Market-making costs in Treasury bills: A benchmark for the cost of liquidity

Journal of Banking & Finance 34 (2010) 2146–2157

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Market-making costs in Treasury bills: A benchmark for the cost of liquidity Mark D. Griffiths a,1, James T. Lindley b, Drew B. Winters c,* a

Department of Finance, Farmer School of Business, Miami University, Oxford, OH, United States Department of Finance, College of Business, University of Southern Mississippi, Hattiesburg, MS, United States c Department of Finance, Rawls College of Business, Texas Tech University, Lubbock, TX, United States b

a r t i c l e

i n f o

Article history: Received 20 April 2009 Accepted 2 February 2010 Available online 6 February 2010 JEL classification: G10

a b s t r a c t We focus on market-making costs by examining the daily bid–ask spreads for off-the-run, one-month Treasury bills around two liquidity-changing events. Event one, Salomon Brothers’ supply shock, results in a roughly 2.5-basis-point increase in the spread because of an increase in ask prices; and event two, the Long-Term Capital Management demand shock, results in a doubling of the spread because of a decrease in bid prices. Our results provide a benchmark for researchers examining bid–ask spreads of securities that include a liquidity premium, a risk premium, and an asymmetric information premium. Ó 2010 Elsevier B.V. All rights reserved.

Keywords: Liquidity Bid–ask spread Market-making costs

1. Introduction A key component of efficient financial markets is the bid–ask spread. Hasbrouck (1999) notes that the bid–ask spread decomposes into market-making (transaction) costs and asymmetric information costs, but that these component costs are difficult to separate empirically. Several authors attempt to decompose equity bid–ask spreads, with the following being a small sample of the mixed results. Stoll (1989) decomposes spreads into adverse information costs of 43%, inventory holding costs of 10%, and order processing costs of 47%. However, Glosten and Harris (1988) and George et al. (1991) report that the percentage of the spread attributable to asymmetric information is small. George et al. (1991) find no evidence of inventory holding costs, while Madhavan and Smidt (1991) find weak inventory effects. Conversely, Huang and Stoll (1997) find a substantial inventory component (average of 28.65%) in spreads, and Bollen and Christie (2009) find that inventory risk is a significant determinant of spreads. Thus, while there is strong theoretical agreement on the components of the bid–ask spread, there is no consensus as to the relative size of each component.

* Corresponding author. Tel.: +1 806 742 3350; fax: +1 806 742 3197. E-mail addresses: mgriffi[email protected] (M.D. Griffiths), [email protected] (J.T. Lindley), [email protected] (D.B. Winters). 1 Currently visiting at Stern School of Business, New York University, NY, United States. 0378-4266/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2010.02.004

One factor common to these studies is that the markets examined are complex, with several factors affecting the bid–ask spreads. In this paper, we investigate Treasury bill (T-bill) spreads because T-bills are one of the least complicated markets. Specially, we examine daily off-the-run one-month T-bill spreads for the period June 1988 to June 2001 (inclusive) and find two market-driven structural breaks that reveal an increase in spread management activity. A detailed examination of the changes in spread management around these two events is the focus of this paper. The first break corresponds to Salomon Brothers’ attempt to corner the two-year Treasury note (T-note) market (1991), which results in dealers systematically increasing the ask price to charge a premium for holding inventory and facilitating trades. The second event (1998) is the collapse of Long-Term Capital Management (LTCM), which results in dealers systematically lowering the bid price to reduce their inventory while continuing to facilitate trades.2 Both events signal changes in the cost of providing liquidity. Our empirical results provide benchmarks for the minimum cost of liquidity in financial markets. Toward this goal of providing a benchmark for market-making costs, we observe that (1) after the Salomon squeeze, the minimum spread in the off-the-run market is 4 basis points (bps) compared to a 2-bp spread before the event; (2) regression results for the Salomon squeeze show roughly a 2.5-bp increase in the spread due to an increase in the ask price by those dealers making inventory 2 Chatrath et al. (2009) find evidence of dealer inventory management in two-year and 10-year Treasury note bid–ask spreads.

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available in an illiquid market; and (3) dealers double the spread (through a four-tick reduction in the bid) to avoid accumulating inventory in an illiquid market. 2. Background We frame our discussion and analysis with an adaptation of the Huang and Stoll (1997) model of the bid–ask spread from equities to T-bills. The adapted model provides a reference for discussing (1) why a sample of off-the-run T-bills is most appropriate for our analysis, (2) the type of structural break we examine and why structural breaks in spreads provide important information, and (3) the testable implications of our analysis. 2.1. The spread model Huang and Stoll’s (1997) model of the bid–ask spread is based on asymmetric information and inventory management cost. They begin with an equation of the intrinsic value of a security:

S V t ¼ V t1 þ a Q t1 þ et ; 2

ð1Þ

where Vt = the intrinsic value of the stock; S = the constant spread; a = the percentage of the half-spread attributable to adverse selection; Q t1 = buy/sell indicator that equals +1 for buyer-initiated trades that occur above the midpoint, 1 for seller-initiated trades that occur below the midpoint, and 0 for trades at the midpoint; and et = the serially uncorrelated public information shock. Stock prices are a function of yesterday’s price plus information generated by the last trade and a public information component. Next, Huang and Stoll define the midpoint of the bid–ask spread because this estimate is observable, while the true value, Vt is not observable in equities:

Mt ¼ V t þ b

S 2

t1 X

Qi

ð2Þ

i¼1

where b is the portion of the spread attributable to inventory control costs. Accordingly, the change in the midpoint is

S DM t ¼ ða þ bÞ Q t1 þ et : 2

ð3Þ

Finally, the transaction price in the Huang and Stoll model is

S Pt ¼ Mt þ Q t þ gt ; 2

ð4Þ

where gt is a rounding error to account for price discreteness. T-bills have considerably different pricing parameters than equities that require adjustments to the Huang and Stoll equitybased model. The fixed terminal payment creates a predictable component in the daily change in market value because price is partially determined by the time remaining to maturity. Thus, if the market rate is unchanged, and the default risk is invariant, the T-bill’s value increases continuously as the time to maturity decreases. The intrinsic value is determined by only three factors: the face value, market interest rates, and the number of days to maturity. Thus, the value equation for T-bills can be written as

V t ¼ ½$1000  ð$1000  r t  ðn=360Þ;

ð5Þ

where Vt is the intrinsic value of the instrument.3 Huang and Stoll’s Vt1 is replaced with a deterministic price that has a positive drift as 3 While T-bills are available in $1000 increments, a round lot transaction in the secondary market for T-bills is $5 million.

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maturity decreases. a 2S Q t1 ¼ 0. a is the percentage of the halfspread attributable to adverse selection. We assume that a = 0 because there is no adverse selection due to asymmetric information about future payoffs for the instrument (we discuss this point in detail in the next section). Since the price of a T-bill is well defined, public information shocks enter the intrinsic price only through a shock to market interest rates (rt). All other new public information influences an investor’s tastes and preferences and appears only in the transaction price. Huang and Stoll model the midpoint of the spread (Mt) because the intrinsic value of equity (Vt) is not observable, and such a reference point is needed for transaction prices. We bypass this construct because the intrinsic value of a T-bill is observable and can be used as the reference point in transaction prices. However, we do need to retain the inventory adjustment process. Accordingly, our transaction price model becomes4

Pt ¼ V t þ

t1 SX Q þ ut þ gt : 2 i¼1 i

ð6Þ

We drop b from the inventory component of the price since a = 0, and we allow the order processing costs to be part of managing T-bill inventory. ut represents public information shocks not related to market interest rates, and gt continues as the rounding error for discrete prices. Public information shocks make holding a T-bill inventory either more or less desirable. When T-bill inventory becomes more desirable, there will be a discrete increase in the ask price, and, similarly, less desirable holdings are reduced through a discrete decrease in the bid price. After any such adjustment, individual dealers adjust their bid and ask prices based on the accumulation of recent transactions. Instead of individual dealer quotes, our data are representative, end-of-the-day quotes best represented with the following transaction price model:

S Pt ¼ V t þ Q t þ ut þ gt : 2

ð7Þ

In this model, 2S Q t represents a bid or ask price based on the normal spread in a well-functioning market that compensates dealers for making the market, and ut represents a discrete adjustment to either the bid or the ask price stemming from the transaction price adjustment for the change in the desirability of T-bill inventory.5,6 Thus, ut represents shocks that influence tastes and preferences, but not intrinsic value. 4 A review of O’Hara (1995) and Hasbrouck (2008) reveals that changing the level of inventory to a new desired level has not been modeled in the microstructure literature. We present here a stylized discussion of change in inventory levels in the T-bill market within the framework of our modified Huang and Stoll model. 5 Ahn et al. (1996) suggest that order processing costs vary little over a few months time. Accordingly, changes in transaction prices from market shocks reflect changes in the transaction value of inventory. 6 Brandt and Kavajecz (2004) contend that permanent price changes are from information, while temporary price movements are from inventory, and conclude that the permanent price changes they find must be from information in the order flow. The idea that temporary price changes relate to inventory was initially developed in the equities markets. The argument is that if a dealer has too much inventory, temporarily dropping the price attracts buyers and moves inventory. Similarly, if a dealer does not have sufficient inventory, then the dealer temporarily raises the price to attract sellers. However, Fleming and Rosenberg (2008) state that because it is redeemed, fixed-income inventory is unique and thus provides a regular disposition that is distinct from other secondary markets. Clearly, T-bills have this disposition feature. Specifically, T-bills mature in a short time and pay a defined, default-free cash flow, so the dealer has the alternative of holding to maturity rather than dropping the price and selling. Additionally, if the dealer needs cash immediately, T-bills can be repoed instead of lowering the price to sell. A T-bill dealer does not have to raise prices since they can borrow T-bills in the repo market. Alternatively, they can buy close substitutes because the Treasury auctions new bills weekly. Thus, due to the unique nature of T-bill inventory, inventory effects lead to permanent price changes in T-bills.

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Table 1 Price changes for 3- and 10-bp shocks. Interest rate shock

Price change per $1000

Price change per $5,000,000

Three-month T-bill (calculated on 91 days) 10 bp $0.25 3 bp $0.08

$1250 $400

One-month T-bill (calculated on 30 days) 10 bp $0.09 3 bp $0.025

$450 $125

2.2. The experimental setting In developing Eq. (7), a = 0 and b = 1, hence the bid–ask spread is solely a liquidity cost for inventory management. Thus, our analysis of T-bill spreads directly addresses the goal of the paper to develop a benchmark for market-making costs in T-bills. In this section, we provide the rationale for setting a = 0, which is equivalent to asserting the absence of asymmetric information in T-bill prices. Easley et al. (1997) maintain that informed traders execute trades based on private signals about changes in value, while uninformed traders execute trades for liquidity reasons and for portfolio rebalancing purposes (i.e., changes in tastes and preferences). Evans and Lyons (2002) find that order flow provides valuable information about numerator effects (future cash flows) and denominator effects (discount rates). Because a T-bill has only one future cash flow, which is known in amount and timing with certainty, any private signals must relate to the discount rate.7 There is no doubt that market interest rates (and thus the discount rate on T-bills) are uncertain and that investors can have different beliefs about future changes in interest rates. However, it is difficult to envision how a trader can gain private information about the true value of the discount rate on T-bills. Since it is an empirical question whether economic gains are possible and economically attractive within the operating parameters of the T-bill market, we calculate the potential payoff available to a dealer who has unique information about future T-bill prices. We start with the assumption of a 5% market yield (the average ask yield for our sample is 4.85%) and examine reasonable shocks relative to the $1000 face value of a T-bill and the market standard round lot of $5,000,000. Using daily changes in the ask yield on three-month, on-the-run T-bills across our sample period we determine that: 71% of all daily changes are 3 bps or less, 89% of all daily changes are 6 bps less and 97% of all changes are 10 bps or less. Based on these facts, we present price changes for 3- and 10-bp shocks in Table 1. With more than 70% of daily shocks at 3 bps or less, asymmetric information about ‘‘normal” shocks would be worth no more than $400 per $5 million invested in three-month T-bills. Private information about a very large shock would be worth $1250 per $5 million invested in three-month T-bills (a .008% rate of return). Thus, the size of these gains does not appear to be economically sufficient to warrant the investment of any resources necessary to capture them. In other words, it is possible that a > 0, but our example suggests that, at most, a is near 0 and thus assuming a = 0 is not unreasonable. If private information does exist as to the size, timing, and direction of interest rate shocks and the above gains are attractive, we would expect traders to attempt to profit from the private information when the most uncertainty about the true value of 7 Fleming and Rosenberg (2008, p. 7) note that ‘‘(g)iven that these is no asymmetric information about Treasury cash flows, the ability of market participants to forecast future price changes is probably limited.” They suggest that any private information must be related to the discount rate.

the security exists. The greatest uncertainty surrounding the true value of a security is at the time of issue. Following the issue, trading occurs and the price moves until an equilibrium price is reached. During this trading, private information about the true value would be highly valuable and relatively easy to exploit. Accordingly, dealer spreads would be wider to minimize losses to informed traders. The US Treasury issues new T-bills every week, and, if attractive private information exists, one would expect to see wider spreads for on-the-run T-bills.8 During our sample period, the average bid–ask spread for on-the-run three-month bills is 2 bps, the minimum spread on a round-trip in T-bills. Accordingly, dealers do not appear to price any asymmetric information risk at a time when the true value may be the most uncertain.9 Seeking confirmation of our contention that there is no asymmetric information in the valuation of T-bills, we interviewed a senior fixed-income officer at a major Wall Street trading house during our sample period. Specifically, we asked if he knew of any participant in the T-bill market who actively looked for asymmetric information and if he knew of any situations in which traders attempted to profit from asymmetric information in T-bills. The trading manager’s response is paraphrased below. First, it is possible that someone has a better optimizer for predicting economic statistics, but, if this happens, they will not tell anyone about it as long as they can use it to generate abnormal returns.10 Second, in all my years on the Street, I have never heard even a hint of this happening, nor have I seen any evidence of front-running behavior consistent with private information about economic value. Third, the information that would be valuable is information about the intended actions of the Federal Reserve and the US Treasury, but both entities are very careful to release this information to all market participants at the same time.11 Finally, while I saw no evidence of private asymmetric information about the economic value of a bill, it is an advantage to know when a potential counterparty needs to buy or sell a particular bill. Thus, the fixed-income trading manager supports our contention that private information about intrinsic values in T-bills is unlikely, while the need to trade does impact transaction prices. The benefits of knowing when an individual wants to trade a particular bill brings up the issue of imbedded information in the order flow, an issue requiring some explanation in the context of T-bills. First, when a dealer knows a client wants to buy or sell a particular bill, the dealer has an information advantage. However, the advantage is not knowledge about the intrinsic value of the bill. Instead, it is the knowledge about the client’s liquidity requirements that has potential value. This situation is thus similar to the upstairs market in equities. Second, while dealers do observe the order flow of their T-bill clients, off-the-run T-bills trade much more infrequently than other securities, making any information available from observing order flow much more difficult to extract. Third, although dealers trade in an interdealer broker market and 8 Treasury bills trade in the ‘‘when-issued” market between primary dealers prior to trading in the on-the-run secondary market. Unfortunately, spread data for this market are limited to best inside quote data and are not conducive to analysis. Nonetheless, some price discovery is certainly advanced at this time. 9 Fleming and Rosenberg (2008) note that holding inventory creates pure inventory risk for dealers, but that adverse selection risk from trading with parties with private information is small. 10 Mizrach and Neely (2008) examine Treasury note cash and futures markets and find that the information effects of macroeconomic announcements are subsumed by liquidity effects. 11 We are aware of one situation of ‘‘insider trading” in Treasury instruments in which a Goldman Sachs trader called clients with information that the Treasury was about to stop selling 30-year T-bonds 30 minutes before the public announcement of the change. The case was settled in 2003, with Goldman agreeing to pay a $9.3 million settlement to the Securities and Exchange Commission.

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observing order flow from a particular dealer might be valuable, all orders in the interdealer broker market are strictly anonymous. In addition, off-the-run T-bills typically have less than 10 trades in a day, with about one-third of all trading days having zero trades in the interdealer broker market. Consequently, the trades seldom follow a pattern and are not clustered timewise. Thus, T-bill trading provides little or no information to observers and makes it highly unlikely that participants can acquire economically valuable asymmetric information from observing order flow. Trading in Treasury securities within a given maturity occurs in both the on-the-run market and the off-the-run market. On-therun instruments are the most recently issued contract, while offthe-run instruments are all previously issued contracts. When Tbills go off-the-run, there is a reduction in uncertainty. The market price of the new on-the-run T-bill is determined in an auction dominated by a few dealers. Therefore, the market must match newly issued T-bills with the secondary market demand before an equilibrium price is determined. The off-the-run T-bills have been through this pricing process, so their market value is already established. Barclay et al. (2006) note that trading volume in the interdealer broker market declines by 75% when T-bills go off-the-run and trading volume in the interdealer broker market declines by 90% when T-notes go off-the-run. The transition from the on-the-run market to the off-the-run market occurs literally overnight.12 Along with a dramatic decline in volume, the market for off-therun Treasury securities changes from an electronic market (80– 90% of the on-the-run market) to a telephone market (about 90% of the off-the-run market). Barclay et al. (2006) contend that the off-the-run market is telephonic because trading is more difficult due to a thin market with small number of participants. Accordingly, the off-the-run T-bill market provides an ideal experimental setting for analyzing the cost of demanding liquidity. In particular, we examine off-the-run T-bills with 1 month to maturity because there is strong evidence of periodic liquidity squeezes in onemonth money market securities, but not in longer-term money market instruments (see Griffiths and Winters, 2005). 2.3. Structural breaks and market events To determine major changes in inventory management, we examine the data for structural breaks. We note that Jordan and Kuipers (2005) report a general lack of information in T-bill spreads because they seldom change, so any structural break in a time series of T-bills spreads would suggest a significant change in dealer behavior. There are two types of structural breaks observable in bid–ask spreads: (1) a shift in the amount dealers charge for market making, and (2) a shift in how dealers are managing their inventories. Referring back to Eq. (7), the constant spread (S) compensates dealers for making the market. Any change in a dealer’s desired level of inventory leads to a change in his or her bid–ask quotes and is captured by u in our model. Accordingly, we begin our analysis by searching for structural breaks in the bid–ask series, since the changes in the average spread are the result of dealers changing how they are managing their inventory. Fig. 1 is a plot of the daily spreads for off-the-run one-month Tbills for the period 6/13/88 through 6/30/01 inclusive and contains over 3200 daily spread observations. We visually inspect this plot as a first step in identifying structural breaks in the spread data and 12 The average volume of any single off-the-run T-note is less than 1% of an on-therun note. However, in a given maturity (for example, two-year notes), there is always only one on-the-run security and multiple off-the-run securities, so the total market volume of the off-the-run notes ‘‘only” declines by 90% when compared to the on-therun security.

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observe six instances: (1) March 1989, (2) August 1989, (3) February 1990, (4) June 1991, (5) November 1996, and (6) October 1998. We employ a Chow test to determine if the visually identified breaks are statistically significant changes in the average spread. Following Jordan and Jordan (1996), we test whether bid–ask spreads in the 6 weeks before the identified break are significantly different from those in the 6 weeks following the break. In the interest of brevity, we discuss the results but do not present the model or the results in tabular form (available upon request). The first break point (March 1989) is negative and significant at the 1% level, with a parameter estimate showing an average decrease of 2.1 bps in the spread. The second point (August 1989) is not significant at the 10% level. The third point (February 1990) is negative and significant at the 1% level, with a parameter estimate showing an average decrease of 2.2 bps in the spread. The fourth point (June 1991) is positive and significant at the 5% level, with the parameter estimate indicating that average spreads increased 1.8 bps following the break. The fifth point (October 1996) is negative and significant at better than the 1% level. The parameter estimate indicates that the average spreads decreased 3.2 bps. The sixth point (September 1998) is positive and significant at better than the 1% level, with the parameter estimate revealing that the average spreads increased by 2.0 bps. Next, we determine if there was a major market event associated with the five mean shifts. We discuss the shift dates with a senior officer in a major brokerage house who traded US government securities throughout our sample period. The officer quickly identifies three of the break point dates as coinciding with (1) Salomon Brothers’ attempts to corner the two-year T-note market at the May 1991 auction (June 1991), (2) the Asian financial crisis (October 1996), and (3) the collapse of LTCM (September 1998). We then perform an in-depth review of financial events around the two remaining dates: March 1989 and February 1990. Inspection of the March 1989 data reveals the decrease in spreads occurs on 3/20. The major events in March 1989 are (1) the merger of Time Inc. and Warner Communications (3/4), (2) the bankruptcy of Eastern Airlines (3/9), and (3) the Exxon Valdez oil spill (3/24). February 1990 is similarly innocuous. Inspection of the data reveals the decrease occurs on 2/20, with three major events during the month as (1) the announcement of German reunification (2/ 13), (2) the bankruptcy of Drexel, Burnham, Lambert on (2/13), and (3) the indictment of Exxon related to the Exxon Valdez oil spill (2/27). Thus, we conclude that these two structural breaks represent changes in the cost of making a market without any news to change how dealers manage T-bill inventory. A brief summary of the three identified events is presented below. In the May 22, 1991 two-year note auction, the US Treasury sold $12.29 billion of two-year notes. In subsequent testimony before Congress, Salomon Brothers’ executives reported controlling 94% of the notes sold through competitive bids and 86% of all the notes sold in the auction. This far exceeds the 35% limit for a single bidder in a Treasury auction,13 and Salomon was sanctioned for this act.14 Jordan and Jordan (1996) examine the price impact of Salomon’s actions and conservatively estimate that the notes were overpriced by 16 bps for approximately 6 weeks following the auction. The Salomon event restricted the availability of Treasury

13 See US Department of the Treasury, Securities and Exchange Commission, and Board of Governors of the Federal Reserve System, 1992, Joint Report on the Government Securities Market, Washington, DC, US Government Printing Office. 14 John Gutfreund, the Salomon chairman, and four other senior executives were released by Citibank immediately. Subsequently, Salomon agreed to pay $290 million to settle civil charges that it had broken the rules at nine auctions. As part of the accord, Salomon settled civil antitrust charges involving its dealings with other parties.

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Fig. 1. Daily bid–ask spread for off-the-run one-month T-bills for the period 6/13/88–6/30/01.

securities and presaged an extended period of increased failures to deliver on transactions in Treasury securities. The Asian financial crisis began at the end of 1996, which coincides with the second break in our data. However, there is also a change in our data source around this same date, which could affect the integrity of any analysis of the crisis. Prior to the break, the Federal Reserve Bank of New York (Fed) collected the data on T-bill yields, which were then provided to the Wall Street Journal. Around the break point, the Fed discontinued collecting these data, and the Wall Street Journal switched to Cantor Fitzgerald as its source for T-bill yields.15 We are not able to determine if the break in the spread data is a response to the Asian financial crisis or the result of the change in the data source. Thus, we remove this break point from our analysis. In the late summer and early fall of 1998, the financial news was reporting that LTCM was seriously undercapitalized. LTCM held $125 billion in assets on a capital base of only $4 billion, and the Federal Reserve was concerned about adverse market consequences should the hedge fund be forced to liquidate its assets to meet current obligations. To prevent market liquidity problems, the Federal Reserve Bank of New York worked with major creditors and LTCM counterparties, resulting in private sector parties providing $3.5 billion of additional capital in exchange for 90% of the equity of the hedge fund. The recapitalization of LTCM occurred on September 23, 1998.16 The collapse of LTCM occurred during an extended period of global financial concerns during which the providers of liquidity were reluctant to take long positions in financial securities inventory. The result of our sample selection process is that two of the five statistically significant means changes in Fig. 1 were also associated

15 The Federal Reserve data ‘‘best reflect the day’s activities” and are representative of the results provided by a survey of various dealers. Cantor Fitzgerald data are the weighted average of the activity processed by this interdealer broker and are representative of near-end-of-day prices. 16 While the problems and recapitalization of LTCM were prominent in the financial news, LTCM was not an isolated problem. Fleming (2000) and the Committee on the Global Financial System (1999) argue that it was part of a global financial crisis that lead to a flight to quality and to liquidity. The report from the Committee on the Global Financial System titled ‘‘A Review ofFinancial Market Events inAutumn 1998” is available from the Bank for International Settlement (October 1999).

with market events that are amenable to economic analysis of inventory management. These two breaks coincide with the Salomon squeeze in the two-year T-note market and the collapse of LTCM. The remaining empirical analysis is a detailed examination of daily spread changes before and after the two events to determine if dealers systematically altered their inventory management after each event through altering bid or ask prices. 2.4. Prices changes from exogenous supply and demand shocks In this section, we employ our bid–ask spread model to discuss the impact of the two events that we retain for further analysis. If either shock creates a change in the market yield on T-bills, we would expect the intrinsic value of T-bills to change, but dealers would continue to process orders in a normal manner. That is, Tbill prices would change but the size of the spread would remain constant, reflecting that the cost of liquidity and tastes and preferences for holding T-bills remain unchanged. The Salomon squeeze and the collapse of LTCM are potentially shocks to the tastes and preferences of T-bill holders and prospective T-bill holders. Such shocks to the tastes and preferences enter our model through the variable ut. Salomon Brothers’ attempt to influence the May 1991 two-year note auction restricted the supply of two-year T-notes available in the market. If the impact of their activities remain isolated in the two-year T-note market, we would expect no change in the behavior of T-bill dealers. However, the actions by Salomon Brothers to control the supply of one Treasury security may have created concerns among other dealers about the available supply of other Treasury securities.17 Concerns about the availability of Treasury securities would make having T-bill inventory more valuable. Thus, we expect a positive ut that increases the ask price. The collapse of LTCM saw a flight to both quality and liquidity. The flight to quality is evident in Fig. 2, as the spread between risk classes of commercial paper increased while the yield on T-bills fell following the 17 Specifically, the two-year note that Salomon squeezed went ‘‘fails” in the repo market. This meant that dealers wishing to sell short Treasuries (either directly or through repos) could not be sure the other Treasury securities were not free of squeezes, and they reacted by increasing prices.

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6.5

Rates (in percent)

6 5.5 5 4.5 4 3.5

Fig. 2. Money market interest rates for 30-day AA commercial paper, A2P2 commercial paper, and three-month Treasury bills surrounding the September 23, 1998 collapse of LTCM from 6/1/98–5/28/99.

collapse. However, a flight to quality and a flight to liquidity can have offsetting effects for dealers in the T-bill market. Treasury securities are default-free, so a flight to quality would be a flight toward T-bills. This, in turn, leads to an increase in investor demand, making T-bill dealers’ inventory more valuable, and we would see a positive ut that increases the ask price. However, Scholes (2000) notes that liquidity providers were unwilling to take long-horizon positions (hold inventory) during this period. The decline in demand for inventory would result in a negative ut and a decreasing bid price. Given that we observe an increase in the bid–ask spread following the collapse of LTCM, the question remains as to which side of the spread changes, the bid or the ask. Thus, it is an empirical question as to which effect dominates. 3. Primary data set and basic descriptive statistics The off-the-run one-month T-bill spread data (Fig. 1) covers the period from 6/13/88 through 6/30/01 and is the primary data set for our analysis. During this sample period, new Treasury bills with four-week terms to maturity were not issued in the regular auction process. Consequently, all of the data are for T-bills originally issued with at least 13 weeks to maturity.18 In addition, since T-bills are issued only one day per week, a true one-month bill is available only on one day in each week. This has important ramifications for our testing methods, as discussed in Section 4.2. Accordingly, our primary data set is for the T-bill closest to one-month (30 days) from maturity.19 The daily data for off-the-run T-bills (from 8/1/89 through 10/ 15/96) are collected from Treasury security quote sheets compiled by the Federal Reserve Bank of New York.20 The Fed quote sheet data are collected from market participants (Treasury securities dealers) that the Fed believes to be reliable. The Fed produced its daily quote sheet prior to 8/1/89, but we do not have access to those sheets. Instead, from 6/13/88 through 7/31/89, we collect 18 We note that during our sample period, the Treasury issued cash management bills as needed and some cash management bills might have been issued with one month to maturity, but if this happened it would be unusual and irregular. 19 This series will vary in maturity from 31 days down to 27 days. Clearly, 32 days to maturity is closer to 30 than 27 days. However, 33 and 32 days to maturity occur on weekends, and thus market data are not available. 20 Duffee (1996) notes that month-end CRSP Treasury data for the period from February 1959 through December 1994 come primarily from the same source we use, namely, the Fed quote sheets.

Table 2 Descriptive statistics on average daily bid yields, ask yields, and bid–ask spreads for off-the-run one-month T-bills for the period 6/13/88–6/30/01. Mean (%) Panel A: Summary statistics Bid yields 4.92 Ask yields 4.85 Bid–ask 0.07 spread Bid–ask spread (bps)

Median (%)

Mode (%)

Inter-quartile range (%)

4.88 4.82 0.08

4.86 4.82 0.04

4.01–5.47 3.93–5.39 0.04–0.10

Count

Percentage

Panel B: Spread frequencies for off-the-run one-month T-bills for the period 6/13/ 88–6/30/01 2 110 3.35 3 3 0.10 4 1212 38.90 5 4 0.13 6 70 2.25 7 135 4.33 8 431 13.83 9 1 0.03 10 1046 33.57 12 83 2.66 Greater than 12 20 0.85 Data Sources: 06/13/88–07/31/ 89 Wall Street Journal. 08/01/89–10/15/96 Federal Reserve Bank of New York Daily Quote Sheets. 10/16/96–06/30/01 Cantor Fitzgerald.

data from the Wall Street Journal. The Wall Street Journal reporting format is identical to the Fed quote sheets for the period from 6/13/ 88 through 7/31/89. Beginning on 8/1/89, we have access to both the Fed daily quote sheet and the Wall Street Journal, and random comparisons between the two sources find them to be identical on each day examined. Data for the period from 10/16/96 through 6/ 30/01 are also collected from the Wall Street Journal. For the period from 10/16/96 through 6/30/01, the Federal Reserve did not produce its daily quote sheet, so the Wall Street Journal had to find an alternate source for its Treasury securities data; the data reported during this period are provided by Cantor Fitzgerald.21 In addition to the off-the-run one-month T-bill data, we collect on-the-run three-month T-bill data from the Fed quote sheets. Starting on 7/31/01 (8/2/01), the US Treasury began auctioning (issuing) T-bills with an initial maturity of 4 weeks. We collect data from the Wall Street Journal from 8/2/01 through 6/28/02 for the on-the-run one-month (4 weeks) T-bill. For all bills, we collect daily end-of-the-day values of the annualized bid and ask yields. Table 2, Panel A, provides summary statistics on (1) bid yields, (2) ask yields, and (3) the bid–ask spread for off-the-run T-bills for the entire sample. The mean bid yield is 4.92%, while the mean ask yield is 4.85%. Thus, the mean bid–ask spread is 7 bps. An average spread of 7 bps is 5 bps larger than the minimum spread for a round-trip in the T-bill market and is also 5 bps larger than the average spread for the on-the-run three-month T-bill spread for the period of 8/1/89 through 10/15/96. After removing the first 21 trading days following the initial issuance of the new onemonth T-bill, the following 193 trading days have spreads of 1 bp 93% of the time (179 out of 193 observations). A spread of 1 bp implies that dealers are not anticipating round-trip trading in new

21 When the Fed stopped providing its daily quote sheet it made the following statement: ‘‘The Federal Reserve Bank of New York has ended publication of the daily treasury securities price quotes. The final issue was posted to the ftp site on October 15, 1996. This reflects the Fed’s decision to discontinue daily collection and compilation of Treasury prices from its trading counterparties. No other Federal Reserve statistical releases will be affected. There are various sources that provide similar information on the Internet.

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Table 3 Average daily spread frequencies for off-the-run one-month T-bills for 6 months before and after the May 22, 1991 Salomon Brothers’ attempt to corner the two-year T-note market. Spread (bps)

Before May 22, 1991

After May 22, 1991

2 4 6 8 10 12

72.95% 22.13 2.46 0.82 0.82 0.82

0.00% 15.57 0.00 0.00 84.43 0.00

one-month T-bills; thus, they are holding to maturity.22 The substantial difference in spreads between the on-the-run and the off-the-run T-bills provides prima facie evidence that liquidity is a concern for dealers in the off-the-run market. These results reinforce Fleming’s (2002) findings that dealers increase the spread for off-the-run T-bills. The difference in spreads between on-the-run and off-the-run T-bills measures the additional compensation that dealers receive for providing liquidity. Brandt and Kavajecz (2004, Table 1) show that net order flow for on-the-run T-bills with maturities of 6 months or less is about double the net order flow of similar T-bills that are just off-the-run. Table 2, Panel B, provides the frequencies across our sample for the various bid–ask spreads for the off-the-run T-bill. The most frequently observed spread (38.9% of the time) is 4 bps. A close second (33.57% of the time) is a spread of 10 bps, while the third (13.83% of the time) is 8 bps. Each of the remaining spreads appears less than 5% of the time. The concentration of spreads on even numbers suggests dealer spread management. The spread frequencies reported in Table 2 show spread clustering consistent with spread management, and there were significant breaks in the average spreads around the two identified market events. The remainder of the paper focuses on an analysis of the periods around Salomon Brothers’ squeeze and the collapse of LTCM. We isolate each event by examining spreads over 6 months on either side of the events. Using spreads minimizes the effect of any trend in the level of interest rates, and 6 months of data provide a sufficient number of observations for our tests while limiting the distance from each event.

regression format to determine how the dealers manage the spread. Finally, we provide some information on dealer T-bill positions and some robustness checks. 4.1. Spread analysis Table 3 provides frequencies for the 6 months before and the 6 months after the Salomon squeeze. Before the squeeze, 72.95% of the spreads are 2 bps with 22.13% at 4 bps. A spread of 2 bps is the minimum spread on a round-trip in this market and is consistent with dealers simply processing orders and moving T-bills in a highly liquid market. After the squeeze, 84.43% of the spreads are 10 bps with 15.57% at 4 bps. The differences in spread frequencies are consistent with dealers increasing their spreads because of the concerns about available supplies. We note that the increase in the minimum spread from 2 bps to 4 bps is indicative of an increase in the regular cost of market making (S). The additional jump to a majority of the spreads at 10 bps is indicative of a change in spread management (ut). 4.2. Regression analysis To explore further the change in dealer behavior, we estimate regressions for daily spread changes (Eq. (8)) with appropriate day-of-the-week controls per Flannery and Protopapadakis (1988):

Spreadt  Spreadt1 ¼ bMon D1 þ bTu D2 þ bWed D3 þ bTh D4 þ bFri D5 þ bTB ðTBt  TBt1 Þ þ et ;

ð8Þ

where Di = 0/1 dummy variable that where D1 = 1 on Monday, D2 = 1 on Tuesday, D3 = 1 on Wednesday, D4 = 1 on Thursday and D5 = 1 on Friday, TBt = annualized yield for the on-the-run three-month T-bill on day t, which is included to control for changes in the general level of short-term interest rates.23

Jordan and Jordan (1996) calculate that the T-notes involved in Salomon’s attempt to corner the market in May 1991 were overpriced by about 16 bps for approximately 6 weeks. Although Salomon’s actions directly affected only the auction of two-year Tnotes, our initial analysis indicates that their actions had fartherreaching implications and generated concerns about the availability of Treasury securities. In this section, we provide a more detailed analysis of the bid–ask spread around the Salomon squeeze, demonstrating that Salomon’s actions had a contagion effect on other Treasury securities. We divide our analysis into three parts, beginning with a detailed frequency analysis of spreads to determine when the dealers manage the spread. Next, we analyze changes in spreads, changes in bids, and changes in asks in a

All regressions are estimated using ordinary least squares (OLS) with White (1980) adjustment for heteroscedasticity and are estimated for the six-month period before and after the squeeze. In standard event study analysis, it is typical to measure the difference in the two regimes using a dummy variable to capture the mean size in the regime shift. Because of the limited maturity of Treasury bills, it is important to be able to examine the differences, before and after the event, of the time series changing from a 27day instrument on Fridays to a 31-day instrument on Mondays. Accordingly, we run two separate regressions. The regression results from before the squeeze are presented in Panel A of Table 4. The frequency analysis shows that dealers do not actively manage the spread before the squeeze, and we do not expect to find any significant spread changes. Confirming our expectations, the model has no explanatory power and no significant parameter estimates confirming that dealers are not managing the spread based on days to maturity. Following the Salomon squeeze, dealers increase and actively manage the size of the spread. Panel B of Table 4 contains the results for the 6-month period following the squeeze. The model pro-

22 On 8/2/01, the Treasury issued for the first time in a regular auction T-bills with original maturity of one-month. We collect the time series of on-the-run spreads for the first 214 trading days of this new maturity T-bill. Our analysis of daily spreads suggests that the first 21 trading days following the introduction of this new instrument comprise a start-up period where the bid–ask spread is 8 bps for each of the 21 days. Following this start-up period, there are only 13 days over the next 193 days where the spread is not 1 bp. On those 13 days, the spreads are 3 days at 2 bps, 2 days at 3 bps, 5 days at 5 bps, and 1 day each at 6 bps, 10 bps, and 15 bps.

23 We collected data on the bid and ask yields for on-the-run three-month T-bills for only a portion of the entire sample period covered by our off-the-run data. For our regression analysis, we collected daily secondary market yields for three-month Tbills for the entire sample period from the FRED database at the Federal Reserve Bank of St. Louis. We chose three-month T-bills as a proxy for the general level of shortterm interest rates because Griffiths and Winters (2005) find no evidence of regularities in this instrument. We recognize that by using a three-month yield to proxy for the general level of one-month rates, we are mismatching maturities.

4. Salomon Brothers’ May 22, 1991 attempt to corner the Treasury market

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M.D. Griffiths et al. / Journal of Banking & Finance 34 (2010) 2146–2157 Table 4 Average daily bid–ask spread results for off-the-run one-month T-bills for the 6 months before and after Salomon Brothers’ May 22, 1991 attempt to corner the two-year T-note market. Estimate

p-Value

Panel A: Before May 22, 1991 Monday Tuesday Wednesday Thursday Friday 3-month TB F-stat Adj R-sq.

0.0002 0.0032 0.0036 0.0040 0.0012 0.0182 0.74 0.0133

0.9620 0.3326 0.2635 0.2244 0.5695 0.5186 0.6220

Panel B. After May 22, 1991 Monday Tuesday Wednesday Thursday Friday 3-month TB F-stat Adj R-sq.

0.0244 0.0070 0.0068 0.0048 0.0023 0.0208 5.13 0.1690

<0.0001 0.1343 0.1378 0.3184 0.6206 0.7016 0.0001

The equation being estimated is Spreadt  Spreadt1 ¼ bMon D1 þ bTu D2 þ bWed D3 þ bTh D4 þ bFri D5 þ bTB ðTBt  TBt1 Þ þ et , where: Di = 0/1 dummy variable that where D1 = 1 on Monday, D2 = 1 on Tuesday, D3 = 1 on Wednesday, D4 = 1 on Thursday and D5 = 1 on Friday, TBt = annualized yield for the on-the-run three-month T-bill on day t, which is included to control for changes in the general level of short-term interest rates. All regressions are estimated using OLS with White’s (1980) adjustment for heteroscedasticity and estimated for the six-month period before and after the squeeze.

vides some explanatory power on daily spread changes. We find a significant and negative parameter estimate on Mondays (at better than the 1% level), reflecting a spread decrease of about 2.5 bps24 from the bid–ask spread on the previous Friday. The data series is constructed such that a typical week has the 31-day bill on Monday, the 30-day bill on Tuesday, and so on, with the 27-day bill on Friday. The negative parameter estimate is consistent with a smaller spread on the 31-day bill than the 27-day bill, and it is consistent with dealers increasing the spread at 30 days to maturity. That is, dealers increased the cost for making T-bills with 30 days or less to maturity available. The insignificant parameter estimates for the remainder of the week reveal a positive drift in the spread as dealers increase spreads across the remainder of the week. In the preMay 22, 1991 period, dealers made no such distinction between the 27-day and the 31-day Treasury bills, which reflects a lack of market concern over their availability. In an attempt to determine how the dealers change the spread, we reestimate the regression model (Eq. (8)), with the change in the bid and ask as the dependent variable. Since the Salomon squeeze is a supply restriction, we expect dealers managing their spread to increase their ask price (decrease ask yield) to earn compensation for providing T-bills. Panel A of Table 5 presents the regression results on the bid and ask for the 6-month period before the squeeze. During this period, dealers conduct business in the traditional manner. Dealers are not managing the spread, and no day-of-the-week parameter estimates are significant at any standard level. Panel B of Table 5 provides the regression results on the bid and ask spread for the 6month period after the squeeze. The only significant day-of-theweek parameter estimate is the Monday parameter estimate on the ask. The increase in the ask yield, with no change in the bid yield, reveals that dealers are increasing the price for making 24 The yield data is recorded as percentage yields so, for example, the parameter estimate for the Monday spread change of 0.0244 represents 2.44 bps.

Table 5 Average daily bid and ask results for the 6 months before and after Salomon Brothers’ May 22, 1991 attempt to corner the two-year T-note market. Bid

Ask

Estimate

p-Value

Estimate

p-Value

Panel A: Before May 22, 1991 Monday 0.0369 Tuesday 0.0102 Wednesday 0.0499 Thursday 0.0017 Friday 0.0093 3-month TB 0.4949 F-stat 2.19 Adj R-sq. 0.0559

0.1607 0.6924 0.0535 0.9466 0.7134 0.0277 0.0484

0.0371 0.0070 0.0463 0.0057 0.0074 0.5131 2.02 0.0480

0.1730 0.7920 0.0829 0.8304 0.7752 0.0273 0.0691

Panel B: After May 22, 1991 Monday 0.0354 Tuesday 0.0159 Wednesday 0.0124 Thursday 0.0263 Friday 0.0328 3-month TB 1.3585 F-stat 3.87 Adj R-sq. 0.1238

0.2510 0.5806 0.6591 0.3739 0.2552 <0.0001 0.0015

0.0597 0.0230 0.0193 0.0311 0.0305 1.3792 4.67 0.1528

0.0547 0.4276 0.4963 0.2951 0.2917 <0.0001 0.0003

The equation being estimated is Yieldt  Yieldt1 ¼ bMon D1 þ bTu D2 þ bWed D3 þ bTh D4 þ bFri D5 þ bTB ðTBt  TBt1 Þ þ et , where: Yieldt = the ask yield or bid yield (as appropriate) on day t. Di = 0/1 dummy variable that where D1 = 1 on Monday, D2 = 1 on Tuesday, D3 = 1 on Wednesday, D4 = 1 on Thursday and D5 = 1 on Friday, TBt = annualized yield for the on-the-run three-month T-bill on day t, which is included to control for changes in the general level of short-term interest rates. All regressions are estimated using OLS with White’s (1980) adjustment for heteroscedasticity and estimated for the six-month period before and after the squeeze.

inventory available at 30 days to maturity. In other words, the lower ask yields starting on Tuesday (30-day maturity) are consistent with earning a higher compensation for providing T-bills. The difference in the Monday parameter estimates of the bid and ask is consistent with the 2.5 bps spread decreases reported in Table 4. Our regression results reflect that dealers increase their ask quotes following the Salomon squeeze to charge a premium for making inventory available. Fig. 1 reveals that the pattern in spreads following the Salomon squeeze persists until November of 1996. Given that our methods are basically event study methods, the question arises about how the impact of this event can persist for 5 years. We believe the answer lies in a market-wide problem in fixed-income securities, the beginning of which is signaled by the Salomon squeeze. The Salomon squeeze causes dealers to have fails in Treasury transactions, making having inventory more valuable. Specifically, a fails occurs where securities are not delivered as agreed for a transaction. An examination of dealer fails to deliver on Treasury securities shows that, prior to the Salomon squeeze, fails are not a significant issue. However, after the Salomon squeeze, fails are a large and reoccurring problem through the middle of 1996.25

4.3. Salomon event robustness checks We conduct three robustness checks on our results for this event. First, the Federal Reserve collects the net T-bill position for the primary security dealers on a weekly basis and reports the data in the Federal Reserve Bulletin. We collect these data from 3/20/91 through 7/24/91 and plot the data in Fig. 3. Following the squeeze, dealers increase their ask price in response to the chang25 Security dealers’ fails data are available at http://www.newyorkfed.org/markets/ primarydealers.html.

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Fig. 3. Primary dealers net T-bill positions ($US millions) surrounding the May 22, 1991 Salomon Brothers’ squeeze from 3/20/91–7/24/91.

ing market conditions, which should result in an increase in inventory. Fig. 3 shows that from the low on May 15, 1991, dealer net Tbill positions increased by $15 million over the next 10 weeks.26 This result supports our hypothesis and is consistent with our results for the Salomon squeeze. Second, to verify that the regression results are associated with a maturity break point of 30 days to maturity, we construct a new time series data set for the T-bill closest to 30 days to maturity but with 30 days or less to maturity. This moves the weekly maturity break in the time series from Monday to Tuesday. We reestimate all of our regressions on this new time series, and the weekly break moves to Tuesday, with all the results supporting that 30 days to maturity is an important maturity break point for dealer inventory management. Third, we collect bid and ask data for the T-bill closest to 45 days to maturity. We find that 87.70% of the spreads are 2 bps before the squeeze, and 97.96% are 4 bps following the squeeze (as compared to the bill closest to 30 days, which had 72.95% of the spreads at 2 bps before the squeeze and 84.43% of the spreads at 10 bps after the squeeze). The 45-day T-bill spread increase reveals that there are increased concerns about T-bill availability across maturities, resulting in a higher price for liquidity in the shorter bills. The smaller spread increase further supports the importance of 30 days to maturity in dealer pricing behavior.

5. Collapse of LTCM (September 23, 1998) 5.1. Spread analysis Table 6 contains the spread frequencies for the 6 months on either side of the LTCM collapse. The frequencies show a onespread market at 4 bps 97.5% of the time before the collapse. However, after the collapse, there are two distinct spreads in the market: 22.13% of the spreads are 4 bps and 77.87% of the spreads are 8 bps. Clearly, the event causes increased spreads. Unlike the Salomon squeeze, the minimum spread does not change around the LTCM collapse, indicating that there was no change in normal market-making costs. Instead, the spread increase reflects a change in 26 Fleming and Rosenberg (2008) plot dealer net T-bill position in their Fig. 2A. They show (as we do in our Fig. 3) that May 15, 1991 is a low point in dealer T-bill positions for the early 1990s. Their figure also shows that following the Salomon squeeze, the net dealer T-bill positions are relatively higher for a prolonged (around 6 months) period of time.

Table 6 Average daily spread frequencies for off-the-run one-month T-bills for the 6 months before and after the September 23, 1998 collapse of LTCM. Spread (bps)

Before September 23, 1998

After September 23, 1998

2 4 6 8 10 12

1.67% 97.50 0.00 0.83 0.00 0.00

0.00% 22.13 0.00 77.87 0.00 0.00

inventory management. However, further analysis is needed before we can comment on whether the increase in spreads supports a flight to quality or a flight to liquidity. 5.2. Regression analysis Next, we estimate regression models (Eq. (8)) for before and after the collapse for the same reasons as outlined in Section 4.2. The results are reported in Table 7 where Panel A reports the pre-collapse results and Panel B reports the post-collapse results. The results in Panel A show Monday as the only statistically significant parameter estimate (at the 5% level or better). However, since this parameter estimate is only about one-third of one basis point, attaching any economic significance to it is unwarranted. In Panel B of Table 7, we report the regression results for the 6 months following the LTCM collapse. There are four significant parameter estimates on the day-of-the-week variables: negative parameters on Monday and Friday and positive parameters on Tuesday and Wednesday. The 31-day spread (Mondays) averages are 3.99 bps less than the 27-day spread (the previous Friday). The positive Tuesday parameter estimate measures the move from 31 days to maturity to 30 days, revealing that the 30-day spread is 3.84 bps on average higher than the 31-day spread. Following the collapse, 30 days to maturity is an important break point in the life of T-bills. The Wednesday and Friday parameter estimates represent one-half basis point or less, and therefore are not economically significant. Table 8 contains the estimations of Eq. (8) on changes in bid and ask yields. The results from the 6 months before the collapse are presented in Panel A. There are significant parameter estimates for both the bids and asks on Mondays and Thursdays. Parameter estimates for Mondays are positive for both the bid and ask, and

M.D. Griffiths et al. / Journal of Banking & Finance 34 (2010) 2146–2157 Table 7 Average daily bid–ask spread results for off-the-run one-month T-bills for the 6 months before and after the September 23, 1998 collapse of LTCM. Estimate Panel A: Before September 23, 1998 Monday Tuesday Wednesday Thursday Friday 3-month TB F-stat Adj R-sq. Panel B: After September 23, 1998 Monday Tuesday Wednesday Thursday Friday 3-month TB F-stat Adj R-sq.

p-Value

0.0031 0.0004 0.0009 0.0003 0.0022 0.0557 3.67 0.1171

0.0181 0.7410 0.4193 0.8171 0.0692 <0.0001 0.0022

0.0399 0.0384 0.0040 0.0000 0.0050 0.0023 137.06 0.8700

<0.0001 <0.0001 0.0396 0.9794 0.0112 0.8388 <0.0001

The equation being estimated is Spreadt  Spreadt1 ¼ bMon D1 þ bTu D2 þ bWed D3 þ bTh D4 þ bFri D5 þ bTB ðTBt  TBt1 Þ þ et , where: Di = 0/1 dummy variable that where D1 = 1 on Monday, D2 = 1 on Tuesday, D3 = 1 on Wednesday, D4 = 1 on Thursday and D5 = 1 on Friday, TBt = annualized yield for the on-the-run three-month T-bill on day t, which is included to control for changes in the general level of short-term interest rates. All regressions are estimated using OLS with White’s (1980) adjustment for heteroscedasticity and estimated for the six-month period before and after the recapitalization.

Table 8 Average daily bid and ask results for the T-bill closest to 30 days until maturity for the 6 months before and after the September 23, 1998 collapse of LTCM. Bid yields Estimate Panel A: Before September 23, 1998 Monday 0.0532 Tuesday 0.0049 Wednesday 0.0080 Thursday 0.0516 Friday 0.0244 3-month TB 0.3324 F-stat 4.97 Adj R-sq. 0.1646

Panel B: After September 23, 1998 Monday 0.1201 Tuesday 0.0386 Wednesday 0.0330 Thursday 0.0331 Friday 0.0609 3-month TB 0.8751 F-stat 7.04 Adj R-sq. 0.2290

Table 7, Panel A. Both Thursday parameter estimates are negative, and the size of the parameter estimates are almost identical. This outcome is consistent with no change in the absolute size of the spread. Panel B of Table 8 contains the post-collapse results. Both the bid and ask have significant and positive parameter estimates on Mondays, reflecting that yields increased and prices fell on Mondays as the time series moves from 27-day T-bills to 31-day T-bills. The difference between the parameter estimates is consistent with the about 4 bps decrease in the spread on Mondays (relative to the previous Friday) reported in Panel B of Table 7. None of the other day-of-the-week parameter estimates is statistically significant. However, on Tuesday, the difference in the size of the parameter estimates is almost 4 bps, which is consistent with the almost 4 bps spread increase reported in Table 7. The parameter estimates are a result of approximately a 3.9 bps increase in the bid yield, while the ask yield did not change. An increase in the bid yield (a decrease in the bid price) is consistent with dealers lowering their bid price at the 30-day point to avoid acquiring inventory following the collapse. This supports Scholes (2000) statement that following the LTCM collapse, providers of liquidity are unwilling to take the long positions necessary to keep the market liquid. As with the Salomon squeeze, Fig. 1 reveals that the effects of the LTCM collapse persisted for several years (through the end of the sample in June 2001). Scholes (2000) notes that, following the LTCM collapse, liquidity providers are unwilling to take longhorizon positions (hold inventory). Fleming (2000) and the Committee on the Global Financial System (1999) suggest that that collapse of LTCM occurred during a global financial crisis, which would provide for the persistence of a market-wide environment where dealers manage inventory with a bias toward not acquiring additional inventory.

Ask yields p-Value

Estimate

p-Value

0.0087 0.7796 0.6537 0.0047 0.1861 0.1004 0.0001

0.0563 0.0053 0.0090 0.0519 0.0265 0.2768 4.70 0.1549

0.0066 0.7678 0.6235 0.0054 0.1583 0.1793 0.0003

Bid Estimate

2155

Ask p-Value

Estimate

p-Value

0.0028 0.2920 0.3666 0.3680 0.1032 0.0001 <0.0001

0.1600 0.0002 0.0369 0.0330 0.0559 0.8774 8.52 0.2699

<0.0001 0.9960 0.3159 0.3727 0.1378 0.0001 <0.0001

The equation being estimated is Yieldt  Yieldt1 ¼ bMon D1 þ bTu D2 þ bWed D3 þ bTh D4 þ bFri D5 þ bTB ðTBt  TBt1 Þ þ et , where: Yieldt = the ask yield or bid yield (as appropriate) on day t. Di = 0/1 dummy variable that where D1 = 1 on Monday, D2 = 1 on Tuesday, D3 = 1 on Wednesday, D4 = 1 on Thursday and D5 = 1 on Friday, TBt = annualized yield for the on-the-run three-month T-bill on day t, which is included to control for changes in the general level of short-term interest rates. All regressions are estimated using OLS with White’s (1980) adjustment for heteroscedasticity and estimated for the six-month period before and after the recapitalization.

the difference in the size between the two parameters is comparable to the one-third basis point change in the spread reported in

5.3. LTCM event robustness checks As with the Salomon event, we again conduct three robustness checks on our results for the LTCM event. First, Fig. 4 plots the weekly data on the investment positions of the Treasury’s primary dealers for the 9 weeks on either side of the September 23, 1998 LTCM recapitalization. The plot shows that, prior to the collapse, the primary dealers held net long positions in T-bills of approximately $4 billion. After the collapse, the dealers held net short positions in excess of $5 billion. Thus, primary dealers changed their behavior after the collapse; going from holding a pre-collapse T-bill inventory to providing post-collapse T-bill positions through repo agreements.27 This change in inventory positions is consistent with our regression results. Dealers moved toward liquidity and away from holding inventory. Second, we re-create the alternative 30-day time series that moves the weekly maturity break in the time series from Monday to Tuesday. We repeat all the empirical analysis of the LTCM event and find that all the results on the alternative time series support our primary results and the fact that 30 days to maturity remains an important liquidity break point for dealer inventory management. We also examine the spread frequencies for the 45-day maturity series. Prior to the collapse, over 95% of the spreads in this series are 4 bps. Afterwards, over 95% of the spreads in this series remain at 4 bps. This provides additional support that 30 days to 27 Fleming and Rosenberg (2008) examine net dealer T-bill positions from July 1990 through June 2006 and find that dealer positions are ‘‘mostly positive” over their sample period. They plot net T-bill positions in their Fig. 1, and a review of the figure suggests that the end of 1998 is the most pronounced period of dealer net short positions across their sample.

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$10,000 $5,000 $0 ($5,000) ($10,000)

11/25/1998

11/11/1998

10/28/1998

10/14/1998

9/30/1998

9/16/1998

9/2/1998

8/19/1998

8/5/1998

7/22/1998

($15,000)

Fig. 4. Primary dealers net T-bill positions ($US millions) surrounding the September 23, 1998 LTCM recapitalization from 7/21/98–11/25/98.

maturity was a unique liquidity-related focal point for dealers around the collapse of LTCM.

6. Robustness checks in our data Our data are not transaction data. Instead, they are daily observations where the data source attempts to provide one number that best represents the day’s activity. Clearly, when a data source provides only a ‘‘representative” number, there is the potential that the numbers do not reflect the market behavior accurately. Here, we address concerns about non-representative data. First, there have been concerns expressed in the literature about the Fed quote sheet data. Duffee (1996, Appendix) notes that the Fed surveys dealers to get quotes and then generates a composite (representative) price. However, since the mid 1980s, the Fed only collects bid prices and creates ask prices from a spread curve that is constant day to day. Jordan and Kuipers (2005) concur by stating the ask prices are generated from a representative spread. However, Duffee compares the Fed bid and ask quotes to an alternative market source and concludes that the Fed quotes better represent true market values. Jordan and Kuipers compare the Fed quotes to representative end-of-the-day quotes from GovPX and conclude that the Fed quotes are superior. Accordingly, the Fed quote sheet data would appear to provide the best daily representative prices. We use the Fed data only for the first event and find that the ask changes following the event. Since Duffee states that the Fed uses the same spread every day, the change in the ask suggests that dealers changed their behavior following the Salomon squeeze in a sufficiently significant manner to change the Fed’s calculation of the spread. In addition, we note that changes in the ask yields generated by the Fed are consistent with our hypothesis and with the observed change in dealer inventory (Fig. 3). Second, the data covering the two events come from two different sources. The Federal Reserve collects its T-bill data through a survey of the dealers. From the survey results, the Fed generates its representative numbers. Cantor Fitzgerald is an interdealer broker that examines its daily activity in T-bills and generates its representative numbers from its activity.28 These are two very different processes that are unlikely to create stark breaks in spread exactly at market events, unless the event triggers the break. 28

Cantor Fitzgerald is a brokerage service for Wall Street’s fixed-income interdealer community. They provide the Wall Street Journal summary information based on the market transactions.

Third, while we have logical reasons why the data are reliable, a second source of market data would be useful. However, finding a second source of market data has proven difficult. GovPX transaction data would be the best solution, but GovPX starts on June 17, 1991 and therefore does not span the Salomon squeeze. Bloomberg would be another good alternative, but Bloomberg does not archive off-the-run one-month T-bill data.29 The best additional data we are able to obtain is from Citibank’s Yield Book Calculator. However, this source contains only one dealer’s activity and only reports bid-side yields. Accordingly, we examine the correlations between the bid yield in our primary data and the bid yields in the dealer yield book. For the Salomon event, the correlation on rate levels between the Fed data and the yield book data is 0.9967, and the correlation between rate changes is 0.9077. These are very high correlations, and we conclude that the third-party data support our contention that the primary data present a reasonable picture of the market. For the LTCM event, the correlation on rate levels between the Cantor Fitzgerald data and the yield book data is 0.9672, and the correlation between the rate changes is 0.6302. The correlations between the rate changes are significant at better than the 1% level. However, it is instructive to remember that we are comparing rate changes from an interdealer broker versus an individual dealer. A plot of the data (not provided for brevity) indicates that we should estimate the correlations before and after the event. Before the event, the levels (changes) correlation is 0.7965 (0.3765), while after the event the levels (changes) correlation is 0.9559 (0.6802). The dramatic increase in the correlations after the collapse of LTCM is consistent with market participants changing their behavior and acting more similarly than before the event. This would happen only if the data capture market behavior. In addition, we estimate the bid yield regression reported in Table 8 on the yield book data. The results from the yield book data are qualitatively similar.

7. Conclusion Our goal in this paper is to provide reasonable benchmarks for the cost of dealer market-making activities priced into the bid–ask spread. We analyze liquidity shocks in the off-the-run T-bill market because it is the least complex financial market and provides the smallest opportunity for asymmetric information. Using data from this market, we explore two financial crises that result in severe shocks to the financial system: Salomon Brothers’ attempt to corner the two-year T-note market and the collapse of LTCM. Our results provide several important insights about the cost of market-making activities. First, the on-the-run T-bill market is highly liquid, and our results show that spreads in the on-therun market are consistently at 2 bps. This is the minimum spread on a round-trip and provides a baseline reference for spreads in the less liquid off-the-run market. Following the Salomon squeeze, dealers moved the off-the-run market to a minimum spread of 4 bps. Second, the Salomon squeeze is a supply restriction, and the dealers moved from 73% of the spreads at 2 bps before the squeeze to more than 84% of the spreads at 10 bps after the squeeze. The regression results show a 2.5 bps increase in the spread through increasing the ask price when dealers managed the spread across the 30 days to maturity break point. Third, around the time of the collapse of LTCM, the dealers increased their spread from 4 bps to 8 bps through decreases in bid prices, indicat29 We note that Duffee uses data from the New York Times to compare to the Fed quote sheet and finds that the Fed quote sheets better represent true values. Duffee notes that Bloomberg was the source of the New York Times data from 1987 through 1994.

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