morning of October 15. However, the timing of the window and the 

distinctive pattern of rapid price movements seem unlikely to have been driven by changes in 

growth expectations or even the effects of positioning unwinds. To better understand the nature 

and timing of the unusual price movements in the event window, Section 3 of this report 

examines several possible channels through which price formation could have been affected. 

19
Section 3: Key Findings on October 15 and the Event Window 

To shed further light on the developments of October 15 and in particular on the events 

leading up to and including the event window, staff examined participant-level transaction and 

order-book data from the primary locations for price discovery in the cash and futures markets. 

While the analysis revealed no single cause for the near round-trip in prices during the event 

window, data did highlight a number of important developments in the market before and during 

the event window, including a significant increase in volume, sizeable changes in market 

participation, a decline in market depth, shifts in the net initiated order flow, and certain notable 

trading behaviors, which together provide insight into the nature of the event. The analysis also 

revealed that changes to the Treasury market structure over recent years have been significant; 

these changes are likely important context for understanding the abnormally sharp period of 

volatility and for assessing the risk of reoccurrence of such an event. 

Specifically, this section reviews analysis related to: 

• trading activity and positioning, 

• patterns of aggressive and passive trading activity, 

• order book participation and liquidity provision, 

• large transactions and orders, and 

• notable trading activity. 

 Because analysis is ongoing and as noted earlier the data are an incomplete snapshot of 

the U.S. interest rate complex, the findings discussed in this report are necessarily preliminary 

and not comprehensive. Nonetheless, the analysis presented provides information that is useful in 

understanding the market conditions and the movements in prices on October 15, and it will 

serve as a foundation for future work in the area of Treasury market structure and functioning. 

Trading Activity and Positioning 

Analysis of trading activity and position changes was conducted in order to determine to 

what extent trading activity, large transactions, or changes in positions help explain the price 

dynamics during the event window. In total, the analysis found that on the day as a whole, the 

20
two most active groups of participants were PTFs and bank-dealers, at levels consistent with 

their participation on the control days. Price movements in the event window coincided with a 

sharp increase in the total volume and share of trading generated by PTFs, with a decline in the 

share of trading generated by bank-dealers, but analysis thus far does not provide evidence that 

the abnormal price movements during the event window were caused by significant position 

changes. 

 On October 15, the data show that PTFs and bank-dealers, in that order, accounted for the 

largest shares of trading volume in both the cash and futures markets. In particular, PTFs 

accounted for more than 50 percent of the total trading volume across various maturities in both 

cash and futures markets, while bank-dealers accounted for roughly 30 to 40 percent of volume 

in the cash market, but under 20 percent in the futures market, perhaps owing to the greater 

variety of futures market participants trading directly or through a Futures Clearing Member 

(“FCM”) (Tables 3.1 and 3.2). The share of trading activity attributable to the various types of 

market participants on October 15 does not stand out as unusual relative to recent history. In 

particular, the PTF shares of trading volume in both the cash and futures markets on October 15 

were similar to those on the control days (Tables 3.3 and 3.4). In terms of message traffic, 

defined as new orders, cancellations and modifications to existing orders, our analysis shows that 

PTFs represented around 80 percent of traffic in both futures and cash on October 15 (Tables 3.5 

and 3.6). This was only slightly higher than for the control days, suggesting that their quoting 

behavior did not materially change. 

During the event window, the data show that the relative share of PTF trading activity 

increased as prices and volumes rose sharply (9:33 to 9:39 ET), comprising about 73.5 percent 

and 68.4 percent of trading volume in 10-year note cash and futures markets, respectively, while 

the relative share of bank-dealer trading activity declined to 21.4 percent and 14.1 percent. 

Though the share of trading shifted toward PTFs, both PTFs and bank-dealers experienced an 

increase in their absolute level of trading volumes during this time, given the sharp increase in 

overall volumes. As the price quickly retraced its previous gains (9:39 to 9:45 am ET), the data 

show that the share of PTF trading activity declined somewhat from its elevated levels to 62.3 

and 56.6 percent at the same time as the share of bank-dealer activity rose substantially to 33.6 

and 24.5 percent in cash and futures, respectively (Figures 3.1 and 3.2). 

21
Data from October 15 and control days show that the trading volume of PTFs and bankdealers in the cash and futures markets is highly concentrated in the most active firms. Tables 3.7 

and 3.8 show some measures of the concentration of activity among bank-dealers and PTFs in 

the cash and the futures markets on October 15 and on the control days, based on shares of 

trading volume. Generally speaking, the data show that there were only small differences in 

concentration levels between October 15 and the control days. In both the cash and futures 

markets, the concentration of PTFs was always much higher than that of bank-dealers. 

In the cash market, for instance, the 10 most active PTFs (out of a total of 37) conducted 

94 percent of the trading activity of all PTFs on October 15, while the 10 most active bankdealers (out of a total of 44) accounted for 79 percent of the trading activity of all bank-dealers. 

A commonly used concentration metric, the Herfindahl-Hirschman Index (HHI) was about 0.23 

for the top 10 PTFs, reflecting moderate concentration, while that of the top ten bank-dealers was 

0.11, reflecting little concentration.18 The concentration findings were generally similar for the 

futures market, with readings a bit lower for PTFs, which are more numerous in futures than in 

cash, and a bit higher for bank-dealers, which are less numerous. 

Despite the surge in trading volume during the event window, available data from the 

cash market do not show a significant change in net position by any specific participant type at 

that time (Figure 3.3). In the futures market, several participant types experienced small to

modest changes in their aggregate net position during the event window. For example, hedge 

funds accumulated short and long positions, respectively, in the first half of the event window, 

which then unwound to varying degrees in the second half of the window (Figure 3.4). 

																																																												

18 The HHI is a commonly accepted measure of market concentration calculated by squaring the market share of 

each firm and then summing the resulting numbers. It takes values between 1/N and 1, where N is the number of 

firms, with values of the index closer to 1 indicating a higher degree of concentration. The Department of Justice 

classifies markets as unconcentrated, moderately concentrated, and highly concentrated according to their HHI. See: 

http://www.justice.gov/atr/public/guidelines/hmg-2010.html#5c for further details.

22
By contrast, in the period preceding the event window, various types of futures market 

participants, as a group, appeared to accumulate more significant positions following the data 

release at 8:30 am ET, including asset managers (who on net purchased futures and accumulated 

net long positions) and, to a lesser degree, bank-dealers and hedge funds (who on net sold futures 

and accumulated net short positions for periods of time). These results are consistent with the 

notion that a significant net change in positions by some market participants is likely important 

in understanding the large reaction to the retail sales data and the subsequent sharp increase in 

prices and volatility, but it does not appear to explain the round-trip in prices during the event 

window itself. Given these observations, questions about the specific factors triggering the move 

and later reversal remain open. 

Patterns of Aggressive and Passive Trading Activity 

During periods of significant directional market moves, an imbalance between the 

volume of buyer-initiated versus seller-initiated trades is commonly observed, with more buyer 

(seller) initiated trades during periods of steep price increases (decreases). A similar imbalance in 

this so called “net aggressive” trade flow was observed on October 15 during the event 

window.19 The analysis suggests that PTFs and bank dealers were the main contributors to the 

pattern of net aggressive flows, consistent with their large share of overall trading volume, with 

PTFs accounting for much of the imbalance in aggressive flows during the event window across 

futures and cash markets. At the same time, there is strong evidence from a study of net passive 

flows to suggest that PTFs, as a group, also remained engaged as liquidity providers throughout 

the event window, thus pointing towards more than one type of PTF strategies at work. 

The net aggressive trade flow on October 15 in both the cash and futures markets was 

buyer-initiated during the first part of the event window when prices were rising and seller-

																																																												

19 Every trade by definition comprises an “aggressive” and a “passive” side with the “passive” defined to be the 

standing order to buy or sell an instrument in the order book, while the "aggressive" order is that which is executed 

when matched against a standing "passive" order. The term “aggressive” therefore should not be seen as having a 

negative connotation in this context. 

23
initiated during the second part as prices were falling, as seen in Figures 3.5 and 3.6. When 

decomposed by participant type, data show that both bank-dealers and PTFs were the main net 

aggressive buyers of Treasury futures as prices rose, and net aggressive sellers as prices fell. In 

the cash market, PTFs showed similar behavior. Bank-dealer flows, by contrast, appeared to be 

neutral in the cash market during the first part of the event, shifting to primarily seller initiated 

during the second part (as price declined). When considered across the cash and futures markets 

together, PFTs account for much of the net aggressive flows during the event window, consistent 

with their elevated share of overall trading volume. 

A similar breakdown of the net passive trade flow by participant type in Figures 3.7 and 

3.8, shows that PTFs were large net passive sellers during the first part of the event window and 

net passive buyers during the second part of the event in both cash and futures, consistent with 

market making activity and their large contribution to the depth at the top of the order book 

throughout the event window. Notably the PTF pattern of net passive flows closely mirrors the 

(inverted) pattern of PTF aggressive flows, such that, as a group, their net position remained 

largely unchanged throughout the event window. In contrast, net passive bank-dealer flows were 

not indicative of significant market making activity during the event window. 

One possible explanation for the symmetry of the observed net aggressive buying and net 

passive selling flows by PTFs could be that this is evidence of hedging activity associated with 

the market making activities of these firms. For example, in a rising price environment, a market 

participant employing a market-making strategy would tend to see their passive offers to sell 

securities executed more often, and therefore over time accumulate a large negative position. In 

order to limit the market exposure associated with a sizeable short position, such a participant 

might choose to partially offset the passive sales by aggressively buying securities. In that case, 

the aggressive buy orders would only serve to decrease the exposure the firm accumulated in the 

process of market making activity and not be reflective of a directional market view. 

However, an algorithm-level analysis from the event window on October 15 suggests that 

the aggressive buying during the first part of the event window was unlikely to be hedging flows 

arising from such market making activities. Indeed, this analysis indicates that aggressive buyerinitiated PTF trade flows during the first part of the event window mainly stemmed from trades 

24
that served to increase, rather than decrease, the exposures associated with pre-existing positions 

at the time of each trade (Figures 3.9 and 3.10). In total, the analysis suggests that multiple types 

of trading strategies were deployed by PTFs during the event window. Some PTF algorithms 

appear to explain the considerable amount of net passive market making activity that was 

witnessed across cash and futures over the event window and likely was an important 

contributing factor to the absence of price gapping despite the unprecedented large price swings. 

Another, and equally significant, group of PTF strategies appears to have aggressively traded in 

the direction of price moves during the event window, accounting for the bulk of the overall 

aggressive trading imbalance observed. 

As for bank-dealers, in the futures market the pattern of net aggressive trading during the 

first half of the event window was dominated by flows that increased exposure, similar to what 

was observed for PTFs (Figure 3.12). In the cash market however, where bank-dealer flows are 

likely to be motivated in significant part by the need to hedge client activity conducted in the 

dealer-to-customer market, the net bank-dealer flow was largely neutral during the initial part of 

the event, but switched to exposure reducing aggressive selling as prices subsequently declined 

(Figure 3.11).20 

Order Book Participation and Liquidity Provision 

Analysis of order book participation and bid-ask spreads, important measures of the 

liquidity available in the market, was conducted in order to determine whether changes in the 

provision of liquidity were associated with the price dynamics during the event window. The 

data show that the two largest groups providing liquidity, PTFs and bank-dealers, both took 

actions to reduce their risk exposure to volatility during the event window, even as they 

																																																												

20 While PTFs in general carry little inventory overnight, bank dealers do routinely end trading sessions with sizable 

long or short positions both in the cash and futures markets. In addition, bank-dealers historically have also 

warehoused significant positions accumulated from client trades in the cash market. It is therefore much more 

difficult in the cash market to identify exposure reducing vs exposure increasing trades for bank-dealers based on the 

data available to staff (inter-dealer cash platform and futures data only). 

25
continued to trade. In general, the analysis shows that the spike in trading volume and volatility 

coincided with a sizeable reduction in the depth of orders provided by PTFs and with the posting 

of much wider bid-ask spreads by bank-dealers. In addition, for brief periods, bank-dealers were 

absent from the offer side of the cash market. 

Market depth, which represents the amount of standing orders at the various prices 

available on the bid and ask sides of the order book, initially followed a typical pattern on the 

morning of October 15, but then deteriorated throughout the day. Figures 3.13 and 3.14 show 

depth for the cash and futures markets, respectively, at the first 10 price levels of the order book 

from 8:00 to 16:00 ET.21 In both markets, there was a sharp drop in market depth just ahead of 

the 8:30 ET retail sales data release that morning, which is a normal pattern ahead of a scheduled 

macroeconomic data release. Market participants reduce the level of potential risk exposure 

ahead of such known events by paring back the volume of standing orders they place on trading 

venues, reducing the risk that they end up transacting at an undesirable price. 

Market depth recovered immediately after the 8:30 ET data release, but then diminished 

quite dramatically over the next hour in the run-up to the event window as Treasury yields 

gradually declined, a pattern consistent with the typical inverse relationship between volatility 

and market depth. During this period, there was evidence of some sizeable buy orders and 

position changes that coincided with notable reductions in the depth of the order book, although 

they did not result in price gapping. For example, near the beginning of the event window, two 

buy market orders were executed in the 10-year futures market—one for 3,000 contracts at 

9:33:45 and one for 2,100 contracts at 9:34:07—both of which coincided with reductions in 

market depth.22 Around these and a number of other similar transactions, the liquidity consumed 

when the orders were executed was not fully replenished with new order submissions, leaving 

the market with reduced depth of book—particularly on the offer side. Thus the data show that 

																																																												

21 In futures markets the top 10 levels represent the entire book visible to subscribers, whereas in cash the entire 

order book is visible in the most detailed commercial data feeds. 22 The notional contract size for the 10-Year Treasury Note future is $100,000.

26
the most extreme period of volatility, the event window, began at a time when market depth was 

thus already low, likely increasing the transaction costs and price impact of larger trades. Market 

depth then dropped even further during the event window as volatility rose. Given the rapid 

movement in prices over this short time interval, and thus the speed with which the best bid and 

offer levels changed, there was obviously a reduced amount of time for the depth of book to be 

replenished at the prevailing price. 

Decomposing the decline in market depth by participant type shows that PTFs and bankdealers adjusted their liquidity provision on October 15 in very different ways. Data from the 

control days show that, in “normal” market conditions, PTFs provide the bulk of the available 

depth at or near the highest bid price or the lowest ask price (the “top of the book”) in both the 

cash and futures markets. On October 15, the drop in available depth that followed the data 

release at 8:30 ET was due primarily to a large reduction by PTFs of the limit orders they left 

standing in the cash and futures markets (Figures 3.15 and 3.16). The decline in limit orders 

made available by bank-dealers was less substantial in absolute terms because they routinely 

leave far fewer limit orders standing near the top of the book. 

In the initial part of the event window, however, there were periods of time when bankdealers had no, or very few, orders in the offer side of the cash market. When prices rose during 

the initial part of the event window, the PTF share of the order book increased to an average of 

80 percent of depth at the top 3 levels of the book in cash and 64 percent in futures, with dealers 

accounting for less than 15 percent. As prices declined in the second part, the PTF share of order 

book depth declined to an average of around 65 percent in cash and 52 percent in futures, while 

the bank dealer share rose to around 30 percent of depth in both cash and futures (Figures 3.17 

through 3.22). Thus the pattern of order book participation largely mirrored the pattern of trading 

volume shares observed above. 

27
Although they significantly reduced their depth of orders, the data also show that PTFs as 

a group continued to provide the majority of order book depth and a tight spread between bid and 

ask prices throughout the day, even during the event window (Figures 3.23 and 3.24).23 In 

contrast, during the event window, the bank-dealers that remained present in the market 

significantly widened their bid-ask spreads such that they only provided limit orders at a 

substantial distance from the top of the book.24 

In very broad terms, therefore, PTFs, as a group, reacted to the event of October 15 

primarily by reducing limit order quantities, while the bank-dealers reacted by widening bid-ask 

spreads and, for brief periods of time, removing their offers to sell securities. Both actions served 

as risk management strategies by reducing the number and size of orders that could be executed, 

and also caused a sharp drop in the supply of liquidity to the market. 

An analysis of the relative supply of liquidity in the bid and offer sides of the order books 

by participant type shows certain imbalances in the provision of liquidity during the event 

window (Figures 3.21 and 3.22). PTFs, as a group, contributed to the order book in a relatively 

balanced fashion throughout the window, often providing standing bids and offers of 

approximately similar sizes, though at low absolute levels. In contrast, the balance of bids and 

offers supplied by bank-dealers was considerably more variable during the event window. 

During the first part of the event window, as dealers intermittently removed their orders to sell 

securities in the cash market, the balance of their remaining orders became skewed toward 

purchase orders. Despite limited and at times imbalanced order book participation, bank-dealers 

continued to trade actively during the event window, and indeed the absolute volume of their 

trading increased. 

																																																												

23 For instance, in the futures market, the average bid-ask spread on October 15 was equal to 1.2 ticks, where 1 tick 

is equal to 1/32 of a percentage point in price terms. This compares to an average of about 1.05 tick on our lowvolatility control days.

24 During the brief moments in the event window when bank-dealers, as a group, only posted purchase orders in the 

cash market, no bank-dealer bid-ask spreads genuinely existed. 

28
Around 9:39 ET, the sudden visibility of certain sell limit orders in the futures market 

seemed to have coincided with the reversal in prices. Recall that only 10 levels of order prices 

above and below the best bid and ask price are visible to futures market participants. Around 

9:39 ET, with prices still moving higher, a number of previously posted large sell orders 

suddenly became visible in the order book above the current 30-year futures price (as well as in 

smaller size in 10-year futures). The sudden visibility of these sell orders significantly shifted the 

visible order imbalance in that contract, and it coincided with the beginning of the reversal of its

price (the top of the price spike). Most of these limit orders were not executed, as the price did 

not rise to their levels. 

Large Transactions and Orders 

 The data for October 15 do not show an exceptionally large trade or a series of related 

large trades that appear to be the direct cause of the sharp price movements observed during the 

event window. Such trades, had they existed, might have been a sign of a “fat-finger” operational 

or systems error, or of severe stress by a particular market participant. Buy orders of reasonably 

large sizes were seen both preceding and at the beginning of the event window in the futures 

market, but they did not directly coincide with substantial price movements (Figure 3.25). 

Moreover, trades of the sizes seen in this event occur regularly, for instance during the control 

days of the data sample, and they generally do not generate very large price movements. 

Historically, sizable market and stop-loss orders have on occasion precipitated or 

exacerbated large moves in financial asset prices, particularly when they lead to trade executions 

far from the price that prevailed when they were initiated. Market orders are often used by 

market participants in CLOBs when they wish to conduct a transaction for a financial instrument 

irrespective of the price, perhaps for hedging purposes or to ensure the timely change in a 

position. There were only a limited number of market orders seen in the data for October 15, 

including two (described in the preceding section) that occur at the start of the event window; 

none of these appear to have resulted in sizeable changes in prices. 

Stop-loss orders are conditional resting orders that execute in the direction of a price 

move after a trigger price is hit. These orders are often used by market participants to protect 

29
against incurring substantial losses when the price of an asset is moving substantially. The stoploss order activity that existed on October 15 across the array of interest rate futures contracts 

was small on October 15, and very limited “buy volume” was triggered during the upward price 

movement of the event window as a result of these orders. Additionally, the trades that did result 

from these resting orders were executed very close to the trigger price, suggesting they did not 

prompt sizeable changes in prices. There is no stop-loss order type on cash platforms.25 

While the large orders did not result in significant price movement leading up to or 

during the event window, they did coincide with notable reductions in market depth preceding 

and at the start of the event window. Indeed, it seems that large orders were thus more likely to 

have contributed to the erosion in liquidity that occurred in the hour preceding the event window, 

rather than serving as a driving factor of prices during the event window itself. 26 

Notable Trading Activity

Analysis of transaction and order book data during the event window revealed two 

notable patterns in activity on October 15, high levels of cancellations and self-trading, but 

whether this activity contributed to the rapid price movements is unknown. 

First, the number of new order submission and cancellation messages sent to the futures 

exchange and the cash trading platforms on October 15 was very high, as would be expected on a 

highly volatile day. In the futures market, this was accompanied by temporary sharp increases in 

latency, the time required by the matching engine to process incoming orders. Figure 3.26 shows 

that message rates for new orders and cancellations across Treasury futures contracts were 

elevated during the event window, though they varied considerably over the trading day. The 

																																																												

25 In both cash and futures markets, stop-loss orders could also be directly generated by traders or their computers in 

real time, and not entered ahead of time on the trading platforms. These orders would not be captured as “market 

orders” in the available data. 26 Additionally, there was a somewhat elevated level of trading volume in exchange-for risk and block trades in the 

futures market. Such transactions take place outside the CLOB. Data limitations prevent a thorough analysis of the 

potential impact of such trades, but further study of the dynamic between trades done off of electronic venues and 

market conditions may be warranted. 

30
largest of these spikes in message rates coincided with the cancellations of a very large number 

of limit orders outside the visible order book, and thus far from the current market price (Figure 

3.27); these cancellations were made by a relatively small number of firms with minimal trading 

volume on the day. 

Given the finite capacity of any matching engine to simultaneously process messages and 

execute matches between buyers and sellers, extremely high message rates appeared to cause 

trading platform latency to temporarily jump higher (Figure 3.28).27 Figure 3.29 shows the 

message rate and latency build-up within a single second around 9:34 ET at millisecond 

resolution, illustrating how a peak message rate of around 40 messages per millisecond results in 

a gradual slowing down of the response time of the matching engine. Once the messaging rate 

fell, trading platform latency quickly returned to previous low levels. While the message 

cancellations observed very near the beginning of the event window were not a direct cause of 

price movements at the time given their distance from the market price, the associated latency 

would have affected the trading speeds of other market participants by increasing the time lag 

between initial order entry and possible execution on the platform. As some market participants 

monitor latency and include it as a variable in their trading strategies, sudden changes in latency 

would cause them to adjust their behavior. 

The cancellation activity witnessed in the invisible futures order book also resulted in a 

highly volatile total order book depth (including visible and invisible orders) in the futures 

market (Figure 3.30). In the futures market, the portion of the order book that is not visible to 

market participants (that portion that rests at levels outside the top 10 best bid and offer price 

levels) can represent anywhere from 50 to 90 percent of total market depth—witnessed both on 

October 15 and the control days. 

																																																												

27 Such latency is defined as the time between when an order is generated by a firm and when it gets added to the 

order book at the exchange.

31
A second notable aspect of trading on October 15 was the heightened level of self-trading 

during portions of the event window. Self-trading, for the purpose of this report, is defined as a 

transaction in which the same entity takes both sides of the trade so that no change in beneficial 

ownership results. Self-trades appeared in both cash and futures market data at varying levels 

across firms and time periods.28 In the cash market for 10-year Treasury securities, for example, 

self-trading represents 5.6 percent of the total activity on control days, and 4.2 percent on 

October 15 (Table 3.9). 

The bulk of self-trading in cash and futures markets was observed among PTFs, perhaps 

due to the fact that such firms can run multiple separate trading algorithms simultaneously. For 

instance, one of these algorithms could specialize in placing buy or sell limit orders at the top of 

the order book while another could specialize in initiating trades given specific conditions in that 

market, potentially leading one algorithm to end up being matched with another algorithm from 

the same firm.29 In addition to PTFs, the cash data also showed a very small amount of selftrading by bank-dealers and hedge funds, some of which are also known to trade algorithmically. 

In the futures market, the share of self-trading was generally similar to the cash market, and the 

																																																												

28 At times, self-trading may reflect unlawful conduct. For example, unlawful self-trades may constitute “wash 

sales.” In the futures markets, “wash sales” involve a purchase and sale of the same delivery month of the same 

futures contract at the same or similar price, made without an intent to take a genuine, bona fide position in the 

market, and instead, are intended to negate risk or price competition. In the securities markets, for example, a “wash 

trade” is a transaction that does not result in a change of beneficial ownership when there is a fraudulent or 

manipulative purpose behind the trade. This report is not making any findings on the legality of any self-trading 

that occurred on the days covered in this analysis.

29 In general both cash and futures platforms provide a “self-match prevention” flag which allows participants to 

avoid self-trading by having the matching engine reject such matches. Some exchanges require the use of the selfmatch prevention for certain participant types, whereas for others use is voluntary. Where voluntary, not all market 

participants choose to use that feature. In addition, many firms have internal self-match prevention tools which may 

provide more customized solutions than those at the exchange. See http://www.cmegroup.com/globex/trading-cmegroup-products/self-match-faq.html for related information from the CME Group. See also 

http://www.gpo.gov/fdsys/pkg/FR-2014-05-07/pdf/2014-10384.pdf for rules adopted by FINRA to address patterns 

of unintentional self-trading by its members in securities, including the cash Treasury market. The FINRA rule 

defines “self-trades” as securities transactions that result from the unintentional interaction of orders originating 

from the same firm that involve no change in the beneficial ownership of the security. Among other things, the rule 

requires FINRA members to have policies and procedures in place that are reasonably designed to review their 

trading activity for, and prevent, a pattern or practice of self-trades resulting from orders originating from a single 

algorithm or trading desk, or from related algorithms or trading desks..

32
activity was also almost exclusively limited to PTFs. In contrast to the cash market, however, 

there was a higher incidence of self-trading on October 15 (4.2 percent) than on the control days 

(2.7 percent) for the 10-year contract (Table 3.10). 

During the event window, the data showed that the share of overall transactions resulting 

from self-trading was substantially higher than average. At the 10-year maturity, it reached 14.9 

percent and 11.5 percent for cash and futures, respectively, during the move up in prices in the 

event window (Figure 3.31). During the retracement, when the price moved back down rapidly, 

the share of self-trading declined to 1.2 percent and 4.8 percent in cash and futures, respectively. 

Moreover, the concentration of self-trading volume among PTFs was very high in both markets 

during the event window. Another aspect of self-trading flows during the event window was its 

directional nature (Figures 3.32 and 3.33). For example, between 9:33 and 9:39 ET, the 

cumulative net aggressive buyer- minus seller-initiated self-trade volume increased by around 

$160 million in the cash 10-year note, accounting for close to one-fifth of the total imbalance 

between buyer and seller initiated trades observed over that time interval.30 

Summary of Analytical Findings 

Analysis of participant-level data in the cash and futures markets did not reveal a clear, 

single cause of the price movement during the event window on October 15. However, the data 

did highlight a number of important characteristics of the event. They revealed that PTFs 

remained the dominant participant type during the event window: they produced high trading 

volumes during the event window, and continued to provide liquidity to the cash and futures 

order books, though at much reduced levels. Bank-dealers also increased their trading volumes, 

but to a much lesser extent, and provided less liquidity in the order books by widening their 

spreads and withdrawing for brief periods from the offer side of the book. The dynamics that 

drove continued trading at such volume during that short period of time remain an open question. 

																																																												

30 Qualitatively similar patterns, albeit larger in magnitude, were observed in the 5-year note in the cash market, 

where self-trading accounted for about one-third of net aggressive trade volume between 9:33-9:39 ET.

33
During the event window, an imbalance between the volume of buyer-initiated versus 

seller-initiated trades was observed, with more buyer-initiated trades as prices rose in the first 

part of the event window, and more seller-initiated trades as prices fell in the second part of the 

event window. Such imbalances are common during periods of significant directional market 

moves. Both bank-dealers and PTFs initiate these liquidity removing trades, though PTFs 

account for the largest share. At the same time, there is strong evidence to suggest that PTFs, as a 

group, also remained engaged as liquidity providers throughout the event window, thus pointing 

towards more than one type of PTF strategies at work. 

 Data from the markets analyzed in this study did not reveal any market orders, stop-loss 

orders, or other types of transactions of highly unusual size that appear to have driven the price 

movements in the event window. A few buy orders of large, but not unusual size, ahead of the 

event window were associated with a reduction in market depth in the cash and futures markets. 

Market depth reached particularly low levels around the start of the event window, and, in the 

absence of new orders to replenish the order book, did not fully recover for a period of time. 

Importantly, analysis shows that in the Treasury cash and futures markets, the nature of trading 

and types of market participants have changed significantly over the last decade. 

34
Section 4: Observations about Treasury Market Structure 

The preceding analysis does not reveal a single cause for the volatility seen on October 

15—particularly so for the event window. However, as explored further in this section, the data 

do suggest that the structure of the U.S. Treasury market has evolved notably in recent years. In 

particular, the increased use of electronic trading and the shifting roles of market intermediaries, 

especially in cash Treasuries, as well as changes in end investors, may have resulted in changes 

to how liquidity is provided and demanded, and to the characteristics of that liquidity. An 

improved understanding of these issues sheds additional light on how the events of October 15 

may have transpired, and raises questions about whether events such as October 15 could occur 

more frequently going forward. 

Increase in electronic trading 

Electronic trading has become an increasingly important feature of the modern Treasury 

market. While it has been well established in the futures market since the late 1990s, it had its 

start in the interdealer cash market in the early 2000s, when the interdealer brokers first launched 

their electronic platforms for Treasuries, and opened access to non-dealers. These electronic 

trading platforms enabled automated trading – a subset of electronic trading that relies on 

computer algorithms for trading decisions, execution, and booking. As a strategy, a variety of 

firms employ automated trading of some form, from small proprietary trading operations to large 

hedge funds and traditional broker-dealers. 

The vast majority of trading in the interdealer cash Treasury market takes place in the 

most recently issued securities (also known as “on-the-run” or “benchmark” securities) given – 

and contributing to – their superior liquidity. Trading in the on-the-run securities tends to 

concentrate on the electronic platforms, with interdealer trading in seasoned securities (also 

known as “off-the-run”) taking place primarily through traditional voice-assisted brokers. 

35
The growth in electronic trading has contributed to a marked shift in the composition of 

participants in the interdealer cash market over time.31 Until 1992, for example, the interdealer 

brokers only allowed primary dealers, as designated by the Federal Reserve Bank of New York, 

to access their trading platforms. In 1992, the interdealer brokers expanded access to all entities 

that were netting members of the Government Securities Clearing Corporation (now the Fixed 

Income Clearing Corporation, or FICC). Over time, other entities gained access to the platforms 

through their prime brokers, who themselves had access, and the platforms in recent years 

granted direct access to an even wider range of participants, including those outside the FICC 

netting membership. In particular, PTFs gained access to the platforms in the mid-2000s. 

Because of the broader access to futures markets, such firms have been a core presence in those 

markets for a longer period. 

 PTFs now account for more than half of the trading activity in the futures and 

electronically brokered interdealer cash markets, as shown earlier for October 15 and control 

days. Moreover, activity among PTFs tends to concentrate among a small number of firms, more 

so than activity among dealers (Tables 3.7 and 3.8). While these data suggest that PTFs’ share of 

activity on October 15 as a whole was not significantly outsized, they point to the key role a 

small number of PTFs play on a routine basis in liquidity provision and price discovery. 

The growth in automated trading has also raised questions about evolving risks. 

Automated trading can occur at speeds that exceed the capacity of manual detection and 

intervention, posing a challenge to traditional risk management protocols, and forcing market 

participants, trading platforms, and clearing firms to develop internal risk controls and processes 

																																																												

31 In contrast to the interdealer market, where brokers facilitate anonymous trades between counterparties, dealer-tocustomer trades occur directly between broker-dealers and end investors. A good deal of the dealer-to-customer 

activity still takes place over the phone. However, electronic trading has also permeated this market, albeit to a 

lesser extent and in a different manner from the interdealer market. In particular, electronic trading in the dealer-tocustomer market takes place through execution platforms in which market participants can request quotes from 

multiple dealers, or through single dealer platforms in which dealers stream quotes to their customers. The analysis 

in this report does not speak to this segment of the market.

36
to manage the potential for rapidly changing market and counterparty risk exposures.32 Risks 

associated with automated trading have been acknowledged by a range of market participants 

and regulators, and a summary of such risks can be found in Appendix C.33 

Changes in liquidity supply 

The growth in electronic trading, competitive pressures, regulation, and other factors 

have led to changes in the suppliers of liquidity and the manner of liquidity provision in recent 

years, with implications for the characteristics and measurement of liquidity. Dealers have 

traditionally acted as short- to medium-term liquidity providers, often buying or selling from 

customers in large amounts, holding a portion of these positions across days, and maintaining a 

large balance sheet to support such positions. Dealers profit from liquidity provision via 

collecting the bid-ask spread, but also from coupon income and potential price appreciation on 

positions taken in their market-making capacity, and from insights gathered from their views of 

customer flows. Moreover, dealers may, in an effort to satisfy client expectations, be willing to 

provide liquidity to clients even when doing so may not be immediately profitable. 

However, the entrance of a broader range of firms in the market and the expansion of 

electronic trading has increased competitive pressures. Market making revenues are now 

distributed across a broader set of entities and entity types, consistent with a competitive 

marketplace. Anecdotally, market participants have claimed that dealers are more circumscribed 

in the utilization of their balance sheet to intermediate markets in order to better manage their 

exposure and risk. 

																																																												

32 Given central clearing in futures, counterparty risk exposures there are managed by the clearing house. 33 These risks are also discussed in the Treasury Market Practices Group white paper “Automated Trading in 

Treasury Markets,” http://www.newyorkfed.org/tmpg/. The white paper includes a set of recommendations for 

market participants, to supplement previously issued best practices, in response to these risks. The CFTC has also 

implemented numerous rules addressing risk management concerns in futures. Through its recent Concept Release 

on Automated Trading, CFTC is engaged in consultations with market participants on the need for additional risk 

control related regulations for automated trading in the futures markets. 

http://www.cftc.gov/ucm/groups/public/@newsroom/documents/file/federalregister090913.pdf

37
Some market participants have argued that recent regulatory initiatives have increased 

trading and inventory costs and forced a reduction in risk-taking, prompting them to shift their 

allocation of capital away from market making for low margin transactions, and instead towards 

other business areas that may yield greater returns on equity. Indeed, some of this capital 

reallocation could have been expected from regulatory changes intended to increase the 

resiliency of financial institutions and of the financial system.34 

There is evidence that dealers have pared their risk-taking in recent years in reaction to 

the crisis, new competitive pressures, new regulations, and other factors. Financial assets of 

security brokers and dealers fell sharply during the crisis, for example, and have since stagnated 

(Figure 4.1). With respect to the Treasury market in particular, commentators sometimes point 

to the aggregate net positions of primary dealers as a gauge of dealer risk taking (Figure 4.2), but 

the offsetting nature of different dealers’ positions in this measure limits its value for this 

purpose. Perhaps more informative is dealers’ gross Treasury positions (Figure 4.3), which sum 

across short and long positions, and an estimate of dealers’ market making positions (Figure 4.4). 

Both measures plunged during the crisis, but more than fully recovered in subsequent years. As 

noted by some market commentators, however, dealer positions relative to outstanding debt have 

fallen in recent years given the growth in debt outstanding. 

In contrast to dealers, PTFs commonly act as short-term liquidity providers, buying and 

selling frequently in small amounts, but rarely taking significant, unhedged intraday positions 

and typically ending the day with little net directional exposure. Because they take on little net 

exposure, and, in cash Treasuries, rely on prime brokers for their financing and clearing needs, 

many PTFs can be more thinly capitalized than typical broker-dealers. Most PTFs do not trade 

on behalf of clients and instead restrict their trading activity to proprietary positions. As a result, 

																																																												

34 Evidence to date is limited on whether regulatory changes have affected dealer market-making behavior. For 

example, an analysis of the sharp rise in yields in 2013 showed that dealers reduced their holdings of Treasury and 

other fixed income product positions at the time, but found no quantitative evidence that such behavior was directly 

related to diminished risk-taking capabilities. See “Dealer Balance Sheet Capacity and Market Liquidity during the 

2013 Selloff in Fixed-Income Markets,” by Tobias Adrian, Michael Fleming, Jonathan Goldberg, Morgan Lewis, 

Fabio Natalucci, and Jason Wu, Liberty Street Economics blog post, October 16, 2013. 

38
these firms make trading decisions, including liquidity provision decisions, primarily on the basis 

of immediate profitability and the level of market risk, rather than as a service offered in the 

context of existing customer relationships that are intended to be profitable over time. 

Analysis of order book data in the cash and futures markets shows distinctive patterns in 

how PTFs and bank-dealers adjust their provision of liquidity in volatile periods. As discussed 

earlier, bank-dealers tended to manage risk on October 15 by adjusting the price at which they 

were willing to transact – widening the spread between their bids and offers as price volatility 

increased, and withdrawing from the offer side of the market during the period of the sharpest 

price rise – rather than relying fully on adjustments to the quantity of orders made available 

(though this was an additional factor). In contrast, PTFs almost exclusively reduced their risk by 

trimming the quantity of standing orders to buy and sell, but generally maintained, as a group, a 

narrow spread between their bid and offer prices. This behavior may reflect the advantages of 

speed: PTFs can likely monitor and respond to price movements more quickly than many other 

market participants, adjusting their provision of liquidity very rapidly, and thus manage their 

price risk more dynamically. 

While PTFs are able to provide very tight bid-ask spreads even in a volatile market, and 

help ensure that prices rapidly reflect new information, such firms typically do not wish to 

establish significant positions, and may not have the capital to compete for large buy and/or sell 

orders. Moreover, unlike dealers, PTFs do not have access to a customer business to assess 

current and latent demand for liquidity and therefore have to spend considerable effort on 

processing the information content of incoming order flow. To ensure that their quotes do not 

become stale and result in unprofitable trades, such firms quickly cancel or modify existing 

quotes if they sense the price is about to move in an unfavorable direction. Such behavior is a 

rational risk management device for PTFs operating in markets where bid and offer quotes are 

binding (as opposed to some markets where quotes are indicative or can otherwise be 

withdrawn). 

Changes in liquidity demand 

39
The demand for liquidity is also changing, in parallel with the changes in liquidity 

supply. The most notable factors may be the growth of Treasury debt outstanding, the changing 

shares of debt held by different investor groups, and the increased concentration of holdings 

among money managers.35 These developments raise concerns that transaction flows, and the 

associated demand for liquidity, may become larger and more volatile in an environment where 

the business of liquidity provision and operating technology of markets have gone through 

fundamental changes. 

The amount of Treasury debt outstanding grew sharply during and after the financial 

crisis as net debt issuance rose to finance the federal government’s budget deficits. Marketable 

debt outstanding totaled $12.6 trillion as of April 30, 2015, up from $4.3 trillion on June 30, 

2007, before the crisis.36 All else equal, the growth in debt outstanding could be expected to 

increase liquidity demands as security ownership and flows increase correspondingly. 

 Aside from the growth in debt outstanding, there is the question of whether the allocation 

of that debt has shifted towards investors who are typically liquidity seekers in the secondary 

markets. Figure 4.5, for example, shows that the share of Treasury securities held by mutual 

funds (including closed-end funds and exchange-traded funds) has increased in recent years, 

from just under 4 percent in the years preceding the financial crisis to 6.7 percent at the end of 

March 2015.37 Mutual funds provide daily liquidity to their investors – investing cash or meeting 

redemptions – and hence demand immediate liquidity from dealers. Figure 4.6 shows that the 

magnitude and volatility of these flows, as measured on a monthly basis, has increased markedly 

in recent years. 

																																																												

35 For further discussion of the trends affecting market making, see CGFS Papers No 52, “Market-making and 

proprietary trading: industry trends, drivers and policy implications,” Report submitted by a Study Group 

established by the Committee on the Global Financial System, November 2014, http://www.bis.org/publ/cgfs52.pdf. 36 These figures include holdings of the Federal Reserve, which totaled $0.8 trillion on June 30, 2007 and $2.5 

trillion on April 30, 2015.

37 These percentages include Federal Reserve ownership of Treasury securities in the denominator. If Federal 

Reserve ownership is not included, then the share increases from about 4.5 percent in the years preceding the crisis 

to 8.2 percent at the end of March 2015. 

40
That said, ownership of Treasury securities has shifted in numerous ways. Ownership by 

foreign investors, for example, which is the residual investor group in Figure 4.5, increased from 

37.8 percent at the end of 2003 to 47.3 percent at the end of March 2015. This category includes 

foreign central banks and sovereign wealth funds, which may have a reduced need for marketmaking services relative to other investors because their investment horizon may be longer. On 

balance, the shift in ownership among investor types does not clearly point to an increased or 

decreased the demand for market-making services, although the absolute growth in the level of 

the debt has likely increased the demand for such services. 

Aside from shifts in ownership among investor groups, there is evidence that 

concentration within the money management industry has increased. Some market participants 

suggest that this concentration may lead to more concentrated demands for liquidity provision, 

presumably under the idea that assets within a given money management firm are more likely to 

be managed in a similar way than assets managed by several different firms. That is, portfolio 

allocation decisions, and hence liquidity demands, may be more dependent on the portfolio 

allocation decisions of a small number of large financial institutions. 

Implications of structural changes for liquidity 

The overall implications of these structural changes for Treasury market liquidity, and 

whether they suggest that events like October 15 are more likely, are unclear. By some metrics, 

market liquidity is as good or almost as good as it has ever been. Bid-ask spreads in the 

interdealer market remain close to the narrowest they have ever been, after having increased 

sharply during the 2007-09 financial crisis (Figure 4.7). The “noisiness” of Treasury yields, 

which measures the dispersion of market yields around a fitted yield curve, shows a similar 

pattern (Figure 4.8). Trading volume of primary dealers is somewhat lower than observed in the 

years before the crisis (Figure 4.9), but trading volume of benchmark securities in the interdealer 

market, which reflects activities among a broader set of firms, is near historically high levels for 

41
the 5- and 10-year notes (Figure 4.10).38 Trading volume in futures, in contrast, has not 

exhibited a strong upward or downward trend since the first half of 2007, before the financial 

crisis (Figure 4.11). 

Some other liquidity measures exhibit some deterioration of liquidity. Order book depth 

of benchmark notes in the interdealer market reached post-crisis highs in early 2013, but 

declined during the 2013 selloff in the fixed income markets, and again in late 2014 (Figure 

4.12). Price impact coefficients, which measure how much prices tend to change for buy or sell 

orders of a given size, spiked up on October 15 and remain somewhat higher than they were in

early 2014 (Figure 4.13). 

While some measures demonstrate a favorable liquidity environment, liquidity remains a 

concern among some market observers because of the structural market changes that have 

occurred. There are also concerns because of liquidity reductions during times of stress, such as 

on October 15 and during the selloff in fixed income markets in 2013, although the relationship 

between volatility and liquidity is obviously not just a recent phenomenon. In particular, while 

liquidity often is high, and, on average, may have benefited from the advent of electronic trading, 

the changing nature of liquidity provision may have increased the likelihood of periodic episodes 

of intraday volatility, such as that observed on October 15. That is, as the speed of market 

activity has increased, substantial variations in market liquidity may have become more frequent 

than in the past. 

In addition, the high levels of liquidity observed in recent years have come in an 

environment of unusually low market volatility and high security supply, which tend to benefit 

liquidity.39 Liquidity could naturally be expected to deteriorate somewhat if and when volatility 

																																																												

38 Trading volume for the 2-year note remains far below its highs in 2007 and 2008, likely reflecting the low 

volatility and trading interest in the note during the prolonged period of overnight rates at the zero lower bound.

39 In fact, when evaluating trading activity, some market participants prefer turnover measures, which take the 

quantity of outstanding debt into account, and which have shown a sharp decline as debt outstanding has grown. 

However, it is not obvious that one would expect trading volume to vary one-for-one with the level of outstanding 

debt and hence unclear whether this is a better measure of activity.

42
rises.40 Some market participants have expressed concern that a shift in investors’ views, in an 

environment where the volatility of investor flows have increased and where dealers have pared 

back their market making capacity, could pose a challenge to market liquidity. 

The potentially higher risks of spikes in volatility and declines in liquidity raise questions 

about the appropriate response. This is complicated by the fact that rapid price changes and 

changes in the level of liquidity can also arise naturally in response to new developments and 

high uncertainty. To mitigate the likelihood and effects of sharp intraday rises in volatility or 

changes in liquidity, market participants and venues have adjusted trading strategies and risk 

management practices to better prepare for such episodes. The CME Group, for example, 

introduced a new rule regarding price fluctuation limits for certain interest rate futures and 

options contracts in December 2014 to “deter sharp price movements that may, for example, be 

driven by illiquid central limit order books prevailing from time to time in otherwise liquid 

markets.”41

Aside from the potentially higher risks of increases in volatility and declines in liquidity, 

the changing nature of liquidity provision means that the measurement of liquidity may need to 

change. Indeed, one measure of liquidity, trade size, has decreased markedly in recent years in 

both the interdealer and futures markets (Figures 4.14 and 4.15), but that decrease at least 

partially reflects the adoption of automated trading in electronic markets with pre-trade 

transparency. More traditional liquidity metrics continue to look favorable, as mentioned above, 

but may provide only a partial view into liquidity availability as a whole. The ability of liquidity 

providers to detect orders and react to new information is nearly instantaneous, and can lead to 

rapid shifts in the state of the limit order book. Average bid-ask spreads and market depth, 

though often indicative of general market conditions, may need to be complemented by other 

																																																												

40 Volatility tends to be positively related to trading activity, but negatively related to more direct measures of 

market liquidity, such as bid-ask spreads and depth. 41 See https://www.cmegroup.com/tools-information/lookups/advisories/ser/files/SER-7259.pdf

43
measures in light of these changes to obtain a more meaningful picture of the state of market 

liquidity in the current market structure.

44
Section 5: Next Steps 

The events and analysis of October 15 have highlighted the importance of the depth and 

liquidity of the U.S. Treasury market, and have led to important questions about the implications 

of structural and other changes currently underway in the market. The staff of the Treasury, 

Board, FRBNY, CFTC, and SEC plan to continue to analyze the events of October 15 and 

examine changes in the U.S. Treasury market structure, and have identified four areas for further 

work. These include: 

• Further study of the evolution of the U.S. Treasury market and the implications for 

market structure and liquidity, 

• Continued monitoring of trading and risk management practices across the U.S. Treasury 

market and a review of the current regulatory requirements applicable to the government 

securities market and its participants, 

• An assessment of the data available to the public and to the official sector on U.S. 

Treasury cash securities markets, and 

• Continued efforts to strengthen monitoring and surveillance and promote inter-agency 

coordination related to the trading across the U.S. Treasury market. 

Understanding the implications of changes to U.S. Treasury market structure and liquidity 

The U.S. Treasury market is the deepest and most liquid government securities market in 

the world. However, an event like October 15 highlights the need to better understand various 

factors that are impacting liquidity in the U.S. Treasury market, especially during stressed market 

conditions. These include changes in intermediation, automated trading, regulation, and buyand sell-side participation that may have altered trading practices as well as the sources and 

characteristics of liquidity provision. Because of the U.S. Treasury market’s unique role, 

changes in market structure and liquidity could also have implications for the cost of financing 

the government, how these instruments are used as risk-free benchmarks for pricing financial 

instruments, the costs borne by investors transacting in these markets, and the implementation of 

monetary policy. In order to better understand the key factors underlying the current market 

45
structure and how they influence the liquidity of the U.S. Treasury market, the following next 

steps are recommended: 

• Continue to study U.S. Treasury market structural changes and implications for market 

functioning and efficiency. 

• Continue to support existing forums where market participants, the public, and regulatory 

agencies discuss issues related to investment trends, market structure, risk management 

and automated trading in financial markets, including the U.S. Treasury market 

• Host a public-private sector conference, and encourage associated studies, addressing 

changes to the U.S. Treasury market and the effects on liquidity provision, risk 

management and market participation 

U.S. Treasury market regulatory requirements and trading and risk management practices 

The introduction and rapid growth of automation in the U.S. Treasury market over the 

past decade has brought benefits as well as challenges to trading practices and risk and internal 

control systems. Risk controls at firms and trading venues must be able to monitor order and 

trade activity at the increased speeds made possible by this automation. In recent years, many 

trading platforms and firms have improved their risk management practices to better align with 

the faster trading environment, often aided by best practices drafted by industry or public bodies. 

The trend toward increasingly automated and algorithmic trading on trading platforms is also 

being addressed by various regulatory efforts underway by the SEC, the CFTC and others. To 

further these steps, the public and private sectors must continue to work together to address the 

outstanding risks, including operational risks and risks that may harm liquidity provision or price 

discovery. 

Market structure changes also suggest that a review of the regulatory requirements 

applicable to the government securities market would be worthwhile. The GSA, which provides 

for the registration of government securities brokers and dealers engaging in transactions in the 

cash markets for government securities, requires Treasury to adopt rules with respect to financial 

responsibility and related practices of government securities brokers and dealers. In order to 

prevent fraudulent and manipulative acts and practices and to promote just and equitable 

46
principles of trade, the GSA also authorizes the appropriate regulatory agencies (the SEC and 

federal bank regulators) to issue regulations, in consultation with Treasury, with respect to 

transactions in government securities for the entities they regulate. The enforcement authority for 

these rules sits with the SEC, FINRA or the appropriate bank regulator. There are, however, 

several differences in the current regulatory requirements applicable to the government securities 

market as compared to other U.S. securities, commodities and derivatives markets that may be 

worthy of examination. The authors of this report: 

• Support a review of the current regulatory requirements applicable to the government 

securities market and its participants. 

• Suggest studying the implications of a registration requirement for firms conducting 

certain types of automated trading in the U.S. Treasury market and for government 

securities trading venues. 

• Support industry and other efforts to develop, and regularly revisit, best practices for 

market conduct related to voice and automated trading in cash and futures markets. 

• Continue to review policies and risk management practices at U.S. Treasury trading 

venues to assess risks posed to trading, risk transfer, and price discovery, as well as 

clearing and settlement risks associated with the increased speeds of automation. 

Publicly available data around U.S. Treasury market activity and functioning 

Public transparency surrounding trading activity in the U.S. Treasury market is critical to 

investor confidence and the liquidity of these markets. Data related to order and trade activity is 

currently published by various agencies and trading venues, informing the public, on an ongoing 

basis, about market prices, trading volumes, market participant inventories and trends in market 

risk and liquidity. However, differences in pre- and post-trade public reporting exist, especially 

between futures and Treasury cash securities, and the level of publicly available information is 

often not consistent across Treasury market venues or products. For example, in the futures 

markets, anonymous transaction information is visible to market participants and aggregated 

position information for large traders is publically available; futures venues and participants also 

provide regulatory information on participant-level transactions and positions. Given the 

fragmented nature of trading in the Treasury cash markets, transaction data is available only for a 

47
subset of trades and only to those trading on a specific platform or venue. The staffs of the 

Treasury, Board, FRBNY, CFTC, and SEC are committed to continuing to appropriately 

enhance the information made public about the U.S. Treasury market. The authors of the report: 

• Plan to conduct an assessment of the sufficiency of publicly available information on 

Treasury cash securities market transaction pricing and pre- and post-trade trading 

activity, including a review of possible post-trade transaction reporting by government 

securities broker-dealers and banks. 

• Support efforts to enhance public reporting on U.S. Treasury market venue policies and 

services. 

Inter-agency monitoring and oversight of the U.S. Treasury market 

Activity related to U.S. Treasury trading often extends beyond individual regulatory 

boundaries; it encompasses not only the primary and secondary cash securities markets, but repo 

markets, futures contracts which reference U.S. Treasuries, and Treasury exchange-traded funds 

traded as equities. This diversity in trading venues and participants often leaves any individual 

regulator with only a partial view of Treasury risk transfer and price discovery. As firms are able 

to access multiple markets over very short time frames, they become ever more interconnected, 

resulting in significantly faster risk and information transmission. These trends call for 

continued cooperation among the official sector to ensure that the monitoring of market activity 

and liquidity is as effective and coordinated as possible. 

The Inter-Agency Working Group for Treasury Market Surveillance (IAWG) was formed 

to improve monitoring and surveillance and strengthen interagency coordination with respect to 

the U.S. Treasury markets following the Salomon Brothers auction bidding scandal in 1992, and 

today consists of staff from the Treasury, Board, FRBNY, the CFTC, and the SEC.42 Since its 

																																																												

42 The IAWG was formed in 1992 by the Treasury, the SEC, and the Board, to strengthen monitoring, surveillance 

and interagency coordination in respect to the Treasury market. Its initial efforts were focused on developing a 

48
inception it has been useful in providing a regular forum for the participating entities to 

collaborate on issues related to Treasury market structure, functioning, and participation, such as 

the events of October 15. In support of their ongoing respective responsibilities, the staff of the 

participating entities plan to work together to: 

• Review, and enhance where necessary, the mission, responsibilities, resources, 

capabilities, and tools of the IAWG to help ensure that it meets the needs associated with 

effective monitoring and oversight of the U.S. Treasury market, while continuing to 

leverage the resources and mandates of its participating entities. 

• Develop a standing multilateral information sharing agreement between the participating 

entities of the IAWG to allow access to – subject to appropriate protections and 

restrictions – confidential, non-public data, information, and analysis related to cash 

securities and futures markets on an ongoing, and event driven, basis.

• Assess the frequency and type of additional data reporting to the official sector necessary 

to continue to effectively monitor the functioning of the Treasury market and meet the 

IAWG mission.

																																																																																																																																																																																																			

framework for enhanced market surveillance for Treasury Securities. See U.S. DEPARTMENT OF THE TREASURY,

SECURITIES AND EXCHANGE COMMISSION, AND BOARD OF GOVERNORS OF THE FEDERAL RESERVE SYSTEM, JOINT 

REPORT ON THE GOVERNMENT SECURITIES MARKET (U.S. Government Printing Office, January 22, 1992) (the 

“1992 Report”), at xii-xiv. http://www.treasury.gov/resource-center/fin-mkts/Documents/gsr92rpt.pdf . See also

THE U.S. DEPARTMENT OF THE TREASURY, SECURITIES AND EXCHANGE COMMISSION, AND BOARD OF GOVERNORS 

OF THE FEDERAL RESERVE SYSTEM, JOINT STUDY ON THE GOVERNMENT SECURITIES MARKET (U.S. Government 

Printing Office, March 1998) (the “1998 Study”) at 14, 

http://www.treasurydirect.gov/instit/statreg/gsareg/gsareg_gsr98rpt.pdf. See also OFFICIAL SURVEILLANCE AND 

OVERSIGHT OF THE GOVERNMENT SECURITIES MARKET, William J. McDonough, FRBNY Quarterly Review, Spring 

1992-93.

49
Appendix A: Participant-Type Classification and Trading Characteristics of PTFs 

and Bank-Dealers 

Category Typical Characteristics

Bank-Dealer

Bank owned dealer engaged in client trading and market 

making, mix of high and low frequency, directional 

trading, some of which may stem from client flows, SECregistered broker-dealer.

Non-bank Dealer

Independent dealer, mainly trading for clients (agency 

investing) or market making, mix of high and low 

frequency, occasional directional trading stemming from 

client flows, SEC-registered broker-dealer. 

Hedge Fund

Agency investing (rule 144A), no leverage restrictions, 

some automated market making but mostly low frequency 

trading, may be registered as broker-dealer, but does not 

have clients as in a typical broker-dealer business model. 

Asset Manager

Agency investing for retail or institutional investors, 

limited leverage, long investment horizon, intermittent 

large directional flows spanning multiple trading sessions, 

low frequency trading. 

Principal Trading 

Firm (PTF) 

Principal investor, deploys proprietary automated trading 

strategies, low latency typically key element of trading 

strategies, may be registered as broker-dealer but does not 

have clients as in a typical broker-dealer business model. 

The trading and quoting behavior of bank-dealers and PTFs exhibit different 

characteristics, though activity varies considerably by specific firm. The tables below show 

statistics across a range of indicators from October 15, with the wide range spanned by the 5th to 

95th percentiles an indication of the broad set of trading strategies and tools employed within 

each participant group. In particular, some bank-dealer and hedge fund trading patterns exhibit 

characteristics of high frequency trading while many smaller PTFs clearly are not trading 

rapidly. 

50
At the median, the data show that PTFs traded more frequently and, on average, in 

smaller size per trade than bank-dealers. The median PTF end-of-day positions were much 

smaller than that of the median bank-dealer, both in absolute terms and especially relative to 

trading volume. The ratio of the number of trades over the number of orders (known as the “fill 

ratio”) was also smaller for the median PTF than for the median bank-dealer, as would be 

expected given the high messaging rates exhibited by many PTFs. All of these PTF statistics are 

consistent with those on control days. This is likely because a significant share of PTF activity 

focuses on the provision of short-term liquidity on both sides of the market, and as such their 

high observed trading volume in the Treasury market does not translate into net changes in their 

positions across a trading session. An analysis of account-level data in the Treasury futures 

market over a number of days that include October 15 shows that more than 80 percent of 

trading in the 10-and 30-year contracts represented short-term intraday turnover.43

																																																												

43 High observed trading volume in a market does not necessarily translate into net changes in overnight positions

(defined as “open interest”). In various futures markets, the daily volume can be at least as large as the net positions 

carried overnight. As such, one cannot always interpret a day with a high trading volume as an indicator of changing 

end-investor interest. 

51
Firm Classification Profile in Cash Treasuries (10 Year)

PTF Bank-Dealer

 5% Median 95% 5% Median 95%

# of Firms 37 44

Number of Orders 10 2,096 195,040 4.15 97 22,517

Trades Filled (count) 3.6 141 7,140 3 60 2,384

# of Fill Messages 3.8 190 7,310 5.15 96.5 3,004

Trade Volume ($ millions) 4 292 18,707 11.3 314 8,403

Average Trade Size ($ millions) 1 1.4 3.0 1.3 2.7 4.3 

Absolute End-of-Day Position 

(scaled by total volume) 0 0.044 0.644 0.0105 0.190 0.888

Max Absolute Intraday Position 

(scaled by total volume) 0.006 0.153 0.644 0.08 0.283 0.914

Fill Ratio* 

(# of fills / # of orders) 0.004 0.153 0.740 0.063 0.649 0.853

Fraction of Aggressive Trades 

(count) 0.165 0.398 0.671 0 0.405 0.843

Fraction of Aggressive Trades 

(volume) 0.144 0.418 0.721 0.0 0.485 0.845

Firm Classification Profile in Treasury Futures (10 Year)

PTF Bank-Dealer

 5% Median 95% 5% Median 95%

# of Firms 66 21

Number of Orders 42.25 7,499 155,814 33 526 54,250

Trades Filled (count) 20.8 1,404 20,649 25 347 9,681

# of Fill Messages 59.3 2,260 53,230 110 4,142 24,832

Trade Volume (# of contracts) 180.3 9,535 325,839 467 59,141 228,092

Average Trade Size 

(# of contracts) 1.5 6.9 27.4 6.5 32.0 72.7 

Absolute End-of-Day Position 

(scaled by total volume) 0 0.011 0.415 0.015 0.083 0.289

Max Absolute Intraday Position 

(scaled by total volume) 0.001 0.021 0.341 0.008 0.089 0.409

Fill Ratio* 

(# of fills / # of orders) 0.019 0.41 0.96 0.084 0.75 0.91

Fraction of Aggressive Trades 

(count) 0.024 0.27 0.73 0.15 0.51 0.73

Fraction of Aggressive Trades 

(volume) 0.027 0.28 0.74 0.28 0.54 0.76

Fraction of Automated Trades 94.8% 33.8% 

Fraction of Automated Messages 99.6% 82.8%

52
Appendix B: Cross-Market Activity between Cash and Futures Markets 

To illustrate the incidence of cross-market activity, an index was developed to illustrate 

the level of activity occurring in a coincident manner across different markets. Figures B.1-B.6 

show the cross market activity index between the 10-year cash and various futures markets, with 

activity for the top 10 bank-dealers shown in the left hand column, and activity for PTFs in the 

right hand column. October 15 is plotted in red and the mean daily index for the April 2014 

control days is shown in blue. 

The cross market activity for the 10-year note and 10-year note futures contract indicates 

that trades by the top 10 PTFs on the CME are frequently followed by trades by the same group 

of firms in the cash market with a lag of roughly 5 milliseconds (Figure B.2). The time gap 

between these trades is just above the theoretical minimum latency between the Chicago area, 

where the CME matching engine is located, and eastern New Jersey, where the cash platform 

matching engines are located. Similarly but less frequently the inverse occurs, with trades in the 

cash market by the largest PTFs followed by trades at the CME by the same group of firms with 

a similar level of latency. For bank-dealers, the activity is much less pronounced. But in both 

cases it is clear that the cross market activity is elevated on October 15 compared to the control 

days, which may be consistent with the hypothesis that more trading opportunities arise between 

the two markets at times of extreme volatility and high volume. 

There is little evidence of heightened cross-market activity between the Treasury market 

and markets that are not as directly related to interest rates, such as the S&P 500 E-Mini (ES) 

futures (Figures B.5 and B.6). Overall, the observed cross-market activity patterns appear 

consistent with known automated trading strategies that involve a nearly instantaneous response 

to common trading signals or that seek to arbitrage short-lived opportunities across related 

interest-rate products. As noted, such linkages are witnessed in other markets as well—for 

example between equity index futures and the cash equity market. 

53
Appendix C: Potential Risks of Automated Trading

Increases in the use of automated trading by a wide range of market participants has helped 

reduce costs and increase market efficiency for many market participants, but has introduced 

or changed a number of risks: 

Operational risks: Such risks are inherent in all financial transactions but of particular 

concern with automated trading given the potential speed of execution. Operational risks 

range from malfunctioning and incorrectly deployed algorithms to algorithms reacting to 

inaccurate or unexpected data. 

Market liquidity risks: Even when a firm executes its strategy perfectly, risks to market 

liquidity may arise from abrupt changes in trading strategies. Such risks are naturally greater 

when such shifts occur at firms with higher trading activity and/or if firms react to an event 

in a similar manner.

Market integrity risks: Market integrity is undermined by acts of manipulation or attempts 

thereof, fraud, disruptive trading, such as spoofing, and other unlawful trade practices, such 

as wash sales and pre-arranged trading.44 Such risks are not new to financial markets, but 

automated trading can provide traders with new tools to engage in such unlawful conduct, for 

many reasons, including creating false impressions of market depth, trading volume, and 

prices. 

Transmission risks: Data from October 15 and other days reveal a high and consistent 

presence of cross market trading between the futures exchange and the cash interdealer 

market platforms. Such trading is generally beneficial at promoting the quick incorporation 

																																																												

44 “Spoofing” involves entering orders with the intent to cancel them before execution and may be undertaken for 

various reasons, including to provide a misleading view of depth. “Wash sales” for purposes of the 

futures/derivatives markets are defined in footnote 31, supra. 

54
of fundamental information into prices across markets, but can sometimes result in the 

transmission of shocks based on erroneous orders or other idiosyncratic factors.45 

Clearing and settlement risks: Traditionally, firms trading on the interdealer platforms have 

cleared through the Fixed Income Clearing Corporation (FICC), which offers central clearing 

services for cash Treasury securities. However, as PTFs have gained access to the platforms, 

they have remained outside the FICC membership and clear with each other either bilaterally, or through a prime broker for trades executed with a FICC member.46 The 

significance of trading volume of firms outside the FICC membership—now larger in 

aggregate than that of FICC netting members—raises the question of whether trades cleared 

for non-CCP members are processed as prudently as those for firms inside the CCP. Trades 

cleared outside the CCP may not be subject to the same level of settlement risk mitigation 

techniques such as margin collection, disciplined clearing fund balance requirements, and 

pre-defined loss sharing arrangements. 

Risks to effective risk management: The speed of trade execution might make critical risk 

mitigation devices, such as trade and position monitoring systems or margin, less effective. 

For example, margin could be insufficient if an exposure grew faster than margin could be 

collected. 

																																																												

45 Because PTFs are privately held and generally face lighter regulation than registered broker-dealers, limited 

financial information is available about many PTFs, including about their financial linkages—and thus their 

potential to transmit risk—to traditional financial institutions. 46 Such a prime brokerage relationship facilitates clearance between a non-FICC member and a FICC member, as 

the prime broker is a netting member of the FICC. The use of prime brokers by PTFs is facilitated by the cash 

market trading venues which act as “introducing brokers” to the prime broker, assuming the credit risk of the PTFs

which are active on their platform. 

55

Section 2 Figures 

Figure 2.1: 10-Year Treasury Yield on October 15 

(Cash) 

Figure 2.2: Intraday Price and Volume in 10-Year 

Treasury (Futures)

Figure 2.3: 10-Year Volume and Market Depth 

(Cash) Figure 2.4: Cost of Conducting Trade (Cash)

Figure 2.5: Historical Intraday Yield Ranges for 10-

Year Treasury (Cash)

Figure 2.6: Historical Full Day Yield Changes for 10-

Year Treasury (Cash)

1.85

1.90

1.95

2.00

2.05

2.10

2.15

2.20

8:00 10:00 12:00 14:00 16:00

%

Note: 1-minute observations; Yield is inversely related to price; Unless 

otherwise noted, intraday figures show October 15 US trading hours

Source: Staff calculations, based on data from Bloomberg.

Retail 

Sales 

Event 

Window

127

128

129

130

131

0

100

200

300

400

500

600

700

800

900

1000

8:00 10:00 12:00 14:00 16:00

$ per 100 

par % of avg Volume Price (RHS)

Note: 1-minute intervals; Volume as percent of average of previous 

30 trading days

Source: Staff calculations, based on data from Bloomberg.

Retail 

Sales 

Event 

Window

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0

50

100

150

200

250

300

350

400

450

8:00 10:00 12:00 14:00 16:00

$ millions Depth Volume (RHS) $ millions

Note: 5-minute intervals; Sum of top 3 levels

Source: Staff calculations, based on data from BrokerTec.

Retail 

Sales 

Event 

Window

100

101

102

103

104

105

0

5

10

15

20

25

30

35

40

45

 8:00 10:00 12:00 14:00 16:00

Sell $100 mm

Buy $100 mm

Price (RHS)

256ths %

Note: 1-minute intervals; Spread between best bid/ask and volumeweighted average price to complete trade; $ per $100 par

Source: Staff calculations, based on data from BrokerTec. 

Event 

Window

Retail 

Sales

0

50

100

150

200

250

300

0 5 10 15 20 25 30 35 40 45 50 55

Count

Intraday Range (high minus low, bps)

Note: Daily observations; 10/1998-10/2014

Source: Staff calculations, based on data from Bloomberg.

October 15

(37 bps)

Coordinated 

Rate Cut,

2008 (43 bps) Calendar Guidance/

U.S. Downgrade, 

2011 (40 bps)

LSAP 1, 2009 

(55 bps)

Average (8 bps)

0

50

100

150

200

250

300

350

0 5 10 15 20 25 30 35 40 45 50 55

Count

Absolute Intraday Change (bps)

Note: Daily observations; 10/1998-10/2014

Source: Staff calculations, based on data from Bloomberg.

Average (5 bps)

October 15 

(6 bps)

57
Figure 2.7: Cross-Asset Performance Figure 2.8: Leveraged Funds Net Positioning in 3-

Month Eurodollar Futures

   

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

-2

-1

0

1

2

3

4

5

6

8:00 8:30 9:00 9:30 10:00

# of SDs S&P E-Mini Future

10 Year Treasury Future

Spot Gold

EUR/USD

Note: 5-second observations; Daily standard deviation of returns since 

2006; Front month contract

Source: Staff calculations, based on data from Bloomberg.

Retail 

Sales 

Event 

Window

-2

-1

0

1

2

3

2008 2009 2010 2011 2012 2013 2014 2015

# contracts 

(millions)

Note: Weekly observations

Source: Staff calculations, based on data from CFTC (Current Traders in 

Financial Futures) 

October 15

58
Section 3 Tables and Figures 

Table 3.1: Volume on October 15 by Firm Type 

(Cash) 

Table 3.2: Volume on October 15 by Firm Type 

(Futures)

Firm Type 5 Year 10 Year 30 Year 

Asset Manager 0.0% 0.0% 0.0% 

Bank/Dealer 30.1% 35.7% 40.6% 

Hedge Fund 9.0% 5.6% 6.0% 

PTF 57.1% 54.8% 51.4% 

Non-bank Dealer 3.9% 3.9% 2.0% 

Total Volume 

($ millions) 150,386 123,198 21,915 

Note: Unless otherwise noted, tables are based on entire trading 

session, 19:30 prev. day – 17:30 ET 

Source: Staff calculations, based on data from BrokerTec. 

Firm Type 5 Year 10 Year Long Bond 

Asset Manager 4.0% 3.3% 2.3% 

Bank/Dealer 11.5% 17.6% 10.9% 

Hedge Fund 7.8% 8.3% 8.7% 

PTF 69.3% 59.4% 64.9% 

Non-Bank Dealer 1.4% 2.4% 2.1% 

Other 6.0% 8.9% 11.2% 

Total Volume 

($ millions) 498,546 779,519 229,408 

Note: Unless otherwise noted, tables are based on entire trading session, 18:00 

prev. day – 16:00 ET; Front month contract 

Source: Staff calculations, based on data from CME Group. 

Table 3.3: Volume on Control Days by Firm Type 

(Cash) 

Table 3.4: Volume on Control Days by Firm Type 

(Futures) 

Table 3.5: Message Counts on October 15 by Firm 

Type (Cash) 

Firm Type 5 Year 10 Year 30 Year

Asset Manager 0.1% 0.1% 0.0% 

Bank/Dealer 38.4% 34.7% 40.5% 

Hedge Fund 4.8% 3.9% 9.4% 

PTF 52.6% 56.3% 46.6% 

Non-bank Dealer 4.2% 5.0% 3.5% 

Avg Daily Volume 

($ millions) 53,077 44,088 9,860 

Firm Type 5 Year 10 Year Long Bond 

Asset Manager 5.2% 3.6% 3.5% 

Bank/Dealer 17.0% 15.8% 9.2% 

Hedge Fund 11.6% 15.3% 13.6% 

PTF 52.4% 51.1% 59.1% 

Non-Bank Dealer 2.9% 2.0% 2.4% 

Other 10.9% 12.2% 12.3% 

Avg Daily Volume

($ millions) 132,087 248,373 61,280 

Source: CME Group 

Table 3.6: Message Counts on October 15 by Firm 

Type (Futures) 

Note: Includes all messages 

Source: Staff calculations, based on data from BrokerTec.

Firm Type 5 Year 10 Year 30 Year 

Bank/Dealer 15.1% 23.6% 13.8% 

Hedge Fund 1.9% 1.6% 0.2% 

Non-bank Dealer 0.6% 0.5% 0.2% 

PTF 82.3% 74.3% 85.8% 

Total 

(# of messages) 

2,786,237 2,700,735 1,637,309 

   

Firm Type 5 Year 10 Year Long Bond 

Bank/Dealer 4.8% 10.2% 8.0% 

Hedge Fund 6.1% 2.6% 4.8% 

Non-bank Dealer 0.4% 0.2% 0.3% 

PTF 82.0% 79.1% 80.1% 

FCM 1.9% 2.6% 2.1% 

Other 4.9% 5.3% 4.7% 

Total 

(# of messages) 

2,069,213 2,853,563 1,865,663

Source: Staff calculations, based on data from CME Group. 

Note: Control days: 04/02/2014 - 04/17/2014 

Source: Staff calculations, based on data from BrokerTec. 

Note: Includes all new, modify, and delete messages 

Source: Staff calculations, based on data from CME Group. 

59
Table 3.7: Firm Concentration in 10-Year Treasury 

(Cash) 

Table 3.8: Firm Concentration in 10-Year Treasury 

(Futures)

10/15/2015 Control Days 

Bank/Dealer PTF Bank/Dealer PTF 

# of Participants 44 37 45 40 

Top 10 Volume 

Share 79% 94% 77% 93%

Top 10 HHI 0.11 0.23 0.11 0.23 

Firm Type HHI 0.07 0.20 0.07 0.20 

All Firms HHI 0.07 0.07 

Note: Control days: 04/02/14 – 04/17/14; HHI is Herfindahl-Hirschman Index 

Source: Staff calculations, based on data from BrokerTec. 

Table 3.9: Self-Trading in 10-Year as % of Type 

Volume (Cash) 

10/15/2015 Control Days 

Bank/Dealer PTF Bank/Dealer PTF

# of Participants 21 65 21 65 

Top 10 Volume 

Share 89% 83% 91% 86% 

Top 10 HHI 0.11 0.22 0.11 0.18 

Firm Type HHI 0.09 0.15 0.10 0.14 

All Firms HHI 0.05 0.04 

Note: Control days: 04/02/14 – 04/17/14; Front month contract 

Source: Staff calculations, based on data from CME Group. 

Table 3.10: Self-Trading in 10-Year as % of Type 

Volume (Futures)

   10/15/2015 Control Days 

Bank/Dealer 1.4% 1.3% 

Hedge Fund 1.2% 1.0% 

Non-bank Dealer 0.0% 0.7% 

PTF 6.7% 9.0% 

All Firms 4.2% 5.6% 

Note: Control days: 04/02/2014 - 04/17/2014 

Source: Staff calculations, based on data from BrokerTec. 

   10/15/2015 Control Days 

Asset Manager 0.10% 0.00% 

Bank/Dealer 0.60% 0.40% 

Hedge Fund 0.20% 0.70% 

Non-Bank Dealer 0.30% 0.20% 

PTF 6.40% 4.40% 

All Firms 4.20% 2.70% 

Note: Control days: 04/02/2014 - 04/17/2014; Front month contract 

Source: Staff calculations, based on data from BrokerTec. 

   

60
Figure 3.1: 10-Year Intraday Volume by Firm Type 

(Cash) 

Figure 3.2: 10-Year Intraday Volume by Firm Type 

(Futures)

Figure 3.3: 10-Year Net Position by Type (Cash) Figure 3.4: 10-Year Net Position by Type (Futures)

Figure 3.5: 10-Year Cumulative Net Aggressive 

Volume (Cash)

Figure 3.6: 10-Year Cumulative Net Aggressive 

Volume (Futures)

   

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

8:00 10:00 12:00 14:00 16:00

$ millions Bank/Dealer

PTF

Other

Note: 5-minute intervals; Gross volume

Source: Staff calculations, based on data from BrokerTec. 

Event

Window

Retail 

Sales 

0

5,000

10,000

15,000

20,000

25,000

8:00 10:00 12:00 14:00 16:00

$ millions Bank/Dealer

PTF

Hedge Fund

Asset Manager

Other

Note: 5-minute intervals; Gross volume; Front month contract

Source: Staff calculations, based on data from CME Group.

Retail 

Sales

Event 

Window

100

101

102

103

104

105

-800

-600

-400

-200

0

200

400

600

800

1,000

8:00 8:30 9:00 9:30 10:00

$ per 100 

$ millions par

Bank/Dealer Hedge Fund

PTF Other

Price (RHS)

Note: 1-second observations

Source: Staff calculations, based on data from BrokerTec.

Event 

Window

Retail 

Sales

127

128

129

130

131

-4,000

-3,000

-2,000

-1,000

0

1,000

2,000

3,000

4,000

5,000

8:00 8:30 9:00 9:30 10:00

$ per 100 

$ millions par

Asset Manager Bank/Dealer

Hedge Fund PTF

Other Price (RHS)

Note: 1-second observations; Front month contract

Source: Staff calculations, based on data from CME Group.

Event 

Window

Retail 

Sales

102.0

102.5

103.0

103.5

104.0

104.5

105.0

-600

-400

-200

0

200

400

600

800

1,000

9:30 9:33 9:36 9:39 9:42 9:45

$ per 100 

$ millions par

PTF Bank/Dealer

Other Hedge Fund

Aggregate Price (RHS)

Note: Stacked 1-second observations

Source: Staff calculations, based on data from BrokerTec. 

128.0

128.5

129.0

129.5

130.0

130.5

131.0

-1,500

-1,000

-500

0

500

1,000

1,500

2,000

2,500

3,000

3,500

9:30 9:33 9:36 9:39 9:42 9:45

$ per $100 

$ millions par

PTF Bank/Dealer

Hedge Fund Asset Manager

Other Aggregate

Price (RHS)

Note: Stacked 1-second observations; Front month contract

Source: Staff calculations, based on data from CME Group. 

61
Figure 3.7: 10-Year Cumulative Net Passive Volume 

(Cash) 

Figure 3.8: 10-Year Cumulative Net Passive Volume 

(Futures)

Figure 3.9: 10-Year PTF Cumulative Net Aggressive 

Volume (Cash)

Figure 3.10: 10-Year PTF Cumulative Net Aggressive 

Volume (Futures)

Figure 3.11: 10-Year Bank/Dealer Cumulative Net 

Aggressive Volume (Cash)

Figure 3.12: 10-Year Bank/Dealer Cumulative Net 

Aggressive Volume (Futures)

   

102.0

102.5

103.0

103.5

104.0

104.5

105.0

-1,000

-800

-600

-400

-200

0

200

400

9:30 9:33 9:36 9:39 9:42 9:45

$ per 100 

par $ millions

Other PTF

Hedge Fund Bank/Dealer

Aggregate Price (RHS)

Note: Stacked 1-second observations

Source: Staff calculations, based on data from BrokerTec. 

128.0

128.5

129.0

129.5

130.0

130.5

131.0

-3000

-2500

-2000

-1500

-1000

-500

0

500

1000

9:30 9:33 9:36 9:39 9:42 9:45

$ per $100 $ millions par

PTF Bank/Dealer

Asset Manager Hedge Fund

Other Aggregate

Price (RHS)

Note: Stacked 1-second observations; Front month contract

Source: Staff calculations, based on data from CME Group.

102.0

102.5

103.0

103.5

104.0

104.5

105.0

-600

-400

-200

0

200

400

600

800

9:30 9:33 9:36 9:39 9:42 9:45

$ per 100 

par $ millions Exposure Increasing

Exposure Decreasing

Price (RHS)

Note: 1-second observations

Source: Staff calculations, based on data from BrokerTec.

128.0

128.5

129.0

129.5

130.0

130.5

131.0

-1,000

-500

0

500

1,000

1,500

2,000

9:30 9:33 9:36 9:39 9:42 9:45

$ per 100 

par

$ millions Exposure Increasing

Exposure Decreasing

Price (RHS)

Note: 1-second observations; Front month contract

Source: Staff Calculations, based on data from CME Group .

102.0

102.5

103.0

103.5

104.0

104.5

105.0

-400

-300

-200

-100

0

100

200

9:30 9:33 9:36 9:39 9:42 9:45

$ per 100 

par $ millions

Exposure Increasing

Exposure Decreasing

Price (RHS)

Note: 1-second observations

Source: Staff calculations, based on data from BrokerTec.

128.0

128.5

129.0

129.5

130.0

130.5

131.0

-200

0

200

400

600

800

1,000

1,200

9:30 9:33 9:36 9:39 9:42 9:45

$ per $100 

$ millions par

Exposure Increasing

Exposure Decreasing

Price (RHS)

Note: 1-second observations; Front month contract

Source: Staff calculations, based on data from CME Group.

62
Figure 3.13: 10-Level Market Depth (Cash) Figure 3.14: 10-Level Market Depth (Futures)

Figure 3.15: Depth at Top 3 Levels by Type (Cash) Figure 3.16: Depth at Top 3 Levels by Type (Futures)

Figure 3.17: Depth by Type during Event Window 

(Cash)

Figure 3.18: Depth by Type during First Part of 

Event Window (Cash)

100

101

102

103

104

105

-100

100

300

500

700

900

1,100

1,300

1,500

8:00 10:00 12:00 14:00 16:00

$ per 100 

par $ millions Depth Price (RHS)

Note: 1-second observations; Sum of top 10 levels

Source: Staff calculations, based on data from BrokerTec. 

Retail 

Sales

Event 

Window

127

128

129

130

131

0

1,000

2,000

3,000

4,000

5,000

6,000

8:00 10:00 12:00 14:00 16:00

$ per 100 

par $ millions Depth Price (RHS)

Note: 1-second observations; Sum of top 10 levels; Front month contract

Source: Staff calculations, based on data from CME Group.

Event 

Window

Retail 

Sales

100

101

102

103

104

105

0

20

40

60

80

100

120

140

160

180

8:00 10:00 12:00 14:00 16:00

$ per 100 

par

$ millions PTF

Bank/Dealer

Price (RHS)

Note: 1-minute moving average; Sum of top 3 levels

Source: Staff calculations, based on data from BrokerTec. 

Retail 

Sales 

Event 

Window

126

127

128

129

130

131

-100

100

300

500

700

900

1100

8:00 10:00 12:00 14:00 16:00

$ per 100 

par $ millions

PTF

Bank/Dealer

Price (RHS)

Note: 1-minute moving average; Sum of top 3 levels; Front month contract

Source: Staff calculations, based on data from CME Group. 

Retail 

Sales

Event 

Window

102

103

104

105

-200

-160

-120

-80

-40

0

40

80

120

160

200

9:30 9:33 9:36 9:39 9:42 9:45

$ millions

Other Bank/Dealer

PTF Price

Note: Stacked 1-second observations; Top 3 levels

Source: Staff calculations, based on data from BrokerTec. 

Offer

Bid

$ per 100 

par

102

103

104

105

-200

-160

-120

-80

-40

0

40

80

120

160

200

9:33 9:34 9:35 9:36 9:37 9:38 9:39

$ millions

Other Bank/Dealer

PTF Price

Note: Stacked 1-second observations; Top 3 levels; Front month contract

Source: Staff calculations, based on data from BrokerTec. 

Offer

Bid

$ per 100 

par

63
Figure 3.19: 10-Year Depth by Type during Event 

Window (Futures) 

Figure 3.20: 10-Year Depth by Type during First Part 

of Event Window (Futures)

Figure 3.21: 10-Year Share of Depth by Type during 

Event Window (Cash) 

Figure 3.22: 10-Year Share of Depth by Type during 

Event Window (Futures)

Figure 3.23: 10-Year Bid/Ask Spreads by Type (Cash) Figure 3.24: 10-Year Bid/Ask Spread by Type 

(Futures)

   

128

129

130

131

-4000

-3000

-2000

-1000

0

1000

2000

3000

4000

9:30 9:33 9:36 9:39 9:42 9:45

Other Bank/Dealer

PTF Price (RHS) $ millions

$ per 100 

par

Note: Stacked 1-second observations; Top 3 levels

Source: Staff calculations, based on data from CME Group. 

Offer

Bid

128

129

130

131

-4000

-3000

-2000

-1000

0

1000

2000

3000

4000

9:33 9:34 9:35 9:36 9:37 9:39

Other Bank/Dealer

PTF Price (RHS) $ millions

$ per 100 

par

Note: Stacked 1-second observations; Top 3 levels; Front month contract

Source: Staff calculations, based on data from CME Group. 

Offer

Bid

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

9:30 9:33 9:36 9:39 9:42 9:45

Other Hedge Fund Bank/Dealer PTF

Note: 1-second observations; Top 3 levels

Source: Staff calculations, based on data from BrokerTec. 

Offer

Bid

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

9:30 9:33 9:36 9:39 9:42 9:45

Hedge Fund FCM Other Bank/Dealer PTF

Note: 1-second observations; Top 3 levels; Front month contract

Source: Staff calculations, based on data from CME Group.

Offer

Bid

101

102

103

104

105

-100

-80

-60

-40

-20

0

20

40

60

80

100

8:00 10:00 12:00 14:00 16:00

$ per 100 

par 256ths

PTF

Bank/Dealer

Price (RHS)

Note: 1-second observations; $ per $100 par

Source: Staff calculations, based on data from BrokerTec. 

Retail 

Sales

Event 

Window

126

127

128

129

130

131

-80

-60

-40

-20

0

20

40

60

80

8:00 10:00 12:00 14:00 16:00

$ per 100 

256ths par

Bank/Dealer

PTF

Price (RHS)

Note: 1-second observations; $ per $100 par; Front month contract

Source: Staff calculations, based on data from CME Group. 

Retail 

Sales 

Event

Window

64
Figure 3.25: 10-Year Timeline of Large Aggressive Trades (Futures)

Figure 3.26: Messages Processed by Matching Engine 

by Message Type (Futures) 

Figure 3.27: Messages Processed by Matching Engine 

Outside Visible 10 Levels (Futures)

Figure 3.28: Latency on US Treasury Futures 

Matching Engine (Futures)

Figure 3.29: Latency in US Treasury Futures 

Matching Engine at 9:34:03 (Futures)

127

128

129

130

131

0

500

1,000

1,500

2,000

2,500

3,000

3,500

8:00 8:30 9:00 9:30 10:00

$ millions $ per $100 par Bid Depth Ask Depth Buy Volume Sell Volume (LHS) Price (RHS)

Note: Trades > 2000 contracts; $200 million notional; Top 10 levels; Front month contract

Source: Staff calculations, based on data from CME Group. 

127

128

129

130

131

-6,000

-4,000

-2,000

0

2,000

4,000

6,000

9:00 9:10 9:20 9:30 9:40 9:50 10:00

$ per 100 

par

# of 

messages 

per second

New Cancel Price (RHS)

Note: 1-second intervals; Includes all US Treasury futures contracts 

Source: Staff calculations, based on data from CME Group.

Event 

Window

127

128

129

130

131

-6,000

-4,000

-2,000

0

2,000

4,000

6,000

9:00 9:10 9:20 9:30 9:40 9:50 10:00

$ per 100 

par

# of 

messages 

per second

New Cancel Price (RHS)

Note: 1-second intervals; Includes all US Treasury futures contracts 

Source: Staff calculations, based on data from CME Group. 

Event 

Window

128

129

130

131

0

5

10

15

20

25

30

35

40

9:00 9:10 9:20 9:30 9:40 9:50 10:00

$ per 100 

ms par

Avg Latency Price (RHS)

Note: 1-second intervals; Includes new, modify, and delete messages; 

Includes all US Treasury futures contracts

Source: Staff calculations, based on data from CME Group. 

Large 

Cancellation

(9:34:03)

Event 

Window

0

10

20

30

40

50

60

70

80

90

0

10

20

30

40

50

60

70

0 100 200 300 400 500 600 700 800 900

ms # of Messages (RHS) count

Avg Latency

Note: 1-millisecond intervals; Include new, modify, delete; All US 

Treasury futures contracts 

Source: Staff calculations, based on data from CME Group. 

65
Figure 3.30: 10-Year Total Market Depth (Futures) Figure 3.31: Self Trades as % of Total Volume

Figure 3.32: 10-Year Cumulative Net Aggressive Self 

Trade Volume (Cash)

Figure 3.33: 10-Year Cumulative Net Aggressive Self 

Trade Volume (Futures) 

   

127

128

129

130

131

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

8:00 10:00 12:00 14:00 16:00

$ per 100 

$ millions par Depth Price (RHS)

Note: 1-second observations; Front month contract

Source: Staff calculations, based on data from CME Group. 

Event 

Window

Retail 

Sales

102

103

104

105

0%

5%

10%

15%

20%

25%

9:00 9:10 9:20 9:30 9:40 9:50 10:00

$ per 100 

par

5 Year (Cash) 10 Year (Cash)

10 Year (Futures) Price (Cash, RHS)

Note: 1-minute intervals

Source: Staff calculations, based on data from BrokerTec, CME Group.

Event 

Window

102.0

102.5

103.0

103.5

104.0

104.5

105.0

-20

0

20

40

60

80

100

120

140

160

180

9:30 9:33 9:36 9:39 9:42 9:45

$ per $100 

$ millions par

Self Trade Volume Price (RHS)

Note: 1-second intervals

Source: Staff calculations, based on data from BrokerTec. 

128.0

128.5

129.0

129.5

130.0

130.5

131.0

-40

-20

0

20

40

60

80

100

120

140

160

9:30 9:33 9:36 9:39 9:42 9:45

$ per $100 

$ millions par

Self Trade Volume Price (RHS)

Note: 1-second intervals; Front month contract

Source: Staff calculations, based on data from CME Group. 

66
Section 4 Figures 

Figure 4.1: Financial Assets of Security Brokers and 

Dealers 

Figure 4.2: Net Treasury Positions of Primary 

Dealers

Figure 4.3: Gross Treasury Positions of Primary 

Dealers

Figure 4.4: Estimated Treasury Market-Making 

Positions of Primary Dealers

Figure 4.5: Ownership of Treasury Debt by Investor 

Group Figure 4.6: Government Bond Fund Flows

0

1

2

3

4

5

6

1990 1994 1998 2002 2006 2010 2014

$ trillions

Note: Quarterly observations; Total financial assets of security brokers and 

dealers as reported in the financial accounts of the United States

Source: Staff calculations, based on data from Federal Reserve Board.

-200

-150

-100

-50

0

50

100

150

2002 2004 2006 2008 2010 2012 2014

$ billions

Note: 4-week moving average

Source: Staff calculations, based on data from FRBNY. 

0

1

2

3

4

5

6

7

8

9

0

100

200

300

400

500

600

2002 2004 2006 2008 2010 2012 2014

$ billions %

Position

Position as % of Debt Outstanding,

ex-Fed (RHS)

Note: 4-week moving average; Sum of dealers’ short and long positions 

Source: Staff calculations, based on data from FRBNY.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0

50

100

150

200

250

2002 2004 2006 2008 2010 2012 2014

$ billions %

Position

Position as % of Debt Outstanding, exFed (RHS)

Note: 4-week moving average; Smaller of each dealer’s short and long 

position in each reporting bucket, aggregated across dealers and buckets 

Source: Staff calculations, based on data from FRBNY.

0

10

20

30

40

50

60

2004 2006 2008 2010 2012 2014

%

Mutual funds Pension funds

Households State and local governments

Depository institutions Money market funds

Insurance companies Other financial institutions

Nonfinancial businesses Federal Reserve

Note: Mutual funds includes closed-end funds and exchange-traded funds; 

Pension funds includes government retirement funds

Source: Staff calculations, based on data from Federal Reserve Board.

-10

-8

-6

-4

-2

0

2

4

6

8

10

2004 2006 2008 2010 2012 2014

$ billions

Note: Total net monthly flows; Some funds own agency debt securities and 

MBS in addition to Treasury securities

Source: Staff calculations, based on data from Morningstar.

67
Figure 4.7: Treasury Benchmark Bid-Ask Spreads 

(Cash) Figure 4.8: Treasury Yield Curve Fitting Errors

Figure 4.9: Primary Dealer Treasury Volumes Figure 4.10: Treasury Benchmark Volume (Cash)

Figure 4.11: Treasury Active Contract Volume 

(Futures) Figure 4.12: Benchmark Order Book Depth (Cash)

   

0

1

2

3

4

5

6

7

8

9

2004 2006 2008 2010 2012 2014

256ths 2 Year 5 Year 10 Year

Note: 21-day moving average; $ per $100 par; On-the-run notes

Source: Staff calculations, based on data from BrokerTec.

0

5

10

15

20

25

2004 2006 2008 2010 2012 2014

bps

Note: 21-day moving average; Absolute yield curve fitting errors for 2-10-

year coupon securities from the Nelson-Siegel-Svensson model of 

Gurkaynak, Sack, and Wright 

Source: Staff calculations, based on data from Federal Reserve Board.

0

50

100

150

200

250

300

350

400

450

500

2004 2006 2008 2010 2012 2014

$ billions Other Inter-Dealer

Note: 4-week moving average; Dealers’ average daily trading volume; All 

US Treasury securities

Source: Staff calculations, based on data from FRBNY.

0

10

20

30

40

50

60

70

2004 2006 2008 2010 2012 2014

$ billions 2 Year 5 Year 10 Year

Note: 21-day moving average; Daily trading volume; On-the-run notes

Source: Staff calculations, based on data from BrokerTec.

0

20

40

60

80

100

120

140

2004 2006 2008 2010 2012 2014

$ billions 2 Year 5 Year 10 Year

Note: 21-day moving average; 8:20 - 15:00; Most active contract

Source: Staff calculations, based on data from CME Group.

0

1

2

3

4

5

6

2004 2006 2008 2010 2012 2014

$ billions 2 Year 5 Year 10 Year

Note: 21-day moving average; Top 3 levels; On-the-run notes

Source: Staff calculations, based on data from BrokerTec.

68
Figure 4.13: Benchmark Price Impact (Cash) Figure 4.14: Benchmark Trade Size (Cash)

Figure 4.15: Treasury Active Contract Trade Size 

(Futures)

   

0.0

0.5

1.0

1.5

2.0

2.5

3.0

2004 2006 2008 2010 2012 2014

32nds per 

$100 

million

2 Year 5 Year 10 Year

Note: Slope coefficients from weekly regressions of 5” price changes 

on 5” net order flow for the on-the-run notes

Source: Staff calculations, based on data from BrokerTec.

0

5

10

15

20

25

30

2004 2006 2008 2010 2012 2014

$ millions 2 Year 5 Year 10 Year

Note: 21-day moving average; Average trade size; On-the-run notes

Source: Staff calculations, based on data from BrokerTec. 

0

1

2

3

4

5

6

7

8

9

2004 2006 2008 2010 2012 2014

$ millions 2 Year 5 Year 10 Year

Note: 21-day moving average; 8:20 - 15:00 ET

Source: Staff calculations, based on data from CME Group. 

69
Appendix B Figures 

Figure B.1: Top 10 Bank/Dealers: 10Y Cash vs 10Y 

Futures Figure B.2: Top 10 PTF: 10Y Cash vs 10Y Futures

Figure B.3: Top 10 Bank/Dealers: 10Y Cash vs 

Eurodollar Futures

Figure B.4: Top 10 PTF: 10Y Cash vs Eurodollar 

Futures

Figure B.5: Top 10 Bank/Dealers: 10Y Cash vs EMini Futures Figure B.6: Top 10 PTF: 10Y Cash vs E-Mini Futures

0.00

0.05

0.10

0.15

0.20

0.25

-100 -50 0 50 100

10/15/2014 (Event Window)

10/15/2014 (Whole Day)

Control Days (Mean)

Note: Millisecond observations

Source: Staff calculations, based on data from BrokerTec, CME Group .

Index

Cash Led Futures Led

0.00

0.05

0.10

0.15

0.20

0.25

-100 -50 0 50 100

10/15/2014 (Event Window)

10/15/2014 (Whole Day)

Control Days (Mean)

Note: Millisecond observations

Source: Staff calculations, based on data from BrokerTec, CME Group. 

Index

Cash Led Futures Led

0.00

0.05

0.10

0.15

0.20

0.25

-100 -50 0 50 100

10/15/2014 (Event Window)

10/15/2014 (Whole Day)

Control Days (Mean)

Note: Millisecond observations

Source: Staff calculations, based on data from BrokerTec, CME Group. 

Index

Cash Led Futures Led

0.00

0.05

0.10

0.15

0.20

0.25

-100 -50 0 50 100

10/15/2014 (Event Window)

10/15/2014 (Whole Day)

Control Days (Mean)

Index

Note: Millisecond observations

Source: Staff calculations, based on data from BrokerTec, CME Group. 

Cash Led Futures Led

0.00

0.05

0.10

0.15

0.20

0.25

-100 -50 0 50 100

10/15/2014 (Event Window)

10/15/2014 (Whole Day)

Control Days (Mean) Index

Note: Millisecond observations

Source: Staff calculations, based on data from BrokerTec, CME Group. 

Cash Led Futures Led

0.00

0.05

0.10

0.15

0.20

0.25

-100 -50 0 50 100

10/15/2014 (Event Window)

10/15/2014 (Whole Day)

Control Days (Mean)

Index

Note: Millisecond observations

Source: Staff calculations, based on data from BrokerTec, CME Group. 

Cash Led Futures Led

70

THE D

EPARTMENT OF THE TREASURY

1789

U.S. Securities & Exchange Commission

100 F Street, NE

Washington, D.C. 20549

(202) 551-4120

www.sec.gov

U.S. Commodity Futures Trading Commission

Three Lafayette Centre, 1155 21st Street, NW

Washington, D.C. 20581

(202) 418-5000

www.cftc.gov

Federal Reserve Bank of New York

33 Liberty Street

New York, NY 10045

(212) 720-5000

www.newyorkfed.org

Board of Governors of the Federal Reserve System

Constitution Avenue NW

Washington, DC 20551

(202) 452-3000

www.federalreserve.gov

U.S. Department of the Treasury

1500 Pennsylvania Avenue

Washington, D.C. 20220

(202) 622-2000

www.treasury.gov