1

The Effects of Quantitative Easing on Interest Rates*

Arvind Krishnamurthy 1and Annette Vissing-Jorgensen2

June 22, 2011

Abstract: We evaluate the effect of the Federal Reserve’s purchase of long-term Treasuries and 

other long-term bonds ("QE1" in 2008-2009 and "QE2" in 2010-2011) on interest rates. Using 

an event-study methodology that exploits both daily and intra-day data, we find a large and 

significant drop in nominal interest rates on long-term safe assets (Treasuries, Agency bonds, 

and highly-rated corporate bonds). This occurs mainly because there is a unique clientele for 

long-term safe nominal assets, and the Fed purchases reduce the supply of such assets and hence 

increase the equilibrium safety-premium. We find only small effects on nominal (defaultadjusted) interest rates on less safe assets such as Baa corporate rates. The impact of quantitative 

easing on MBS rates is large when QE involves MBS purchases, but not when it involves 

Treasury purchases, indicating that a second main channel for QE is to affect the equilibrium 

price of mortgage-specific risk. Evidence from inflation swap rates and TIPS show that expected 

inflation increased due to both QE1 and QE2, implying that reductions in real rates were larger 

than reductions in nominal rates. Our analysis implies that (a) it is inappropriate to focus only on 

Treasury rates as a policy target because QE works through several channels that affect particular 

assets differently, and (b) effects on particular assets depend critically on which assets are 

purchased.

 

1 Kellogg School of Management, Northwestern University and NBER

2 Kellogg School of Management, Northwestern University, NBER and CEPR

* We thank Olivier Blanchard, Greg Duffee, Charlie Evans, Ester Faia, Monika Piazzesi, Tsutomu Watanabe, and 

participants at seminars and conferences at the Chicago Fed, ECB, San Francisco Fed, Princeton University, 

Northwestern University, the NAPA Conference on Financial Markets Research, and CEMFI for their suggestions.

We thank Kevin Crotty and Juan Mendez for research assistance.
2

1. Introduction

The Federal Reserve has recently pursued the unconventional policy of purchasing large 

quantities of long-term securities, including Treasuries, Agency bonds, and Agency Mortgage 

Backed Securities (quantitative easing, or “QE”). The stated objective of quantitative easing is to 

reduce long-term interest rates in order to spur economic activity.3 There is significant evidence 

that QE policies can alter long-term interest rates. For example, Gagnon, Raskin, Remache, and 

Sack (2010) present an event-study of QE1 that documents large reductions in interest rates on 

dates associated with positive QE announcements. Swanson (2010) presents confirming eventstudy evidence from the 1961 Operation Twist, where the Fed/Treasury purchased a substantial

quantity of long-tem Treasuries. Apart from the event-study evidence, there are papers that look 

at lower frequency variation in the supply of long-term Treasuries and documents causal effects 

from supply to interest rates (see, for example, Krishnamurthy and Vissing-Jorgensen (2010)).4

While it is clear from this body of work that QE lowers long-term interest rates, the 

channels through which this reduction occurs are less clear. The main objective of this paper is 

to evaluate these channels. We review the principal theoretical channels through which QE may 

operate and then examine the event-study evidence with an eye towards distinguishing among 

these channels. We furthermore supplement previous work by adding evidence from QE2 and 

evidence based on intra-day data. Studying intra-day data allows us to document price reactions 

and trading volume in the minutes after the main announcements, thus increasing confidence that 

any effects documented in daily data are causal.

It is necessary to understand the channels of operation in order to evaluate the success of 

policy. Here is an illustration of this point: Krishnamurthy and Vissing-Jorgensen (2010) 

(hereafter, KVJ) present evidence for a channel whereby changes in long-term Treasury supply 

work through altering the safety premia on near zero-default-risk long-term assets. That is, under 

their theory, QE works particularly to lower the yields of bonds which are extremely safe, such 

as Treasuries or Aaa bonds. But, even if a policy affects Treasury interest rates, such rates may 

not be policy relevant. Most economic activity is funded by debt that is not as free of credit risk 

as Treasuries or Aaas. For example, only a small fraction of corporate bonds are rated Aaa or 

Aa, with the majority of corporate debt being A, Baa, or lower. If the objective of QE is to 

 

3

http://www.newyorkfed.org/newsevents/speeches/2010/dud101001.html

4 Other papers in the literature that have examined Treasury supply and bond yields include Bernanke, Reinhart and 

Sack (2004), Greenwood and Vayanos (2010), D’Amico and King (2010), and Hamilton and Wu (2010). 
3

reduce interest rates paid by the majority of corporations and households which may then spur 

spending and economic growth, then examining supply effects on Treasury rates could be 

misleading.5 One of the principal findings of this paper is that a Treasuries-only QE policy will 

have significant effects on long-term Treasury rates and rates on highly-rated corporate bonds, 

but smaller effects on lower-rated corporate bonds, and mortgage rates, both of which are very 

policy relevant. The large reductions in mortgage rates due to QE1 appear to be driven by the 

fact that QE1 involved large purchases of agency MBS. For QE2, which involved only Treasury 

purchases, we find a substantial impact on Treasury rates, but almost no impact on MBS rates. 

Clouse, Henderson, Orphanides, Small, and Tinsley (2000) make similar arguments in their 

discussion of the theoretical channels for quantitative easing, but do not offer empirical evidence 

distinguishing among the channels. A principal contribution of the present paper is to use a 

variety of asset market data, including derivatives data, to distinguish among the channels of QE.

The next section of the paper lays out the channels through which QE may be expected to 

operate. We then present an event study of QE1 and evaluate the channels. Section 3 presents a 

similar event study and evaluation of channels for QE2. Section 4 presents regression analysis 

building on the work from KVJ to provide estimates of the expected effects of QE on interest 

rates. Section 5 concludes. All tables and graphs appear at the end of the paper.

2. Channels

a. Duration Risk Channel

Vayanos and Vila (2009) offer a theoretical model for the duration risk channel. The model 

produces a risk premium on a bond of maturity t that is the product of the duration of a maturity t 

bond and the aggregate amount of duration risk borne by the marginal bond market investor. By 

purchasing long-term Treasuries, Agency debt, or Agency MBS, policy can reduce the duration 

risk in the hands of investors and thereby alter the yield curve, particularly reducing longmaturity bond yields relative to short-maturity yields. To deliver these results the model departs 

from a frictionless asset pricing model. The principal departure that generates the duration risk 

 

5 A good example to illustrate this point is to consider the behavior of Treasury Bill rates in the fall 2008 period. 

Such rates were close to zero and substantially below most of other corporate borrowing rates. It would have been 

incorrect to look at the low Treasury Bill rate and conclude that credit was easy – the low rates reflect an extremely 

high investor preference for extremely safe and liquid assets.
4

premium result is the assumption that the bond market is segmented and that there is a subset of 

risk-averse investors who bear (all or a lot of) the interest rate risk in owning bonds. 

An important but subtle issue in using the model to think about QE is to ask whether the 

model applies narrowly to a particular asset class (e.g., only the Treasury market) or whether it 

applies broadly to all fixed income instruments. For example, if the segmentation is of the form 

that some investors had a special demand for 10 year Treasuries, but not 10 year corporate bonds 

(or mortgages or bank loans), then the Fed’s purchase of 10 year Treasuries can be expected to 

affect Treasury yields, but not corporate bond yields. More broadly, if segmentation 

assumptions describe activity in only the Treasury market, then the model has implications for 

Treasury market yield curves, but not other fixed income yield curves. Vayanos and Vila (2009) 

do not take a stand on the issue. Greenwood and Vayanos (2010) offer evidence for how a 

change in the relative supply of long-term versus short-term Treasuries affects the spread 

between long-term and short-term Treasury bonds. This evidence also does not settle the issue, 

because it only focuses on Treasury data.

Recent studies on QE have interpreted the model as being about the broad fixed income 

market (see Gagnon, Raskin, Remache, and Sack, 2010), and that is how we proceed.

6 Under this 

interpretation, the duration risk channel has two principal predictions:

i. QE decreases the yield on all long-term nominal assets, including Treasuries, 

corporate bonds, and mortgages.

ii. The effects are proportional to the duration of a bond, with larger effects for longer 

duration assets.

b. Liquidity Channel

The QE strategy involves purchasing long-term securities and paying by increasing reserve 

balances. Reserve balances are a more liquid asset than long-term securities. Thus, QE 

increases the liquidity in the hands of investors and thereby decreases the liquidity premium on 

the most liquid bonds. There are many theoretical references for the liquidity channel. Almost 

all of the work on the effects of open market operations describes a liquidity channel.

 

6

Note however that the broad fixed income market is much larger than the Treasury market, so that, under the 

Vayanos and Vila (2009) model, the Fed’s purchase of duration risk should be expected to have a smaller effect on 

interest rates via this broad market channel.
5

It is important to emphasize that this channel implies an increase in Treasury yields. 

That is, it is commonly thought that Treasury bonds carry a liquidity price premium, and that this 

premium has been high during particularly severe periods of the crisis. An expansion in liquidity 

can be expected to reduce such a liquidity premium and increase yields. This channel thus 

predicts that:

i. QE raises Treasury yields, rather than lowers them.

ii. QE produces large effects for liquid assets, and no effects for illiquid assets.

c. Safety Premium Channel

Krishnamurthy and Vissing-Jorgensen (2010) offer evidence that there are significant clienteles 

for long-term safe (i.e., near zero-default-risk) assets that lower the yields on such assets.7The 

evidence comes from relating the spread between Baa bonds and Aaa bonds (or Agency bonds) 

to variation in the supply of long-term Treasuries, over a period from 1925 to 2008. They report 

that when there are less long-term Treasuries, so that there are less long-term safe assets to meet 

clientele demands, the spread between Baa and Aaa bonds rises. 

The safety channel of KVJ is not the same as the risk premium of a standard asset pricing 

model; it reflects a deviation due to clientele demand. A simply way to think about investor 

willingness to pay extra for assets with very low default risk is to plot an asset’s price against its

expected default rate. KVJ argue that this curve is very steep for low default rates, with a slope 

that flattens as the supply of Treasuries increases. Figure 1 illustrates the distinction. The bottom 

line is the C-CAPM value of a risky bond. As default risk rises, the price of the bond falls. The 

distance from this line up to the middle (solid) line illustrates the safety premium of KVJ; for 

bonds that have very low default, the bond price rises as a function of the safety of the bond. 

The figure also illustrates the dependence of the safety premium on the supply of long-term 

Treasuries. The distance from the bottom line to the top line is the safety premium for a smaller 

supply of safe assets. The clientele demand shifts the premium up. This dependence on the 

premium on the supply of long-term Treasuries is how KVJ distinguish a standard risk premium 

explanation of defaultable bond pricing with the clientele-driven safety demand.

 

7 Many discussions of the effects of QE refer to a “portfolio balance channel,” without being precise on the nature 

of investor portfolio demand underlying this channel. The safety channel of KVJ makes precise a portfolio balance 

channel: investors have a clientele demand for near-zero default risk assets.
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This same effect may be expected to play out in QE. However, there is a subtle issue in 

thinking about different asset classes in QE: Treasury and Agency bonds are clearly safe in the 

sense of offering an almost sure nominal payment; however Agency MBS has significant 

prepayment risk which means that it may not meet clientele safety demands. The safety channel 

thus predicts that:

i. QE involving Treasuries and Agencies lowers the yields on safe assets.

ii. The largest effects should occur for the safest assets, with no effects on low-grade 

debt such as Baa bonds or bonds with prepayment risk such as MBS.

We expect Baa bonds to be the relevant cutoff for these safety effects. First, such bonds 

are the boundary between investment grade and non-investment grade securities, so that if driven 

by safety clientele demands, the Baa bond forms a natural threshold. More rigorously, 

Longstaff, Mithal and Neis (2006) use credit default swap data from December 2000 to October 

2001 to show that the component of yields that is hard to explain by purely default risk 

information is about 50 bps for Aaa and Aa rated bonds, and about 70 bps for lower-rated bonds, 

suggesting that the cutoff for bonds whose yields are not affected by safety premia is somewhere 

around the A/Baa rating.

d. Signaling Channel

Eggertson and Woodford (2003) argue that non-traditional monetary policy can have a beneficial 

effect in lowering long-term bond yields only if such policy serves as a credible commitment by 

the central bank to keep interest rates low even after the economy recovers (i.e., lower than what 

a Taylor rule may call for). Clouse, et. al. (2000) argue that such a commitment can be achieved 

when the central bank purchases a large quantity of long duration assets in QE. If the central 

bank raises rates, it takes a loss on these assets. To the extent that the central bank weighs such 

losses in its objective function, purchasing long-term assets in QE serves as a credible 

commitment to keep interest rates low. Furthermore, some of the Federal Reserve 

announcements regarding QE explicitly contained discussion of the Federal Reserve’s policy on 

future Federal Funds rates. 

The signaling channel affects all bond market interest rates, since lower future Federal

Funds rates, via the expectations hypothesis, can be expected to affect all interest rates. We 

examine this channel by measuring changes in the prices of the Federal Funds futures contract, 
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as a guide to market expectations of future Federal Funds rates. Piazzesi and Swanson (2008) 

show that these futures prices reflect a risk premium, in addition to such expectations. As we 

explain, we adjust the futures prices to remove risk premium effects. The signaling channel 

should have the largest impact in lowering short/intermediate maturity rates rather than long 

maturity rates, since the commitment to keep rates low only lasts until the economy recovers and 

the Fed can sell the accumulated assets. Given forecasts of the duration of the current lowgrowth period, such maturities will be in the 1 to 3 year range. Federal Funds futures contracts 

only extend out only to 2 years. Thus, we also examine the yields on bonds of different 

maturities to discern this effect.

e. Prepayment Risk Premium Channel

QE1 involved the purchase of $1.25tn of Agency MBS. Gabaix, Krishnamurthy, and Vigneron 

(2007) present theory and evidence that mortgage prepayment risk carries a positive risk 

premium, and that this premium depends on the quantity of prepayment risk borne by mortgage 

investors. The theory requires that the MBS market is segmented and that a class of arbitrageurs 

who operate predominantly in the MBS market are the relevant investors in determining the 

pricing of prepayment risk. This theory is similar to the Vayanos and Vila (2009) explanation of 

the duration risk premium, and more broadly fits into theories of intermediary asset pricing (see 

He and Krishnamurthy, 2010).

This channel is particularly about QE1 and its effects on MBS yields, which reflect a 

prepayment risk premium:

i. QE1 lowers MBS yields relative to other bond market yields.

ii. QE2, which does not involve MBS purchases, does not affect MBS yields.

f. Default Risk Channel

Lower grade bonds such as Baa bonds carry higher default risk than Treasury bonds. QE may 

affect the quantity of such default risk as well as the price (i.e. risk premium) of the default risk.

If QE succeeds in stimulating the economy, we can expect that the default risk of corporations 

will fall, and hence Baa rates will fall. Moreover, standard asset pricing models predict that 

investor risk aversion will fall as the economy recovers, implying a lower default risk premium. 

Finally, extensions of the intermediary pricing arguments we have offered above for pricing 
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prepayment risk can imply that increasing health/capital in the intermediary sector can further 

lower the risk premium on default risk.

g. Inflation Channel

To the extent that QE is expansionary, it increases inflation expectations, and this can be 

expected to have an effect on interest rates. In addition, some commentators have argued that 

QE may increase tail risks surrounding inflation.

8

 That is, in an environment where investors are 

unsure about the effects of policy on inflation, policy actions may lead to greater uncertainty

over inflation outcomes. Others have argued that aggressive policy decreases uncertainty in the 

sense that it effectively combats the possibility of a deflationary spiral. Ultimately, this is an 

issue that can only be sorted out by data. We propose looking at the implied volatility on interest 

rate options, since a rise in inflation uncertainty will plausibly also lead to a rise in interest rate

uncertainty and implied volatility. The inflation channel thus predicts:

i. QE increases the rate on inflation swaps as well inflation expectations as measured by 

the difference between nominal bond yields and TIPS.

ii. QE may increase or decrease interest rate uncertainty as measured by the implied 

volatility on swaptions.

3. Evidence from QE1

a. Event Study

Gagnon, et. al., (2010) provide an event study of QE1 based on the announcements of long-term 

asset purchases by the Federal Reserve in the late-2008 to 2009 period (“QE1”). These policies 

included purchase of mortgage-backed securities, Treasury securities and Agency securities. 

Gagnon, et. al., (2010) identify eight event dates beginning with the 11/25/08 announcement of 

the Fed’s intent to purchase $500 bn of Agency MBS and $100bn of Agency debt, and running 

into the summer of 2009. We focus on the first five of these event dates (11/25/2008, 12/1/2008, 

12/16/2008, 1/28/2009, and 3/18/2009), leaving out three later event dates on which only small 

yield changes occurred.

There was considerable turmoil in financial markets in the period from the fall of 2008 to 

the spring of 2009, which makes inference from an event-study somewhat tricky. Some of the 

 

8

See Calomiris and Tallman, 2010, op-ed, “In Monetary Targeting, Two Tails are Better than One.”
9

assets we consider, such as corporate bonds and CDS, are less liquid. During a period of low 

liquidity, the prices of such assets may react slowly in response to an announcement. We deal 

with this issue by presenting two-day changes for all assets (from the day prior to the day after 

the announcement). In the data, for high liquidity assets like Treasuries, two-day changes are 

almost the same as one-day changes. For low liquidity assets, the two-day changes are almost 

always higher than one-day changes; there appears to be a continuation pattern in the yield 

changes. 

The second issue that arises is that we cannot be sure that the identified events are in fact 

important events, or the dominant events for the identified event day. That is, other significant 

economic news arrives through this period and potentially creates measurement error problems 

for the event-study. To increase our confidence that QE1 announcements were the dominant 

news on the five event dates we study, Figure 2 presents graphs of intraday movements in 

Treasury yields and trading volume for each of the event dates. The figure is based on data from 

BG Cantor and the data graphed is for the on-the-run 10 year Treasury bond at each date. Yields 

graphed are averages by the minute and trading volume graphed is total volume by minute. The 

vertical lines indicate the minute of the announcement, defined as the minute of the first article 

covering the announcement in Factiva. These graphs show that the events identify significant 

movements in Treasury yields and Treasury trading volume and that the announcements do 

appear to be the main piece of news coming out on the event days, especially on 12/1/2008, 

12/16/2008 and 3/18/2009. For 11/25/2008 and 1/28/2009, the trading volume graphs also 

suggests that the announcements are the main events, with more mixed evidence from the yield 

graphs for those days.

Table 1 presents data on two-day changes in Treasury, Agency, and Agency MBS yields 

around the main event-study dates, spanning a period from 11/25/08 to 3/18/09. Over this period 

it became evident from Fed announcements that the government intended to purchase a large 

quantity of long-term securities. Across the five event dates, interest rates fell across the board 

on long-term bonds, consistent with a contraction of supply effect. Now consider the channels 

through which the supply effect may have worked. 

In all tables we provide tests of the statistical significance of the rate changes 

documented, focusing on the total change shown in the last row of each table (for QE1 and QE2 

separately). Statistical significance is assessed by regressing the daily changes for the variable in 
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focus on 4 dummies: A dummy for whether there was a QE1 announcement on this day, a 

dummy for whether there was a QE1 announcement on the previous day, a dummy for whether 

there was a QE2 announcement on this day, and a dummy for whether there was a QE2 

announcement on the previous day. This regression is estimated on daily data from 2008 to 

November 4, 2011, using OLS but with robust standard errors to account for heteroscedasticity. 

F-tests for the QE dummy coefficients being zero are then used to assess statistical significance. 

When testing for statistical significance of 2-day changes, the F-test is a test of whether the sum 

of the coefficient on the QE dummy (QE1 or QE2) and the coefficient on the dummy for a QE 

announcement (QE1 or QE2) on the previous day, is equal to zero. We are not able to present 

statistical tests for results involving CDS rates because we only have CDS data for the main 

event days.

b. Duration Risk Channel

Consistent with the duration risk hypothesis, the yields of many longer term bonds in Table 1 fall 

more than the yields of shorter maturity bonds. The exception here is the 30 year Treasury bond, 

where the yield falls less than the 10 year bond. 

However, there is other evidence that the duration risk channel cannot explain. There are 

dramatic differences in the yield changes across the different asset classes. Agency bonds, for 

example, experience the largest fall in yields. The duration risk channel cannot speak to these 

effects as it only prescribes effects that depend on bond maturity. The corporate bond data also 

cannot be explained by the duration risk channel. Table 2 presents data on corporate bond yields

of intermediate duration (around 4 year duration) or long duration (around 10 year duration), as 

well as these same yields with the impact of changes in CDS rates taken out. We adjust the yield 

changes using CDS changes to remove any effects due to a changing default risk premium and 

thereby isolate duration risk premium effects. The CDS rates used are for 5-year tenor. The CDS 

adjustment makes a substantial difference in interpreting the evidence. In particular, there is a 

large fall in CDS rates for lower grade bonds on the event dates, suggesting that default risk/risk 

premia fell substantially with QE, consistent with the default risk channel (we discuss this further 

below). Given the CDS adjustment, there is only a small change in the yields of Baa and lower 

bonds. Moreover, there is no apparent pattern across long and intermediate maturities in the 
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changes in CDS-adjusted corporate bond yields.9 These observations suggest that we need to 

look to other channels to understand the effects of QE.

c. Liquidity Channel

The most liquid assets in Table 1 are the Treasury bonds. The liquidity channel predicts that 

these yields should increase with QE. They do not increase; however, note that they fall much 

less than the yields on Agency bonds which are less liquid. That is the Agency-Treasury spread 

falls with QE. For example, the 10 year spread falls by 199-107=92 basis points. This is a 

relevant comparison because 10 year Agencies and Treasuries have the same default risk 

(especially since the government placed FNMA and FHLMC into conservatorship in September 

2008), and are duration matched. Thus this spread isolates a liquidity premium. Consistent with 

the liquidity channel, we see that the equilibrium price premium (yield discount) for liquidity 

falls substantially.

d. Safety Channel

The Agency bonds will be particularly sensitive to the safety effect. These bonds are not as 

liquid as the Treasury bonds, but do have almost the same safety as Treasuries. The fall in 10 

year Agency yields is 199 bps, which is the largest effect in the table. This suggests that the 

safety channel is one of the dominant channels for QE1. As we have just noted, Treasuries fall 

less than the Agencies because the liquidity effect runs against the safety effect.

The corporate bond evidence is also consistent with a safety effect. The CDS-adjusted 

yields on Aaa bonds, which are close to default free, fall substantially, while there is close to no 

effect on the non-investment grade bonds. Finally, since Agencies are safer than Aaa corporate 

bonds, the safety channel prediction that the former bond yields fall more than the latter is also 

confirmed in the data.

e. Signaling Channel

Figure 3 graphs the yields on the monthly Federal Funds futures contract, for contract maturities 

from March 2009 to October 2010. The pre-announcement average yield curves are computed 

 

9 We show below that there is no tenor effect in CDS rates so using CDS rates of different tenor for long and 

intermediate corporate bonds is unlikely to change our conclusion about the absence of a duration risk channel.
12

on the day before each event and then averaged across event dates. The post-announcement 

average yield curve is computed likewise for the day after the event dates. The graph shows that, 

on average, each QE announcement “shifts” an anticipated rate hike cycle by the Fed later by a 

little over one month. For the March 2010 contract, the total effect of the five QE 

announcements is to shift anticipated rate increases later by 6.3 months (although, see below for 

a caveat and adjustment that implies a true shift of 5.2 months). This effect is consistent with the 

signaling channel whereby the Fed’s portfolio purchases signals a commitment to keep the 

Federal Funds rate low.

Table 5 reports the one and two-day change in the yields of the 3rd month, 6th month, 12th

month, and 24th month futures contracts, across the five event dates. We aggregate by, for 

example, the 3rd month rather than a given contract-month (e.g., March), because it is more 

natural to think of the information in each QE announcement as concerning how long from today 

rates will be held low. On the other hand, for plotting a yield curve it is more natural to hold the 

contract-month fixed.

There are two important issues in interpreting these changes. First, the 24th month contract is 

extremely thinly traded, so that one should take the data from that contract with a grain of salt. 

Second, Piazzesi and Swanson (2008) show that Federal Funds futures contracts include a risk 

premium so that there is considerable error in simply inferring expected future Federal Funds 

rates from these contracts. For example, they report that the 6th month futures contract has an 

average risk premium of 31 basis points, using data from 1988 to 2005. Moreover, they show 

that this risk premium varies positively with the level of short-term rates (so that when rates are 

lower, the risk premium is lower), and that it varies negatively with employment growth or other 

measures of the business cycle. These effects need to be accounted for in inferring future rate 

expectations from the futures contract prices.

To deal with this issue, we proceed as follows. Using monthly data from December 1988 to 

November 2011, we replicate the Piazzesi-Swanson result on the forecasting power of the futures 

contract. In particular we estimate the relation between the realized Federal Funds rate n-months 

from today and the yield on the today’s futures contract of maturity n. We also include data on 
13

the annual growth rate of non-farm payrolls (this month relative to same month the prior year), 

following Piazzesi and Swanson. For n=3, the coefficient on the futures contract is 0.92

(significant at 1% level), while for n=6, the coefficient is 0.82 (significant at 1% level). These 

numbers are very close to the Piazzesi-Swanson results. The 0.82 number for n=6 indicates that 

a decrease in today’s yield on the futures contract of 10 basis points leads to subsequent decrease 

of the Federal Funds rate, 6 months from today, of 8.2 basis points. The difference of 10 - 8.2

=1.8 basis points is a risk premium that an investor can earn by purchasing the futures contract 

today. The 0.82 coefficient thus is an adjustment factor that we multiply the 6

th month number in 

Table 2 by in order to infer the change in the market expectations of the Federal Funds rate 6-

months from today. Because of data limitations (short time series of data for Fed funds futures 

beyond 6 month maturity), we only run the regression using n=3 and n=6. However, we are 

most interested in inferring changes in expectations in the 12th month. Based on the pattern that 

the n=3 coefficient is less than the n=6 coefficient, it is likely that the true coefficients for the 

12th month is less than or equal to 0.82. If we use 0.82 as an adjustment factor, we find that twoday change in the expected Federal Funds rates 12 months out is 27 basis points. This is an upper 

bound on the signaling effect on the short rate 1 year out both because of the use of the 0.82 

coefficient, and because QE likely improved the employment outlook which will tend to reduce 

the risk premium in the Fed funds futures, implying that some additional component of futures 

rate decline is due to a reduction in the risk premium rather than expected future short rates. For 

the 24th month contract, this computation gives an upper bound of 33 bps.

Note from Table 1 that the 1-year Treasury yield falls by 25 bps over the QE1 event 

dates. This fall is thus likely due mainly to the signaling channel.

As we have explained, the signaling channel predicts the largest effects in bonds with 

maturities between 1 and 3 years, with only small effects for bonds of say 10 year maturities. 

This is clearly rejected by the data. Longer term Treasuries and Agencies fall more than the 3

year (and shorter) bond yields. In the corporate bonds of Table 2, there is no apparent maturity 

effect. Thus, to understand the more substantial movements of long-term rates we need to look 

to other channels and, in particular, the safety channel.
14

f. Prepayment Risk Channel

Agency MBS yields fall by 128 bps for 30-year bonds and 98 bps for 15-year bonds. There are 

two ways to interpret this evidence. It is possible that this is due to a safety effect – the 

government guarantee behind these MBS may be worth a lot to investors so that these securities 

carry a safety premium. The safety premium then rises, as with the Agency bonds, decreasing 

Agency MBS yields. On the other hand, the Agency MBS carry significant prepayment risk and 

are unlikely to be viewed as safe in the same way as Agency bonds or Treasuries (where safety 

connotes the almost certainty of nominal repayment). In empirically analyzing Agency MBS 

rates as well as the rate on 30-year conventional household mortgages (using historical data 

going back to the 1960s, but not reported here for brevity) we have found no effects of Treasury 

supply on the spreads between Baa rates and Agency MBS, which leads us to conclude that 

Agency MBS rates, like Baa rates, do not carry safety or liquidity premia.

Yet, Agency MBS fall more than Agency bonds. We think that a more likely explanation 

is market segmentation effects as in Gabaix, Krishnamurthy and Vigneron (2007). The 

government purchase of MBS reduces the prepayment risk in the hands of investors, and thereby 

reduces MBS yields. The effect is higher for the 30 year than the 15 year because the longer 

bonds carry more prepayment risk.

Additionally, Fuster and Willen (2010) show that the large reductions on agency MBS 

rates around 11/25/2008 were quickly followed by reductions in mortgage rates offered by 

mortgage lenders to households.

g. Default Risk Channel

We have noted earlier from Table 2 that QE appears to reduce default risk or default risk premia, 

which particularly affects the interest rates on lower grade corporate bonds. Table 3 offers more 

data on this observation, presenting two-day changes in CDS rates for each event day. The CDS 

rates are for approximately 440 firms for each of the five event dates. We compute the CDS rate 

as the equally weighted average across firms.

The table shows that CDS rates of the lowest risk firms (quintile 1, with average CDS 

rates of 82 basis points) do not change appreciably with QE. There is a clear pattern across the 

quintiles, going from Q1 to Q5, whereby higher credit risk firms experience the largest fall in 
15

CDS rates. This evidence suggests that QE had a significant effect through default risk and 

default risk premia. 

Table 3 also presents the CDS changes sorted by underlying CDS tenor. There does not 

appear to be any tenor effect on the CDS changes. This evidence is further confirmation that 

there is no “duration” effect on corporate bonds. That is, we earlier showed that CDS-adjusted 

corporate bond yields do not reflect a duration risk premium effect. We adjusted the corporate 

bond yields, at all maturities, using the 5 year CDS (which is the most liquid contract). Table 3 

shows that our conclusions would likely have been the same had we maturity-matched the CDS 

to the corporate bond yields. 

h. Inflation Channel

The above analysis focuses on nominal rates. To assess effects on real rates, one further needs 

information about the impact of QE1 on inflation expectations. Table 4 presents the relevant 

data.

The first columns in the table are for inflation swaps. The 10-year inflation swap is the 

fixed rate in the 10-year zero coupon inflation swap, and thus a market-based measure of 

expected inflation over the next 10 years (see Fleckenstein, Longstaff and Lustig (2010) for 

information on the inflation swap market). This data suggests that inflation expectations 

increased by between 36 and 95 basis points, depending on maturity.

The second set of columns present data on TIPS yields. We compare these yield changes 

to those from nominal bonds to evaluate the change in inflation expectations. Based on the 

evidence of the existence of significant liquidity and safety premia on Treasuries, it is 

inappropriate to compare TIPS to nominal Treasuries. Instead we compare the TIPS to the CDSadjusted Baa bond. From Table 1, the CDS-adjusted long maturity Baa bond falls in yield by 31 

bps, while the intermediate maturity bond falls in yield by 26 bps. Matching the 31 bps change 

to the 187 bps change in the 10 year TIPS, we find that inflation expectations increased by 156

bps at the 10 year horizon. At the 5 year horizon, based on the 26 bps change in CDS-adjusted 

intermediate maturity Baa bond and 144 bps change in TIPS, we find that inflation expectations 

increased by 118 bps.

Together these two sets of data suggest that the impact of Fed purchases of long-term 

assets on expected inflation was large and positive. 
16

We also evaluate the inflation uncertainty channel. The last column in Table 4 reports 

data on implied volatilities from interest rate swaptions (i.e., the option to enter into an interest 

rate swap). The data is the Barclays implied volatility index. The underlying tenor for the swap 

ranges from 1 year to 30 year, involving options that expire from 3 months to 20 years. The 

index is based on the weighted average of implied volatilities across the different swaptions. 

The average volatility measure over the QE time period is 103 bps, so that the fall of 37 

bps is substantial. Thus, it appears that QE reduced rather than increased inflation uncertainty. 

The other explanation for this fall in volatility is segmented markets effects. MBS have 

an embedded interest rate option that is often hedged by investors in the swaption market. Since 

QE involved the purchase of MBS, investors have a smaller demand for swaptions and hence 

implied volatility on swaptions fall. This latter explanation is often the one given by 

practitioners for changes in swaption implied volatilities. Notice, however, that volatility is 

essentially unchanged on the first QE1 event date, which is the event that drives the largest 

changes in MBS yields. This could indicate that the segmented markets effects are not important, 

with volatility instead driven by the inflation uncertainty channel.

i. Summary

QE1 significantly reduced yields on safe assets, including Treasuries, Agencies and highly-rated 

corporate bonds, with liquidity effects working in the opposite direction. For the rates such as the 

Baa corporate bond or MBS yields, QE has effects through a reduction in default risk/default risk 

premia and a reduced prepayment risk premium. There is also evidence that QE decreased the 

yields on shorter maturity bonds via the signaling channel. On the other hand, there is little 

evidence for the duration risk premium channel. There is evidence that QE increased inflation 

expectations, but reduced inflation uncertainty. This latter point implies that real rates fell for a 

wide variety of borrowers.

Finally, note that these effects are all sizable and probably much more than we should 

expect in general. This is because the November 2008 to March 2009 period is an unusual 

financial-crisis period in which the demand for safe assets was heightened, segmented market 

effects were apparent across many markets, and intermediaries suffered from serious financing
17

problems. In such an environment, supply changes should be expected to have a large effect on 

interest rates. 

3. Evidence from QE2

a. Event Study

We perform an event study of QE2 similar to that of QE1. There are two relevant sets of events 

in QE2. First, in the 8/10/2010 FOMC statement, the committee announces:

“the Committee will keep constant the Federal Reserve's holdings of securities at their current 

level by reinvesting principal payments from agency debt and agency mortgage-backed 

securities in longer-term Treasury securities.”

Prior to this announcement, market expectations were that the Fed would let its MBS portfolio 

runoff,10 thereby reducing reserve balances in the system and allowing the Fed to exit from its 

non-traditional monetary policies. Thus, the announcement of the Fed’s intent to continue QE

revised market expectations. Moreover, the announcement indicated that the QE would shift 

towards longer-term Treasuries, and not Agencies or Agency MBS as in QE1. As a back-of-theenvelope computation, suppose that the prepayment rate for the next year on $1.1tn of MBS was 

20%.11 Then the announcement indicated that the Fed intended to purchase $220bn of 

Treasuries over the next one year, and $176bn over the subsequent year, etc. It is unclear from 

the announcement how long the Fed expected to keep the re-investment strategy in place.

The 9/21/10 FOMC announcement reiterates this message:

“The Committee also will maintain its existing policy of reinvesting principal payments from its 

securities holdings.”

 

10 See Fed Chairman Bernanke’s Monetary Policy Report to Congress on July 21, 2010, discussing the 

"normalization” of monetary policy. The issue is also highlighted in Bernanke’s testimony on March 25, 2010 on 

the Federal Reserve’s exit strategy.

11 The Fed’s holdings of MBS on August 4, 2010 was $1,118bn, while it was $914bn on June 22, 2011 (source: H4 

report of the Federal Reserve). That is an annualized decline of 20.6%. 
18

The second type of information for QE2 pertains to the Fed’s intent to expand its purchases of 

long-term Treasury securities. The following announcement is from the 11/3/2010 FOMC 

statement:

“The Committee will maintain its existing policy of reinvesting principal payments from its 

securities holdings. In addition, the Committee intends to purchase a further $600 billion of 

longer-term Treasury securities by the end of the second quarter of 2011.”

The 11/3 announcement was widely anticipated. According to the Wall Street Journal, a WSJ 

survey of private sector economists in early October of 2010 found that the Fed was expected to 

purchase about $750 billion in QE2.12 Based on this, one would expect the 11/3/2010 

announcement to have little effect (estimates in the press varied widely, but the actual number of 

$600 bn was in the range of numbers commonly mentioned). The two prior announcements are 

more likely to have been among the main events changing market expectations about QE2.

Figure 4 presents intraday data on the 10-year Treasury bond yield around the 

announcements times of the FOMC statements. The 8/10 announcement appears to be

significant news for the Treasury market, reducing the yield in a manner that suggest that market 

expectations over QE were revised up. The 9/21 announcement is qualitatively similar. At the 

11/3 announcement, Treasury yields increased by then fell some. The reaction suggests that 

markets may have priced in more than a $600bn QE announcement.

In our event study, we aggregate across the 8/10 and 9/21 events, which appear to be 

driven by upward revisions in QE expectations. We do not add in the change from the 11/3 

announcement as it is unclear whether only the increase in yields after than announcement or 

also the subsequent decrease was due to QE2 (furthermore, the large two-day reaction to the 11/3 

announcement may not be due to QE2 since a lot of it happened the morning of 11/4 around the 

time new numbers were released for jobless claims and productivity).

Additionally, we present information for both 1-day changes and 2-day changes, but 

focus on the 1-day change in our discussion. This is because market liquidity had normalized by 

the fall of 2010, and looking at the 2-day changes would therefore likely add noise to the data.

 

12

 WSJ, Oct 26, 2010, "Fed Gears Up for Stimulus". 
19

b. Analysis

Table 6 provides data on the changes in Treasury, Agency and Agency MBS yields over the 

event dates. Table 7 provides data on changes in corporate bond yields, CDS, and CDS-adjusted 

corporate yields. For this window, we did not obtain CDS data by rating category, and instead 

relied on the Markit investment grade and high yield CDS indices, matching these indices with 

corporate bond yields for the relevant category.

Here are the main conclusions we draw based on this data, in terms of the channels we have 

discussed earlier:

 There does not appear to be a duration risk premium channel. It is not the case that 

longer term Treasuries, Agencies, Agency MBS, or the CDS-adjusted corporate bonds 

move more in yield than shorter term securities. In addition, while the CDS-adjusted 

investment grade corporate bond yield falls, that of the high yield corporate bond rises.

 There does not appear to be a liquidity channel. Treasury and Agency yields fall by 

nearly the same amounts, so that their spread, which can measure liquidity, appears 

unchanged. This result is plausible because liquidity premia in markets were quite low in 

late 2010, as market liquidity conditions had normalized. Consider the following data (on 

10/22/2010):

Treasury Bill Tier 1 Non-Financial CP

1 week 10bps 19bps

1 month 12 21

3 month 12 23

The premium on the more liquid 1 week bill relative to the 3 month bill is only 2 basis 

points. The premium on the more liquid 3 month bill relative to 3 month CP is only 11 

basis points. The latter premium also reflects some credit risk and tax effects. Part of the 

reason why liquidity premia are so low is that government policy had already provided a 

large supply of liquid assets to the private sector. Consider that the Fed had already 

increased bank reserves substantially. In June 2007, reserve balances totaled $44bn. As 

of September 2010, reserve balances totaled close to $1,040bn. Furthermore, the 
20

government had increased the supply of Treasury bills from $865bn to $1783bn over this 

same period. These arguments suggest that the effects on liquidity premia should be 

negligible via the liquidity channel.

 There is significant evidence for a safety channel. Agency yields and Treasury yields, 

which are both near zero-default risk fall in yield. Agency MBS which carries 

prepayment risk and hence is not safe in the sense of Treasuries, does not fall appreciably 

in yield. Moreover, the CDS-adjusted investment grade bond yield also falls, but only 7 

bps (intermediate) and 12 bps (long) compared to the 18 bps for the safer Treasuries and 

Agencies. Finally, the CDS-adjusted high yield bond rises in yield rather than fall.

 There is some evidence for a credit risk channel as the high yield CDS falls by 21 bps.

 There is significant evidence of the signaling channel affecting short-term rates. The 12th

month Federal Funds futures contract from Table 5 falls by 4 bps; with the 0.82

adjustment factor, this translates to a 3.2 bps fall in expectations of the Federal Funds rate 

one year out. The 24th month contract (which is thinly traded) falls by an adjusted 

amount of 9 bps. The 1 year Treasury yield falls by 1 bps. Again we need to look to 

other channels to understand the substantially larger move in longer term rates.

Table 8 provides data on inflation swaps and TIPS yield for the event dates to analyze effects on 

inflation expectations.

 Inflation expectations rise with QE2. The 10 year inflation swap rises by 5 bps. The 10 

year TIPS falls by 25 bps. Comparing this number to the CDS-adjusted rise in high yield 

bonds of 11 bps, we find that inflation expectations rise by 36 bps. While the inflation 

swaps and the TIPS both point to a rise in inflation expectations, there is a dramatic 

difference in the numbers from each asset market. We are unsure as to why this happens.

 The implied volatility on swaptions falls by 3 bps, indicating a slight decrease in inflation 

uncertainty.

c. Summary

The QE2 data strongly suggests that the primary channel for the Treasuries-only policy on is

through the safety channel causing Treasury, Agency and highly-rated corporate bond yields to 
21

decline. This is a negative note for the policy as it indicates that nominal rates that are highly 

relevant for household and many corporations -- mortgage rates and rated on lower-grade 

corporate bonds were less affected by the policy. The main effect on the latter was through the 

signaling channel which lowered rates on shorter-term bonds. On the other hand, there is 

significant evidence for an increase in inflation expectations, suggesting that real rates fell for all 

borrowers. This is a more positive note for the policy.

4. Regression Analysis of the Safety Channel

The event-study evidence is useful in identifying channels for QE. While it provides some 

guidance on the magnitudes of the effects through QE, it is hard to precisely interpret the 

numbers because event study measures are dependent on the dynamics of expectations through 

the event. That is, the asset market reaction depends on the change in the expectation of QE over 

the event. We have no direct way of precisely measuring such an expectations change, nor 

determine whether the event study is likely to over- or understate the effects of QE. In addition, 

the QE1 event occurs in highly unusual market conditions, so that it is hard to extrapolate 

numbers from that period to more normalized conditions. As such, it is valuable to find 

alternative approaches to estimating the impact of QE. In this section, we use regression analysis 

to provide such estimates.

a. KVJ regressions

We build on the regression analysis from KVJ to estimate the effect of a purchase of long-term 

securities via the safety channel. We focus on the safety channel because it appears to be a 

dominant effect from the event studies. 

The KVJ regression approach can be explained through Figure 1. Consider the yield (or 

price) difference between a low default risk bond, such as a Treasury, and a Baa bond. This 

yield difference includes both a default risk premium due to standard risk considerations and a 

safety premium component due to clientele demands for particularly safe assets. We disentangle 

the default risk and safety premium by observing that the safety premium is decreasing in the 

supply of safe assets, including Treasuries, while the default risk component can be controlled 
22

for using empirical default measures. The empirical approach is to regress the Baa-Treasury 

spread on the supply of Treasuries as well as standard measures of default.

In KVJ, we mainly focus on the effect of changes in the total supply of Treasuries, 

irrespective of maturity, on bond yields. For evaluating QE, we are interested more in asking 

how a change in the supply of long-term Treasuries will affect yields. Accordingly, we construct 

a maturity-based measure of debt supply as follows. For each Treasury issue, we compute the 

market value of that issue multiplied by the duration of the issue divided by 10.

13 We normalize 

by 10 to express the supply variable in “ten-year equivalents.” We then sum these values across 

Treasury issues with remaining maturity of 2 years or more. Denote the sum as LONG-SUPPLY. 

We also construct the (unweighted) market value across all Treasury issues (TOTAL-SUPPLY), 

including those with a remaining maturity of less than 2 years.

We then regress the spread between the Moody’s Baa corporate bond index and the longterm Treasury yield (Baa-Treasury) on the ln(LONG-SUPPLY/GDP) instrumented by TOTALSUPPLY/GDP, and squares and cubes of TOTAL-SUPPLY/GDP. The regression includes 

default controls of stock market volatility (i.e., standard deviation of weekly stock returns over 

the preceding year) and the slope of the yield curve (10 year Treasury yield minus 3-month 

yield). The regressions are estimated via OLS, with standard errors adjusted for an AR(1) 

correlation structure. It is important to instrument for LONG-SUPPLY because the maturity 

structure of government debt is chosen by the government in a way that could be correlated with 

spreads. TOTAL-SUPPLY is a good instrument for LONG-SUPPLY and plausibly exogenous to 

the safety premium. See KVJ for further details of the estimation method. The regressions are 

estimated using annual data from 1949 to 2008. The regression is:

 

instrumented by TOTAL-SUPPLY/GDP, and squares and cubes of TOTAL-SUPPLY/GDP.

The term is the premium of interest in this regression. We 

evaluate the effect of a QE by evaluating this premium term at the pre-QE and post-QE values of 

LONG-SUPPLY.

 

13 We use monthly data on prices and bond yields from the CRSP Monthly US Treasury Database base to 

empirically construct the derivative of price with respect to yield. The derivative is used to compute the duration.
23

The β coefficient is -0.83 (t-statistic = -5.83). If we construct the spread as Aaa-Treasury, 

the result is -0.51 (t-statistic = -4.64). For the Baa-Aaa spread, the result is -0.31 (t-statistic = -

4.64).

As we explain in KVJ, the Baa-Treasury spread reflects both a liquidity premium and a 

safety premium. The coefficient from the Baa-Aaa regression is a pure read on the safety 

premium, because Baa bonds and Aaa bonds are equally illiquid. However, it is an 

underestimate of the safety effect as may be reflected in Treasuries or Agencies because while 

Aaa are safe, they still contain more default than Treasuries or Agencies. For example, Moody’s 

reports that over 10 years, the historical average default probability of a bond that is rate Aaa 

today is 1% (while it is likely close to 0% for Treasuries and is close to 10% for Baa bonds). 

The Baa-Treasury spread is likely an overestimate of the safety premium. That is, it has 

the advantage that the Treasury yield is close to default free. However, Treasuries are an order

of magnitude more liquid than Baas, so that the spread also contains a substantial liquidity 

premium. We discuss estimates using both the Baa-Aaa regression and the Baa-Treasury 

regression.

a. Estimates for QE1

Gagnon, et. al, (2010) report that in 10-year equivalents the Fed had purchased $169bn of 

Treasuries, $59bn of Agency debt, and $573bn of Agency MBS by Feb 1, 2010. The total 

purchase up to this date was $1.625tn and the anticipated total was $1.725tn. We scale up the 

numbers up to Feb 1, 2010 by 1.725/1.625 to evaluate the effect of the total purchase.

Agency debt and Treasury debt are equally safe during the QE period, while Agency 

MBS carries prepayment risk. Thus, if we consider only the Treasuries and Agencies purchased, 

and ask what effect this will have on the Baa-Aaa spread using the regression coefficient of -

0.31, we find that the effect is 4 bps (we also use the fact that the end of 2008 LONGSUPPLY/GDP = 0.140 for this computation). As we have noted, this is smaller than the true 

safety effect because Aaa corporate bonds are not as safe as either Agencies or Treasuries. As an 

upper bound, even if we use the Baa-Treasury coefficient (which includes a liquidity premium), 

the estimate is 11 bps. Although the event study may not identify the precise economic impact 

of QE for reasons we have discussed earlier, our regression estimates still appear quite small.
24

However, we have neglected an important aspect of the crisis. The regressions 

coefficients are estimates of an “average” demand for safety; for evaluating QE we are more 

interested in the demand function as of the Fall of 2008 and Winter of 2009. It is likely that 

demand during the crisis was elevated relative to an average period. One way of seeing this is to 

note that the CDS-adjusted Baa spread minus the CDS-adjusted Aaa spread averages 1.87% in 

the sample from 11/25/08 to 3/23/09. This number is an estimate of the relative safety value of 

the Aaa bonds over the Baa bonds. We can also estimate the historical average safety premium

by evaluating ln(LONG-SUPPLY/GDP) at the 2008 level and multiplying by the -0.31

coefficient. This computation yields 0.61%. That is, the safety premium over the QE period was 

roughly 3 times the average level. The larger effects obtained from the QE1 event study than the

regression approach suggest that changes in Treasury supply have much larger impact on the 

safety premium in times of unusually high safety demand than they do in average times.

b. Estimates for QE2

In QE2, the Fed announced that it will purchase $600bn of Treasuries and rollover the maturing 

MBS into long-term Treasuries. We suggested earlier that the latter effect translates to a 

purchase of $220bn over the next year, and $176bn for the following year, if the policy was kept 

in place. For the sake of argument, let us suppose that the market expects the policy to be in 

place for only one year then the total effect is to purchase $820bn of Treasuries. 

The impact of an $820bn Treasury purchase can have a large effect on safety premia. 

However, QE2 occurs during normalized market conditions, so that the -0.31 coefficient 

estimates are appropriate during this period. For example, as of 10/22/2010, the spread between 

Baa rates and Aaa rates was 107 bps and the spread between Aaa rates and the 20 year Treasury 

bond was 111 bps. Averages for 1919-2008 are: Baa-Aaa=118 bps and Aaa-Treasury=81 bps. 

Thus the premia during QE2 are large and similar to historical averages.

The $820bn of Treasuries translates to $511bn of 10-year equivalents, based on the 

planned maturity breakdown provided by the Federal Reserve Bank of New York.

14

 The LONGSUPPLY/GDP ratio at the end of 2009 was 0.165. Based on these numbers, using the -0.31 

coefficient, we find that QE2 should increase the safety premium by 7 bps. Using the upper 

 

14 http://www.newyorkfed.org/markets/lttreas_faq.html
25

bound coefficient of -0.83, we estimate an effect of 21 bps. These numbers seem in line with 

those from the event study. 

5. Conclusion

We document that QE1 and QE2 significantly lower nominal interest rates on Treasuries, 

Agencies and highly-rated corporate bonds, driven mainly by an increase in the safety price 

premium of assets with near-zero default. We estimate that the impact of QE1 on the safetypremium reduced yields by more than 100 bps (based on agency yields), with QE2 having an 

effect of about 20 bps. However, we show that QE1 and (announced) QE2 purchases of longterm Treasuries (and other long-term bonds in the case of QE1) have had a smaller (defaultadjusted) effect on lower-grade corporate bonds. Furthermore, the impact of quantitative easing 

on MBS rates is large when QE involves MBS purchases (QE1), but not when it involves only 

Treasury purchases (QE2), indicating that a second main channel for QE is to affect the 

equilibrium price of mortgage-specific risk. We also find that the signaling channel had a 

significant effect in lowering the yields on shorter maturity bonds. Finally, evidence from 

inflation swap rates and TIPS show that expected inflation increased substantially due to QE1 

and modestly due to QE2, implying that reductions in real rates were larger than reductions in 

nominal rates.

By analyzing the differential impact of QE on a host of interest rates, our findings shed 

light on the channels through which QE affects interest rates and shows that which interest rates

will be affected the most depends crucially on which assets are purchased. Methodologically, we 

show that derivatives prices from credit-default swaps and inflation swaps can provide central 

inputs to macroeconomic policy assessment.
26

References

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at the Zero Bound: An Empirical Assessment”, Brookings Papers on Economic Activity, (2), pp. 

1-78

Clouse, James, Dale Henderson, Athanasios Orphanides, David Small, and Peter Tinsley, 2000,

“Monetary Policy when the Nominal Short-Term Interest Rate is Zero,” Topics in 

Macroeconomics, 3(1), article 12.

D'Amico, Stefania, and Thomas B. King, 2010, "Flow and Stock Effects of Large-Scale Treasury 

Purchases," Finance and Economics Discussion Series 2010-52. Washington: Board of 

Governors of the Federal Reserve System, September.

Eggertson, Gauti and Michael Woodford, 2003, Brookings Papers on Economic Activity, (1), pp. 

139–233.

Fleckenstein, Matthias, Francis A. Longstaff, and Hanno Lustig, 2010, "Why does the Treasury 

Issue TIPS? The TIPS-Treasury Bond Puzzle", working paper, UCLA.

Fuster, Andreas and Paul S. Willen, 2010, ``$1.25 Trillion is Still Real Money: Some Facts 

About the Effects of the Federal Reserve's Mortgage Market Investments'', working paper, 

Federal Reserve Bank of Boston.

Gabaix, Xavier, Arvind Krishnamurthy and Olivier Vigneron, 2007, "Limits of Arbitrage: 

Theory and Evidence from the Mortgage-Backed Securities Market," Journal of Finance, 62(2), 

pages 557-595, 04. 

Gagnon, Joseph, Matthew Raskin, Julie Remache, and Brian Sack, "Large-Scale Asset Purchases 

by the Federal Reserve: Did They Work?" Federal Reserve Bank of New York Staff Report no. 

441, March 2010.
27

Greenwood, Robin and Dimitri Vayanos, 2010, “Bond Supply and Excess Bond Return,”

working paper, LSE.

Hamilton, James D., and Jing Wu, 2010, "The Effectiveness of Alternative Monetary Policy 

Tools in a Zero Lower Bound Environment," working paper. San Diego: University of 

California, San Diego.

He, Zhiguo and Arvind Krishnamurthy, 2009, “Intermediary Asset Pricing,” working paper, 

Northwestern University.

Krishnamurthy, Arvind and Annette Vissing-Jorgensen, 2010, “The Aggregate Demand for 

Treasury Debt,” working paper, Northwestern University.

Longstaff, Francis A., Sanjay Mithal and Eric Neis, 2005, "Corporate Yields Spreads: Default 

Risk or Liquidity? New Evidence from the Credit-Default Swap Market," Journal of Finance, 

60(5), pages 2213-2254.

Swanson, Eric, 2010,“Let’s Twist Again: A High-Frequency Event-Study Analysis of Operation 

Twist and Its Implications for QE2,” working paper, Federal Reserve Bank of San Francisco.

Vayanos, Dimitri and Jean-Luc Vila, 2009, “A Preferred Habitat Model of the Term Structure of 

Interest Rates,” working paper, LSE Financial Markets Group.
28

Figure 1. Safety Premium on Bonds with Near-Zero Default Risk

Default Probability

Price

CCAPM Value: Price = E[M * Risky Payoff]

Baa Rating
29

Figure 2. Intra-day Yields and Trading Volume on QE1 Event Days

Panel A. Yields

3.05

3.1

3.15

3.2

3.25

Yield

7 8 9 101112131415161718

Nov 25, 2008

2.65

2.7

2.75

2.8

2.85

2.9

Yield

7 8 9 101112131415161718

Dec 1, 2008

2.2 2.3 2.4 2.5 2.6

Yield

7 8 9 101112131415161718

Dec 16, 2008

2.5

2.55

2.6

2.65

2.7

Yield

7 8 9 101112131415161718

Jan 28, 2009

2.5 2.6 2.7 2.8 2.9

3

Yield

7 8 9 101112131415161718

Mar 18, 2009
30

Panel B. Trading Volume

Note: This figure is based on data purchased from BG Cantor and the data graphed is for the onthe-run 10 year bond at each date. Yields graphed are averages by the minute and trading volume 

graphed is total volume by minute. The vertical lines indicate the minute of the announcement, 

defined as the minute of the first article covering the announcement in Factiva.

0

5000

10000

7 8 9 101112131415161718

Nov 25, 2008

0

5000

10000

7 8 9 101112131415161718

Dec 1, 2008

0

5000

10000

7 8 9 101112131415161718

Dec 16, 2008

0

5000

10000 15000 20000

7 8 9 101112131415161718

Jan 28, 2009

0

5000

10000 15000 20000

7 8 9 101112131415161718

Mar 18, 2009
31

Figure 3. Yield Curves from Fed Funds Futures, pre- and post QE1 Event Days

Notes: The figure graphs the yields (in %) on the Federal Funds futures contract, by contract 

maturity. The yields are computed the day prior to the QE1 event dates and again the day after 

the event dates. All of the pre-event yields, and all of the post-event yields, are then averaged 

across events. All data are from Bloomberg.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

PRE-ANNOUNCEMENT AVERAGE

POST-ANNOUNCEMENT AVERAGE
32

Figure 4. Intra-day Yields and Trading Volume on QE2 Event Days

Panel A. Yields

2.74 2.76 2.78

2.8

2.82 2.84

Yield

7 8 9 10 11 12 13 14 15 16 17 18

Aug 10, 2010

2.55

2.6

2.65

2.7

Yield

7 8 9 10 11 12 13 14 15 16 17 18

Sep 21, 2010

2.5

2.55

2.6

2.65

Yield

7 8 9 10 11 12 13 14 15 16 17 18

Nov 3, 2010
33

Panel B. Trading Volume

Note: See note to Figure 2.

0

25000 50000

7 8 9 10 11 12 13 14 15 16 17 18

Aug 10, 2010

0

25000 50000

7 8 9 10 11 12 13 14 15 16 17 18

Sep 21, 2010

0

25000 50000

7 8 9 10 11 12 13 14 15 16 17 18

Nov 3, 2010
34

Table 1. Treasury, Agency and Agency MBS yields on QE1 event dates

Two-day changes (in basis points)

Date Event Treasuries yields (constant 

maturity)

Agency yields Agency MBS 

yields

30 

year 

10 year 5 

year

1 

year

10 

year

5 year 3 year 30 

year

15 

year

11/25/2008

Initial 

announcement-24 -36 -23 -2 -76 -57 -42 -75 -147

12/1/2008 Bernanke 

speech -27 -25 -28 -13 -67 -50 -28 -10 58

12/16/2008 FOMC 

statement -32 -33 -15 -5 -39 -26 -28 -30 -7

1/28/2009 FOMC 

statement

31 28 28 4 28 27 16 6 16

3/18/2009 FOMC 

statement -21 -41 -36 -9 -45 -44 -38 -19 -18

Above 5 dates Above 5 

events -73*

-107**-74 -25*-199***-150**-120***-128**-98

Note: The Treasury yields are from FRED (the constant maturity series). The agency yields are for 

FNMA bonds and the MBS yields are for the current coupon GNMA. Both are from Bloomberg.

* denotes significance at 10% level, ** denotes significance at 5% level and *** denotes significance at 

1% level.
35

Table 2. Corporate Yields, and Corporate Yields Adjusted by CDS on QE1 Event Dates

Two-day changes (in basis points)

Corporate Yields

Aaa 

long

Aa 

long

A 

long

Baa 

long

Ba 

long

B 

long

Aaa 

int

Aa 

int A int

Baa 

int

Ba 

int B int

11/25/2008 -28 -18 -23 -19 -4 4 -17 -15 -18 -18 1 -47

12/1/2008 -24 -24 -21 -17 -13 28 -21 -15 -18 -8 -5 6

12/16/2008 -43 -37 -45 -39 1 -11 -19 -21 -24 -27 -28 -42

1/28/2009 34 17 17 14 -16 -25 12 8 7 3 -32 -25

3/18/2009 -16 -21 -21 -20 -28 -39 -43 -50 -39 -26 -18 -22

Above 5 

dates -77 -83**

-93**-81**-60**-43 -88**-93**-92**-76**-82***-130***

Credit Default Swaps (5 year tenor)

Aaa Aa A Baa Ba B

11/25/2008 3 -1 -5 -19 -35 -32

12/1/2008 2 7 12 1 0 124

12/16/2008 5 -4 -5 -17 -15 13

1/28/2009 -3 -5 -8 -9 -17 -118

3/18/2009 -1 -2 -4 -7 -14 -45

Above 5 

dates 6 -5 -10 -50 -81 -58

Corporate Yields-Credit Default Swaps

Aaa 

long

Aa 

long

A 

long

Baa 

long

Ba 

long

B 

long

Aaa 

int

Aa 

int A int

Baa 

int

Ba 

int B int

11/25/2008 -31 -17 -18 0 31 36 -20 -14 -13 1 36 -15

12/1/2008 -26 -31 -33 -18 -13 -96 -23 -22 -30 -9 -5 -118

12/16/2008 -48 -33 -40 -22 16 -24 -24 -17 -19 -10 -13 -55

1/28/2009 37 22 25 23 1 93 15 13 15 12 -15 93

3/18/2009 -15 -19 -17 -13 -14 6 -42 -48 -35 -19 -4 23

Above 5 

dates -83 -78 -83 -31 21 15 -94 -88 -82 -26 -1 -72

Note: The corporate yield indices are from Barclay's and downloaded from Datastream. The CDS rates 

by ratings are Moody’s indices. * denotes significance at 10% level, ** denotes significance at 5% level 

and *** denotes significance at 1% level.
36

Table 3. CDS Rates on QE1 Event Dates, by Quintile of Credit Risk and Tenor

Two-day changes (in basis points)

QE1 event 

day Tenor

Mean 2-day change, by quintile of 5-year CDS rate (level) as of day before the 

particular QE1 event day

Q1 (lowest) Q2 Q3 Q4 Q5 (highest)

11/25/2008 10 years -3 -8 -14 -23 -182

11/25/2008 5 years -3 -8 -14 -24 -157

12/1/2008 10 years 3 6 8 8 -149

12/1/2008 5 years 2 6 10 11 -102

12/16/2008 10 years -5 -8 -13 -27 -184

12/16/2008 5 years -4 -8 -13 -30 -187

1/28/2009 10 years 0 -7 -11 -26 -37

1/28/2009 5 years 1 -5 -11 -25 -38

3/18/2009 10 years -2 -4 -9 -15 -88

3/18/2009 5 years -2 -3 -10 -18 -106

All 5 days 10 years -7 -22 -38 -84 -640

All 5 days 5 years -6 -18 -39 -86 -591

Mean 5-year CDS rate level as of day prior to the QE1 event day:

Q1 (lowest) Q2 Q3 Q4 Q5 (highest)

All 5 days 82 159 318 669 3395

Note: The CDS rates are obtained from Datastream. The number of firms covered is around 440 for each 

of the five event dates.
37

Table 4. Inflation Swaps, TIPS, and Implied Interest Rate Volatility on QE1 Event Dates

Two-day changes (in basis points)

Date Event Inflation swaps TIPS real yields (constant 

maturity)

Interest 

rate 

volatility

30 

year

10 

year

5 year 1 year 20 

year

10 

year

5 year

11/25/2008 Initial 

Announcement 1 -6 -28 48 -22 -43 5 1

12/1/2008 Bernanke 

speech 15 27 11 -40 -38 -34 -5115

-7

12/16/2008 FOMC 

Statement 4 37 35 -17 -45 -57 -83 -20

1/28/2009 FOMC 

Statement 14 15 -6 5 15 6 13 0

3/18/2009 FOMC 

Statement 2 22 24 45 -45 -59 -43 -11

Above 5 

dates

Above 5 

events 36** 95** 36 41 -135***

-187***-144***-37***

Note: Inflation swap rates and interest rate volatility (ticker BBOX) is from Bloomberg. TIPS yields are 

from FRED. * denotes significance at 10% level, ** denotes significance at 5% level and *** denotes 

significance at 1% level.

 

15 The constant maturity TIPS data from the Federal Reserve website indicates that the 5 year TIPS fell by 244 bps 

on the 12/1/2008 event. We think this is a data error. We examined the yield movements in the underlying TIPS 

bonds, with maturities ranging from 2013 to 2015. These bonds change in yield from -39 bps to -58 bps, with an 

average change of -51 bps. We report the -51 bps number in the table. 
38

Table 5. Federal Funds Futures Yields over QE1 and QE2 Event Dates

One and two-day changes (in basis points)

QE1 (5 events) QE2 (8/10 and 9/21)

Contract Maturity 1-day 2-day 1-day 2-day

3rd month -26.5*-27.5*0 0

6th month -26.5*-27 -0.5 -0.5*

12th month -36.5***-33**-4

***

-5

**

24th month -59***-40 -11***-16***

Note: All data are from Bloomberg. * denotes significance at 10% level, ** denotes significance at 5% 

level and *** denotes significance at 1% level.

Table 6. Treasury, Agency and Agency MBS Yields on QE2 Event Dates

One and two-day changes (in basis points)

Date Event Changes Treasuries yields (constant 

maturity)

Agency yields Agency MBS 

yields

30 

year 

10 

year 

5 

year

1 

year

10 

year

5 

year

30 

year

15 

year

8/9/2010 to 

11/4/2010

Day before 8/10 

FOMC statement 

to day after 11/3 

FOMC 

statement)

3 -33 -50 -5 -41 -53 -36 -26

8/10/2010 FOMC meeting 1-day -1 -7 -8 -1 -7 -9 1 -5

2-day -8 -14 -10 -1 -13 -9 -8 -4

9/21/2010 FOMC meeting 1-day -8 -11 -9 0 -11 -9 -7 1

2-day -13 -16 -10 -1 -16 -10 4 5

11/3/2010 FOMC meeting 1-day 16 4 -4 0 5 -5 -5 -2

2-day 11 -10 -11 -1 -10 -14 -13 -3

8/10 and 9/21

1-day -9

*

-18***-17***-1 -18***-18***-6 -4

2-day -21***-30***-20***-2 -29***-19***-4 1

Note: Data sources are as for QE1. * denotes significance at 10% level, ** denotes significance at 5% 

level and *** denotes significance at 1% level.
39

Table 7. Corporate Yields, and Corporate Yields Adjusted by CDS on QE2 Event Dates

One and two-day changes (in basis points)

Date Changes Corporate yields Credit default 

swaps

Corporate yields-CDS

Inv 

grade 

long

Inv 

grade 

intermediate

High 

yield 

long

High 

yield 

intermediate

Inv.

grade

High 

yield

Inv 

grade 

long

Inv 

grade 

intermediate

High 

yield 

long

High 

yield 

intermediate

8/9/2010 to 

11/4/2010 -11 -51 -58 -120 -13 -70 2 -38 12 -50

8/10/2010 1-day 2 -3 -5 3 -2 -11 4 -1 6 14

2-day -6 -6 -3 17 1 2 -7 -7 -5 15

9/21/2010 1-day -9 -9 -5 -3 3 -10 -12 -12 5 7

2-day -13 -10 -10 0 4 -4 -17 -14 -6 4

11/3/2010 1-day 11 -2 17 -4 -2 -6 13 0 23 2

2-day 2 -13 10 -19 -3 -15 5 -10 25 -4

8/10 and 

9/21

1-day -7 -12***-10*** 0 0 -21 -7 -12 11 21

2-day -19***-16***-13** 17*5 -2 -24 -21 -11 19

Note: The corporate yield indices are from Barclay's and downloaded from Datastream. The CDS rates 

are from the Financial Times and are for 5-year tenor. * denotes significance at 10% level, ** denotes 

significance at 5% level and *** denotes significance at 1% level.
40

Table 8. Inflation Swaps, TIPS, and Implied Interest Rate Volatility on QE2 Event Dates

One and two-day changes (in basis points)

Date Event Changes Inflation swaps TIPS real yields (constant 

maturity)

10 year 

interest 

rate 

volatility

30 

year

10 

year

5 

year

1 

ye

ar

20 

year

10 

year

5 year

8/9/2010 to 

11/4/2010

Day before 8/10 FOMC 

statement to day after 

11/3 FOMC statement)

37 17 16 19 -53 -60 -54 -1

8/10/2010 FOMC meeting 1-day 5 -1 -3 0 -10 -9 -8 -2

2-day -2 0 -3 -4 -6 -9 -5 -3

9/21/2010 FOMC meeting 1-day 6 6 6 -1 -14 -16 -14 -1

2-day 6 4 7 9 -17 -20 -18 -2

11/3/2010 FOMC meeting 1-day 6 -3 2 1 4 1 -6 -2

2-day 1 -10 4 14 2 -5 -14 -3

8/10 and 9/21 1-day 11*** 5 3 -1 -24***

-25***-22***-3

***

2-day 4 4 4 5 -23***-29***-23***-5

***

Note: Data sources are as for QE1. * denotes significance at 10% level, ** denotes significance at 5% 

level and *** denotes significance at 1% level.