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Liquidity in a Market for Unique Assets: Specified Pool and TBA Trading in the Mortgage Backed Securities Market
Pengjie Gaoa, Paul Schultza, and Zhaogang Songb
Abstract Agency mortgage-backed securities are traded simultaneously in a market for specified pools and in a forward (TBA) market. Cheapest-to-deliver pricing in the TBA market increases liquidity because buyers do not bear costs of analyzing the mortgage pool. Pooling is not complete in the TBA market though, prices differ across dealers. TBA trading also increases liquidity by allowing dealers to hedge specified pool inventory. Dealers hedge almost all specified pool inventory for pools that are closely matched by TBA contracts. Specified pools that do not match TBA trading as closely are hedged less frequently. Specified pools that match TBA characteristics are cheapest to trade.
August, 2014
Preliminary, do not quote. Not for circulation.
aMendoza College of Business, University of Notre Dame. bBoard of Governors of the Federal Reserve.
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Banks and other mortgage originators typically sell their mortgages as part of a pool of mortgages
in a mortgage backed security (MBS). The pooling of mortgages for MBS occurs in two ways. Large
originators can assemble a pool of conforming mortgages and issue them through one of three agencies:
Fannie Mae, Freddie Mac, or Ginnie Mae. Small mortgage originators will typically sell loans to the
agencies through the cash window, and the agency will poll them with other mortgages into an MBS.
Large originators may also do this if they don’t want to keep loans on their balance sheet until a pool is
assembled. In pooling mortgages, the agencies attempt to pool similar loans together. Agency MBS are
pass-through securities. All MBS issued on an underlying mortgage pool represent a proportional claim
on the principal and interest payments of the underlying loans. After their initial sale to investors, MBS
trade freely in the over-the-counter market.
The secondary market for MBS is among the largest, most active, and most liquid of all securities
markets. At first glance, it is surprising that the market is so liquid because each MBS is unique,
composed of specific mortgages with their own prepayment characteristics. In this paper, we study the
institutional feature of this market that allows it to work so well – its structure of parallel trading in a to-
be-announced (TBA) forward market in MBS and a specified pool market in which specific MBS are
traded.
Agency MBS are default-free. Despite this, MBS with the same coupon and maturity can differ
significantly in value because of differences in the risk of prepayment. Mortgages give the borrower an
option to prepay the loan. This option may be exercised when interest rates fall and borrowers can switch
to new lower rate mortgages. Conversely, if mortgage rates rise, borrowers who would otherwise prepay
and move may be more likely to remain in a house. The value of the prepayment options that mortgage
lenders have written vary from MBS to MBS depending on the ability and willingness of the mortgage
holders to prepay. Homeowners with low credit scores, or who hold mortgages with high loan-to-value
ratios, or who hold mortgages that have been recently refinanced are regarded as less likely to exercise
their prepayment option. MBS composed of mortgages like these are considered to be particularly
valuable. On the other hand, borrowers who take out large mortgages are thought to be more financially
sophisticated and more likely to exercise prepayment options. MBS with a lot of large mortgages are less
valuable.
It is difficult to model prepayment. Stanton (1995) notes that some mortgages are not prepaid
even when their coupon rate is below current mortgage rates. He also observes that there is seasonality in
prepayments and that there is a “burnout” factor in prepayment. That is, if mortgages haven’t been repaid
after a decline in interest rates, it indicates that the remaining mortgage holders in a pool cannot easily
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refinance and prepayments will decline. To produce estimates of prepayments that approximate empirical
observations, Stanton relies on the assumption that different mortgage holders face different and probably
unobservable costs of prepayment.
The idiosyncratic prepayment risk of individual mortgages may be reduced through
diversification when the mortgages are assembled into MBS, but MBS differ significantly in the
characteristics of the constituent mortgages. The average size of the loans, refinancing history,
homeowner credit scores, house locations, loan-to-value ratios and other factors differ significantly across
MBS’s and are related to their prepayment risk. Each MBS has a unique combination of these
characteristics and each therefore carries its own unique prepayment risk. A potential buyer or seller of
these securities needs to use the characteristics of the mortgages in an MBS to evaluate the prepayment
risk and determine its value. Many potential buyers of MBS do not have the expertise to evaluate
prepayment risk, and face a winners curse in this market.
TBA trading provides a solution to the winner’s curse problem. Specific mortgage pools are not
traded in the TBA market. Instead, buyers and sellers agree on six parameters; the price, par value of the
securities, settlement date, maturity, coupon rate and issuer (Fannie Mae, Freddie Mac, or Ginnie Mae).
Sellers are expected to deliver the cheapest pool that meets the agreed-upon parameters. By purchasing in
the TBA market, uninformed investors pay the same price as sophisticated investors. Every buyer in the
TBA market, regardless of sophistication, has the same likelihood of receiving a more or less desirable
MBS.1 Because of this, the TBA market for MBS is among the most liquid securities markets even
though each MBS is a unique asset with unique prepayment risks.
The TBA market functions so well in part because the MBS that trade there are relatively
homogeneous. This is because the pools with the lowest prepayment risk can be sold for higher prices in
the specified pool market. The MBS traded in the TBA market have similar high risk of prepayment.
The TBA market contributes to the liquidity of the specified pool market by allowing dealers to
hedge their specified pool inventory risk. Trades in the MBS market are large, often in the tens of millions
of dollars. Without active efforts to manage inventory, dealers can acquire large long or short positions
that will leave them exposed to risks from changing interest rates or changing values of prepayment
options. The highly liquid TBA market allows dealers to hedge the risk of specified pool positions with
offsetting positions in the TBA market.
1 Pagano and Volpin (2012) consider a market for asset‐backed securities in which some potential buyers cannot evaluate information needed for pricing the securities. They show that opaqueness can make the market more liquid.
4
In this paper, we find that, as expected, prices of specified pools are higher than TBA pools with
the same coupon and maturity. Also as expected, we find that trading costs are much lower in the TBA
market than in the specified pool market. Specified pools that are eligible for TBA trading carry lower
trading costs. Specified pools with the same maturity and coupon as actively traded TBA contracts also
have lower trading costs than other specified pools.
An unexpected finding is that the market recognizes that pools traded in the TBA market are not
perfect substitutes. The TBA market is an over the counter market without central clearing or settlement.
Investors pay different prices for contractually identical TBA trades with different dealers on the same
day. These price differences do not appear to be a result of counterparty risk. Less active dealers tend to
get higher prices when they sell in the TBA market than more active dealers. A possible explanation is
that less active dealers may be more likely to deliver a more valuable MBS, while the most active dealers
are more sophisticated and are more likely to sell an attractive MBS in the specified pool market.
Finally, we document that the TBA market appears to play a critical role in allowing dealers to
hedge specified pool inventory. The great majority of dealers match changes in their specified pool
inventory with offsetting changes in their TBA inventory. Dealers are less likely to hedge inventories of
specified pools with maturities or coupons that do not match TBA maturities and coupons. They are also
less likely to hedge specified pools that are not TBA eligible. Specified pools that are less likely to be
hedged have higher round-trip trading costs on average.
The rest of the paper is organized as follows. Section I discusses how the secondary market for
MBS operates. Section II documents that pooling in the TBA market is only partial, and that the identity
of counterparties affects prices. Section III compares liquidity in the TBA and specified pool markets. In
Section IV, we show that dealers use the TBA market to hedge specified pool positions. This improves
liquidity in the specified pool market. Section V draws conclusions.
I. How the Market for MBS Works
In the forward market, or TBA market for mortgage-backed securities, buyer and seller agree on a
price for agency mortgage backed securities to be delivered later. The actual securities to be delivered are
not determined. Instead, six parameters are specified – the issuer (Fannie Mae, Freddie Mac, or Ginnie
Mae), maturity, coupon, price, par amount, and settlement date. Contracts settle once a month. The TBA
market settlements are up to three months out, with contracts for the next two months particularly active.
5
Sellers have an incentive to deliver the cheapest possible mortgage backed securities that meet the TBA
specifications - and buyers assume they will.
Equilibrium in the MBS market is a pooling equilibrium for MBS with a high probability of
prepayment, and a separating equilibrium for MBS with low prepayment risk. The pooling of MBS with
high prepayment risk occurs through trading in the TBA market. Low prepayment risk MBS will trade in
the TBA market if the price discount from being pooled with higher prepayment risk MBS is smaller than
the cost of convincing buyers of their low risk status. If a MBS has a low enough prepayment risk, it
becomes worthwhile to bear the costs of revealing its value to investors, and the MBS will trade in the
specified pool market. If the borrowers are more sophisticated, they are more likely to prepay and
refinance when interest rates fall. Holders of large mortgages, for example, are more likely to prepay and
hence pools of these mortgages are unlikely to trade as specified pools. Other mortgage backed securities
trade in the specified pool market because their mortgages have non-standard features (e.g. jumbo loans)
and are ineligible for TBA trading.
In this paper, we examine MBS trading using a dataset of all trades by all dealers who are FINRA
members over the May 16, 2011 through April, 2013. Data for each trade includes the maturity, coupon,
and issuer of the MBS, the price, par value, trade date, trade time, and settlement date for the trade, and
identifying numbers for dealers in the trade. Data includes both interdealer trades and trades between
dealers and customers, and both TBA and specified pool trades
Table 1 provides some summary statistics for MBS trading. Panel A reports the number of trades
of various types, and the volume from these trades. As is also noted by Vickery and Wright (2013), 90%
of mortgage backed security volume is in the TBA market. It is also interesting that that interdealer trades
account for almost half the volume in the TBA market, but a much smaller proportion of specified pool
volume. An explanation for greater interdealer trading in the TBA market could be that dealers lay off
inventory in the TBA market by trading with other dealers, and also hedge specified pool inventory with
interdealer TBA trades.
Panel A of Table 1 also provides information on the numbers of different types of TBA trades.
Over the May, 2011 through April, 2013, there are more than 3.3 million TBA trades. Outright trades
make up the majority of TBA trades. Dollar rolls are the second most common type of trade. Dollar rolls
are similar to repos. The seller of a dollar roll sells the front month TBA contract and simultaneously buys
a future month contract with the same characteristics at specified prices. Dollar rolls differ from repos in
that the securities that are purchased for delivery in the later month are “substantially similar” to the one
sold in the front month rather than the same securities. In addition, in a dollar roll, the buyer of the front
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month contract receives coupon and principal payments over the month. Dollar rolls tend to be larger size
trades, and are responsible for most of the buy, sell, and interdealer volume.
Stipulated trades are TBA trades in which the buyer requires the seller to deliver pools with
additional stipulated characteristics. The buyer could, for example, specify that no more than a certain
percentage of mortgages are on homes in California. Stipulated dollar rolls are dollar rolls that stipulate
additional characteristics of pools to be delivered. They are less common, and account for less than
30,000 trades.
There are about 1.66 million trades of specified pools. The great majority of these are TBA –
eligible, and could be sold in the TBA market if the seller so desired. The other pools may contain jumbo
loans or loans with credit scores that make them ineligible for TBA trading. Interdealer trades make up a
far smaller proportion of specified pool trades than TBA trades. As we show later, interdealer TBA trades
are used to manage specified pool inventory.
Panel B of Table 1 provides information on trade sizes. Trade sizes are far smaller for specified
pools than for TBA trades. Interdealer specified pool trades have an average size of only $3.32 million
dollars. Trade sizes are right-skewed, so the majority of trade sizes are smaller. Only 6.7% of specified
pool interdealer trades for at least $10 million par. It is interesting that specified pool trades with
customers tend to be larger than interdealer specified pool trades. The mean size trade with customers is
for $6.49 million par value, and 10.7% of the trades are for $10 million or more.
TBA trades are much larger than specified pool trades. Dollar rolls are especially large. The mean
trade size for interdealer trades of dollar rolls is $59.64 million while the mean size for trades with
customers is over $100 million.
Panel C of Table 1 reports the proportion of trades of different types for dealers with different
levels of activity. There are a large number of dealers, but trading is heavily concentrated in a few dealers.
Active dealers tend to do most of their trading in the TBA market, while inactive ones trade mainly in
specified pools. The table doesn’t show results for individual dealers, but the single most active dealer
accounts for 17.3% of all trades, but made almost no trades in the specified pool market. For the ten most
active dealers, the average proportion of volume from specified pools is 13.55%. For the twenty next
most active dealers, the proportion of volume from specified pools averages 26.16%. For dealers ranked
101 – 758 by number of trades, the proportion of volume from specified pools reaches 87.82%. Large
dealers trade mainly in the TBA market. Less active dealers trade mainly in the specified pool market. As
we have seen, TBA trades are usually much larger than specified pool trades. To compete effectively as a
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dealer in the TBA market requires more capital than it takes to trade specified pools – capital that the
inactive dealers may not have.
Panel C also reveals that the proportion of trades that are interdealer trades is higher for more
active dealers than for less active ones. Even for the least active dealers, however, the average proportion
of trades that are interdealer is over 44%.
Figure 1 shows the daily dollar of investor outright purchases from dealers, purchases of dollar
rolls, and purchases of specified pools for our sample period of May, 2011 through April, 2013. Several
facts about MBS trading stand out in the graph. First, the dollar volume of both outright and dollar roll
TBA trading dwarf that of specified pool trading. Specified pool trading is typically on the order of $5
billion to $10 billion per day. In contrast, there are several days when dollar rolls alone account for $100
billion dollars in trading. Total daily purchases in the TBA market typically exceed $50 billion and are
often greater than $100 billion. Second, there is a strong monthly seasonal component in the TBA
volume, particularly for dollar rolls. This reflects investors rolling over positions in the TBA market
before settlement dates. There appears to be seasonal component to specified pool trading, but it is much
weaker. Finally all of the time series appear stationary. There is no appreciable change in volume in the
TBA, dollar roll, or specified pool market over the two-year sample period.
II. Prices in the Specified Pool and TBA Markets
During the sample period, both TBA and specified pool prices increased. This can be attributed to
falling mortgage interest rates over this time period. Figure 2 shows weekly national average mortgage
rates, from Freddie Mac, for 15 and 30 year mortgages for the period from April, 2011 through April,
2013. For this period, 30-year rates were consistently higher than 15 year rates by about 75 basis points.
Rates decline by about 125 basis points between April, 2011 and October, 2012. The decline in rates
explains why prices of mortgage backed securities rose over the sample period. The decline in rates also
made prepayment an attractive option to many mortgage holders during this time.
To compare prices in the TBA and specified pool markets, we first calculate the average price of
interdealer trades in the TBA market for combinations of maturity and coupon for each sponsor, and each
settlement date each day. Likewise we calculate the average price for interdealer trades of specified pools
for maturity and coupon combinations each day. Prices of TBA and specified pool securities cannot be
directly compared because they have different settlement dates. To adjust for this, we calculate the “drop”
as the difference in price between the Fannie Mae TBA with the nearest settlement, and the Fannie Mae
TBA with the second nearest settlement. Fannie Mae TBAs are used because they are the most common.
The daily drop is the drop divided by the number of days between the two settlement dates. We then
8
multiply the daily drop by the number of days between a specified pools settlement date and the nearest
TBA settlement date and add this to the specified pool price. This adjusted specified pool price can then
be compared with TBA prices. In practice though, adjusting for the drop does not have a big impact on
the difference between TBA and specified pool prices.
Figure3 shows average specified pool and average nearest settlement Fannie Mae TBA prices for
each day for MBS with different maturities and coupons. The first figure compares prices of 15 year, 3%
MBS traded in the TBA market with those traded as specified pools. Specified pool and TBA prices are
generally close, but there are fewer specified pool trades, and hence the series of average specified pool
prices appears more volatile. The second graph shows prices of 15 year, 4% TBA and specified pool
trades. Here, specified pool prices are slightly higher, especially toward the end of the period. The third
graph shows 30 year, 4% TBA and specified pool prices. At the start of the sample period, the prices are
very close. Starting in late 2011, the two price series diverge, with specified pool prices typically higher
by 1% to 2% of face value. The last graph shows TBA and specified pool prices for 30 year, 5% MBS. In
this case, specified pool prices are almost always higher.
III. Trading Costs in the Specified Pool and TBA Markets
To date, there has been little academic research on the microstructure of MBS markets.
Bessembinder et al (2013) examine trading of MBS and other structured credit products for the period
from May 16, 2011 through January 31, 2013. They estimate that 90% of TBA trades are over $1 million,
and two-third’s are interdealer trades. For other MBS trades, 39% are over $1 million and only 35% are
interdealer trades. They use a regression approach to look at trading costs as differences in price between
successive customer trades with dealers. A variable for change from sell to buy (+1) or buy to sell (-1) is
included in the regression along with variables for changes in bond and equity indices over the trade
period. One way trading costs are estimated to be 40 basis points for MBS, and just 1 basis point for
trading in the TBA market.
To start, we provide some summary statistics on round-trip trading costs (markups) for dollar
rolls, TBA outright trades, and specified pool trades for our sample period. For each CUSIP each day, we
calculate the weighted average purchase and sales prices using the dollar volume in a trade as the weight.
Only trades between customers and dealers are used. Each observation is one security on one day.
Observations are only included if there is at least one purchase and at least one sale on that day. Markups
are the differences between the average purchase price and average sale price. Table 2 presents average
markups for MBS with 30 years to maturity and coupon rates between 2.5% and 6%, and for MBS with
9
15 years to maturity and coupon rates of 2.5% to 4%. These 12 maturity-coupon combinations account for
95% of the volume in the TBA market during our sample period. We report average markups separately
for TBA dollar rolls, TBA outright trades, and specified pool trades. Markups are reported as percentages,
so 1.000 is 1% of par value.
Markups for dollar rolls are particularly low, typically about one-half of a basis point. Markups on
TBA outright trades range from 0.29 basis points for 30-year, 4% TBA trades to 8.78 basis points for 15-
year, 4% TBAs. The more active the trading in a TBA contract, the lower the transactions costs. Specified
pool markups are much larger. The round trip costs on a 30-year, 3.5% specified pool averaged 10.56
basis points, as compared to 1.79 basis points for a 30-year, 3.5% TBA trade. Likewise, the markup on a
15-year, 3% specified pool averaged 31.47 basis points, while the markup for a similar TBA trade was
0.99 basis points.
Table 2 results suggest that specified pools are much more expensive to trade than TBAs.
Nevertheless, it is possible that we have underestimated trading costs for specified pools. In many cases,
dealers who purchase specified pools need to hold them several days before they can be sold. It is likely
that specified pools with purchases and sales on the same day, like those in Table 2, are more liquid and
have lower markups than other specified pools. This is less likely to be a problem for TBAs, which trade
much more frequently.
To estimate trading costs for MBS when purchases and sales occur days apart, we employ a
regression methodology like that in Bessembinder et al (2013). Each observation is consecutive trades in
an MBS with a specific CUSIP, but the regression includes observations from all CUSIPs with a
particular coupon and maturity. To estimate trading costs, we estimate the following regression:
Δ Δ Δ ∙ ln Δ ∙
Δ ∙ ∙ ln Σ , , 1
where ΔPt is the difference in prices between trade t and trade t-1, ΔQt, is +1 (-1) if trade t-1 is a sale to
(purchase from) a dealer and trade t is a purchase from (sale to) a dealer, lnSize is the natural log of the
par value of the securities in the trade in thousands of dollars, and TBA Eligible is a dummy variable that
takes a value of one if the specified pool is eligible to be traded as a TBA. Five return variables are also
included to capture changes in MBS values when consecutive trades take place on different days. They
are the percentage changes in 1) a U.S. Agency Fixed Rate MBS index, 2) a U.S. Treasury 7-10 year
Bond index, 3) a U.S. Investment Grade Corporate Bond Index, 4) a U.S. Corporate High-Yield Bond
Index, and 5) the S&P 500 index. If consecutive trades occur on the same day, all of these return values
are zero.
10
Table 3 reports the regression results, with Panel A reporting results for specified pools and Panel B
reporting TBA results. The first row in Panel shows coefficients and robust t-statistics for specified pools
with 30 years to maturity and coupons of 2.5%. There are a total of 3,866 pairs of consecutive trades in
specified pools with the same CUSIP. The coefficient on ΔQ is 1.4110. If both of the consecutive trades
were for $1,000, and hence the log of the trade size was zero, and the TBA dummy was zero, round trip
trading costs would be 1.411%. The interactions between ΔQ and trade size and between ΔQ and TBA
eligible indicate that trading costs decrease with trade size and are lower for TBA eligible securities. In
most of the regressions the coefficient for the ΔQ x TBA eligible x trade size term is positive. This
suggests that trading costs do not decrease as fast with trade size for TBA eligible specified pools as they
do for specified pools that are not TBA eligible.
Panel B shows results for regressions using TBA trades. Here, of course, variables for TBA eligibility
are not included in the regressions. Coefficients on ΔQ are much lower for TBA trades than for specified
pool trades, indicating that transactions costs are lower for small TBA trades than for small specified pool
trades. The coefficients on the interaction between ΔQ and trade size are negative, indicating that TBA
trading costs decline with trade size. Regressions in Panel B have far more observations than Panel A
regressions.
Using the regression coefficients in Table 3, we estimate markups for TBA trades, TBA eligible
specified pools, and ineligible specified pools for trades of $100,000, $1,000,000, and $5,000,000.
Results are shown in Table 4.
Markups decrease with trade size for trades of all types. For example, a TBA-eligible specified pool
with a 4% coupon and a 30 year maturity has a markup of 51 basis points for a trade of $100,000, 26
basis points for a trade of $1,000,000, and 8.6 basis points for a trade of $5,000,000. Table 4 also
demonstrates that markups are far lower for TBA trades than for TBA-eligible specified pools, which are,
in turn much lower than markups for specified pools that are not TBA-eligible. The markup for a round-
trip trade of $1,000,000 of 30-year 3.5% securities is 3.3 basis points for TBA trades, 18 basis points for
TBA-eligible specified pools, and 91.5 basis points for other specified pools. There are some maturity-
coupon combinations with few specified pool observations, such as 30-year maturities with coupons of
5.5% or 6%. In these cases there are few observations of specified pools that are not TBA-eligible, and
estimates are imprecise. Nevertheless, the pattern is clear: TBA trades are much cheaper than trades of
TBA-eligible specified pools, which in turn are much cheaper than trades of other specified pools.
IV. Counterparties and Pooling in the TBA Market
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TBA market trading can be thought of as a pooling equilibrium in which MBS that differ in
prepayment risk are nevertheless traded as if they were identical. The TBA market is a bilateral dealer
market and not a centrally cleared market though, so the pooling is incomplete. Counterparties matter in
bilateral dealer markets if they pose different default risks, but there is another reason why counterparties
may matter in the TBA market. In TBA trading, the seller of the MBS can deliver any pool that meets the
specified criteria. It is assumed by the buyer that the seller will deliver the least attractive (that is the most
prone to prepayment) pool. It is possible that different dealers will deliver pools of different quality.
We test this using all sales from dealers to customers of MBS in the TBA market. For maturity-
coupon combinations we estimate the following regression
, , . , , , , , 2
where Pricei,c,t is the price for the ith TBA sale of MBS with CUSIP c from dealers to customers on day t,
Avg.Pricec,t is the trade size weighted average price for TBA sales of CUSIP c on day t, Ln(Trade Sizei,c,t)
is the natural logarithm of the par value of the securities in the sale, and Dealern is a dummy variable that
takes a value of one for sales by dealer n.
Results are reported in Table 5 for the 12 most common maturity-coupon combinations. In each
case, the intercept of the regression is positive and significant, and the coefficient on the log trade size is
negative and significant. Dealers charge lower prices for large TBA purchases than for small TBA
purchases. Of more interest are the f-statistics that test whether fixed effects for dealers are significant.
For three of the less-frequently traded maturity-coupon combinations, f-tests indicate that sale prices do
not differ significantly across dealers. In the other cases, f-statistics indicate that the dealer fixed effects
are quite significant. Different dealers get different prices when selling in the TBA market. For example,
for the 30-year maturity, 3% coupon case, the f-statistic, for 100 dealers and 57,653 remaining degrees of
freedom is 30.531. The p-value for this is 0.000. Different dealers receive different prices for similar sales
in the TBA market.
A more active dealer may be better able to separate high quality pools for sale in the specified
pool market, leaving particularly poor quality pools to sell TBA. Inactive dealers, on the other hand, may
commit to sell in the TBA market, but not be able to separate pools into those that should be sold in the
specified pool market, and those that should be sold TBA. The quality of pools delivered with TBA sales
may be higher for inactive dealers.
12
For each dealer each month, we calculate the total number of TBA purchases from, and sales to
customers over the previous three months. We define inactive dealers as those with 1,000 or fewer trades
in the previous three months. For each TBA CUSIP each day, we calculate the weighted average price
for sales from dealers to customers using the par value of trades as weights. We also calculate the average
trade size for the CUSIP that day, and the average natural log of the trade size. Then, for each sale in a
CUSIP with two or more sales during a day, we regress the sale price on a dummy variable for an inactive
dealer and the difference between the log of the trade size and the average log of trade size for that CUSIP
that day. That is,
3
Table 6 reports results. Panel A reports results when trades of all sizes are included. The first row
is the regression when TBA trades of MBS of all maturities and coupons are included together. The
coefficient on the difference in log sizes is -0.0110. When a dealer sells MBS in a larger trade than the
average for that day, he tends to receive a lower price. The coefficient on the inactive dealer dummy
variable is 0.0728. On average, the price that inactive dealers get when they sell in the TBA market is
7.28 basis points higher than active dealers receive. The robust t-statistic for the inactive dealer dummy is
37.01, reflecting both the significance of dealer identity, and the large number of observations (322,037)
in the regression.
The rest of the rows report results when only trades of particular coupons and maturities are
included in the regressions. In every case, the coefficient on the difference in log sizes is negative and
highly significant. In all cases save one, the coefficient on the inactive dealer dummy is positive and
highly significant. Inactive dealers get higher prices when they sell to customers in the TBA market. In
general, holding maturities constant, higher coupons are associated with larger coefficients on the inactive
dealer dummy. Higher coupons are associated with a higher rate of prepayment, so this suggests that
buyers recognize that they will get pools with lower prepayment rates in the TBA market if they buy from
inactive dealers.
A reader could question whether our adjustment for trade size was adequate and whether the
difference in prices received by active and inactive dealers reflects differences in trade size. To address
this, we repeat the analysis of Panel A, but only include trades that differ in size from the average trade
for that CUSIP on that day by less than $1,000,000. Results are shown in Panel B.
When the sample is restricted in this way, we lose about 95% of the observations. Despite this,
the coefficients on the inactive dealer dummy variables are generally positive and statistically significant.
13
When trades from all maturity-coupon combinations are used, the coefficient on inactive dealer is 0.0902.
Inactive dealers receive prices that are 9.02 basis points more than active dealers. For 9 of the 12 specific
maturity-coupon combinations, coefficients on the inactive dealer dummy are positive and statistically
significant. They are of roughly the same magnitude as the coefficients from the regressions with all
trades.
We rerun the regressions in several alternate forms as robustness checks. We use the simple
average price for a CUSIP each day rather than the weighted average. We also use the difference in trade
sizes rather than the difference in log trade sizes. We also ran the regressions with fixed effects for the
date of the sale. None of these alternative methodologies produced different results – inactive dealers
received higher prices for sales in the TBA market.
We repeat the analysis of dealer sales to customers with a similar analysis of dealer purchases
from customers. For each CUSIP each day, we calculate the weighted price for purchases by dealers from
customers. We also calculate the average log of the trade size for these trades. We then regress the
difference between the price of a purchase and the average purchase price for each CUSIP each day on a
dummy for inactive dealers and the difference between the log trade size and the average log trade size.
Results are reported in Table 7.
Coefficients on the inactive dealer dummy are generally negative and statistically significant. For
example, when all maturity and coupon combinations are included in the regression, the coefficient on the
inactive dealer coefficient is -0.0172. Inactive dealers pay about 1.7 basis points less when purchasing
from customers than do active dealers. When specific maturity-coupon combinations are considered,
coefficients on inactive dealer dummies are generally negative and statistically significant. We repeat the
regressions using only trades with sizes within $1,000,000 of the mean size. Results are shown in Panel
B. Coefficients on the inactive dealer dummy are typically negative, but are not statistically significant in
most of the regressions using a specific maturity-coupon combination.
Results using dealer purchases from investors seem to mirror the results from dealer sales to
investors. In both cases, inactive dealers seem to get better prices. The results are much stronger,
however, for dealer sales to investors. When all trades are included in the same regression, dealers pay
under two basis points less to purchase, but sell for seven to eight basis points more. And, despite larger
sample sizes, the discount that inactive dealers receive when buying from customers is less likely to be
statistically significant than the premium they receive when selling to customers.
14
TBA trading involves bilateral contracts. There is no central clearing, so the identity of one’s
counterparty can matter. There are two potential explanations for why active dealers receive lower prices
when they sell in the TBA market. One is that they are better informed. They are more likely to know
when MBS prices are likely to fall. The second potential explanation is that active dealers are likely to
deliver a less desirable mortgage pool when the TBA trade is settled. Less desirable in this context means
homeowners are more likely to prepay when interest rates fall, and less likely to prepay when interest
rates rise. Active dealers may sell less desirable pool in the TBA market if they are better than inactive
dealers at separating their most desirable pools and selling them in the specified pool market.
V. The TBA Market and Specified Pool Liquidity
Every specified pool is unique, and there are hundreds of thousands of them. That the market is as
liquid as it is owes a lot to parallel trading in the TBA market.
TBA trading contributes to specified pool liquidity in two ways. First, it provides benchmark
prices for specified pools. MBS traded in the TBA market are lower priced because they are more likely
to be prepaid. We would expect price discovery to take place in the TBA market both because it is more
liquid and also because the cheapest-to-deliver pools trade in the TBA market.
The TBA market also allows dealers to hedge positions in the specified pool market. Because
each specified pool is unique, it may take some time for dealers to sell them. The low trading costs in the
TBA market allow dealers to hedge the inventory they intend to sell in the specified pool market cheaply.
It is not clear whether dealers would hedge specified pool inventory from one issuer (say, Fannie
Mae) with offsetting TBA inventory by the same issuer, or whether they would hedge specified pool
inventory from one issuer with TBA trading in other issuers. To examine this, we calculate daily changes
in specified pool and TBA inventory for each dealer for each issuer, maturity, and coupon combination.
One combination, for example, would be 30-year, 3.5%, Freddie Mac MBS. Inventory changes for the
dealer are obtained by adding the par value of all purchases from customers and other dealers, and
subtracting all sales to customers or other dealers. We then estimate the following two regressions of
changes in TBA inventory on changes in specified pool inventory
∆ , ∆ . , , 4
∆ , ∆ . , , 5 .
The α2 coefficient in equation (4) indicates the proportion of specified pool inventory changes that are
offset by TBA trades in MBS issued by the same agency. If dealers completely hedged their specified
15
pool inventory with TBA trades in MBS issued by the same agency, we would expect α2 to equal -1. The
β2 coefficient in equation (5) provides the proportion of specified pool inventory changes that are offset
by TBA trades in MBS issued by all agencies. These regressions are run separately for each dealer. Some
maturity/coupon combinations are not traded over the entire sample period. For example, 30 year 5%,
5.5%, and 6% MBS were only traded in the TBA market in the early part of the sample period. By 2013,
trading in these maturity/coupon combinations had virtually disappeared from the TBA market. Hence, in
estimating these regressions, we use only days when the dealer had a change in specified pool inventory.
Regression estimates are reported in Table 8. The weighted median α2 and β2 coefficients across
dealer regressions are reported. In calculating the coefficients, we weight by the number of observations
in each dealer regression. An examination of Table 8 reveals that the α2 coefficients are typically smaller
in absolute value and further from -1 than the β2 coefficients. For example, for Fannie Mae 30 year 3.5%
coupon MBS, the median α2 coefficient is -0.4756. This suggests that 47.56% of specified pool inventory
changes in 30 year 3.5% Fannie Mae specified pools are offset with Fannie Mae TBA trades. The median
β2 coefficient is -1.0670, suggesting that slightly over 100% of Fannie Mae specified pool inventory
changes are offset when TBA trades in MBS from all issuers are considered.
As a whole, the results in Table 8 strongly suggest that dealers do not differentiate between MBS
issued by different agencies when hedging specified pool inventory. So, henceforth, we study dealer
hedging after pooling together inventory changes across issuers for each maturity/coupon combination
(i.e. 30 years, 3.5%). For each dealer, we regress daily changes in TBA inventory on same-day changes
in specified pool inventory and on lagged changes in TBA inventory. Both TBA and specified pool
inventory changes are aggregated across all issuers for each dealer. That is,
∆ , ∆ . , ∆ , , 6
Panel A of Table 9 summarizes the results across individual dealer regressions for
maturity/coupon combinations. A number of dealers did very little trading in the TBA or in the specified
pool market, making it very difficult to estimate the regression for them. If we cannot estimate all
coefficients for a dealer, that dealer regression is dropped. In addition, in calculating mean and median
coefficients and t-statistics, we weight individual dealer regressions by the number of observations in
them. The fourth column reports the median coefficient on the same day change in specified pool
inventory. If dealers used the TBA market to hedge their specified pool inventory, we would expect these
coefficients to be negative. If the specified pool positions were completely offset, we would expect the
coefficient to equal -1.
16
As an example, consider the results for regressions using 30 year, 4% MBS. These are shown in
the fourth row of the table. Regressions of changes in TBA inventory on changes in specified pool
inventory could be estimated for 52 dealers, and as the next column shows, there were 6,199 observations
across the 52 regressions. The median estimate of the coefficient on changes in specified pool inventory is
-1.0057, suggesting that median dealer offset specified pool inventory changes with TBA trades one-for-
one. The median t-statistic for the coefficient is -7.00, and the mean is -8.15. The evidence that dealers
offset specified pool inventory changes with TBA trades is quite strong.
The other rows of the table report results from regressions using MBS with different maturity-
coupon combinations. The results in these other rows are sometimes stronger and sometimes weaker than
the results for 30 year 4% MBS, but they also indicate that dealers use the TBA market to offset changes
in specified pool inventory.
The weakest results are for the regressions using 30 year 6% MBS. The median coefficient on
change in specified pool inventory is -0.3296. This maturity-coupon combination traded infrequently
during the sample period. The 16 regressions are estimated using only 150 total observations, so these
coefficients are likely to be estimated less accurately than others. In addition, the TBA market for 30 year
2.5% and 30 year 6% MBS is likely to have been less liquid over most of the sample period than the TBA
market for other MBS
The last two columns of the table show the median coefficients on the change on lagged TBA
inventory change, and the median t-statistics. In contrast to the strong evidence that dealers hedge their
specified pool inventory with TBA trades, evidence that lagged TBA inventory changes from the previous
day affect current TBA trading is weak. Coefficients on lagged TBA inventory changes, shown in the
second to the last column, are all negative, but close to zero, as are the median t-statistics. While there is
weak evidence that dealers offset previous-day changes in TBA inventory, it seems likely that dealers
seldom maintain unwanted TBA positions overnight.
Much of the trading in the specified pool market consists of pools with different maturities than
the 30 year and 15 year maturities traded in the TBA market. These specified pool trades cannot be
hedged as well in the TBA market. Nevertheless, it seems likely that some dealers will use the TBA
market to hedge changes in the inventory of specified pools with different maturities. To examine this, we
regress changes in TBA inventory on changes in inventory of specified pools with the same coupon and
maturity, and changes in inventory of specified pools with the same coupon but other maturities. That is
∆ , ∆ . 15 30 , ∆ . . , , . 7
17
Other maturities are 10-14 and 16 -21 years for 15 year TBA inventory changes and 22-29 and 31–35
years for 30 year TBA inventory. Now, a day is included in the dealer regression if there is a change in
the dealer’s TBA inventory, specified pool with the same maturity inventory, or specified pool with a
different maturity inventory.
Summaries of the individual dealer regressions are reported in Panel B of Table 9. Coefficients
and t-statistics are again weighted by the number of observations in each dealer’s regression to produce
the means and medians reported in the table. As in Panel A, coefficients on changes in specified pool
inventories are generally negative and significant. Dealers offset changes in their specified pool inventory
positions by trading in the TBA market.
The last four columns describe the distribution of coefficients, and t-statistics of the coefficients,
for the change in inventory of specified pools with other maturities. The coefficients are negative, but
generally smaller in absolute value than the coefficients on changes in inventory for specified pools with
15 or 30 years maturity. For example, the median coefficient on changes in inventory of specified pools
with odd maturities is -0.4569 for the 30 year 4.5% TBA regressions, and -0.9094 for specified pools with
30 years to maturity and coupons of 4.5%. It appears that dealers hedge their inventory positions in
specified pools with odd maturities, but not as much as those with 15 or 30 years to maturity.
Dealers who acquire inventory positions in specified pools with maturities other than 15 or 30
years do seem to hedge part of their inventory changes in the TBA market. Coefficients are smaller
however, than for changes in inventory of specified pools with 15 or 30 years to maturity. This suggests
that dealers hedge a lower portion of inventory changes in odd maturity specified pools than changes in
specified pool inventories that match TBA maturities.
Specified pools may not be eligible for TBA trading if they contain jumbo loans, if the mortgage
holders do not meet criteria for creditworthiness, or for other reasons. These mortgage pools have
different characteristics than those that trade in the TBA market. Jumbo loans, for example, are thought to
be more likely to be prepaid. These differences mean that TBA positions will not hedge TBA-ineligible
inventory as effectively as they hedge TBA-eligible specific pools. Hence it is interesting to ask whether
dealers hedge positions in specified pools that are not TBA eligible by taking offsetting positions in the
TBA market. To examine this, we run the following regression
∆ , ∆ . , ∆ . , , . (8)
This regression is run separately for every dealer using only specified pools with 30 (or15) years
to maturity. Median coefficients and t-statistics are reported in Panel C of Table 9. In calculating means
and medians, each dealer regression is weighted by the number of observations in the regression. Days are
18
only included in a regression if there was a change in either the TBA-eligible or TBA ineligible specified
pool inventory.
The median α2 coefficients, which can be interpreted as the proportion of TBA-eligible specified
pool that are hedged in the TBA market, range -0.7974 to -0.9863 across the different maturity-coupon
combinations with more than 2,000 total observations. The α3 coefficients provide estimates of the change
in TBA inventory when TBA-ineligible specified pools are traded rather than TBA-eligible specified
pools. In general, these coefficients are closer to zero. There appears to be less hedging of TBA- ineligible
specified pools than TBA-eligible specified pools.
VI. Hedging and Trading Costs in the Specified Pool Market
Dealers who hold an inventory of securities are exposed to the risk of price fluctuations. This is
the inventory holding cost of market making. There is a large literature in market microstructure that
indicates that greater inventory holding costs for dealers leads to greater trading costs. If, however,
dealers are able to hedge their inventory holdings, trading costs need not be affected by dealers’ need to
hold inventory. For example, Battalio and Schultz (2011) find that option market makers needed to hedge
using the underlying stock.
As we have seen, the specified pools that are most likely to be hedged in the TBA market are
TBA eligible with 15 or 30 years maturity. This suggests that specified pools with these characteristics
are likely to be the cheapest to trade. We have already documented that trading costs are lower for TBA
eligible specified pools.
The maturity of specified pools may also affect their liquidity. During our sample period, TBA
trading takes place almost entirely in 15 and 30 years securities. Prices in the TBA market are likely to be
most informative for pricing specified pools with 15 or 30 years to maturity rather than specified pools
with, say, 14 or 26 years to maturity. In addition, it is likely to be easier to hedge specified pool positions
in the TBA market if the specified pools have either 15 or 30 years to maturity. Hence we would expect
specified pools with exactly15 or 30 years to maturity to have lower trading costs than those with
somewhat different maturities.
To test this, we run the following regressions using all specified pool sales to and purchases from
customers:
Δ Δ Δ ∙ ln Δ ∙ Δ ∙
∙ ln ∆ 30 ∆ 30 ∙ ln
Σ , . 9
19
Here, 30Year is a dummy variable that takes a value of one if the specified pool has exactly 30 or 15
years to maturity, and zero if the maturity is longer or shorter. All other variables are the same as in the
previous regressions. Before, we included all MBS with the same coupon and either 15 or 30 years to
maturity in the regressions used to calculate trading costs. Now we include all specified pools with 22-35
years to maturity in the 30-year MBS regressions, and all specified pools with 10-21 years to maturity in
the 15-year MBS regressions. We do not include TBA trades in the regressions.
Results are reported in Panel A of Table 10. For 10 of the 12 maturity-coupon combinations, the
coefficient on the interaction between the quote change and the dummy variable for exactly 30 or 15
years to maturity is negative. This indicates that, at least for small trades, trading costs are lower for
specified pools with exactly 15 or 30 years to maturity than for specified pools with other maturities. The
regression also contains an interaction between the quote-side change, the size of the trade, and the
dummy variable for an exact 30 year or 15 year maturity. The coefficient on this variable is always the
opposite sign of the coefficient on the interaction between quote change and exact 30 year or 15 year
maturity. Thus the only way to tell if having an exact 15 or 30 years decreases trading costs for a
particular size trade is to plug values for the trade size into the regressions.
Transaction cost estimates from the regressions are reported in Panel B of Table 10. Costs when
each trade in the round trip is for $100,000 par value are shown in columns 3-5. Trading costs when the
specified pool trades are TBA eligible and mature in 30 (or 15) years are shown first. The second cost
reported in the column is 0.5509. This indicates that the round-trip trading cost for TBA-eligible specified
pools with 30 years to maturity and a coupon of 3.0% is 0.5509, or 55 basis points on a $100 par value.
The next column reports trading costs when the specified pools are not TBA eligible but mature in 30 (or
15) years. For every maturity/coupon combination, trading costs are greater when the MBS is not TBA
eligible. So, for example, trading costs for specified pools with coupons of 3% and 30 years to maturity
rise from 55 basis points for TBA-eligible pools, to 156 basis points for those that are not TBA-eligible.
The following column provides round-trip trading costs for $100,000 par value specified pools that are
not TBA eligible, and that have maturities within the 22-35 (10-21) year range that are not 30 (15) years.
Trading cost estimates for these odd maturities exceed trading costs for other specified pools for 11 of the
12 maturity-coupon combinations.
Trading costs for round-trips of $1,000,000 par value and $5,000,000 par value are shown in the
following six columns of the table. Regardless of the characteristics of the specified pool, larger trade
sizes are associated with lower proportional trading costs. For a handful of maturity-coupon
combinations, point estimates of round-trip trading costs are negative. In these cases, regressions may
have been estimated with very few trades of $1,000,000 or $5,000,000.
20
For large trades, as with small trades, specified pool trading costs are greater if they are not TBA-
eligible, and greater yet if their maturities are not 30 or 15 years. The greater the similarity between the
characteristics of a specified pool and the characteristics of MBS traded in the TBA market, the lower the
trading costs for the specified pool.
Trading costs are lower for specified pools with characteristics that are most similar to the MBS
that trade in the TBA market. These are also the specified pools for which dealers are most likely to hedge
inventory positions. It is tempting to conclude that dealer’s ability to hedge specified pool inventory
positions lowers trading costs for specified pools, and that may indeed be true. It is also possible though
that TBA trades provide clearer benchmark prices for the specified pools that most closely resemble them,
and the existence of good benchmark prices lowers trading costs.
VII. Conclusions
The secondary market for agency mortgage backed securities is among the largest and most liquid
securities markets in the world. In a way, this is surprising because each of the hundreds of thousands of
MBS is a claim on the cash flows of a different set of mortgages, and is therefore unique. An important
reason for the liquidity of this market is the existence of TBA trading, in which different MBS are traded
in a forward market on a cheapest-to-deliver basis. Round-trip trading costs in the TBA market are tiny,
typically only a few basis points.
TBA trading can be thought of as a pooling equilibrium in which MBS with a range of
prepayment risks are sold to buyers who do not know which MBS they will receive. The pooling,
however, is incomplete. Mortgage backed securities trade in a dealer market, and different dealers are
likely to deliver MBS of different quality. We find that less active dealers receive higher prices when they
sell in the TBA market than more active dealers. We hypothesize that less active dealers may be less able
to separate the best MBS for sale in the specified pool market, and that buyers recognize that they are
more likely to receive an MBS with low prepayment risk when they purchase from an inactive dealer.
Round-trip trading costs are much higher for specified pools. There is considerable variation in
trading costs across different types of specified pools. The pools that are TBA-eligible are cheaper to
trade than TBA-ineligible specified pools. Specified pools with 15 or 30 years to maturity are cheaper to
trade than otherwise similar specified pools with other maturities.
Differences in trading costs across different types of specified pools correspond closely to
differences in dealer hedging of specified pool positions. By examining daily net inventory changes for
specified pools and TBA contracts, we provide strong evidence that dealers offset changes in specified
21
pool inventory by trading similar TBA contracts. This is particularly true for the most active dealers.
Dealers do far less trading in the TBA market to offset positions in TBA-ineligible pools than TBA-
eligible pools. Similarly, they offset changes in inventory of specified pools with 15 or 30 years to
maturity by TBA trading more than they offset changes in inventory of specified pools with other
maturities. Put another way, if a specified pool is very similar to MBS traded in the TBA market, a dealer
will hedge a position in the specified pool through TBA trading. If the specified pool has different
characteristics, it will not be hedged through TBA trading.
Positions in the specified pools that are most similar to the pools traded in the TBA market are
most likely to be hedged. These are also the specified pools that are cheapest to trade.
22
References
Atanasov, Vladimir, and John Merrick, 2013, The effects of market frictions on asset prices: Evidence from agency MBS, working paper, College of William and Mary, available at http://ssrn.com/abstract=2023779.
Battalio, Robert, and Paul Schultz, 2011, Regulatory uncertainty and market liquidity: The 2008 short sale ban’s impact on equity option markets, Journal of Finance 66, 2013-2053.
Bessembinder, Hendrik, William Maxwell, and Kumar Venkataraman, 2006, Market transparency, liquidity externalities, and institutional trading costs in corporate bonds, Journal of Financial Economics 82, 251-288.
Bessembinder, Hendrik, William Maxwell, and Kumar Venkataraman, 2013, Trading activity and transaction costs in structured credit products, Financial Analysts Journal 69 (6), 55-68.
Citigroup Markets Quantitative Analysis, 2011, Specified Pools: Superior Prepayment Profiles Offer Added Value, Citigroup Global Markets.
Pagano, Marco, and Paolo Volpin, 2012, Securitization,Transparency, and Liquidity, Review of Financial Studies 25 (8), 2417-2453.
Song, Zhaogang and Haoxiang Zhu, 2014, Mortgage Dollar Roll, working paper, Board of Governors of the Federal Reserve System.
Stanton, Richard, 1995, Rational Prepayment and the Valuation of Mortgage-Backed Securities, Review of Financial Studies 8 (3), 677-708.
Vickery, James, and Joshua Wright, 2013, TBA Trading and Liquidity in the Agency MBS Market, FRBNY Economic Policy Review, May, 2013, 1-16.
23
Table 1 Summary Statistics for MBS Trading in the TBA and Specified Pool Markets, May, 2011 – April, 2013.
Volume is in $1,000,000’s of face value. Panel A. Total trading by trade type. Number
Sells Volume
SellsNumber
BuysVolume
BuysNumber
Interdealer Volume
Interdealer TBA Trades Outright Trades 342,350 13,794,567 494,661 13,568,672 1,531,919 25,807,536
Dollar Rolls 139,134 16,415,923 147,964 17,020,740 544,153 32,525,031
Stipulated Trades 34,460 1,001,036 39,936 1,125,391 14,624 170,244
Stip. Dollar Rolls 8,456 429,988 17,665 1,026,649 1,711 28,310
Total TBA trading 533,664 32,238,176 691,017 32,144,801 2,092,407 58,531,121
Specified Pool Trades TBA Eligible 291,404 2,459,120 657,974 3,822,160 472,574 1,394,371
Non-Eligible 75,787 394,383 74,512 476,542 89,625 474,355
Total Specified Pool 367,191 2,853,503 732,486 4,298,702 562,199 1,868,726
Panel B. Trade sizes by trade type Interdealer Trades Trades with Customers
Number Avg. Trade Size
($millions) Percent >
$10 million
Number Avg. Trade Size
($millions) Percent >
$10 million Specified Pools 562,067 $3.32 6.7% 1,099,260 $6.49 10.7% TBA Outright 1,531,919 $16.71 37.1% 837,011 $32.64 37.2% TBA Dollar Roll 544,153 $59.64 60.8% 287,158 $116.43 68.1% TBA Stipulated 14,624 $11.64 12.0% 74,396 $28.58 36.4% TBA Stip. Rolls 1,711 $16.55 32.0% 26,121 $55.77 66.1%
Panel C. Dealer activity and trade type. Dealer Ranking by Number of
Trades
Percentage of Trades that are Specified Pools
Percentage of Volume from
Specified Pools
Percentage of Trades that are
Interdealer
Minimum Number Trades
Maximum Number Trades
1-10 23.83% 13.55% 56.37% 267,712 1,508,883 11-30 42.86% 26.16% 55.09% 62,397 237,655 31-50 56.44% 42.08% 53.73% 24,037 61,151 51-100 75.35% 63.29% 51.37% 4,758 22,690
101-758 91.36% 87.82% 44.73% 1 4,587
24
Table 2. Markup estimates for MBS that had same-day purchases and sales.
Each observation is one security on one day. Average prices of purchases and sales are calculated, as well as weighted averages. Markups are the differences between the average purchase price and average sale price. 0.0726 is 7.26 basis points. Observations with markups of more than $10 or less than -$10 are excluded.
TBA Dollar Rolls TBA Outright Trades Specified Pools Maturity Coupon Markup Obs. Markup Obs. Markup Obs.
30 2.5% -0.0038 (-0.35)
158 0.0726 (6.86)
614 0.4549 (16.29)
237
30 3.0% 0.0037 (1.42)
1,412 0.0585 (9.11)
2,917 0.2162 (13.76)
1,551
30 3.5% 0.0036 (1.75)
2,445 0.0179 (4.89)
4,514 0.1056 (9.61)
3,483
30 4.0% 0.0036 (1.87)
2,376 0.0029 (0.78)
3,974 0.1284 (7.58)
2,191
30 4.5% 0.0001 (0.05)
2,061 0.0150 (3.46)
3,011 0.1399 (5.48)
1,608
30 5.0% 0.0026 (0.45)
1,774 0.0253 (8.77)
2,244 0.1390 (2.72)
364
30 5.5% 0.0047 (1.95)
1,240 0.0509 (5.86)
1,618 0.0798 (2.24)
33
30 6.0% 0.0044 (1.28)
908 0.0388 (5.83)
1,173 0.8959 (3.14)
11
15 2.5% 0.0055 (2.04)
753 0.0131 (3.28)
1,432 0.1567 (7.75)
897
15 3.0% 0.0066 (2.77)
903 0.0099 (2.41)
1,836 0.3147 (9.86)
1,082
15 3.5% 0.0034 (1.29)
665 0.0057 (1.00)
1,371 0.1217 (4.79)
505
15 4.0% 0.0017 (0.62)
567 0.0878 (6.02)
1,011 0.1263 (2.78)
351
25
Table 3. Regressions of Price Changes on Quote Changes and Interactions Between Quote Changes and Other Variables
We regress changes in price between two consecutive trades of the same MBS on the change in trade type (ΔQ), on the interaction between ΔQ and the sum of the natural logs of the trade sizes of the two consecutive trades, on the interaction between ΔQ and a dummy variable that is one when the specified pool is TBA eligible, on the interaction between ΔQ, trade size and TBA eligibility, and on changes in the 1) a U.S. Agency Fixed Rate MBS index, 2) a U.S. Treasury 7-10 year Bond index, 3) a U.S. Investment Grade Corporate Bond Index, 4) a U.S. Corporate High-Yield Bond Index, and 5) the S&P 500 index. Consecutive trades are always of the same MBS, but trades from all MBS with the same coupon and maturity are included in the regressions. Robust t-statistics are reported in parentheses. Panel A. Specified pool trades only.
Maturity
Coupon
ΔQ
ΔQ x Trade Size
ΔQ x TBA Eligible
ΔQ x TBA Elg x Size
Return Variables
Obs.
R2
30 Years 2.5% 1.4110 (8.21)
-0.0563 (-5.00)
-0.2620 (-0.67)
-0.0052 (-0.23)
Yes 3,866 0.3533
30 Years 3.0% 2.7446 (16.46)
-0.1316 (-12.55)
-1.6959 (-8.85)
0.0788 (6.73)
Yes 18,789 0.1649
30 Years 3.5% 3.1419 (33.08)
-0.1612 (-29.92)
-2.6531 (-22.64)
0.1388 (20.84)
Yes 45,900 0.1861
30 Years 4.0% 2.0582 (11.49)
-0.1008 (-10.09)
-1.0493 (-5.59)
0.0466 (4.42)
Yes 46,982 0.2737
30 Years 4.5% 2.6057 (8.83)
-0.1345 (-7.62)
-1.5244 (-4.97)
0.0739 (4.02)
Yes 34,399 0.1888
30 Years 5.0% 4.6320 (9.67)
-0.2758 (-9.57)
-3.9847 (-7.52)
0.2363 (7.45)
Yes 5,296 0.2767
30 Years 5.5% 9.7998 (4.92)
-0.6160 (-4.81)
-9.9535 (-4.58)
0.6518 (4.68)
Yes 755 0.2549
30 Years 6.0% 1.5939 (0.66)
-0.0395 (-0.14)
-0.2586 (-0.11)
0.0012 (0.00)
Yes 225 0.2281
15 Years 2.5% 1.6578 (3.44)
-0.0821 (-2.91)
-0.1857 (-0.37)
0.0039 (0.13)
Yes 13,137 0.0952
15 Years 3.0% 3.3452 (7.81)
-0.1794 (-6.58)
-1.9187 (-4.33)
0.1070 (3.81)
Yes 19,499 0.0792
15 Years 3.5% 2.1303 (2.97)
-0.0949 (-2.01)
-1.0726 (-1.48)
0.0465 (0.98)
Yes 14,160 0.3055
15 Years 4.0% 5.2279 (3.77)
-0.3215 (-3.72)
-4.4545 (-3.20)
0.2872 (3.31)
Yes 7,964 0.3285
26
Table 3, Panel B. TBA trades only.
Maturity
Coupon
ΔQ ΔQ x Trade
Size Return
Variables
Obs.
R2
30 Years 2.5% 0.7120 (7.45)
-0.0341 (-6.86)
Yes 6,770 0.0957
30 Years 3.0% 0.2422 (20.04)
-0.0112 (-17.87)
Yes 167,165 0.0924
30 Years 3.5% 0.1028 (14.56)
-0.0050 (-13.39)
Yes 212,745 0.0638
30 Years 4.0% 0.0554 (4.32)
-0.0041 (-6.15)
Yes 137,073 0.0522
30 Years 4.5% 0.1002 (5.31)
-0.0051 (-5.27)
Yes 67,942 0.0473
30 Years 5.0% 0.0909 (8.00)
-0.0040 (-7.17)
Yes 31,786 0.0479
30 Years 5.5% 0.1594 (10.57)
-0.0071 (-9.06)
Yes 18,295 0.0683
30 Years 6.0% 0.1493 (9.55)
-0.0067 (-8.07)
Yes 10,807 0.0428
15 Years 2.5% 0.0886 (15.09)
-0.0040 (-12.54)
Yes 51,189 0.1497
15 Years 3.0% 0.0518 (2.56)
-0.0031 (-2.90)
Yes 51,331 0.1028
15 Years 3.5% 0.0356 (2.39)
-0.0025 (-3.17)
Yes 28,478 0.1390
15 Years 4.0% 0.3511 (10.02)
-0.0163 (-8.90)
Yes 13,697 0.0575
27
Table 4 Estimates of Round-Trip Trading Costs for TBA Trades, Specified Pool Trades of MBS that are TBA Eligible, and Other Specified Pool Trades. Estimates are based on regression coefficients in Table 3.
Trades = $100,000 Trades = $1,000,000 Trades = $5,000,000
Maturity
Coupon
TBA TBA
Eligible TBA Non-
Eligible
TBA TBA
Eligible TBA Non-
Eligible
TBA TBA
Eligible TBA Non-
Eligible 30 Years 2.5% 0.3978 0.5825 0.8921 0.2407 0.2992 0.6326 0.1309 0.1013 0.4512 30 Years 3.0% 0.1389 0.5626 1.5328 0.0872 0.3196 0.9268 0.0511 0.1497 0.5033 30 Years 3.5% 0.0565 0.2828 1.6573 0.0333 0.1798 0.9151 0.0171 0.1078 0.3962 30 Years 4.0% 0.0178 0.5100 1.1298 -0.0010 0.2605 0.6656 -0.0142 0.0861 0.3411 30 Years 4.5% 0.0532 0.5228 1.3677 0.0298 0.2435 0.7472 0.0133 0.0483 0.3142 30 Years 5.0% 0.0539 0.2815 2.0921 0.0354 0.0999 0.8222 0.0224 -0.0270 -0.0655 30 Years 5.5% 0.0942 0.1765 4.1265 0.0616 0.3416 1.2899 0.0388 0.4571 -0.6928 30 Years 6.0% 0.0875 0.9825 1.2296 0.0566 0.8061 1.0475 0.0350 0.6828 0.9202 15 Years 2.5% 0.0519 0.7519 0.9017 0.0335 0.3918 0.5236 0.0206 0.1401 0.2593 15 Years 3.0% 0.0234 0.7597 1.6928 0.0093 0.4263 0.8667 -0.0006 0.1933 0.2892 15 Years 3.5% 0.0129 0.6121 1.2564 0.0016 0.3892 0.8194 -0.0063 0.2334 0.5140 15 Years 4.0% 0.2009 0.4574 2.2669 0.1258 0.2994 0.7864 0.0733 0.1889 -0.2484
28
Table 5
, , . , , ,
Maturity Coupon Intercept Log Size Dealer Fixed Effects Adj.R2 30 Yrs. 2.5% 5.4145
(9.54) -0.5628 (-9.11)
F(51, 2,674) = 0.842 p = 0.779 0.0210
30 Yrs. 3.0% 1.1250 (20.71)
-0.1174 (-19.55)
F(100, 57,653) = 30.531 p = 0.000 0.0541
30 Yrs. 3.5% 0.3986 (14.44)
-0.0402 (-13.12)
F(119, 79,894) = 16.480 p = 0.000 0.0255
30 Yrs. 4.0% 0.4088 (9.92)
-0.0385 (-8.45)
F(103, 58,034) = 79.493 p = 0.000 0.1255
30 Yrs. 4.5% 0.6185 (7.99)
-0.0546 (-6.38)
F(101, 32,493) = 97.577 p = 0.000 0.2358
30 Yrs. 5.0% 0.8875 (7.10)
-0.0840 (-6.17)
F(76, 15,785) = 13.403 p = 0.000 0.0621
30 Yrs. 5.5% 3.0974 (9.63)
-0.3020 (-8.19)
F(61, 8,935) = 8.308 p = 0.000 0.0654
30 Yrs. 6.0% 6.5417 (10.80)
-0.6469 (-9.15)
F(45, 5,221) = 7.412 p = 0.000 0.0897
15 Yrs. 2.5% 0.3266 (6.95)
-0.0345 (-6.58)
F(70, 20,613) = 0.434 p = 1.000 -0.0000
15Yrs. 3.0% 0.3877 (6.48)
-0.0407 (-5.91)
F(79, 22,318) = 64.224 p = 0.000 0.1841
15 Yrs. 3.5% 1.0997 (8.79)
-0.1172 (-8.04)
F(68, 13,910) = 17.556 p = 0.000 0.0796
15 Yrs. 4.0% 2.2229 (8.50)
-0.2370 (-7.63)
F(68, 7,183) = 0.364 p = 1.000 0.0037
29
Table 6.
For every TBA CUSIP with two or more sales to customers on the same day, we calculate a size-weighted average price. For a sale i, Pricediffi is the difference between that sales price and the size-weighted average sales price for that CUSIP on the same day. The dealer who participates in sale i to a customer is defined as inactive if it completed fewer than 1,000 trades in the previous three months. For these dealers the dummy variable for inactive takes a value of one, for trades by other dealers, the value of the dummy variable is zero. Sizediffi is the difference between the log of the trade size of trade i, and the average log trade size of all sales in that CUSIP that day. We run the following regression using all TBA sales or all TBA sales in CUSIPs with a particular maturity-coupon combination.
T-statistics, reported in parentheses are robust. Panel A. All trades.
Maturity
Coupon
Inactive Dealer
Difference in Log Sizes
Constant
Obs.
R2
All
0.0728 (37.01)
-0.0110 (-45.24)
0.0142 (45.46)
322,037 0.0261
15 Years 2.5% 0.0340 (7.78)
-0.0040 (-13.27)
0.0055 (9.83)
20,321 0.0181
15 Years 3.0% 0.0253 (7.51)
-0.0034 (-9.03)
0.0054 (9.09)
21,786 0.0103
15 Years 3.5% 0.1614 (14.10)
-0.0036 (-6.92)
0.0029 (3.47)
13,188 0.0901
15 Years 4.0% 0.3321 (15.03)
-0.0090 (-6.34)
-0.0104 (-4.60)
6,389 0.1191
30 Years 2.5% -0.0140 (-0.78)
-0.0137 (-3.36)
0.0150 (2.74)
2,200 0.0073
30 Years 3.0% 0.0591 (15.06)
-0.0137 (-18.95)
0.0178 (20.75)
56,941 0.0251
30 Years 3.5% 0.0464 (14.53)
-0.0125 (-24.60)
0.0191 (28.83)
78,969 0.0226
30 Years 4.0% 0.0505 (11.11)
-0.0138 (-22.05)
0.0200 (23.89)
56,308 0.0243
30 Years 4.5% 0.0521 (10.38)
-0.0148 (-18.27)
0.0209 (18.64)
30,122 0.0312
30 Years 5.0% 0.0857 (6.26)
-0.0050 (-6.93)
0.0042 (3.80)
14,317 0.0198
30 Years 5.5% 0.0885 (7.74)
-0.0051 (-5.79)
0.0014 (1.59)
7,986 0.0522
30 Years 6.0% 0.0460 (6.26)
-0.0026 (-3.46)
-0.0011 (-0.80)
4,334 0.0221
30
Table 6 (continued)
Panel B. Difference between trade size and average trade size is less than $1 million
Maturity
Coupon Inactive Dealer
Difference in Log Sizes
Constant
Obs.
R2
All
0.0902 (17.32)
-0.0050 (-1.65)
-0.0029 (-1.19)
16,705 0.0310
15 Years 2.5% 0.0177 (1.55)
0.0026 (0.43)
-0.0003 (-0.04)
654 0.0016
15 Years 3.0% 0.0744 (4.82)
0.0126 (1.60)
-0.0168 (-2.32)
959 0.0390
15 Years 3.5% 0.4826 (15.63)
0.0032 (0.32)
-0.0254 (-3.65)
1,030 0.3810
15 Years 4.0% 0.3263 (17.22)
0.0191 (1.65)
-0.0468 (-5.24)
1,999 0.1755
30 Years 2.5% -0.1068 (-2.08)
-0.2335 (-2.84)
0.0950 (2.53)
279 0.0777
30 Years 3.0% 0.0165 (1.11)
-0.0323 (-2.34)
0.0279 (2.26)
1,523 0.0166
30 Years 3.5% 0.0477 (4.58)
-0.0017 (-0.31)
0.0066 (1.36)
2,183 0.0101
30 Years 4.0% 0.0574 (2.51)
0.0008 (0.10)
0.0033 (0.47)
1,826 0.0098
30 Years 4.5% 0.0590 (3.02)
-0.0356 (-2.91)
0.0170 (1.96)
1,143 0.0361
30 Years 5.0% 0.2529 (4.10)
-0.0131 (-2.30)
0.0043 (1.54)
820 0.1524
30 Years 5.5% 0.0610 (3.81)
-0.0187 (-5.03)
-0.0002 (-0.05)
1,034 0.0576
30 Years 6.0% 0.0743 (6.97)
-0.0172 (-3.24)
0.0022 (0.55)
684 0.1401
31
Table 7. For every TBA CUSIP with two or more purchases from customers on the same day, we calculate a size-weighted average price. For a purchase i, Pricediffi is the difference between that purchase price and the size-weighted average purchase price for that CUSIP on the same day. The dealer who participates in purchase i from a customer is defined as inactive if it completed fewer than 1,000 trades in the previous three months. For these dealers the dummy variable for inactive takes a value of one, for trades by other dealers, the value of the dummy variable is zero. Sizediffi is the difference between the log of the trade size of trade i, and the average log trade size of all purchases in that CUSIP that day. We run the following regression using all TBA purchases or all TBA purchases in CUSIPs with a particular maturity-coupon combination.
T-statistics, reported in parentheses are robust. Panel A. All purchases.
Maturity
Coupon
Inactive Dealer
Difference in Log Sizes
Constant
Obs.
R2
All
-0.0172 (-15.14)
0.0030 (21.54)
-0.0064 (-25.62)
461,574 0.0018
15 Years 2.5% -0.0155 (-7.16)
0.0051 (16.95)
-0.0086 (-16.43)
29,638 0.0107
15 Years 3.0% -0.0223 (-4.69)
0.0059 (13.82)
-0.0103 (-12.97)
27,269 0.0074
15 Years 3.5% -0.0163 (-4.14)
0.0033 (5.05)
-0.0036 (-2.71)
12,564 0.0035
15 Years 4.0% -0.0101 (-1.62)
0.0019 (2.31)
0.0004 (0.16)
5,482 0.0009
30 Years 2.5% -0.0132 (-0.94)
0.0119 (5.73)
-0.0117 (-4.15)
3,226 0.0093
30 Years 3.0% -0.0164 (-10.48)
0.0061 (25.43)
-0.0146 (-39.46)
107,695 0.0071
30 Years 3.5% -0.0216 (-10.96)
0.0029 (9.87)
-0.0068 (-15.02)
129,867 0.0022
30 Years 4.0% -0.0257 (-9.76)
0.0013 (3.02)
-0.0012 (-1.37)
74,164 0.0016
30 Years 4.5% -0.0265 (-6.13)
0.0008 (1.44)
0.0031 (2.72)
31,416 0.0023
30 Years 5.0% -0.0174 (-2.41)
0.0005 (0.86)
0.0030 (2.53)
14,404 0.0012
30 Years 5.5% 0.0158 (1.55)
0.0008 (1.64)
-0.0006 (-0.57)
8,246 0.0016
30 Years 6.0% 0.0198 (0.97)
0.0012 (1.25)
-0.0006 (-0.31)
4,741 0.0011
32
Table 7. Panel B. Difference between trade size and average trade size is less than $1 million.
Maturity
Coupon Inactive Dealer
Difference in Log Sizes
Constant
Obs.
R2
All
-0.0182 (-3.89)
0.0068 (3.50)
-0.0067 (-4.38)
28,690 0.0016
15 Years 2.5% -0.0354 (-3.31)
0.0061 (1.50)
-0.0074 (-1.90)
1,131 0.0124
15 Years 3.0% -0.0081 (-0.40)
0.0333 (3.29)
-0.0353 (-3.76)
1,385 0.0130
15 Years 3.5% -0.0774 (-2.34)
0.0262 (2.37)
-0.0034 (-0.44)
795 0.0290
15 Years 4.0% -0.0222 (-0.71)
0.0182 (2.23)
0.0017 (0.21)
653 0.0067
30 Years 2.5% -0.0130 (-0.64)
0.0283 (1.23)
0.0001 (0.02)
671 0.0053
30 Years 3.0% -0.0146 (-2.02)
-0.0022 (0.28)
-0.0066 (-1.06)
5,052 0.0009
30 Years 3.5% -0.0101 (-1.26)
-0.0005 (-0.11)
-0.0053 (-1.79)
6,295 0.0003
30 Years 4.0% -0.0142 (-1.94)
-0.0008 (-0.16)
-0.0039 (-1.00)
3,948 0.0009
30 Years 4.5% -0.0056 (-0.24)
0.0013 (0.20)
-0.0030 (-0.59)
1,843 0.0001
30 Years 5.0% -0.0449 (-1.78)
0.0146 (2.55)
-0.0035 (-0.79)
1,013 0.0147
30 Years 5.5% -0.0337 (-1.44)
0.0076 (1.83)
-0.0027 (-0.77)
841 0.0117
30 Years 6.0% -0.0065 (-0.58)
0.0207 (2.05)
-0.0061 (-1.05)
477 0.0225
33
Table 8.
∆ , ∆ . , ,
∆ , ∆ . , ,
Same Agency TBA All Agencies TBA
Maturity
Coupon Number Dealers
Sum of Obs.
Median α2 Coefficient
Median T-statistic
Median β2 Coefficient
Median T-statistic
Fannie Mae 30 Year 2.5% 22 431 -0.0035 -0.20 -1.2854 -6.88 30 Year 3.0% 50 2,818 -0.0348 -1.67 -1.0812 -7.66 30 Year 3.5% 61 4,866 -0.4756 -5.02 -1.0670 -6.32 30 Year 4.0% 67 4,826 -0.7775 -4.48 -0.9935 -5.14 30 Year 4.5% 54 2,914 -0.8973 -3.28 -1.1409 -4.07 30 Year 5.0% 37 1,166 -1.2914 -2.93 -1.3229 -2.67 15 Year 2.5% 53 2,748 0.0008 0.12 -0.9296 -8.58 15 Year 3.0% 68 3,857 -0.0003 -0.19 -0.8897 -3.26 15 Year 3.5% 48 2,519 -0.0430 -0.07 -1.1359 -2.04 15 Year 4.0% 38 1,170 -0.9631 -0.45 -0.8626 -0.46
Freddie Mac 30 Year 2.5% 47 1,173 -0.5200 -3.09 -0.5065 -2.99 30 Year 3.0% 70 3,414 -1.0446 -5.61 -1.0365 -5.34 30 Year 3.5% 84 5,527 -0.7788 -5.61 -0.8937 -6.63 30 Year 4.0% 71 4,568 -0.4202 -3.45 -1.1770 -5.14 30 Year 4.5% 68 2,800 -0.1651 -2.00 -0.9901 -3.71 30 Year 5.0% 48 1,458 -0.1423 -0.71 -0.9852 -2.28 15 Year 2.5% 64 2,339 -0.3575 -1.73 -0.3562 -1.73 15 Year 3.0% 61 3,198 -0.7464 -2.11 -0.7947 -2.40 15 Year 3.5% 60 2,084 -1.0168 -1.97 -0.8992 -2.22 15 Year 4.0% 46 1,262 -0.2046 -0.85 -0.6273 -0.80
Ginnie Mae 30 Year 2.5% 38 1,102 -0.0023 -0.02 -0.0287 -0.20 30 Year 3.0% 42 1,226 0.0000 0.08 0.1386 0.37 30 Year 3.5% 62 1,880 -0.6444 -7.24 -0.8448 -3.69 30 Year 4.0% 47 1,761 -0.7216 -5.19 -1.0001 -2.23 30 Year 4.5% 43 1,174 -0.8321 -5.86 -1.0261 -2.69 30 Year 5.0% 30 554 -1.0489 -2.69 -0.7755 -0.85 15 Year 2.5% 39 1,173 0.0059 0.28 -0.9046 -1.40 15 Year 3.0% 51 1,705 0.0239 0.37 -0.9293 -0.79 15 Year 3.5% 46 1,218 0.2634 0.35 0.0803 0.05 15 Year 4.0% 33 636 -0.4219 -1.00 -1.3078 -1.17
34
Table 9. The impact of daily changes in specified pool inventory on same day changes in TBA inventory of MBS with the same coupon and maturity.
Panel A. Dealer observations weighted by number of days. A day is only included in a dealer regression if the change in specified pool inventory is different from zero. Standard errors of both coefficients must be greater than zero for a dealer regression to be included.
∆ , ∆ . , ∆ , ,
Regression Coefficients on Specified Pool Inventory Change
Coefficients on Lagged TBA Inventory Change
Maturity
Coupon
Number Dealers
Sum of Obs.
Median Coefficient
Mean Coefficient
Median T-statistic
Mean T-statistic
Median Coefficient
Mean Coefficient
Median T-statistic
Mean T-statistic
30 Year 2.5% 18 431 -0.8761 -0.7452 -6.70 -10.77 -0.1347 -0.1375 -0.30 -0.79 30 Year 3.0% 45 3,583 -0.9870 -0.9499 -9.93 -11.29 -0.0130 0.0131 -0.12 -0.07 30 Year 3.5% 59 6,708 -0.8983 -0.8801 -11.27 -11.42 -0.0570 -0.0329 -1.00 -1.06 30 Year 4.0% 52 6,199 -1.0057 -0.9312 -7.00 -8.15 -0.0793 -0.0564 -1.43 -0.88 30 Year 4.5% 49 3,886 -0.9842 -0.5265 -5.41 -6.93 -0.0050 -0.2092 -0.15 0.24 30 Year 5.0% 33 1,935 -0.7478 -0.8350 -3.46 -3.03 -0.0892 0.2010 -0.71 -0.48 30 Year 5.5% 21 454 -1.5847 0.0591 -2.26 -3.11 -2.2644 -3.1094 -0.20 0.51 30 Year 6.0% 16 150 -0.3296 -1.0166 -0.30 -0.19 0.3923 0.3617 0.42 0.81 15 Year 2.5% 41 3,821 -0.9118 -0.9620 -9.68 -11.25 -0.0665 -0.2007 -1.00 -1.05 15 Year 3.0% 50 5,513 -0.7645 -0.7005 -7.57 -7.51 -0.0635 -0.0827 -1.06 -1.11 15 Year 3.5% 48 3,545 -0.9087 -0.7793 -5.32 -5.15 0.0138 -0.0466 0.18 -0.50 15 Year 4.0% 38 2,289 -0.8122 -0.8035 -4.39 -4.81 -0.0177 -0.0699 -0.13 -0.14
35
Table 9, Panel B. Dealer observations weighted by number of days. A day is only included in a dealer regression if the change in specified pool inventory, or the change in specified pool odd maturity inventory is different from zero. Standard errors of both coefficients must be greater than zero for a dealer regression to be included.
∆ , ∆ . 15 30 , ∆ . , ,
Sum of Obs.
Coefficients on Specified Pool Inventory Change
Coefficients on Odd Maturity Specified Pool Inventory Change
Maturity (Years)
Coupon
Number Dealers
Median Coefficient
Mean Coefficient
Median T-statistic
Mean T-statistic
Median Coefficient
Mean Coefficient
Median T-statistic
Mean T-statistic
22-35 2.5% 42 4,987 -0.0967 -0.3387 -1.55 -7.23 -0.0048 -0.0611 -0.24 -0.77 22-35 3.0% 63 6,844 -0.9433 -0.7687 -9.50 -11.11 -0.5333 -0.5576 -2.77 -3.27 22-35 3.5% 83 10,018 -0.7894 -0.7393 -9.18 -10.80 -0.7437 -0.6619 -3.18 -4.50 22-35 4.0% 82 10,929 -0.8428 -0.7423 -7.87 -8.41 -0.7442 -0.6497 -7.31 -9.85 22-35 4.5% 82 10,933 -0.9094 -0.7093 -6.11 -8.18 -0.4569 -0.4406 -6.92 -9.60 22-35 5.0% 75 11,699 -0.6736 -0.4182 -2.50 -3.55 -0.2486 -0.2259 -6.43 -7.50 22-35 5.5% 55 10,964 -0.1560 -0.5257 -0.92 -2.16 -0.1394 -0.1597 -5.90 -8.25 22-35 6.0% 56 10,821 -0.0528 -0.3255 -0.19 -0.35 -0.0626 -0.1097 -3.17 -4.79 10-21 2.5% 65 9,760 -0.7378 -0.6201 -8.29 -8.84 -0.0288 -0.1142 -0.83 -1.61 10-21 3.0% 70 11,157 -0.7567 -0.6704 -7.29 -8.25 -0.0735 -0.1373 -1.37 -2.16 10-21 3.5% 77 10,562 -0.5825 -0.5421 -3.07 -4.22 -0.1174 -0.1441 -1.76 -2.65 10-21 4.0% 74 10,981 -0.6390 -0.6687 -3.74 -5.02 -0.1763 -0.2001 -3.86 -4.68
36
Table 9, Panel C. Dealer observations weighted by number of days. A day is only included in a dealer regression if the change in specified pool TBA-eligible inventory, or the change in specified pool TBA-ineligible inventory is different from zero. Standard errors of both coefficients must be greater than zero for a dealer regression to be included.
∆ , ∆ . , ∆ . , ,
Sum of Obs.
Coefficients on TBA-Eligible Specified Pool Inventory Change
Coefficients on TBA-Ineligible Specified Pool Inventory Change
Maturity
Coupon
Number Dealers
Median Coefficient
Mean Coefficient
Median T-statistic
Mean T-statistic
Median Coefficient
Mean Coefficient
Median T-statistic
Mean T-statistic
30 Year 2.5% 25 1,682 -0.5855 -0.6200 -3.17 -9.19 -0.0189 -0.0607 -0.14 -1.82 30 Year 3.0% 51 5,179 -0.9669 -0.8868 -9.07 -10.71 -0.4440 -0.1971 -1.29 -1.81 30 Year 3.5% 69 8,117 -0.9183 -0.8504 -9.48 -10.77 -0.6235 -0.5169 -1.90 -2.22 30 Year 4.0% 62 7,407 -0.9836 -0.9111 -6.65 -8.11 -0.9291 -0.7398 -1.62 -1.91 30 Year 4.5% 54 4,457 -0.9619 -0.5485 -5.37 -7.00 -0.8025 -0.6852 -0.93 -1.39 30 Year 5.0% 35 2,206 -0.7974 -0.8272 -3.71 -3.30 -0.5708 0.3305 -0.53 -0.66 30 Year 5.5% 17 410 -1.8508 -0.6517 -2.40 -3.91 0.0000 -0.5344 0.00 0.47 30 Year 6.0% 10 126 -0.3629 -0.2826 -0.39 -0.74 -2.0629 -1.7635 -0.17 -0.14 15 Year 2.5% 45 4,713 -0.8925 -0.8942 -8.59 -10.94 -0.4237 -0.5326 -0.71 -1.34 15 Year 3.0% 51 5,961 -0.7891 -0.7058 -7.72 -7.81 -0.1500 -0.1591 -0.36 -0.80 15 Year 3.5% 44 3,940 -0.8859 -0.7883 -5.36 -5.18 -0.1897 -0.1503 -0.23 -0.16 15 Year 4.0% 41 2,404 -0.8413 -0.8029 -4.48 -4.77 -0.3728 -5.1305 -0.33 -0.73
37
Table 10. Panel A. Regressions of consecutive trade changes in the price of specified pools on changes in trade type and interactions between changes in trade type and trade sizes, TBA eligibility, and whether the specified pool maturity is exactly 15 or 30 years. The following regression is run:
Δ Δ Δ ∙ ln Δ ∙ Δ ∙ ∙ ln Δ∙ 30 Δ ∙ 30 ∙ ln Σ , .
ΔPt is the difference in price for two successive trades of a particular specified pool. ΔQt is the difference in trade type between two consecutive trades. It is +1 if trade t-1 was a sale by a customer to a dealer and trade t was a purchase by a customer from a dealer. It is -1 if trade t-1 was a purchase from a customer by a dealer and trade t was a sale to a customer from a dealer. TBA Eligible is a dummy variable that takes a value of one if the specified is eligible for TBA trading and zero otherwise. 30Year is a dummy variable that has a value of one if the specified pool has a maturity of 15 or 30 years, and zero if the maturity is different. Observations from different specified pools are included together in the regressions.
38
Panel A. Regression results.
Maturity
Coupon
ΔQ
ΔQ x 30 (15)Year
ΔQ x TBA Eligible
ΔQ x Trade Size
ΔQ x 30 (15) Year x Size
ΔQ x TBA Elg x Size
Return Variables
Obs.
R2
22-35 Years 2.5% 2.3175 (15.04)
-0.8734 (-3.97)
-0.3596 (-1.33)
-0.0981 (-9.78)
.0376 (2.62)
0.0018 (0.11)
Yes 5,273 0.2725
22-35 Years 3.0% 1.8893 (10.18)
0.8809 (4.95)
-1.7384 (-11.67)
-0.0711 (-5.99)
-0.0606 (-5.40)
.0795 (8.36)
Yes 21,132 0.1577
22-35 Years 3.5% 4.0393 (34.12)
-1.2591 (-13.10)
-2.2529 (-20.78)
-0.2088 (-26.15)
0.0694 (10.41)
0.1143 (16.98)
Yes 53,731 0.2226
22-35 Years 4.0% 3.6452 (37.69)
-0.7740 (-13.87)
-1.8968 (-19.11)
-0.1875 (-28.15)
0.0433 (11.05)
0.0902 (13.66)
Yes 78,032 0.2458
22-35 Years 4.5% 3.7900 (29.73)
-0.7366 (-10.64)
-1.9792 (-15.02)
-0.1937 (-20.31)
0.0280 (5.84)
0.1013 (10.50)
Yes 78,637 0.1375
22-35 Years 5.0% 4.0192 (18.55)
-0.8149 (-3.47)
-2.3581 (-10.73)
-0.1961 (-11.37)
0.0033 (0.22)
0.1301 (7.44)
Yes 81,390 0.0540
22-35 Years 5.5% 3.8340 (15.99)
-1.0610 (-1.11)
-2.3237 (-9.54)
-0.1730 (-8.77)
0.0459 (0.71)
0.1125 (5.60)
Yes 89,313 0.0366
22-35 Years 6.0% 4.4117 (13.56)
-1.1289 (-0.97)
-2.0964 (-6.33)
-0.2045 (-7.45)
0.0635 (0.75)
0.1136 (4.05)
Yes 58,029 0.0605
10-21 Years 2.5% 1.9129 (5.20)
0.0662 (0.44)
-0.5584 (-1.49)
-0.0621 (-2.64)
-0.0130 (-1.38)
-0.0025 (-0.11)
Yes 18,580 0.0756
10-21 Years 3.0% 2.4315 (6.05)
-0.1051 (-0.95)
-0.8883 (-2.20)
-0.1146 (-4.38)
0.0016 (0.23)
0.0391 (1.49)
Yes 33,151 0.0936
10-21 Years 3.5% 2.8167 (8.52)
-0.0860 (-0.99)
-1.6705 (-5.05)
-0.1523 (-7.28)
0.0010 (0.17)
0.1029 (4.90)
Yes 32,511 0.2222
10-21 Years 4.0% 2.7111 (6.77)
-0.3957 (-3.87)
-1.5044 (-3.73)
-0.1706 (-6.37)
0.0142 (2.10)
0.1192 (4.42)
Yes 30,971 0.1891
39
Table 10. Panel B. Trading cost estimates from the regressions.
Trades = $100,000 Trades = $1,000,000 Trades = $5,000,000
Maturity
Coup.
TBA Eligible, 30 Year
Not TBA Eligible, 30
Year
Not TBA Eligible, Not
30 Years
TBA Eligible, 30 Year
Not TBA Eligible, 30
Year
Not TBA Eligible, Not
30 Years
TBA Eligible, 30 Year
Not TBA Eligible, 30
Year
Not TBA Eligible, Not
30 Years 22-35 Yrs 2.5% 0.5450 0.8877 1.4144 0.2752 0.6095 0.9629 0.0867 0.4151 0.6473 22-35 Yrs 3.0% 0.5509 1.5571 1.2343 0.3105 0.9505 0.9069 0.1424 0.5266 0.6780 22-35 Yrs 3.5% 0.2968 1.4966 2.1163 0.1815 0.8549 1.1548 0.1010 0.4063 0.4827 22-35 Yrs 4.0% 0.4758 1.5426 1.9181 0.2265 0.8783 1.0546 0.0523 0.4140 0.4510 22-35 Yrs 4.5% 0.4807 1.5271 2.0058 0.1840 0.7639 1.1137 -0.0234 0.2305 0.4901 22-35 Yrs 5.0% 0.2679 1.4281 2.2130 -0.0213 0.5399 1.3099 -0.2234 -0.0809 0.6786 22-35 Yrs 5.5% 0.3152 1.6025 2.2408 0.2480 1.0172 1.4442 0.2011 0.6081 0.8873 22-35 Yrs 6.0% 0.9339 1.9842 2.5282 0.8075 1.3349 1.5864 0.7193 0.8811 0.9281 10-21 Yrs 2.5% 0.7062 1.2879 1.3410 0.3489 0.9423 1.0550 0.0991 0.7007 0.8552 10-21 Yrs 3.0% 0.7578 1.2861 1.3762 0.4177 0.7659 0.8485 0.1800 0.4024 0.4797 10-21 Yrs 3.5% 0.6141 1.3372 1.4144 0.3911 0.6405 0.7132 0.2352 0.1535 0.2331 10-21 Yrs 4.0% 0.4693 0.8755 1.1402 0.2984 0.1556 0.3548 0.1789 -0.3476 -0.1942
40
Figure 1. TBA and specified pool volume (in thousands of dollars) by day. Includes only purchases by investors from dealers.
$0
$25,000,000
$50,000,000
$75,000,000
$100,000,000
$125,000,000
$150,000,000
5/16/2011 9/11/2011 5/8/2012 11/1/2012 4/30/2012
specified pool
TBA Outright
TBA Dollar Roll
41
Figure 2. Weekly 15 and 30 year mortgage rates. Source: Freddie Mac.
0.00
1.00
2.00
3.00
4.00
5.00
4/28/2011 10/27/2011 4/26/2012 10/25/2012 4/25/2013
15 Year Rate
30 Year Rate
42
Figure 3. Mean Daily Prices of MBS Traded in the TBA and Specified Pool Markets
15 year 3%
15 year 4%
98
99
100
101
102
103
104
105
106
107
108
5/19/2011 12/2/2011 4/30/2012 10/3/2012 4/18/2013
TBA
Specified Pool
100
102
104
106
108
110
5/16/2011 8/8/2011 11/8/2011 4/25/2012
TBA
Specified Pool
43
30 year, 4.0%
30 Year, 5%
98
100
102
104
106
108
110
112
5/16/2011 10/12/2011 3/12/2012 8/8/2012 1/25/2013
TBA
Specified Pool
104
106
108
110
112
114
116
5/15/2011 8/25/2011 1/3/2012 7/18/2012
TBA
Specified Pool