On the international spillovers of US*
Quantitative Easing
Marcel Fratzscher
DIW Berlin, Humboldt-University Berlin and CEPR
Marco Lo Duca
European Central Bank
Roland Straub
European Central Bank
Abstract
The paper analyses the global spillovers of the Federal Reserve’s unconventional monetary
policy measures. First, we find that Fed measures in the early phase of the crisis (QE1) werehighly effective in lowering sovereign yields and raising equity markets, especially in the US
relative to other countries. Fed measures since 2010 (QE2) boosted equities worldwide, while
they had muted impact on yields across countries. Yet Fed policies functioned in a procyclical manner for capital flows to emerging markets (EMEs) and a counter-cyclical way for
the US, triggering a portfolio rebalancing across countries out of EMEs into US equity andbond funds under QE1, and in the opposite direction under QE2. Second, the impact of Fed
operations, such as Treasury and MBS purchases, on portfolio allocations and asset prices
dwarfed those of Fed announcements, underlining the importance of the market repair andliquidity functions of Fed policies. Third, we find no evidence that FX or capital accountpolicies helped countries shield themselves from these US policy spillovers, but rather that
responses to Fed policies are related to country risk. The results thus illustrate how US
unconventional measures have contributed to portfolio reallocation as well as a re-pricing ofrisk in global financial markets.
JEL Codes: E52, E58, F32, F34, G11.
Keywords: monetary policy, quantitative easing, portfolio choice, capital flows, Federal
Reserve, United States, policy responses, emerging markets, panel data.

*

mfratzscher@diw.de, marco.lo_duca@ecb.europa.eu, roland.straub@ecb.europa.eu, European
Central Bank, Kaiserstrasse 29, D-60311, Frankfurt am Main. The paper was previously circulated
under the title “A global monetary tsunami? On the spillovers of US Quantitative Easing”. We would
like to thank participants at the 2012 AEA, EEA and EFA meetings, the 2012 NBER IFM programmemeeting, the German Economic Association Monetary Theory meeting, the Banco de España-WorldBank conference on “Debt and Credit, Growth and Crises”, Banco de México-IMF Seminar July 24-25
on “Regional Macroeconomic Strengths and Challenges”, at seminars at the Federal Reserve Board, theIMF, the ECB, ZEW and Cass Business School, and in particular our discussants Eduardo Fernández
Arias, Joe Gagnon, Steve Kamin, Kate Phylaktis, Ole Rummel and Carmen Reinhart. The views
presented in the paper are those of the authors and do not necessarily represent the views of theEuropean Central Bank or the Eurosystem.

Non-technical summary
The 2007-09 global financial crisis triggered unprecedented policy interventions in the UnitedStates. Besides the more standard counter-cyclical policy measures, the Federal Reserve
launched a new set of non-standard policy tools, which have been labeled as credit-easing or
quantitative easing policies (QE). In the period after the collapse of Lehman Brothers in
September 2008 (often referred to as QE1), measures included (i) an extension of liquidity
operations to support banks and markets, and (ii) large-scale asset purchases (LSAP) of GSE
debt, agency debt, mortgage-backed securities (MBS) and Treasury securities. By contrast,
the second main push by the Federal Reserve started in the second half of 2010 (QE2) andconcentrated primarily on purchases of US Treasury securities.
While most of the debate has focused on the effects of QE on the US economy, foreign
policy-makers – in particular in emerging markets– argued that QE policies have createdexcessive global liquidity and caused an acceleration of capital flows to EMEs. In turn, thiscapital flow surge is widely blamed for appreciation pressures on EME currencies and abuild-up of financial imbalances in EMEs.
To shed some light on this issue, the paper analyses the effects of the Federal Reserve’s
unconventional policies on the US and on 65 foreign financial markets. Importantly, the paper
extends the existing literature by investigating the effects on both global asset prices andcapital flows. For this purpose, we use a relatively novel database of daily portfolio flows into
bond and equity mutual funds, taking primarily a US investor perspective. In this way, we cantrack capital injections into funds (portfolio flows) across countries.
We analyze different types of US unconventional monetary policy measures (liquidity
operations, purchases of MBS and of US Treasuries) in order to understand whether and why
QE1 and QE2 have exerted different effects on US and foreign markets. In contrast with mostof the literature on US QE, which has focused narrowly on announcement effects, we make a
distinction between announcements of Fed interventions and the actual market operations.
The results in the paper illustrate how US monetary policy since 2007 has contributed toportfolio reallocation as well as a re-pricing of risk in global financial markets. First, we findthat Fed measures in the early phase of the crisis (QE1) were highly effective in boostingbond and equity prices, especially in the US, and led to US dollar appreciation. Conversely,
QE2 boosted equity prices worldwide and led to US dollar depreciation. Yet Fed policies
functioned in a pro-cyclical manner for capital flows to EMEs and in a counter-cyclical way
for the US. QE1 triggered a portfolio rebalancing across countries out of emerging markets(EMEs) into the US, while QE2 triggered rebalancing in the opposite direction. This finding
may be interpreted as lending support to the concerns expressed by policymakers in EMEs.

2

Second, the impact of Fed operations, such as Treasury and MBS purchases, on portfolio
allocations and asset prices dwarfed that of Fed announcements. This result underlines theimportance of the market repair and liquidity functions of Fed policies. Third, there is noevidence that FX or capital account policies helped countries shield themselves fromspillovers. We find instead that heterogeneity in the response to Fed policies is related tocountry risk.
The results suggest that there are indeed global spillovers and externalities from monetary
policy decisions in advanced economies. However, the paper is mute on whether suchexternalities are overall positive or negative for other economies. The potentially undesirable
effects of these measures on the pro-cyclicality of EME capital flows need to be weighedagainst potential benefits such as higher economic activity and a better financial market
functioning in the global economy.

3

"This crisis started in the developed world. It will not be overcome … through … quantitativeeasing policies that have triggered … a monetary tsunami, have led to a currency war andhave introduced new and perverse forms of protectionism in the world."

President Rousseff of Brazil (2012)

1 Introduction
The 2007-09 global financial crisis triggered unprecedented policy interventions by central
banks around the globe. After cutting policy rates to close to the zero lower bound, several
central banks started conducting non-standard measures. The Federal Reserve has been one of
the most active, implementing several types of non-standard measures during different
periods. In the period after the collapse of Lehman Brothers in September 2008 (often
referred to as QE1), measures aimed at repairing the functioning of financial markets andfocused mainly on liquidity operations to support banks and on large-scale asset purchases(LSAP) of GSE debt, agency debt, mortgage-backed securities (MBS) and Treasury
securities. By contrast, the second main push by the Federal Reserve started in the second halfof 2010 (QE2) and concentrated primarily on purchases of US Treasury securities, with theprimary aim of stimulating the US economy by lowering yields, and pushing up asset prices
in riskier market segments inducing thereby positive wealth effects. In September 2011, theFederal Reserve launched “Operation Twist”, a commitment to extend the maturity of
securities held on its balance sheet, followed by the announcement in September 2012 of athird round of QE, focusing on the purchase of MBS.
While most of the debate has focused on the effects of QE on the US economy, foreign
policy-makers – in particular in emerging markets, as highlighted by the above quote byPresident Rousseff of Brazil – have been criticizing the Fed’s policies, arguing that these havecreated excessive global liquidity, and thus caused the massive acceleration of capital flows to
EMEs since 2009. In turn, this capital flow surge is widely blamed for appreciation pressures
on EME currencies, a build-up of financial imbalances and asset price bubbles in EMEs, highcredit growth and the threat of an over-heating of the domestic economies. As the above
quote suggests, some see the unconventional monetary policy measures of advanced
economies even as a form of protectionism.
There is indeed a hole in the literature about global effects of unconventional monetary
policies, with most of the literature focusing on the effects of QE on US domestic markets1

1

Most of this work focuses on QE1, generally finding empirical evidence that the announcements of
the Fed’s purchases lowered US yields (e.g. Gagnon et al. 2011; D’Amico and King 2011; Wright,2011), with similar evidence for the UK (Joyce et al. 2011), while yielding more mixed evidence forthe effectiveness of the Term Auction Facility (Thornton, 2010) and MBS purchases (Hancock andPassmore 2011, Stroebel and Taylor 2012). Hamilton and Wu (2011) discuss the effectiveness of4and their underlying channels. Through the portfolio balance channel, QE may not only
trigger a portfolio rebalancing towards more risky domestic assets, but also towards foreign
assets. Similarly, a signaling about future US economic conditions and policy rates, orchanges in risk and liquidity premia in the US, and confidence effects are likely to have
implications for economic conditions and financial markets elsewhere in the world. However,with a few notable exceptions, little analysis has been conducted so far to gauge thesespillover effects.2
This paper analyses the effects of the Federal Reserve’s QE both on the US and on 65 foreign
financial markets. Importantly, the paper does not only investigate the effects on asset prices,as most of the literature does, but also on portfolio decisions by investors. This is important as
portfolio decisions are central for identifying the portfolio balance channel of Fed policies.
For this purpose, we use a relatively novel database of high-frequency, daily portfolio flows
into bond and equity mutual funds, taking primarily a US investor perspective. The advantageof the data is that we do not only track capital injections into US bond and equity funds, butalso inflows into EME and other advanced economy funds.
We analyze different types of US unconventional monetary policy measures (liquidity
operations, purchases of MBS and of US Treasuries) in order to understand whether and why
QE1 and QE2 have exerted different effects on US and foreign markets. An importantdistinction we make is between announcements of Fed interventions and the actual market
operations. Most of the literature on US QE has focused narrowly on the effects
announcements of the two large-scale asset purchase (LSAP) programs, but not the actual
operations and purchases, assuming that only the announcement contain new information,
while the latter do not, or do much less so.
Turning to the empirical results, a first key result highlights the fundamental difference
between QE1 and QE2. QE1 measures have been highly effective in lowering long termyields in the US and elsewhere and in supporting equity prices (especially in the US).
However, they also triggered a strong global rebalancing of investor portfolios out of EMEs
and into US equity and bond funds, thus also inducing overall appreciation of the US dollar.
By contrast, QE2 measures appear to have been largely ineffective in lowering yields
worldwide, have caused sizeable capital outflows, mainly into EME equities, and a marked
US dollar depreciation.
This evidence thus suggests the presence of a portfolio balance channel. QE1 measures
induced mainly a portfolio rebalancing across countries, while QE2 functioned both through a
unconventional monetary policy tools at the zero lower bound. Krishnamurthy and Vissing-Jorgensen
discuss the transmission channels of quantitative easing.
2
Neely (2010) and Bauer and Neely (2012) finds significant and sizeable effects of the Fed’s LSAP onsovereign yields in a small sample of other advanced economies. Chen et al. (2011) document
significant spillovers of QE into Asian financial markets.

5portfolio rebalancing across asset classes (from bonds into equities) and across countries. In
particular the US Treasury purchases under QE2 triggered a large portfolio rebalancing out ofbond markets globally, primarily into EME equity markets.
A second major finding of the paper is that the Fed’s LSAP announcements had overall
comparatively smaller effects than actual Fed operations. This is particularly the case for
portfolio decisions and asset prices outside the US (in EMEs and other advanced economies
AE). This is an important finding because it suggests that investors in no way fully priced in
and reacted only to Fed announcements. To the contrary, the impact of Fed operations has
been dominant, an analysis of which has been missing so far in the literature. We argue that
this finding is sensible, as mere Fed announcements that e.g. aim at repairing dysfunctional
markets alone are insufficient to trigger such a repair. Moreover, actual operations may
contain new information and may help coordinate market participants, a fact that has become
apparent also in non-standard measures of other central banks, such as the ECB’s 3-year longterm refinancing operations (LTROs) of December 2011 and February 2012.
Third, in terms of economic significance, the effects of US QE measures on capital flows to
EMEs have been relatively small compared to other factors, but they have exacerbated the
pro-cyclicality of EME capital flows. In cumulative terms, US unconventional monetary
policy measures together explain EME net equity inflows of 5% and EME net bond outflowsof 6% of the fund’s assets under management in our data set between mid-2007 and 2011. Of
course these are sizeable magnitudes, yet they are moderate relative to the total cumulative
net equity inflows to EMEs of more than 25% and net bond inflows to EMEs of 34% during
this period. By contrast, Fed policy measures have exerted a comparatively larger effect on
asset prices than on portfolio flows. For instance, the effects of Fed policies explain about one
third of the overall depreciation of the US dollar during the 2007-11 period.
However, although Fed policies may explain only a limited share of the large swings in crossborder capital flows during 2007-11, they are found to have magnified the pro-cyclicality ofcapital flows to EMEs, while acted in a counter-cyclical manner for the United States. In late2008-09, Fed measures contributed significantly to net capital outflows from EMEs – in aperiod when EMEs experienced sudden stops and massive capital flight overall – and thensince mid-2009 induced a gradual reversal of these outflows, contributing to the surge incapital inflows to EMEs during that period. Hence one key message of the empirical findingsof the paper is that US unconventional monetary policy measures have not so much affected
the overall magnitude of capital flows to EMEs, but they have magnified the variability andpro-cyclicality of capital flows.
Fourth and finally, there is a substantial degree of heterogeneity in the extent that countries’
capital flows and asset prices react to Fed QE measures. In particular EME policy-makers
may have tried to shield themselves from the described spillover effects, such as through6interventions in foreign exchange markets – with FX reserves of many EMEs increasing
dramatically between 2009 and 2011 – or by introducing capital controls. We find evidence
that countries with better institutions and more active monetary policy have been affected lessby Fed policies. By contrast, there is no evidence that having a pegged exchange rate regime
or a less open capital account helped countries insulate themselves from QE policy spillovers,
conversely, they might have amplified the pro-cyclical impact of Fed policies. This lends
further credence to the hypothesis that the portfolio rebalancing effects of Fed QE policies areat least in part explained by risk and a flight-to-safety phenomenon.
The findings of the paper have a number of implications. They support the argument that US
unconventional monetary policy measures have affected capital flows to EMEs in a procyclical manner, and have raised asset prices globally and weakened the US dollar. Thissuggests that there is indeed an important global dimension to and externalities frommonetary policy decisions in advanced economies. However, the paper is mute on whether
such externalities are overall positive or negative for other economies – as the potentially
undesirable effects of these measure on the pro-cyclicality of EME capital flows need to beweighed against potential benefits such as e.g. through higher economic activity and a better
financial market functioning in the global economy.
The paper is organized as follows. The next section discusses in detail the various elements of
the US monetary policy stance and their objectives and functioning, as well as links thecurrent paper to the related literature and details some of potential the transmission channels.
Section 3 outlines the empirical methodology and data. Section 4 then presents the empiricalfindings, both for the overall effects of US unconventional monetary policy measures as well
as for the various transmission mechanisms. Section 5 summarizes the main findings andconcludes by discussing policy implications.

2 US Non-Standard Monetary Policy Measures
This section provides an overview of the various instruments of the Fed’s tool kit employedduring the period 2007-11, and discusses the different channels through which they function.
2.1. The Fed policy menu
The reversal of the housing boom in the United States and the collapse of the US sub-prime
mortgage market resulted in a crisis of a global dimension in 2008. US policy makers reactedwith a set of policy measures to the economic downturn.
Beside the more standard counter-cyclical policy measures, the Federal Reserve decided to
introduce a new set of non-standard policy tools, which have been labeled credit-easing

7

tools.3 These new facilities dramatically affected both the composition and the size of the
Fed’s balance sheets. In general, the non-standard measures implemented by the Federal
Reserve can be divided into three groups:4 (i) lending to financial institutions, (ii) providing
liquidity to key credit markets, and (iii) large-scale asset purchase program (LSAP). In what
follows, we provide a short description of each of these groups.
In late 2007 and early 2008, the Federal Reserve implemented several programs associatedwith direct lending to financial institutions. These measures intended to address the extremely
limited availability of credit in short-term funding markets, which are used by financial
institutions and other businesses to finance their day-to-day operations.5 The financial crisis
intensified dramatically in the second half of 2008, after the collapse of Lehman Brothers,with many financial market segments all but shutting down. These developments induced theFederal Reserve to implement a number of additional programs with the aim for providing
liquidity to key credit markets, and to reduce funding pressures.6
Both of these facilities can be associated with the central bank’s role as lender of last resort,with the purpose of providing liquidity to the financial sector (see Bernanke, 2009). We willthus subsume both of them under the category of liquidity providing measures by the Fed.
The aim of these policies was to avoid fire-sales of assets by providing a liquidity backstop tofinancial institutions (see Bernanke, 2009). In other words, the Fed’s objectives were to
mitigate the propagation of the crisis through a balance sheet channel (Sarkar, 2009).
These policies therefore have a different impact on the economy than the Fed’s third policy
tool, the so called large scale asset purchases program (LSAP). The overall LSAP composedof asset purchases of mortgage back securities (MBS), and in a later stage of US Treasury
bonds. While the MBS program was introduced with the explicit aim of reducing mortgage
interest rates and stabilizing the housing markets, the ultimate goal of Treasury purchases wasto stimulate economic activity by lowering long term rates to support investment, and by

3

The US Congress passed large-scale countercyclical fiscal packages as response to the crisis. Also,
the Federal Reserve reduced the Fed funds target rate to close to its zero lower bound.
4
See Carlson, Haubrich, Cherny and Wakefiled (2009) for a detailed discussion, Fawley and Neely(2012) for an insightful comparison of different QE policies across advanced countries, and Bekaert etal. (2011) on how monetary policies may affect the transmission of shocks in equity markets.
5
The programs under this category included the Term Auction Facility, which auctioned term loans todepository institutions, as well as the Primary Dealer Credit Facility and the Term Securities Lending
Facility, which provided overnight and term loans to primary dealers, a group of major financial firmsthat have an established trading relationship with the Federal Reserve Bank of New York. Furthermore,
to address a severe US dollar shortage overseas, the Federal Reserve established dollar liquidity swaps
with foreign central banks to help them provide dollar loans to financial institutions.
6
The Federal Reserve established the Asset-Backed Commercial Paper, Money Market Mutual Fund
Liquidity Facility, the Commercial Paper Funding Facility, and the Money Market Investor Funding
Facility. The Fed decided to set up three limited liability companies (Maiden Lane LLCs) to facilitate
lending in support of specific institutions such as Bear Sterns, JP Morgan and AIG.

8

boosting asset prices to stimulate demand.7 The channels through which the Fed’s Treasury
purchases affect longer-term interest rates and financial conditions have been subject to acontroversial debate. One view is that such purchases work primarily through a portfolio
balance channel, which holds that, once short-term interest rates have reached zero, theFederal Reserve’s purchases of longer-term securities affect financial conditions by changing
the quantity and mix of financial assets held by the public (see Bernanke, 2010). Specifically,
the Fed’s strategy relied on the presumption that different financial assets are not perfect
substitutes in investors’ portfolios, so that changes in the net supply of an asset available toinvestors affect its yield and those of broadly similar assets. Thus, one intention of thepurchases of long-term assets is to reduce the yields on the purchased securities and to push
investors into holding other assets. In August 2010, the FOMC stopped the purchase of MBS
and agreed to stabilize the quantity of securities held by the Federal Reserve by re-investing
payments of principal on agency securities into longer term Treasury securities, extending
thereby its Treasury purchases program. The Fed argued that reinvestment in Treasury
securities is more consistent with the FOMC’s longer-term objective of a portfolio made up
principally of Treasury securities. As a result, Treasury purchases by the Fed became thedominant instrument within the LSAP program.
All these measures led to a significant increase in the size and a change in the composition of
the Fed’s balance sheet (see Figure 1). While direct lending to financial institutions played asignificant role at the beginning of the crisis, large scale asset purchases have since become
dominant in the dynamics of the Fed’s balance sheet.

Figure 1

Each of the Federal Reserve’s credit easing strategies—lending to financial institutions,providing liquidity to key credit markets, and purchasing long-term securities—intended tostabilize financial market and real economic activity in the United States. Observers,
however, have argued that beside their domestic impact, credit easing policies affected global
asset prices and were the main driver of the surge in capital flows to emerging economies(EMEs). It is in particular this latter point on which the current paper focuses.

7

The implementation of the LSAP came in several steps. In November 2008 the Federal Reserveannounced plans to purchase the direct obligations of the housing-related government-sponsoredenterprises (GSEs), specifically Fannie Mae, Freddie Mac, and the Federal Home Loan Banks. In
March 2009, the Federal Open Market Committee (FOMC) decided to expand its purchases of agencyrelated securities and to buy long-term government bonds as well. In August 2010 the Fed decided torenew the quantitative easing programme. The list of Fed announcements for the Fed’s LSAP
programme is presented in Table 1.

92.2 Channels of transmission and international repercussions
There are four channels through which Fed unconventional policies may affect portfolio
decisions by investors and asset prices, both domestically and internationally. A first one is aportfolio balance channel. A Fed purchase e.g. of a US Treasury security influences theavailable supply of this asset to private investors. As bond premia should be determined by
the underlying risk characteristics of the asset and the risk appetite of investors, such a Fed
purchase influences yields of the asset only to the extent that the asset is not perfectly
substitutable. A number of studies have indeed referred to the hypothesis that Habitat theories
hold (see Gagnon et al. 2011, D’Amico and King 2011, and Doh 2010).
The signaling channel is a second mechanism through which Fed interventions may influence
asset prices and portfolio decisions. Bond yields may decline via a lower risk-neutralcomponent of interest rates, if Fed announcements or operations are understood by markets tosignal lower future policy rates than was previously expected. Bauer and Rudebusch (2011)stress the importance of this channel for Fed announcements since 2008, and show that thischannel had similar importance as the pure portfolio balance channel via lower term premia.
However, Fed announcements may also provide new information about the current state of
the economy. Such a third channel, or what may be dubbed confidence channel, can affectportfolio decisions and asset prices by altering the risk appetite of investors. For instance, a
Fed LSAP announcement may be understood by markets as indicating that conditions areworse than previously expected, hence triggering a flight to safety (e.g. Neely 2010).
A fourth channel is related to the effects of Fed announcements and operations on thefunctioning of markets, and thus on portfolio decisions and asset prices by affecting e.g.
liquidity premia. In particular the liquidity operations and purchases of MBS, as outlined
above, are likely to have functioned, at least in part, through such a channel by improving
market functioning and decreasing liquidity premia (Joyce et al., 2011; Gagnon et al., 2011).
Three key points need to be emphasized. First, the four channels discussed above are by nomeans mutually exclusive, but several channels may be at work simultaneously. Second, theway non-US portfolio allocations and asset prices are affected by Fed announcements andoperations depends on how foreign assets are considered by investors. For instance, whether aflight-to-safety phenomenon leads to a flight out of non-US bonds depends on the degree towhich such securities are considered “safe” by US investors.
Third, the dominant focus in the literature on Fed unconventional policy measures, as outlined
above, has primarily been on the functioning of the portfolio balance channel in response to
Fed announcements (rather than actual operations). An important caveat is that Fed
announcements do not imply any change in supply of e.g. US Treasury securities at the time
the announcements are made, but they merely indicate that such a change will occur at some10

point in the future, to some degree, and with certain probability (with LSAP announcements
declaring an upper bound of future purchases of various instruments). Hence, a change in theavailable supply of US Treasuries is not the impetus of the portfolio balance channel of suchannouncements. It is rather the change in expectations about future asset prices that triggers
changes in portfolio allocations and current asset prices. This point is also important forunderstanding the rationale for analyzing Fed operations, rather than limiting our study toannouncements as done by most of the literature. One potential objection and concern toanalyzing Fed operations is that they may not contain any new information, as e.g. amounts
and timing about LSAP programs were known at the time of their announcements. Hence,
efficient markets should have priced in fully such information with the announcements.
There are two replies to this point. A first one takes issue with the assumption of efficient
markets implied in this point. Many of the Fed measures were implemented precisely because
markets were not functioning. The Fed’s liquidity support measures to banks and also its
MBS purchases are two examples. The mere announcement of MBS purchases may havebeen much less effective than the actual purchase because the latter restored liquidity tomarkets and allowed investors to adjust their portfolios.
A second point relates to the accuracy of market expectations. Although market participants
may have had a fairly accurate idea about the timing and amounts of Fed operations under the
LSAP programs, they may not have been accurate in their expectations about theeffectiveness of such operations in e.g. re-establishing the functioning of markets orenhancing the prospects of the US or global economy. In addition, Fed operations, by
inducing portfolio rebalancing, can lead to unexpected demand for certain assets, therefore
having an impact on market prices.

3 Empirical Methodology and Data
In this section, we discuss the empirical strategy we employ for assessing the impact of US
unconventional monetary policies on portfolio decisions and asset prices. We conclude thesection by outlining the data used, in particular the fund-level data on portfolio decisions.

3.1 Methodology
Our empirical approach for evaluating the impact of QE is to analyze the response of portfolio
decisions, asset prices and exchange rates to specific unconventional policy actions andevents. Importantly, we differentiate between US and foreign variables (further distinguishing
between EMEs and other AEs). This allows testing whether foreign markets were affecteddifferently from the US, as well as whether different types of investment were influenceddifferently. We evaluate the impact of QE using the following model:

11yi ,t  Ei ,t 1  yi ,t   (    EME DiEME   AE DiAE ) MPt   i ,t(1)

with MPt  [ AN1t , AN 2t , LQt , TRt , MBSt ]'with the dependent variable yi,t being alternatively the net inflows (into bonds or into
equities), expressed in percent of all assets under management, equity price returns, the firstdifference of long term bond yields or the exchange rate return in country i and day t. DEME isa dummy with a value of unity if country i is an emerging economy, and DAE for otheradvanced economies (AEs). Hence the impact of a particular policy measure MPt on the US isgiven by the coefficient , while the additional impact on EMEs and AEs is denoted by therespective coefficients.
We distinguish between two types of unconventional monetary policy measures in theanalysis. Announcements (denoted AN1 and AN2) are impulse dummies equal to 1 for anumber of announcements related to QE1 and QE2 policies, respectively. As stated above,
such announcements mostly occur several weeks or even months before actual operations are
implemented. As it is common in the literature (Gagnon et al. 2011, Wright 2011), we analyze
twelve key announcements by the Fed which are primarily related to Fed purchases orreversals of US Treasuries and span from 2008 to 2010.8 The list of announcements isprovided in Table 1.
The second set of policy measures relates to actual market interventions by the Fed and ismeasured as the weekly changes of outstanding amounts of the following operations in the
Fed balance sheet:9 (i) liquidity support measures for the financial sector (LQt), (ii) purchasesof long term Treasury bonds (TRt), and (iii) purchases of long term mortgage backed
securities and GSE agency debt (MBSt).10 Note that all of these measures can take positive or
negative values, e.g. in the latter case when such operations are reversed.
Importantly, we also include a set of control variables which capture the expected component(Ei,t-1) of changes in portfolio allocations and asset prices for country i at time t. In the basic
8
As commonly done in the literature, we include events QE1, QE3, QE4, and QE5 in the group ofannouncements denoted “AN1” which is related to the first LSAP programme (QE1). We include
events QE10, QE11, and QE12 in the group of announcements denoted “AN2” which is related to thesecond LSAP programme (QE2) – see Table 1 for the list of events/announcements. Events QE2 andQE9 are excluded from the analysis as they occurred on days on which other news dominated financial
markets developments. In the case of QE2, the US and global equity markets collapsed as a result of theofficial news of the US recession. Similarly, negative market news unrelated to the Fed’s
announcement dominated QE9. Events QE6-QE8 announced a reduction or a halt to LSAP programmes,
and have been shown in the literature to have been mostly irrelevant as news for financial markets.
9This classification is based on a lecture of Chairman Ben Bernanke given on 13 January 2009 at the
London School of Economics. http://www.clevelandfed.org/research/trends/2009/0209/02monpol.cfm
10
We separate between purchases of long term mortgage backed securities, and purchases of long-term
treasury bonds, since the latter become prominent following the QE2 announcement in August 2010.

12

setting, we take account of (i) country fixed effects i to capture country-specific, timeinvariant elements, (ii) lagged variables reflecting financial shocks, risk and global market
conditions, such as the option implied volatility on the S&P 500 index (VIX), the 10-year Tbond yield in the US, the liquidity spread (defined as the difference between 3-month OIS
rate and T-Bill yield);11 and (iii) lagged returns of the domestic market return.12 In practice, itturns out that the inclusion of different sets of controls influences only modestly themagnitude of the estimated coefficients, but does not alter the sign or statistical significanceof the estimates.
Three important methodological caveats need to be stressed. First, Fed operations and market
interventions may to some extent not be exogenous, but endogenous to market developments.
For instance, a decision by the Fed to provide more liquidity support to banks is likely to havebeen influenced by market conditions and banks’ needs for liquidity, and thus may have been
higher during weeks when spreads were high, equity markets fell and investors withdrew
capital from markets.
It is very hard to deal with this issue, and we try to do so in several different ways. In
particular, we control for market developments and previous trends in our empirical model, as
outlined above, and also use interventions with lags in the robustness exercise. Moreover, in
the robustness section, we adopt a more sophisticated two-stage approach where we first
calculate the unexpected component of Fed operations and then use it as explanatory variablein the benchmark model. Most importantly, we note that if there is such an endogeneity bias,
removing it should strengthen the estimates of our empirical findings because Fed operations
in most cases were of a “leaning-against-the-wind” type through which the Fed responded tomarket distortions and attempted to remove these.
A second query relates to the speed with which financial markets respond to Fed
announcements and operations. As shown in the literature, asset prices responded mostly
instantaneously to Fed measures. However, portfolio decisions by investors may besubstantially more sluggish in their responses (see e.g. the evidence provided in Forbes et al.
2012). In the benchmark specification, we therefore include Fed policies on the day and thesubsequent day in the estimation of empirical model, while for operations we include them forthe entire week.
A third caveat is about the extent to which Fed announcements and operations have been
anticipated by financial markets. If these policies have been correctly anticipated, then asset
prices and portfolio allocations may have partly adjusted already ahead of the event. The
11

The liquidity spread is defined as the difference between the 3-month Overnight Swap Index (OIS)
rate and the 3-month T-bill yield.
12
There are some differences as to the precise specification of the models for flows and for asset
prices, such as that the estimation for the former includes levels of VIX, of the liquidity spread and of
the 10-year T-bond, while the model for prices includes changes of these variables.

13

previous section discussed why in particular operations may nevertheless still exert someeffects on asset prices and portfolio allocations, even when they do not constitute “news” perse. Nevertheless, as for the potential endogeneity bias, such an anticipation should make theestimated coefficients larger and more significant statistically.

3.2 Data

We use daily data on portfolio equity and bonds investment flows from January 2007 to
December 2010, compiled by the data provider EPFR. The dataset contains daily flows formore than 16,000 equity funds and 8,000 bond funds. EPFR data captures about 5-20% of themarket capitalization in equity and in bonds for most countries, but importantly, it is a fairly
representative sample as shown by Jotikasthira, Lundblad and Ramadorai (2010), Miao andPant (2012) and Fratzscher (2012), with EPFR portfolio flows and portfolio flows stemmingfrom total balance-of-payments data mostly matching quite closely13.
At the fund level, EPFR data provides information on the total assets under management(AUM) at the end of each period, allowing for a distinction between capital flows net ofvaluation changes, and valuation changes (due to asset returns and exchange rate changes) to
calculate each period’s change in AUM. Importantly, in our benchmark specification, we
focus on total net injections into the funds (which abstracts from valuation changes),aggregated at the country level, because these reflect the active decisions of investors about
whether or not to add or reduce investments in a particular fund class. Therefore our focus is
not on analyzing the portfolio allocation strategy of individual fund managers, but rather thatof individual firms or other institutional investors following monetary policy actions.
A caveat to conducting an analysis that compares allocations to equity funds with those tobond funds is that each of these categories is fairly broad, comprising a very heterogeneous
set of financial assets. For instance, bond funds include investments in Treasury securities, i.e.
the very same assets in which the Fed intervened, as well as a broad array of corporate bonds
with a wide spectrum of risk and liquidity. This implies that the empirical analysis yields
merely the average effects across individual market segments.
Table 2 provides summary statistics for the net flows aggregated at the level of the group ofcountries of destination (expressed as percentage of assets under management in thedestination country) for our selected sample of countries, as well as for asset prices. Note thatin our benchmark regression we consider both US and non-US domiciled funds, with US
domiciled funds accounting for more than 80% of the number of funds. Moreover, due to13

Other papers using the EPFR dataset are Forbes et al. (2012), Lo Duca (2012) and Raddatz and
Schmukler (2012).

14

legal restrictions most of the investors in the funds are located in the same domicile as thefund itself. This means that strictly speaking the analysis is from a US investor perspective,
while it can say little about the portfolio decisions of e.g. investors located in EMEs. This isimportant because it implies that investment decisions vis-à-vis EMEs or other AEs imply
cross-border transactions and thus gross capital flows in a balance-of-payments definition. By
contrast, investment decisions vis-à-vis the US do not constitute such balance-of-payments
transactions. For simplicity, we use the terms “capital flows” and “portfolio choice/decision”
interchangeably throughout the paper.
Asset prices comprise returns of domestic equity indices in local currency terms (in percent),
first differences of 10-year government bond yields (in percentage points), and returns of thebilateral exchange rate vis-à-vis the US dollar (and the NEER for the US) with a rise in allcases implying an appreciation of the US dollar (in percent). Table 3 provides summary
statistics for the US monetary policy measures, the asset price variables, as well as a broad setof control variables, both common factors and country-specific, idiosyncratic variables.

4 Empirical Results
This section first presents the findings of the benchmark model (section 4.1), the results forthe economic significance (section 4.2), the robustness analysis (section 4.3) and concludes
with an analysis of the determinants of the cross-country heterogeneity in the effects of Fed
QE policies (section 4.4).

4.1 Benchmark model
The estimated coefficients of the benchmark regression are reported in Table 4 for portfolio
flows, in Table 5 for asset returns/yields and Table 6 for exchange rate reactions to US
monetary policies. The tables show the estimated coefficients of equation (1) for the fivevariables capturing the US unconventional monetary policy measures.14 We organize thediscussion of the findings along the distinction between policy measures that fall under the
QE1 period – primarily QE1 announcements, liquidity operations and MBS purchases, and
QE2 measures – mainly QE2 announcements and Treasury purchases.

Tables 4-6
For the QE1 period of 2008-2009, recall that the main objective of Fed policy was one ofmarket repair and the provision of liquidity to financial institutions, as an extension of theFederal Reserve’s role as a lender of last resort, to avoid a credit crunch in the US economy.

14

The full results with the control variables (as listed in Table 3) are not shown for brevity reasons but
are available upon request.

15

Table 4 indicates that the Fed was fairly successful in pursuing this objective as its policy
measures triggered primarily a portfolio rebalancing across countries, with capital flowing
mainly out of EMEs and into US equity and bond funds. Starting with QE1 announcements,
these triggered mainly inflows into both US equities and, to a lesser extent, into US bonds.
Hence, unlike what has been discussed in the previous literature, the portfolio rebalancing thatappears to have been most pronounced in response to US QE1 announcements has been one
across countries, rather than across asset classes. This portfolio rebalancing pattern is also
clearly visible in the reaction of asset prices as each of the QE1 announcements reduced US
10-year Treasury yields on average by 16 basis points (Table 5), which is consistent with thefindings of the literature and also with the stylized facts of Table A2 (see Appendix). Also thesignificant easing foreign bond yields is in line with that of the literature (see e.g. Neely 2010
for advanced economies’ yields).
A second, crucial element of the Fed’s strategy during the QE1 period was its liquidity
operations. Also these induced a cross-country rebalancing out of EME assets and into US
equities and US bonds (Table 4) and a drop in US bond yields (Table 5), while appreciating
the US dollar as a result (Table 6).15 This finding again seems sensible against the backgroundthe underlying objective of the Fed’s liquidity operations. This role may have implied also a
moral suasion component, i.e. market participants that receive funding from the Fed might be
inclined not to reduce their exposures to the domestic economy, but achieved their desireddeleveraging by selling off foreign asset holdings in EMEs.16 In addition, by expanding thepool of collateral eligible to obtain central bank liquidity, the Fed might have increased thewillingness of investors to hold US assets at times of global liquidity shortages.
As the third main element of QE1 policies, MBS purchases by the Fed induced net inflows
into bond funds of all regions and groups, and net outflows out of US equity funds (Table4),17 while asset prices reacted only weakly (Table 5). This finding is consistent with theargument that MBS purchases helped improve the functioning of particular US bond market
segments, making these more attractive to investors and hence attracting private capital into
funds investing in bond markets. Indeed, the Fed stated as its goal for the MBS purchases to15

Endogeneity might be a problem for liquidity operations, as a decision by the Fed to provide more
liquidity is likely to have been influenced by market conditions and banks’ needs for liquidity, and thus
may have been higher during weeks when spreads were high, equity markets fell and investors
withdrew capital from markets. In section 4.3, we show that the core results (i.e. rebalancing towards
the US and US dollar appreciation) are robust to a number of robustness checks that address
endogeneity concerns.
16
See Rose and Wieladek (2011) for a similar argument in the context of the UK.
17
This finding survives all the robustness checks with the exception of the two step approach aimed ataddressing endogeneity concerns (see section 4.3 and table A9). However, since MBS purchases were
implemented in a rather mechanical manner by calibrating daily purchases to hit the targeted total
quantity of holdings by the last day of the program (Hancock and Passmore 2011), endogeneity is not a
crucial concern for this instrument. Therefore, we pay less attention to the results of the two stage
approach for MBS purchases.

16

“reduce the cost and the increase the availability of credit for the purchases of houses”.18 As
discussed in Hancock and Passmore (2011), the Federal Reserve’s MBS Purchase Program
re-established robust secondary mortgage market, which meant that the marginal mortgage
borrower could be funded via capital markets, which is consistent with our finding of net
inflows into US bond markets.
By contrast, for the QE2 period in 2010 Fed policy measures functioned in a fundamentallydifferent way from those of the QE1 period. In particular, QE2 policies induced a portfolio
rebalancing out of US equities and bonds, and partly into EME equities. This holds for both
QE2 announcements as well as for the Fed’s Treasury purchases (Table 4). Moreover,
Treasury purchases by the Fed also induced a portfolio rebalancing across asset classes, asbond funds in all regions – US, EMEs and other advanced economies, experienced net
outflows and EME equity funds net inflows. When the Federal Reserve buys long-term
government bonds, it crowds out other investors and reduces yields in this market segment.
This raises the demand for more risky assets. Relative to the size of assets under managementof the funds, the effects of US Treasury purchases by the Fed were even larger for many
EMEs than for the US itself, thus suggesting that these operations had a particularly strong
impact on capital flows to EMEs. In fact, the estimates indicate some, albeit small, net
outflows even out of US equities compared to sizeable net inflows into EME equities.
Moreover, opposite to the effects of liquidity operations, US Treasury purchases thus
triggered a stronger risk-taking by fund managers, and in particular with regard to equity
investment in EMEs.
The response of asset prices is in line with the results for portfolio allocations, as Table 5
suggest that QE2 announcements had a substantially smaller effect on US yields than QE1
announcements, reducing them on average by about 2 basis points, which is consistent e.g.
with the findings by Wright (2011). Moreover, Treasury purchases even raised US Treasury
yields slightly (Table 5). Most importantly, both QE2 announcements and Treasury purchasesby the Fed worked to weaken the US dollar significantly (Table 6).
In summary, the findings highlight the fundamental differences between the Fed’s QE1
policies and its QE2 policies. QE1 policies induced mainly a portfolio rebalancing from therest of the world into the US, and in particular into US bond funds, and lowered US bondyields significantly. By contrast, QE2 announcements and Treasury purchases mainly
triggered a portfolio rebalancing in the opposite direction, from US funds into foreign funds,
but also across asset classes, from bonds into EME equities. Among country groups, EMEs
seem to have been more strongly exposed to these spillover effects of Fed policy thanadvanced economies, an issue to which we will turn in more detail below.

18

See http://www.federalreserve.gov/newsevents/press/monetary/20081125b.htm

17

4.2 Economic significance and cyclicality
How important are the effects of US monetary policy measures for changes in portfolio
allocations, asset prices and exchange rates? So far, we have discussed the statisticalsignificance and the underlying mechanisms and channels through which US unconventionalmonetary policy measures have functioned. Yet, we have observed large shifts in portfolio
allocations global capital flows during the crisis in 2007-08 and also since 2009. How muchof this overall pattern and overall magnitude can be explained through such policy measures?
Moreover, has Fed policy functioned in a pro-cyclical or in a counter-cyclical manner, ineither exacerbating or reducing capital flows and asset price movements?

Tables 7 – 8, Figure 2

We conduct two types of analyses to get at this question. First, we calculate the cumulative
effects of the different policy measures on total investment in US, other AE and EME bondfunds and equity funds. Table 7 shows the cumulated effects of each US policy measure at thepeak of the Federal Reserve’s balance sheet exposure, while Table 8 depicts the impact of
the total change over the 2007-11 sample period. The distinction between the two is importantprimarily for the liquidity operations, which reached a peak with a cumulated USD 2,000 bn
in early 2009, but then were unwound to a large extent by the end of 2010. The same analysis
is conducted for asset prices (equity returns, bond yields and exchange rates) in panels B of
Tables 7 and 8.
The second analysis is to cumulate across all five Fed policy measures, however not at one
particular point in time (as in Tables 7 and 8), but rather presenting the evolution of the total
cumulated effect of US monetary policy measures over time. This is what is shown in Figure
2 for equity and bond flows into EMEs, the US and other AEs.
Three main findings emerge. First, the absolute effect of US monetary policy measures on
portfolio allocations, capital flows and asset prices is substantial. For instance, in cumulative
terms, US policy measures together explain EME net equity inflows of 4.4% and EME net
bond outflows of 6.0% as a share of the funds’ assets under managements between mid-2007
and early 2011 (see Table 8). As the size of EME equity assets held by foreigners issubstantially larger than that for EME bond assets, in US dollar terms these figures imply net
inflows of USD 22 bn into EME equities and net outflows of USD 6 bn out of EME bonds
using our mutual fund database.19 Similarly for US funds and other AE funds, Fed non-19

Using IMF CPIS figures for a back-of-the-envelope calculation to get a proxy for the effect onoverall portfolio equity flows and bond flows to EMEs confirms that the magnitudes of these effects
are indeed sizeable (proxied USD 159 bn inflows into EME equities and net outflows of USD 112 bn
out of EME bonds) – see the respective rows in the tables labelled “IMF CPIS”.

18

standard measures induced significant effects on allocations, e.g. cumulative inflows into AE
bonds of 3.7% and net outflows out of US bond funds of 4.7%.
Importantly, these cumulative figures mask the fact that some of the Fed measures exerted
opposing effects on portfolio allocations. Looking at the breakdown by individual Fed
measures in Table 7, for instance, shows that Fed purchases of US Treasuries caused large net
outflows out of US bond funds of 9.7% and out of EME bond funds of 10.5%, while MBS
purchases had the opposite effect inducing net inflows into US bond funds by about 5% andinto EME bond funds by 5%.
The responses of asset prices and exchange rates reveal a similar picture in that Fed policies
have exerted economically meaningful effects on equity returns and bond yields in all threegeographical areas – the US, EMEs and other AEs. Panels B of Tables 7 and 8 show that, forinstance, QE1 announcement raised US equity prices by 4.3% and lowered US 10-year
Treasury yields by 66 b.p. (Table 8), which is in line with the stylized facts presented above.
Similarly, Fed operations – specifically Treasury purchases – exerted even larger effects than
Fed announcements on asset prices in all financial market segments globally. Fed Treasury
purchases raises US equity prices by 15% (and EME and AE equity prices by more than18%), and led to an effective depreciation of the US dollar by 4.8%.

As a second main result, although these effects of Fed policies obviously constitute sizeable
magnitudes in absolute terms, they are moderate compared to the total cumulative changes in
portfolio allocations, capital flows and asset prices when taking a longer-term perspective
over the entire sample period. For instance, the total increase in net equity inflows to EMEs
over the period 2007-11 was more than 25% and in net bond inflows to EMEs 33%, i.e. farlarger than what can be accounted for by Fed announcements and operations. In fact, Figure 2
shows that the control variables (common risk, liquidity and yield factors, and local asset
returns) have been substantially more important as drivers of capital flows to EMEs than US
monetary policy measures. The same holds for allocations to US funds and to other AE funds.
Hence, overall, a key finding is that Fed non-standard measures account for only a small sharein the changes in portfolio allocations and capital flows.
Another important aspect of the results is that capital flows to EMEs have in most cases been
substantially more sensitive to Fed policy measures than flows into US funds or other AE
funds, when measured relative to fund assets under management. This again confirms that
Fed measures have indeed exerted a substantial and economically meaningful effect inparticular on capital flows to EMEs.
A final point on this first overall finding is that the effects of Fed announcements have,overall, been substantially smaller than the effects of actual Fed operations on portfolio flows
and on asset prices. For instance, QE1 announcements caused net inflows of about 1% into19

US bond funds and 1.8% into US equity funds. By contrast, Fed purchases of US Treasuries
lowered the private mutual fund holdings of US bonds by close to 10% and of US equities of
0.8%. A similar finding holds for asset prices, although QE announcement did exert very
substantial effects on equity return and in particular on US Treasury yields.
This finding is important because it challenges the approach in the literature to focus
exclusively on the effects of Fed QE announcements, rather than Fed operations themselves.
It also underlines and confirms the role of the market repair and liquidity provision functionsof Fed policies, which means that the mere announcement or anticipation of such measuresalone do not meet these objectives, but that it takes the operations to truly accomplish thegoals. What the findings also suggest is that while Fed QE announcement indeed triggered
substantial changes in US asset prices, most of the effects on capital flows as well as on asset
prices for EMEs and other AEs were caused by Fed operations. Hence analyzing operations is
key for understanding how the Fed’s unconventional monetary policy measures havefunctioned, and in particular gauging their global repercussions.

Figure 3

As a third main finding, the evidence suggests that US unconventional monetary policy
measures since 2007 have significantly exacerbated the pro-cyclicality of capital flows to
EMEs. By contrast, these Fed measures have worked in a counter-cyclical manner forinvestments in US equity and bond markets, as well as those of other AEs. Figure 2 shows
how during the height of the 2007-08 crisis Fed liquidity operations pulled capital out of
EMEs and into US equity and bond funds. By contrast, during the recovery period of 2009
when overall capital inflows into EME surged, the combination of a partial reversal of Fed
liquidity operations with Treasury and MBS purchases contributed to the capital flow surge
into EME equities. The strongest effect of QE policies on cyclicality is present for bondyields (Panel D of Figure 2), where QE policies induced bond outflows out of EMEs and a
sharp rise in bond yields in late 2008 and 2009, and the reverse since 2010.
Figure 3 reports the correlation (using a centered rolling window of 12 months) between theestimated effects of US monetary policy instruments and the estimated effects of the other,
control variables on portfolio flows. The evolution of the correlation shows that at the peak of
the crisis at the end of 2008 Fed policies amplified the cycle of portfolio flows to EMEs bygenerating outflows when also other factors had a negative impact on flows. Conversely,
during the recovery in 2009 and 2010, US monetary policy interventions generated inflows in
EMEs when also other factors pushed capital to EMEs. Regarding the US, monetary policy
had a counter-cyclical effect as indicated by the negative correlations at the peak of the crisis.

20

4.3 Robustness tests
We conduct a number of robustness checks and extensions to the analysis, in particular inview of the various caveats discussed in section 2.

Tables A3 – A9 (see Appendix)

First, we gauge the sensitivity of the benchmark specification to the inclusion of alternative
controls. This is important because control variables should provide a fairly accurate predictor
for capital flows in the absence of US policy measures, and therefore are needed for theidentification of the effects of monetary policy instruments. In this regard, we include
additional explanatory variables capturing macroeconomic surprises in the G10 and in theregion (with Citigroup as source) where the portfolio of the individual fund is invested (Table
A3). The results are unaffected by this change.
Second, we check whether Fed operations functioned in an asymmetric manner (Table A4) inthat balance expansions had different effects from balance sheet contractions (in fact, as
Figure 1 shows, most of the liquidity injections and MBS purchases were unwound overtime). The results of the benchmark specification are again confirmed in this setting.
Third, we replace the weekly figures on Fed holdings of Treasuries related to unconventional
operations with more detailed daily data provided by the New York Fed on Treasury
purchases related to QE1 and QE2 (Table A6). The results of the benchmark specification are
confirmed in this setting which suggests that the use of daily interpolated figures from weekly
data for some of the monetary policy instruments is not an issue.
Fourth, we use a bootstrap procedure for the estimation of the covariance matrix of theparameters of the econometric model. This approach addresses issues related to theuncertainty about the correct adjustment for the standard errors that could be affected bydifferent forms of heteroskedasticity and cross sectional dependence (Table A7). When using
the bootstrap procedure a few parameters become insignificant, however, this result has no
meaningful impact on the overall conclusions.
Fifth, Table A8 is based on a specification of the benchmark model that splits Treasury
purchases across QE1 and QE2 periods. While other Fed operations can be clearly associatedeither with the QE1 or with the QE2 period, this is not the case for Treasury purchases whichstarted in early 2009 under QE1, and then were used again in a larger scale during the QE2
period. The separation of Treasury purchases in the two periods shows that the relative
rebalancing towards riskier asset and emerging markets generated by Treasury purchases wasstronger under QE2.
Finally, another important caveat discussed in section 2 relates to the potential endogeneity of
Fed operations, as e.g. the purchase of Treasury bonds by the Fed in a particular week may

21respond to common factors affecting also the dependent variable or to existing market
conditions that have been present before the Fed conducts such a purchase. To the extent thatmarket participants anticipate purchases ahead of time, and thus investors have alreadyreacted before purchases are conducted, such a behavior would rather imply a downward biasof the benchmark estimates. Nevertheless, we try to deal with this issue directly in various
ways. The first one is obviously the inclusion of official Fed announcements of such policy
measures as well as appropriate controls that proxy for market conditions, as described indetail in section 3. In the second one, we analyze whether large operations (i.e. the 25 percentof the largest purchases for each instrument) have a different effect on flows and asset prices,with the idea that larger operations may potentially contain a larger unexpected component
than smaller operations (Table A5). Also in this setting, the overall picture in terms of signand size of the coefficients is largely confirmed.
A third way to reduce/eliminate the endogeneity bias is to replace the actual Fed operations
with their unexpected component. Table A9 shows the results of a two-step approach where
the explanatory variables LQt, TRt and MBSt are taken from a first-step regression residual
where purchases in week/day t are explained with indicators related to market conditions.
Concerning the latter, we use intraday data from European markets in a narrow time window
between 12PM and 2PM (CMT) before the opening of US markets, as well as the release of
macroeconomic news, as measured by Citigroup surprise indexes. These variables that
capture or influence market conditions might affect the quantities purchased by the FED.
However, they are not affected by the purchases, as macro news are exogenous and theintraday time window used to calculate indicators of market conditions does not overlap withthe timing of the purchases. While for the Treasury purchases we make use of the daily NY
Fed data and therefore we can calculate daily unexpected purchases,20 this is not possible for
MBS purchases and liquidity operations which are available on a weekly basis only.
Therefore, for MBS and liquidity, we equally split the calculated value of the unexpected
weekly intervention over the week when they took place. In addition, for all the QE
instruments, unexpected purchases are set to 0 in periods when the instruments are not active.
Table A921 shows the results for this setting which confirms the main finding of thebenchmark specification. In addition, it is interesting to note that some puzzles that are20

We adopt the two-stage approach only for Treasury purchases during QE1. While the Fed had someflexibility to adjust purchases during QE1, during QE2 the Treasury purchases were preannounced at
the beginning of each month with a detailed schedule. More specifically, the Fed published at the
beginning of each month a calendar indicating the ISIN of the targeted security, a narrow range for thequantities to be purchased and the date of the operation.
21
The results are particularly relevant for the liquidity operations, as a decision by the Fed to provide
more liquidity is likely to have been influenced by market conditions and banks’ needs for liquidity,
and thus may have been higher during weeks when spreads were high. To the contrary, MBS and

22

present in the results of the benchmark specification disappear. For example, while in thebenchmark specification liquidity operations have large negative effects on US equity prices(despite the large positive portfolio inflows in the US), in the two-stage approach the impact
on equity prices for the US is positive. In addition, the global impact of QE1 announcementson equity prices becomes stronger. This reinforces the conclusion that QE1 instruments
boosted equity prices, especially in the US.

4.4 Country heterogeneity and foreign policy responses to Fed policies
The final exercise is to understand to what extent and why foreign countries are affecteddifferently by US QE policies. As discussed above, especially EME policy-makers haveexpressed concerns about the spillover effects of US QE policies on their economies, andhave tried to react with domestic policy measures to these spillovers, such as through FX
policies, and monetary and fiscal policies. The specific question this final section tries toanswer is whether such policies have been effective in shielding countries from spillovers.
So far, we have grouped the 65 countries in our analysis into three groups – the US, EMEs
and other advanced economies (AEs). A first issue is therefore to illustrate that this groupingis indeed a valid one across country groups. We gauge the cross-country heterogeneity in theeffects of Fed policies by estimating model (1) for each individual country, and thus obtaining
country-specific parameters i:yi ,t  Ei ,t 1  yi ,t   i MPt   i ,t(2)

Figures A1 – A3 show these country parameters for some selected QE instruments. Two main
points emerge. First, the coefficient estimates within each of the groups (EMEs vs. AEs)
display a fairly high degree of homogeneity in that there is a clustering of coefficients withineach group. By contrast, the second finding is that the differences across groups is indeed
substantial, confirming the findings of the benchmark estimation.

Figures A1 – A3

The second main question is why there are differences in the way countries’ portfolio
allocations, asset prices and exchange rates respond to US monetary policy measures. Some
EMEs have responded by e.g. increasing their FX reserve holdings and actively using theirown monetary policy tools to deal with potential spillover effects from the US. Hence
differences in such policies (or in investors’ expectations about them) across countries may, at

Treasury purchases (especially in the QE2 period) were implemented in a rather mechanical manner.
Therefore, endogeneity is not a key concern for the latter two instruments.

23

least in part, explain why US QE measures have affected countries in a heterogeneous way. In
other words, the spillovers from US monetary policy measures may not only have a “pushfactor” component, but also a “pull factor” component in that they depend on the policy
actions of the recipient countries. To test this hypothesis more formally, we modify the
benchmark model (1) in the following way

yi ,t  Ei ,t 1  yi ,t   (0  1 Di ) MPt   i ,t(3)where Di, is a dummy indicating whether country i is classified in the “high” group (i.e. if thecountry scores above a pre-defined threshold), i.e. Di=1, or a “low” group, Di=0, according to
the following pre-determined country characteristics:22
“FX flexibility”: a “high”, i.e. Di=1, indicates that a country is classified as a free
floater pre-crisis in 2007 according to the coarse classification of Reinhart and Rogoff(2004), and Di=0 for peggers. By looking at this category we test whether the foreignexchange regime affects the transmission of US quantitative easing to recipient
countries.

“Central Bank (CB) activism”: “high” countries (i.e. Di=1) are those with an abovemedian coefficient of variation for the central bank interest rate. By looking at thiscategory we test whether an active central bank (or the expectation the monetary
policy will be adjusted to stabilize the economy) affects the transmission of US
quantitative easing on the recipient country.

“Fiscal policy (FP) activism”: “high” countries (i.e. Di=1) are those with above
median coefficient of variation for the structural balance to GDP ratio. By looking at
this category we test whether an active government (or the expectation that fiscal
policy will be intensively used to stabilize the economy) affects the transmission of
US quantitative easing on the recipient country.

“Institutions”: “high” countries (i.e. Di=1) are those with above median institutional
quality according to the average of four indicators of governance in 2007. The
indicators are “Political Stability”, “Rule of Law, “Control of Corruption” and“Regulatory Quality” (see Kaufmann, Kraay and Mastruzzi, 2010). By looking at
this category we test whether institutional quality affects the transmission of US
quantitative easing on the recipient country.

22
Country features are pre-determined in order to account for the possibility that these may be affectedby US monetary policies MPt contemporaneously. Also note that the effect of the dummy Di itself is
captured by the country-fixed effects of the model.

24“Capital Account (KA) Openness”: “high” countries (i.e. Di=1) are those with above
median Chinn-Ito coefficient (Chinn and Ito, 2006). By looking at this category wetest whether capital account openness affects the transmission of US quantitativeeasing on the recipient country.

The main parameters of interest are 0 and 1, which test whether a country characteristic
makes a country more or less vulnerable to a particular US monetary policy measure.

Table 9The empirical estimates for the effects are displayed in Table 9, where column “low-high=0”
tests for the null hypothesis of equality between the coefficient for “low” and “high”
countries, i.e. 1=0.
First, turning to the role of FX policy and macroeconomic policy activism, the findings
indicate that it is in particular an active monetary policy stance that is associated with smaller
spillovers from US QE policies to countries via capital flows. This is evident from the factthat the spillover coefficients are systematically larger for countries with low degree of central
bank activism. By contrast, there is no evidence that countries either with low FX flexibility
or more fiscal activism or systematically exposed differently to QE policies. This is arevealing finding as there is a moderate positive relationship between countries with FX pegs
and more active monetary policy in the data. The findings suggest that it is the use ofmonetary policy that helps countries insulate themselves from QE policies, and not themaintenance of a fixed exchange rate regime.
Second, countries with strong and high-quality institutions are systematically less exposed to
QE policy spillovers than those with weak institutions. Overall, the economic and statisticalrelevance of the institutional variable is among all five dimension the most important one. By
contrast, countries with less open capital accounts tend to be more exposed to QE policy
spillovers. Given that there is a moderate positive relation between lower-quality institutions
and less capital account openness, what these findings suggest is that keeping one’s capital
account closed is not effective to insulating a country from QE policy spillovers, but it israther the quality of institutions that has such insulating properties.
There are a number of caveats to this analysis. Most importantly, as indicated the different
policy dimensions analyzed are not necessarily independent from one another. Moreover,other determinants not analyzed here due to a lack of data availability for the full crosssection of countries, such as the presence of micro- and macroprudential measures during thecrisis, may also have played a role in the transmission of QE policies.

25

Nevertheless, overall the evidence lends further support to the hypothesis that also “pull”
factors played a role for QE spillovers. Specifically, the portfolio rebalancing effects of Fed
QE policies are at least partly explained by risk and a flight-to-safety phenomenon, while
there is no evidence that keeping exchange rates fixed and trying to limit capital accountopenness have helped insulate countries from the global transmission of QE policies. FX andcurrent account policies might have actually amplified the pro-cyclical impact of Fed policies.

5 Conclusions
The paper has analyzed the impact of the Federal Reserve’s QE policies on portfolio flows
and asset prices both in the US and globally. A first key result of the empirical analysis is that
QE1 policies during the first phase in 2008-2009 have triggered a substantial rebalancing in
global portfolios, with investors shifting out of EMEs and other AEs and into US equity andbond funds. This led to a marked US dollar appreciation, while these Fed policies lowered USbond yields and supported equity markets. Thus, given the Fed objective of providing
liquidity to financial markets and institutions and repairing dysfunctional market segments
during the aftermath of the Lehman collapse, these policies seem to have been fairly effectivein doing so, in part by inducing US investors to repatriate capital from abroad. By contrast,
Fed policies during the second phase in 2010 (QE2) induced a portfolio rebalancing in the
opposite direction, pushing capital into EMEs. Importantly, these policies did not seem tohave lowered sovereign yields, and have induced a marked depreciation of the US dollar.
Equally importantly, we find that Fed operations, such as the purchases of Treasuries andMBS through its two LSAP programs, exerted substantially larger effects on portfolio
decisions and asset prices, than Fed announcements of these programs. This is importantbecause most of the literature to date has focused on the market impact of announcements,
while our results emphasize that actual Fed operations are relatively more important forunderstanding portfolio decisions and the re-pricing of risk at the global level.
In addition, our findings indicate that Fed policies exerted larger effects on asset prices thanon capital flows. In fact, relative to the large magnitude of swings in capital flows to EMEs
following the collapse of Lehman Brothers in September 2008, and the subsequent capitalflow surge to EMEs in 2009 and 2010, the share of these movements explained by Fed
monetary policy measures is comparatively modest. However, these Fed policy measures
have significantly exacerbated the pro-cyclicality of capital flows to EMEs – raising outflows
even further in periods when capital flees EMEs, and magnifying inflows when these are
already large. By contrast, Fed policies have functioned in a counter-cyclical fashion forinvestment flows into US equity and bonds funds.

26

Finally, we do not find evidence that policy-makers succeeded in insulating their countries
from spillovers of QE policies by limiting exchange rate flexibility or imposing controls on
capital account openness. These policies might have amplified the pro-cyclical impact of Fed
interventions. Instead, an important determinant of the sensitivity of capital flows to Fed
policy during the crisis has been the institutional quality of countries, suggesting that the
impact of QE policies are partly linked to pull factors in recipient countries, and specificallyto risk and a flight-to-safety phenomenon.
The findings of the paper have a number of implications for policy. First, some of the results
may be interpreted as lending support to concerns expressed by policymakers in EMEs. In
particular, EMEs have been adversely affected by pro-cyclical effects of QE policies,
inducing capital outflows from EMEs when capital is scarce and pushing capital into EMEs,
driving up asset prices and exchange rates, when they already experience high capital inflows
through other sources. Yet, the findings also indicate that foreign policy-makers are notinnocent bystanders. The empirical results show that part of the effect of QE policies on
foreign economies is related to risk, and that sound domestic policies and strong domestic
institutions help insulate countries from US monetary policy spillovers. Thus there may
indeed be a case both for domestic policy reforms as well as for more coordination at the
global level in order to deal with policy spillovers and externalities.

27

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29

Table 1. List of QE events
Date

Description of the event

Impact in
Gagnon etal. (2011)

Impact in
Wright(2011)(1) QE1
Tuesday
25/11/2008

Type of event: FOMC statement – Expansion of QE.
Initial LSAP announcement. The Fed announces purchases of $100
billion in GSE debt and up to 500 billion in MBS. Creation of the Term
Asset-Backed Security Loan Facility (TALF)

-22

0.75

(2) QE1
Monday01/12/2008

Type of event: Bernanke Speech – Expansion of QE.
Chairman Bernanke mentions that the Fed could purchase long-term
Treasuries.

-19

0.84

(3) QE1
Tuesday
16/12/2008

Type of event: FOMC statement – Expansion of QE.
The FOMC “evaluates” the potential benefits of purchasing longer-term
Treasury securities. Also FED funds target rate reduced to the range 00.25

-26

2.22

(4) QE1
Wednesday
28/01/2009

Type of event: FOMC statement – Expansion of QE.
The Fed is ready to expand agency debt and MBS purchases, as well as
to purchase long term treasuries.

14

-0.23

(5) QE1
Wednesday
18/03/2009

Type of event: FOMC statement – Expansion of QE.
The Fed will purchase an additional $750 billion in agency MBS and an
additional $100 billion in Agency Debt. Moreover, the FOMC decided topurchase up to $300 billion of longer-term Treasury securities over thefollowing six months.

-47

3.41

(6) QE1
Wednesday
12/08/2009

Type of event: FOMC statement – Phase out of QE.
The Fed will slow the pace of the LSAP by purchasing the full amount by
the end of October instead of mid- September.

5

0.15

(7) QE1
Wednesday
23/09/2009

Type of event: FOMC statement – Phase out of QE.
The Fed will slow the purchases of agency MBS and agency debt,
finishing the purchases by the end of 2010Q1. Treasury purchases will
still be finished by October 2009.

-3

0.85

(8) QE1
Wednesday
04/11/2009

Type of event: FOMC statement– Phase out of QE.
The amount of agency debt will be halted at $175 billion, instead of $200
billion.

6

0.12(9) QE2
Tuesday
10/08/2010

Type of event: FOMC statement – Expansion of QE.
The Fed will reinvest principal payments from agency debt and agency
mortgage-backed securities in longer-term Treasury securities. Holdingsof Treasury securities will be rolled over as they mature.

NA

0.57

(10) QE2
Friday27/08/2010

Type of event: Bernanke speech – Expansion of QE.
Bernanke mentions potential policy options for further easing, including
additional purchases of long term securities.

NA

-0.83

(11) QE2
Friday15/10/2010

Type of event: Bernanke speech – Expansion of QE.
The Fed is prepared to provide additional accommodation if needed tosupport the economic recovery.

NA

-0.21

(12) QE2
Wednesday
03/11/2010

Type of event: FOMC statement – Expansion of QE.
The Fed will purchase a further $600 billion of longer-term Treasury
securities by the end of the second quarter of 2011, a pace of about $75
billion per month.

NA

-0.05

Note: The column “Impact in Gagnon et al. (2011)” reports the estimated impact of each announcement on the 10 year Treasury
yield, according to Table 1 in Gagnon et al. (2011). The column “Impact in Wright (2011)” reports the estimated impact of eachannouncement according to Table 5 in Wright (2011). The impact is measured as the first principal component of the intradaychange in yields on Treasury futures. The surprises are normalized to have a unit standard deviation and signed so that a positive
number represents falling yields.

30

Table 2 – Summary Statistics for the dependent variables

Variable

Equity Portfolio Inflows

Bond Portfolio Inflows

Equity Returns

Change in Bond Yields

FX Returns

Description

Group

Equity inflows in country i expressed in % of the bondassets invested in country i. Source: EPFR.

Bond inflows in country i expressed in % of the bondassets invested in country i. Source: EPFR.

Local equity market daily returns (in %). For EMEs
Datastream Total Market indexes, for the US S&P500
index. Source: Datastream.

Daily differences of the 10 year Goverment Bond yield(in percentage points). Source: Datastream.

Daily returns of the bilateral exchange rate with the
USD and daily returns of the Nominal Effective
Exchange Rate for the US (in %, positive values
indicate appreciation of the USD in all cases). Source:
Datastream.

Mean Std. Dev. Min

Max

United States

0.00

0.19

-1.08

1.23

Emerging Markets

0.03

0.33

-27.24

25.22

Advanced Economies -0.02

0.23

-7.95

12.11

United States

0.03

0.07

-0.28

0.27

Emerging Markets

0.03

0.24

-3.04

3.27

Advanced Economies

0.00

0.24

-7.99

5.35

United States

-0.01

1.68

-9.41

10.90

Emerging Markets

0.00

1.72

-19.85

23.17

Advanced Economies -0.03

1.64-14.42

16.05

United States

0.00

0.07

-0.47

0.27

Emerging Markets

0.00

0.41

-22.8522.85

Advanced Economies

0.00

0.45

-9.37

9.37

United States

-0.01

0.41

-2.69

1.80

Emerging Markets

0.00

0.85

-16.45

69.32

Advanced Economies -0.01

0.81

-7.07

9.11

Note: The list of the countries included in the sample is in table A1 in the Annex.

31

Table 3 – Summary Statistics for monetary policy related variables and other control variables
Variable

Description

QE I Annoucements (AN1) *,**

Min

Max

Dummy variable equal to 0.5 on the day on the announcement and on
the day following the announcement, for the events 1, 3, 4 and 5 (seetable 1). By setting the dummy to 0.5, the estimated coefficientmeasures the total impact of one annoucement over two days.

01

Dummy variable equal to 0.5 on the day on the announcement and on
the day following the announcement, for the events 10, 11 and 12 (seeQE II Annoucements (AN2) *,**
table 1). By setting the dummy to 0.5, the estimated coefficientmeasures the total impact of one annoucement over two days.

01

Liquidity (LQ) *,**

Change in the amount outstanding of unconventional liquidity
operations and support measures to key credit markets between weekt and week t-1. The weekly change is split evenly among the days of
the week. Scale: USD billions. Source: FED.
Change in the amount outstanding of long term Treasury bonds related
to the LSAP between week t and week t-1. The weekly change issplit evenly among the days of the week. Scale: USD billions. Source:
FED.
Change in the amount outstanding of MBS and GSE debt related to
the LSAP between week t and week t-1. Scale: USD billions. The
weekly change is split evenly among the days of the week. Source:
FED.
VIX Implied volatility on options on the S&P 500 Index (in %).
Source: CBOE via Datastream.

Mean

Std. Dev.

0.6

45.2

-157.6

332.0

4.8

8.6

-6.8

39.7

4.4

17.6

-14.6

162.5

26.03

11.85

9.89

80.86

0.01

2.38

-17.36

16.54

3.68

0.71

2.08

5.25

0.00

0.07

-0.47

0.35

0.30

0.33

-0.13

1.81

First difference of the "Liquidity Spread" (in percentage points)

0.00

0.09

-1.10

0.92

3 month T-bill yield

Yield of the the 3 month Treasury Bill in the US (in %). Source:
Datastream.

1.50

1.80

0.01

5.18

S&P500 Returns *,**

Daily returns of the S&P 500 index (in %)

0.00

1.70

-9.03

11.58

-0.01

1.68

-9.41

10.90

0.01

3.58

-18.00

16.50

-0.01

6.17

-30.2041.40

Treasuries (TR) *,**MBS (MBS) *,**

VIX *Change in VIX **US 10y Bond Yield *Change in US 10y Bond Yield **Liquidity Spread *Change in Liquidity Spread **Local Equity Returns *, **Change in the G10 Surprise
Index
Change in the EME Surprise
Index

First difference of VIX (in percentage points)
Yield of the the10 year Treasury Bond in the US (in %). Source:
Datastream.
First difference of US 10 year Treasury Bond yield (in percentage
points)Overnight Swap Index 3 month rate minus the 3 month T-Bill yield in
the US (in %). Source: Datastream

Local equity market dailyreturns (in %). For EMEs Datastream Total
Market indexes, for the US S&P500 index. Source: Datastream.
Daily change (first difference) in the Economic Surprise Index for theG10. Source: Citigroup.
Daily change (first difference) in the Economic Surprise Index for
Emerging Markets. Source: Citigroup.

Note: * Indicates that the variable is included in the benchmark model for portfolio flows. ** Indicates that thevariable is included in the benchmark model for equity returns, bond yields and exchange rate returns. When includedin the model, variables that are not related to monetary policy are lagged by one period.

32

Table 4: Impact of Fed unconventional monetary policy measures – Portfolio allocations and capital flows
Dependent variable: inflows in Equity Funds(in % of asset under management in the country of destination)

QE I Announcements (AN1)ββ + γEM E

0.44802 ***0.04111 *(.01839)

QE II Announcements (AN2)
Liquidity (LQ)
Treasuries (TR)MBS (MBS)

Controls
Fixed Effects
Number of Observations
R-Squared(.022)

β + γAE
0.08289 ***Dependent variable: inflows in Bond Funds(in % of asset under management in the country of destination)γEM E=0 γAE=0 γEM E-γAE=0
***

***

(.02097)

0.008310.14094 ***-0.00445(.0101)(.0192)(.0167)0.00247 ***-0.00077 ***0.00068 **(.00015)(.00027)(.00029)-0.00128 *0.00621 ***0.00003(.00077)(.00108)(.00133)-0.00209 ***0.000450.00042(.00016)(.0006)(.00037)

***
***

***
***

***
***

***
***

***

β

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=00.23752 ***(.01872)-0.08502 ***0.08410 **

***

***

***

(.01879)(.03823)-0.20395 ***0.02930 **-0.06269 **

***

***

***

(.00944)(.01406)(.0239)

***

***

***

***

***

0.00173 ***-0.00232 ***0.00033(.00014)(.00017)(.00024)-0.01851 ***-0.01988 ***-0.00392 **(.00057)(.00121)(.00176)0.00419 ***0.00434 ***0.00478 ***(.00012)(.0004)(.00046)

Yes
Yes

Yes
Yes

56084
0.03

54429
0.25

Note: The table shows the estimated impact of the different monetary policy instruments on portfolio flows according to equation (1):yi ,t  Ei ,t 1  yi ,t   (    EME DiEME   AE DiAE ) MPt   i ,t(1)

with MPt  [ AN1t , AN 2t , LQt , TRt , MBSt ]'The dependent variable is indicated at the top of the table. Control variables for portfolio flows (not shown for brevity reasons): VIX (t-1), Liquidity spread (t-1), US 10 year yield (t-1), S&P
500 return (t-1), local equity market return (t-1). The description of the dependent and explanatory variables is given in Tables 2 and 3. Sample period: January 2007 to December 2010, daily
observations. Column “β” reports the estimated impact of monetary policy instruments on US flows, while column “β + γEME” (“β + γAE”) reports the estimated impact of monetary policy
instruments on flows into emerging markets (advanced economies). Standard errors of the coefficients are reported in parenthesis. Column “γEME” (“γAE”) indicates the significance of theparameter γ i.e. it tests whether the impact on emerging markets (advanced economies) is statistically different from the impact on the US. Finally “γEME” -“γAE”, indicates whether thecoefficients “γEME” and “γAE” are statistically different. “***”, “**” and “*” indicate significance at the 1%, 5% and 10% confidence levels, respectively.

Table 5: Impact of Fed unconventional monetary policy measures – Equity returns and government bond yields
Dependent variable: Change in 10 year bond yields (in percentage
points)Dependent variable: Equity returns (in %)β
QE I Announcements (AN1)QE II Announcements (AN2)
Liquidity (LQ)
Treasuries (TR)MBS (MBS)

Controls
Fixed Effects
Number of Observations
R-Squared

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=0

***

***

1.08812 ***-0.08615-0.42340 **(.09322)(.20506)(.16762)

***

***

β

β + γEM E

β + γAE-0.16317 ***-0.12211 *-0.05923 *(.01141)(.07099)(.0346)

0.96743 ***0.37365 ***0.44304 ***-0.02050 ***-0.00386-0.01777(.01607)(.12271)(.07349)(.00192)(.00837)(.01807)-0.01411 ***-0.01434 ***-0.01363 ***-0.00037 ***0.00126-0.00027 ***(.0002)(.00143)(.00157)(.00002)(.00125)(.00004)

0.02542 ***0.03043 ***0.03203 ***0.00234 ***-0.00158

0.00007(.00103)(.00622)(.00417)(.00009)(.0018)(.00076)-0.00528 ***-0.00081-0.00203

0.00007 *-0.00041-0.00029(.00018)(.00153)(.00169)(.00004)(.00065)(.00022)

***

*

Yes
Yes

Yes
Yes

56062
0.08

48825
0.01

γEM E

γAE γEM E-γAE=0
**

*
***
**

***

Note: See note to Table 4. Control variables for equity returns and bond yields: change in VIX (t-1), change in the liquidity spread (t-1), change in the US 10 year yield (t-1), S&P 500 return(t-1), local equity market return (t-1).

34

Table 6: Impact of Fed unconventional monetary policy measures – Exchange
rates
Dependent variable: exchange rate return(in %, positive vlues mean appreciation of the USD)β
QE I Announcements (AN1)QE II Announcements (AN2)
Liquidity (LQ)
Treasuries (TR)MBS (MBS)

Controls
Fixed Effects
Number of Observations
R-Squared

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=0

***

***

***

***

***

***

***

-0.84485 ***-0.21177-1.45310 ***(.05801)(.12946)(.07388)-0.06209 ***-0.08910 ***-0.28847 ***(.00426)(.03308)(.05537)

0.00378 ***0.00523 ***0.00435 ***(.00008)(.00096)(.00067)-0.00899 ***-0.00492 **-0.00892 ***(.00037)(.00229)(.00147)0.00427 ***0.00274 **-0.00055(.0001)(.00115)(.00047)

*

Yes
Yes
59205
0.04

Note: See note to Table 4. Control variables for exchange rate returns: change in VIX (t-1), change inthe Liquidity spread (t-1), change in the US 10 year yield (t-1), S&P 500 return (t-1), local equity
market return (t-1). Note that positive values always indicate the appreciation of the USD.

Table 7: Economic significance – Cumulated impact of Fed policy measures –“Peak” impact
A. Portfolio allocations and capital flows
Equity Funds

Bond Funds

US

EME

AE

US

EME

AE

Total Impact of QE I Announcements
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)

1.80%
18,630

0.16%
383
1,474

0.33%
1,417
8,780

0.95%
4,249

-0.35%
-123
-592

0.34%
231
4,575

Total Impact of QE II Announcements
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)

0.02%
333

0.42%
2,914
3,844

-0.01%-107-355-0.61%-6,008

0.09%
96
145

-0.19%-348-2,534

Total Impact of Liquidity Operations
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)

4.88%
62,451-1.46%-5,067

1.29%
7,396

3.33%
14,354-4.37%-2,256-7,338

0.62%
481
8,457

Total Impact of Treasury Purchases
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)-0.80%-11,136

3.45%
17,72531,365

0.01%
38
158

-9.73%-75,417-10.52%-8,210-17,666-2.04%-2,666-27,888

Total Impact of MBS Purchases
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)-2.67%-33,105

0.61%
2,464

0.54%
3,030

5.54%
32,816

5.80%
2,793
9,740

6.31%
5,675
86,069B. Asset prices and exchange rates
Equity prices

Total Impact of QE I Announcements
Total Impact of QE II Announcements
Total Impact of Liquidity Operations
Total Impact of Treasury Purchases
Total Impact of MBS Purchases

Bond yields

US

EME

AE

US

EME

AE

4.30%
2.93%
-23.56%15.08%-6.61%-0.34%
1.12%-23.90%18.31%-1.03%-1.68%
1.33%-22.87%18.25%-2.58%

-0.66
-0.07
-0.69
1.31
0.11

-0.48
-0.01
2.45
-0.87
-0.57

-0.25
-0.06
-0.69
0.11
-0.41

Exchange rate

Total Impact of QE I Announcements
Total Impact of QE II Announcements
Total Impact of Liquidity Operations
Total Impact of Treasury Purchases
Total Impact of MBS Purchases

US

EME

AE

-3.24%-0.19%
7.45%-4.83%
5.71%-0.62%-0.30%
11.44%-2.89%
3.88%-5.61%-0.94%
8.39%-5.78%-0.11%

Note: Figures in millions USD. The total impact of each monetary policy instrument is calculated bymultiplying the estimated coefficient for the operation (see Tables 4 to 6) by the size of the operation ateach period t and by cumulating the effect from the beginning of the programme to the day of the
maximum expansion of the programme. The maximum expansion of the liquidity support measures
was reached at the end of December 2008, while the maximum expansion of MBS purchases wasreached at the end of June 2010. Regarding the other monetary policy instruments, the maximum
expansion was reached at the end of our sample (December 2010). Flows based on IMF CPIS data arecomputed on the basis of the stock of portfolio investment held by US residents in the target group ofcountries (i.e. EMEs and AEs) as of end of 2009.

36

Table 8: Economic significance – Cumulated impact of Fed policy measures –Total impact (over entire sample period)
A. Portfolio allocations and capital flows
Equity Funds

Bond Funds

US

EME

AE

US

EME

AE

Total Impact of QE I Announcements
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)

1.80%
18,630

0.16%
383
1,474

0.33%
1,417
8,780

0.95%
4,249

-0.35%
-123
-592

0.34%
231
4,575

Total Impact of QE II Announcements
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)

0.02%
333

0.42%
2,914
3,844

-0.01%-107-355-0.61%-6,008

0.09%
96
145

-0.19%-348-2,534

Total Impact of Liquidity Operations
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)

0.44%
12,305-0.13%-345-1,226

0.12%
1,491
3,112

-0.29%-2,193-0.40%
-476
-668

0.06%
20
753

Total Impact of Treasury Purchases
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)-0.80%-11,136

3.45%
17,72531,365

0.01%
38
158

-9.73%-75,417-10.52%-8,210-17,666-2.04%-2,666-27,888

Total Impact of MBS Purchases
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)-2.38%-28,197

0.54%
1,995
4,917

0.49%
2,536
12,927

4.92%
26,977

5.13%
2,100
8,616

5.59%
4,379
76,297

Total impact of all operations
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)-0.91%-8,065

4.45%
22,67240,3740.93%
5,376
24,622-4.76%-52,391-6.05%-6,614-10,165

3.75%
1,617
51,203

Total flows
In % Assets
In Million USD (according to EPFR)
In Million USD (according to IMF CPIS)-4.64%-41,22225.43%
130,015
230,923-17.08%-133,251-452,651

27.33%
177,783

33.78%
31,541
56,726-3.94%
16,422-53,742

B. Asset prices and exchange rates
Equity prices

Bond yields

US

EME

AE

US

EME

AE

Total Impact of QE I Announcements
Total Impact of QE II Announcements
Total Impact of Liquidity Operations
Total Impact of Treasury Purchases
Total Impact of MBS Purchases

4.30%
2.93%
-2.11%
15.08%-5.90%-0.34%
1.12%-2.15%
18.31%-0.92%-1.68%
1.33%-2.00%
18.25%-2.31%

-0.66
-0.07
-0.06
1.31
0.09

-0.48
-0.01
0.19
-0.87
-0.52

-0.25
-0.06
-0.05
0.11
-0.37

Total impact of all operations
Total cumulated change over the period

14.30%

16.02%

13.59%

0.63

-1.70

-0.62

-20.31%-0.41%-35.08%

-1.4

-0.23

0.25

Exchange rate
US

EME

AE

Total Impact of QE I Announcements
Total Impact of QE II Announcements
Total Impact of Liquidity Operations
Total Impact of Treasury Purchases
Total Impact of MBS Purchases-3.24%-0.19%
0.54%-4.83%
5.07%-0.62%-0.30%
0.81%-2.89%
3.45%-5.61%-0.94%
0.61%-5.78%-0.69%

Total impact of all operations
Total cumulated change over the period-2.65%

0.45%-12.41%-8.79%

4.24%-7.31%

37

Note: See note to Table 7. The total impactof each monetary policy instrument iscalculated by multiplying the estimatedcoefficient for the operation (see Tables 4to 6) by the size of the operation at eachperiod t and by cumulating the effect fromthe beginning of the programme to
December 2010.

Table 9 – Impact of QE on recipient countries, the role of domestic policies
Fx flexibility
Equity flows

Bond flows

low

high

QE I Announcements (AN1)0.0587 ***0.0971 ***QE II Announcements (AN2)0.1123 ***0.0250Liquidity (LQ)

-0.0001

0.0001QE Treasuries (TR)0.0046 ***0.0013MBS (MBS)-0.0001

0.0009 **QE I Announcements (AN1)-0.0119-0.0230

QE II Announcements (AN2)0.0139-0.0511 **Liquidity (LQ)

-0.0013 *** -0.0010 ***CB activism
low-high=0

***
*

**

0.03350.0726 ***0.1395 ***0.0593 ****0.0652 ***0.0817 ***0.1474 ***0.0072-0.0011 *0.0003**-0.0004-0.0001-0.0007 **0.00040.0071 ***0.0021 **0.0042 ***0.0036 ***0.0058 ***0.00120.0013-0.0001

0.00070.0007 **0.00050.0004-0.0437 *0.00290.0235-0.0480-0.0104-0.0079

0.0325-0.0247 *low-high=0

*

low

high

0.03330.0489 ***Institutions

highlow-high=0

*

low

high

0.03840.0845 ***CA openness
low

high

0.04890.0662 ******0.1646 ***0.0467 *****-0.0010 *0.0002***0.0083 ***0.0013***0.00110.00010.0180low-high=0

low-high=0***-0.0717 ***0.0558***-0.0753 ***0.0411 **-0.0684 ******0.0563 *** -0.0351 **

***

***

-0.0019 *** -0.0009 *****-0.0009 *** -0.0016 ***-0.0021 *** -0.0002***-0.0024 *** -0.0006 ***

***

**

-0.0103 *** -0.0142 ***-0.0187 *** -0.0080 ******-0.0202 *** -0.0103 ******0.0047 ***0.0049 ***QE Treasuries (TR)-0.0142 *** -0.0115 ***-0.0182 *** -0.0122 ***MBS (MBS)0.0046 ***0.0040 ***0.0045 ***FP activism

low0.0048 ***0.0040 ***0.0040 ***0.0046 ***0.0044 ***Note: Estimated impact of the different monetary policy instruments on the dependent variable according to equation (3), with the US being excluded from the sample. Column “low” (“high”)
reports the estimated impact of QE policies on countries that score below (above) a predetermined threshold in the category indicated at the top. Column “low-high=0” tests for the null
hypothesis of equality between the coefficient for “low” and “high” countries. Categories: for “Fx flexibility”, “high” countries are those classified as free floaters in 2007 according to thecoarse classification of Reinhart and Rogoff (2004); “Central Bank (CB) activism”, “high” countries are those with above median coefficient of variation for the central bank interest rate (thecoefficient of variation is calculated between 2000 and 2007); “Fiscal policy (FP) activism”, “high” countries are those with above median coefficient of variation for the structural balance to
GDP ratio (the coefficient of variation is calculated between 2000 and 2007); “Institutions”, “high” countries are those with above median institutional quality according to the average of four
indicators of governance in 2007. The indicators are “Political Stability”, “Rule of Law, “Control of Corruption” and “Regulatory Quality” (the World Bank, World Governance Indicators,
methodological issues are discussed in Kaufmann, Kraay and Mastruzzi, 2010); “Capital Account (CA) Openness”, “high” countries are those with above median Chinn-Ito coefficient in 2007.

Figure 1
Unconventional operations in the Fed balance sheet
2,500,000

Liquidity
Treasuries

2,000,000

MBS and GSE debt

1,500,000

1,000,000

500,000

Source: Federal Reserve.
Note: Amounts outstanding in USD millions.

39

Nov 10Aug 10

May 10Feb 10

Nov 09Aug 09

May 09Feb 09

Nov 08Aug 08

May 08

Feb 08

Nov 07

Aug 07

May 07

Feb 070Figure 2
Cumulated impact of US quantitative easing and other control variables
A - Equity portfolio flows(In % of asset under management in country of destination)

United States
20

Other Factors

15

Monetary Policy

10

Actual

5
0
‐5
‐10
‐15
‐20
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Mar‐10

Sep‐10Mar‐10

Sep‐10Mar‐10

Sep‐10Emerging markets
30

Other Factors

25

Monetary Policy

20

Actual

15
10
5
0
‐5
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Advanced Economies
5
0
‐5
‐10
‐15
‐20

Other Factors
Monetary Policy
Actual‐25
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐0940

Sep‐09Figure 2 (continued)
Cumulated impact of US quantitative easing and other control variables
B - Bond portfolio flows(In % of asset under management in country of destination)

United States
40

Other Factors

30

Monetary Policy

20

Actual

10
0
‐10
‐20
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Mar‐10

Sep‐10Mar‐10

Sep‐10Mar‐10

Sep‐10Emerging markets
50
40
30
20

Other Factors
Monetary Policy
Actual

10
0
‐10
‐20
‐30
‐40
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Advanced Economies
10
5
0
‐5
‐10
‐15
‐20
‐25

Other Factors‐30

Monetary Policy‐35

Actual‐40
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐0941

Sep‐09Figure 2 (continued)
Cumulated impact of US quantitative easing and other control variables
C – Equity prices(Returns in %)United States
40
30
20
10
0
‐10
‐20

Other Factors

‐30
‐40

Monetary Policy‐50

Actual‐60
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Mar‐10

Sep‐10Mar‐10

Sep‐10Mar‐10

Sep‐10Emerging markets
30
20
10
0
‐10
‐20

Other Factors‐30

Monetary Policy‐40

Actual‐50Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Advanced Economies
30
20
10
0
‐10
‐20
‐30

Other Factors‐40

Monetary Policy‐50

Actual‐60
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐0942

Sep‐09Figure 2 (continued)
Cumulated impact of US quantitative easing and other control variables
D – Bond yields(Change in percentage points)

United States
1
0.5
0
‐0.5
‐1
‐1.5

Other Factors‐2

Monetary Policy‐2.5‐3Mar‐07

Actual

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Mar‐10

Sep‐10Mar‐10

Sep‐10Mar‐10

Sep‐10Emerging markets
4

Other Factors

3

Monetary Policy

2

Actual10‐1‐2Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Advanced Economies
2.5
2
1.5
1
0.5
0
‐0.5
‐1

Other Factors‐1.5

Monetary Policy‐2‐2.5
Mar‐07

Actual

Sep‐07

Mar‐08

Sep‐08Mar‐0943

Sep‐09Figure 2 (continued)
Cumulated impact of US quantitative easing and other control variables
E – Exchange rate
(Returns in %)United States
15

Other Factors

10

Monetary Policy
Actual

5
0
‐5
‐10
‐15
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Mar‐10

Sep‐10Mar‐10

Sep‐10Mar‐10

Sep‐10Emerging markets
20
Other Factors

15
10

Monetary Policy
Actual50‐5‐10Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Advanced Economies
15

Other Factors

10

Monetary Policy

5

Actual

0
‐5
‐10
‐15
‐20
Mar‐07

Sep‐07

Mar‐08

Sep‐08Mar‐09Sep‐09Note: The cumulated contributions of different explanatory variables are calculated according to the benchmark modelin Tables 4-6.

44

Figure 3
Cyclicality of QE policies: correlation between the contribution of US quantitative easing and thecontribution of other control variables to portfolio flows

-.5

0

.5

Equity Portfolio Flows

01jul2008

01jan200901jul2009

United States
Emerging Markets

01jan2010

01jul201001jan2011

Advanced Economies

-.5

0

.5

Bond Portfolio Flows

01jul2008

01jan200901jul2009

United States
Emerging Markets

01jan2010

01jul201001jan2011

Advanced Economies

Note: the correlation is calculated over a twelve-month rolling window. Contributions of different explanatory
variables are calculated according to the benchmark model in Table 4.

45

APPENDIX

46

Table A1: countries included in the sample

Emerging Markets
Argentina
BrazilBulgaria
Chile
China
Colombia
Croatia
Czech Republic
Ecuador
Egypt
Estonia
Hong Kong
Hungary
India
Indonesia
Kazakhstan
Korea
KuwaitLatviaLithuania
Malaysia
MexicoMoroccoNigeria
Pakistan
Peru
Philippines
PolandRomania
RussiaSaudi Arabia
SerbiaSingapore
Slovak Republic
Slovenia
South Africa
TaiwanThailandTurkeyUkraineVenezuela, Rep. Bol.
VietnamAdvanced Economies

ARG
BRA
BGR
CHL
CHN
COL
HRV
CZE
ECU
EGY
EST
HKG
HUN
IND
IDN
KAZ
KOR
KWT
LVA
LTU
MYS
MEX
MAR
NGA
PAK
PER
PHL
POL
ROM
RUS
SAU
SRB
SGP
SVK
SVN
ZAF
TWN
THA
TUR
UKR
VEN
VNM

Australia
Austria
Belgium
CanadaDenmark
FinlandFranceGermany
GreeceIrelandIsraelItaly
Japan
Netherlands
New Zealand
NorwaySpain
SwedenSwitzerland
UK
USA

47

AUS
AUT
BEL
CAN
DNK
FIN
FRA
DEU
GRC
IRL
ISR
ITA
JPN
NLD
NZL
NOR
ESP
SWE
CHE
GBR
USA

Table A2 – Stylized facts of QE announcements and asset prices
A. United States
Change in S&P Change in 10y
500 Index
yield
QE 1
QE 2
QE 3
QE 4
QE 5
QE 6
QE 7
QE 8
QE 9
QE 10
QE 11
QE 12

QE I (1 to 5)
QE I (1,3,4,5)
QEII (9 to 12)
QEII (10,11,12)

Change in
NEER

4.19
-4.94
4.18
0.04
0.79
1.84
-1.96
2.03
-3.41
0.19
0.93
2.30

-0.34
-0.27
-0.33
0.30
-0.40
-0.11
-0.07
0.06
-0.15
0.05
0.01
-0.13

-0.41
0.15
-2.46
0.24
-2.90
-0.77
0.68
-0.52
1.16
0.18
0.34
-0.70

4.17
9.15 **-0.09
3.36-1.04 ***
-0.78 ***
-0.21
-0.06

-5.37 ***
-5.52 ***
1.02
-0.15

B. Emerging Markets

QE 1
QE 2
QE 3
QE 4
QE 5
QE 6
QE 7
QE 8
QE 9
QE 10
QE 11
QE 12

QE I (1 to 5)
QE I (1,3,4,5)
QEII (9 to 12)
QEII (10,11,12)

Change in thelocal equity
price index

Change in 10y
yield

Change in FX
USD

1.60
-2.03
1.53
0.28
1.52
0.88
0.09
1.21
-1.30
0.56
-0.05
1.33

-0.18
-0.12
-0.06
-0.04
-0.02
0.02
-0.04
-0.03
-0.04
-0.01
0.01
-0.02

-0.34
0.51
-1.64
0.63
-1.52
-0.52
0.18
-0.61
0.99
0.10
0.29
-0.57

2.89 ***
5.07 ***
0.43
1.86 ***-0.69 **
-0.48
-0.08
-0.02

-2.93 ***
-3.52 ***
0.93 **-0.25

48

Table A2 (continued)
C. Advanced Economies

QE 1
QE 2
QE 3
QE 4
QE 5
QE 6
QE 7
QE 8
QE 9
QE 10
QE 11
QE 12

QE I (1 to 5)
QE I (1,3,4,5)
QEII (9 to 12)
QEII (10,11,12)

Change in thelocal equity
price index

Change in 10y
yield

Change in FX
USD

1.26
-3.56
0.75
0.94
0.92
1.87
-1.12
2.20
-2.83
0.83
0.00
0.69

-0.11
-0.15
-0.17
-0.07
-0.12
-0.01
-0.05
0.07
-0.02
0.01
0.02
0.00

-0.24
0.37
-4.30
1.27
-4.45
-1.09
0.66
-0.98
2.08
0.04
0.41
-1.20

0.28
3.86 ***-1.33
1.51 **-0.63 ***
-0.48 ***
0.02
0.04

-7.65 ***
-8.06 ***
1.47 ***-0.74 *

Note: The upper part of the table shows the cumulated (two-day) impact of each announcement, i.e. thechange of the dependent variable between the closing price on the day before the event (t-1) and the closing
price on the day after the event (t+1). See Table 1 for the description of the events/announcements. The lower
part of the table shows the total impact of different groups of announcements (with the impact of each of theannouncements being cumulated over two days). The number in the parenthesis indicates the events includedin the group. More specifically, the total impact for groups of announcements has been calculated using thefollowing regression:yt = c + β Dt + εt
Where Dt is a dummy equal to (1 / N*2) on each of the N announcements in the group and on the following
day. By constructing the dummy in this way, the coefficient β measures the total impact of the group ofannouncements. “***”, “**” and “*” indicate significance at the 1%, 5% and 10% confidence level
respectively.

49

Table A3: Alternative model specifications – Additional control variables
Inflows in Equity Funds

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)Liquidity (LQ)

Treasuries (TR)MBS (MBS)ββ + γEM E

β + γAE0.44599 ***0.007290.00246 ***-0.00141 *-0.00212 ***0.03908 *0.13990 ***-0.00078 ***0.00608 ***0.000420.08086 ***-0.00547
0.00067 **-0.00010
0.00038

Number of Observations
R-Squared

Liquidity (LQ)

Treasuries (TR)MBS (MBS)
Number of Observations
R-Squared

Liquidity (LQ)

Treasuries (TR)MBS (MBS)
Number of Observations
R-Squared

***
***
***
***
***

***
***

***
***
***

***

β

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=00.23431 ***-0.20577 ***0.00171 ***-0.01853 ***0.00419 ***-0.08819 ***0.02751 *-0.00235 ***-0.01990 ***0.00433 ***0.08079 **-0.06450 ***0.00031-0.00394 **0.00478 ***

***
***
***

***
***
***
***

***
***
***
***

54429
0.25

Equity returns

Change in 10 year bond yields

β

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=0

1.09831 ***0.97287 ***-0.01389 ***0.02369 ***-0.00573 ***-0.07597
0.37908 ***-0.01412 ***0.02870 ***-0.00125-0.41327 **0.44850 ***-0.01341 ***0.03028 ***-0.00247

***
***

***
***

***

*

56062
0.08

β

β + γEM E

β + γAE-0.16317 ***-0.02050 ***-0.00037 ***0.00234 ***0.00007 *-0.12211 *-0.00386
0.00126-0.00158-0.00041-0.05923 *-0.01777-0.00027 ***0.00007-0.00029

γEM E

γAE γEM E-γAE=0
**

*
**

***
***

48825
0.01

exchange rate return (positive means appreciation of the USD)

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)γEM E=0 γAE=0 γEM E-γAE=0

56084
0.03

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)

Inflows in Bond Fundsβ

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=0-0.83597 ***-0.05981 ***0.00393 ***-0.00981 ***0.00410 ***-0.20288-0.08685 **0.00538 ***-0.00575 **0.00256 **-1.44421 ***-0.28619 ***0.00451 ***-0.00974 ***-0.00072

***

***
***

***
***

***

***

*

59205
0.04

Note: See notes to Tables 4 to 6. Fixed effects and control variables as in the benchmark model. The following additional control variables are included: the economic surprise index for the
G10 and economic surprise index for emerging markets.

50

Table A4: Alternative model specifications – Asymmetric impact of monetary policy operations
Inflows in Equity Funds

Dependent variable:β
QE I Announcements (AN)0.46195 ***QE II Announcements (AN) 0.06052 ***Liquidity (LQ)
0.00398 ***Treasuries (TR)0.00074
MBS (MBS)-0.00300 ***Number of Observations
R-Squared

β + γEM E

β + γAE

0.05162 **0.11795 ***-0.00008
0.00246 **0.00133 **0.07466 ***0.017110.00267 ***-0.00230
0.00042

Liquidity (LQ)

Treasuries (TR)MBS (MBS)
Number of Observations
R-Squared

Liquidity (LQ)

Treasuries (TR)MBS (MBS)
Number of Observations
R-Squared

***
***
***

***
***
***
**
***

***

***
***
**

β

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=00.21529 ***-0.08788 ***0.00560 ***-0.01411 ***0.00187 ***-0.04644 ***0.03080 ***-0.00223 ***-0.01747 ***0.00440 ***0.06145 *-0.02903
0.00313 ***-0.00560 ***0.00441 ***

***
***
***
***
***

***
***
***
***
***

***
**
***
***

54429
0.26
Equity returns

Change in 10 year bond yields

β

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=0

1.32454 ***0.81252 ***-0.02504 ***0.03665 ***-0.00319 ***-0.02193
0.23950 *-0.01834 ***0.02619 ***0.00190-0.31871 *
0.31729 ***-0.01912 ***0.03222 ***-0.00006

***
***
***

***
***
***

***

*

56062
0.08

β
-0.16043 ***-0.02951 ***-0.00060 ***0.00192 ***0.00026 ***β + γEM E-0.12578-0.00007
0.00148-0.00163-0.00045

β + γAE-0.05603-0.02277-0.00047 ***-0.00002-0.00022

γEM E

γAE γEM E-γAE=0
**

***
***
**
*

48825
0.01

exchange rate return (positive means appreciation of the USD)

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)γEM E=0 γAE=0 γEM E-γAE=0

56084
0..03

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)

Inflows in Bond Fundsβ

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=0-0.90001 ***0.02718 ***0.00696 ***-0.00597 ***0.00275 ***-0.28432 **-0.00345
0.00911 ***-0.00322
0.00154-1.49625 ***-0.12369 **0.00829 ***0.00294-0.00325 ***

***

***
**

***
*

***
***

*
***

59205
0.04

Note: See notes to Tables 4 to 6. Differentiation between expansionary and tightening monetary policy operations. Only the coefficients of expansionary monetary policy operations arereported; otherwise as in the benchmark model.

51

Table A5: Alternative model specifications – Large monetary policy interventions only
Inflows in Equity Funds

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)Liquidity (LQ)

Treasuries (TR)MBS (MBS)ββ + γEM E

β + γAE

0.43863 ***0.02016 **0.00385 ***-0.00266 ***-0.00260 ***0.05292 **0.13311 ***-0.00049
0.00627 ***0.00082

0.08587 ***-0.00206
0.00219 ***-0.00006
0.00036

Number of Observations
R-Squared

QE I Announcements (AN)QE II Announcements (AN)Treasuries (TR)MBS (MBS)

Treasuries (TR)MBS (MBS)
Number of Observations
R-Squared

***
***
**
***

***
***
***

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=00.23159 ***-0.19357 ***0.00484 ***-0.01826 ***0.00319 ***-0.06750 ***0.01001-0.00282 ***-0.01920 ***0.00452 ***0.08042 **-0.06152 **0.00226 ***-0.00357 **0.00466 ***

***
***
***

***
***
***
***
***

***

***
**
***
***

54429
0.24
Change in 10 year bond yields

β

β + γEM E

β + γAEγEM E=0γAE=01.22356 ***0.87922 ***-0.02407 ***0.02988 ***-0.00448 ***0.040940.30477 **-0.01883 ***0.03877 ***0.00086-0.30034 *0.37373 ***-0.01917 ***0.03881 ***-0.00066

***
***
**

***
***
**
*
**

***

γEM E-γAE=0

β-0.16010 ***-0.02267 ***-0.00059 ***0.00247 ***0.00015 ***

56062
0.02

β + γEM E-0.13275
0.00206
0.00152-0.00232-0.00054

48825
0.01

exchange rate return (positive means appreciation of the USD)

Dependent variable:

Liquidity (LQ)

***
***
***
***
***

β

Equity returns

Number of Observations
R-Squared

QE I Announcements (AN)QE II Announcements (AN)γEM E=0 γAE=0 γEM E-γAE=0

56084
0.03

Dependent variable:

Liquidity (LQ)

Inflows in Bond Fundsβ

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=0-0.88047 ***-0.04568 ***0.00670 ***-0.00988 ***0.00369 ***-0.26291 **-0.06204 *0.00874 ***-0.00584 ***0.00221 *-1.48497 ***-0.25996 ***0.00791 ***-0.00741 ***-0.00173 ***

***

***
***

***
***

***

***

*

59205
0.04

Note: See notes to Tables 4 to 6. Only purchases/operations larger than the 75th percentile are considered; otherwise as in the benchmark model.

52

β + γAE-0.05656-0.01895-0.00045 ***0.00016-0.00028

γEM E

γAE γEM E-γAE=0
**

**
*

***
***
*

Table A6: Alternative model specifications – Using daily data on Treasury bond purchases
Inflows in Equity Funds

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)Liquidity (LQ)

Treasuries (TR)MBS (MBS)ββ + γEM E

β + γAE0.45090 ***0.007200.00250 ***0.00047-0.00212 ***0.03829 *0.11899 ***-0.00080 ***0.00563 ***0.00058

0.08577 ***-0.01216
0.00071 **0.00206
0.00040

Number of Observations
R-Squared

QE I Announcements (AN)QE II Announcements (AN)Treasuries (TR)MBS (MBS)

Treasuries (TR)MBS (MBS)
Number of Observations
R-Squared

***
***

***
***
**

***

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=00.25656 ***-0.16052 ***0.00186 ***-0.00806 ***0.00388 ***-0.06352 ***0.07146 ***-0.00216 ***-0.00777 ***0.00401 ***0.08704 **-0.04520 **0.00025-0.00146
0.00482 ***

***
***
***

***
***
***
***
*

***
***
***
***

54429
0.25
Change in 10 year bond yields

β

β + γEM E

β + γAEγEM E=0γAE=01.05505 ***0.92808 ***-0.01449 ***0.01016 ***-0.00469 ***-0.11687
0.30171 **-0.01467 ***0.01796 ***-0.00015-0.44881 ***0.34746 ***-0.01389 ***0.02363 ***-0.00136

***
***

***
***

**
***

***
**

γEM E-γAE=0

56062
0.08

β

β + γEM E

β + γAE-0.16547 ***-0.02454 ***-0.00040 ***0.00106 ***0.00013 ***-0.12154 *0.003150.00126-0.00168-0.00044-0.06097 *-0.01210-0.00030 ***-0.00130-0.00027

γEM E

γAE γEM E-γAE=0
**

***
**

***
*
*

48825
0.01

exchange rate return (positive means appreciation of the USD)

Dependent variable:

Liquidity (LQ)

***
***
***
***
***

β

Equity returns

Number of Observations
R-Squared

QE I Announcements (AN)QE II Announcements (AN)γEM E=0 γAE=0 γEM E-γAE=0

56084
0.03

Dependent variable:

Liquidity (LQ)

Inflows in Bond Fundsβ

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=0-0.83306 ***-0.05295 ***0.00393 ***-0.00260 ***0.00403 ***-0.20115-0.09694 ***0.00537 ***0.001540.00257 **-1.44111 ***-0.28032 ***0.00451 ***-0.00237-0.00079 *

***

***
***

***
***

***

*
***

**

59205
0.04

Note: See notes to Tables 4 to 6. The weekly aggregate data on Treasury purchases are replaced with daily purchases data released by the New York Fed; otherwise as in the benchmark model.

53

Table A7: Alternative model specifications – bootstrapped standard errors
Inflows in Equity Funds

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)Liquidity (LQ)

Treasuries (TR)MBS (MBS)
Number of Observations
R-Squaredβ

β + γEM E

β + γAEγEM E=0γAE=00.44599 **0.007290.00246 **-0.00141-0.00212

0.03908 *0.13990 ***-0.00078 ***0.00608 ***0.000420.08086 ***-0.00547
0.00067 **-0.00010
0.00038

**
***
***
**
*

**

Liquidity (LQ)

Treasuries (TR)MBS (MBS)
Number of Observations
R-Squared

Liquidity (LQ)

Treasuries (TR)MBS (MBS)
Number of Observations
R-Squaredβ

β + γEM E

β + γAE

γEM E

***
***
***

0.23431 *-0.20577 **0.00171-0.01853 ***0.00419 ***-0.08819 ***0.02751 **-0.00235 ***-0.01990 ***0.00433 ***0.08079 **-0.06450 **0.00031-0.00394 **0.00478 ***

**
**
***

*

γAE γEM E-γAE=0

**

***
***
***
***

54429
0.02
Equity returnsβ

β + γEM E

β + γAE

1.09831 *0.97287 ***-0.01389 ***0.02369 ***-0.00573 **-0.07597
0.37908 ***-0.01412 ***0.02870 ***-0.00125-0.41327 **0.44850 ***-0.01341 ***0.03028 ***-0.00247

Change in 10 year bond yields
γEM E

γAE γEM E-γAE=0

**

**
**

*

β

β + γEM E

β + γAE-0.16317 ***-0.02050 *-0.00037
0.002340.00007-0.12211 *-0.00386
0.00126-0.00158-0.00041-0.05923 *-0.01777-0.00027 ***0.00007-0.00029

56062
0.08

48825
0.01
exchange rate return (positive means appreciation of the USD)

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)γEM E-γAE=0

56084
0.02

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)

Inflows in Bond Fundsβ

β + γEM E

β + γAE-0.83597 **-0.05981
0.00393 ***-0.00981 ***0.00410 **-0.20288-0.08685 ***0.00538 ***-0.00575 **0.00256 **-1.44421 ***-0.28619 ***0.00451 ***-0.00974 ***-0.00072

γEM E

γAE γEM E-γAE=0
**

***
***

***

***

59205
0.04

Note: See notes to Tables 4 to 6. Standard errors are computed with a bootstrap; otherwise as in the benchmark model.

54

γEM E

*

γAE γEM E-γAE=0

Table A8: Alternative model specifications – Differentiating between Treasury bond purchases during QE1 and QE2Inflows in Equity Funds

Dependent variable:

Inflows in Bond Fundsβ

β + γEM E

β + γAEγEM E=0γAE=00.05312 **0.13789 ***-0.00062 *0.01068 ***0.003300.000170.10173 ***-0.00480
0.00089 ***0.00758 ***-0.00586 ***-0.00025

***
***
***

***

Treasuries QE1 (TR1)
Treasuries QE2 (TR2)
MBS (MBS)0.46789 ***0.008410.00269 ***0.00667 ***-0.00785 ***-0.00283 ***

***
***

**
***

Number of Observations
R-Squared

56084
0.03

QE I Announcements (AN)QE II Announcements (AN)Liquidity (LQ)

γEM E-γAE=0***

β + γAEγEM E=0γAE=0-0.04185
0.38935 ***-0.01377 ***0.05618 ***0.02460 ***-0.00228-0.35380 **0.47162 ***-0.01277 ***0.07156 ***0.01358 ***-0.00484 ***

***
***

***
***

Treasuries QE1 (TR1)
Treasuries QE2 (TR2)
MBS (MBS)

1.10098 ***0.96932 ***-0.01392 ***0.03329 ***0.03005 ***-0.00526 ***

***

***
***

Number of Observations
R-Squared

56062
0.08

γAE γEM E-γAE=00.32508 ***-0.19686 ***0.00272 ***0.01866 ***-0.04680 ***0.00058 ***-0.03916 *
0.01567-0.00184 ***-0.00574 ***-0.02998 ***0.00289 ***0.14477 ***-0.06546 ***0.00104 ***0.02044 ***-0.02295 ***0.00256 ***

***
***
***
***
***
***

***
***
***
***
***

***
***
***
***
***

γEM E-γAE=0*β

β + γEM E

β + γAE-0.16864 ***-0.02259 ***-0.00042 ***-0.00011
0.00448 ***0.00035 ***-0.12698 *-0.00575
0.00121-0.00394
0.00025-0.00020-0.06532 *-0.02032-0.00033 ***-0.00292 *0.00242 ***-0.00001

γEM E

γAE γ EM E-γAE=0
**

**

***

**
*
***
**

**

48825
0.01

exchange rate return (positive means appreciation of the USD)

Dependent variable:ββ + γEME

β + γAEγEM E

γAE γEM E-γAE=0-0.23086 *-0.09613 ***0.00500 ***-0.01522 ***0.00004
0.00354 ***-1.47200 ***-0.29571 ***0.00412 ***-0.01954 ***-0.00405 *0.00022

***

***
***

***
***

Treasuries QE1 (TR1)
Treasuries QE2 (TR2)
MBS (MBS)-0.86534 ***-0.06958 ***0.00353 ***-0.02058 ***-0.00433 ***0.00509 ***

***

**

Number of Observations
R-Squared

59205
0.04

Liquidity (LQ)

γEM E

Change in 10 year bond yields

β + γEM E

QE I Announcements (AN)QE II Announcements (AN)β + γAE

Equity returns
βLiquidity (LQ)***

β + γEM E

54429
0.26

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)

***
***

β

Note: See notes to Tables 4 to 6. Differentiation between Treasury bond purchases during QE1 (from March to October 2009) and QE2 (after August 2010); otherwise as in the benchmark
model.

55

Table A9: Alternative model specifications – Two-stage approach
Inflows in Equity Funds

Dependent variable:

Inflows in Bond Fundsβ

β + γ EM E

β + γAEγEM E=0γAE=0γEM E-γAE=0β

β + γEM E

β + γAEγEM E

γAE γEM E-γAE=00.04072
0.11275 ***-0.00157 **0.001030.001630.00124 **0.14308 ***-0.01218-0.00007-0.00228 ***-0.00598 ***-0.00078 **

***
***
***
***
***
***

***

**
***
*
*
***
***

0.16586 ***0.02254 ***0.00113 ***0.00308 ***-0.00947 ***-0.00076 ***-0.14472 ***0.06593 ***-0.00164 ***-0.00356 ***-0.01886 ***-0.00349 ***0.08168 ***0.014530.00027-0.00078-0.00694 ***-0.00291 ***

***
*
***
***
***
***

***

***

Treasuries QE1 (TR1)
Treasuries QE2 (TR2)
MBS (MBS)0.33987 ***-0.00025
0.00135 ***0.00770 ***-0.00485 ***-0.00100 ***

***
***
**
***

***
***
***

Number of Observations
R-Squared

52170
0.03

QE I Announcements (AN)QE II Announcements (AN)Liquidity (LQ)

51219
0.26
Equity returns

Dependent variable:ββ + γEM E

β + γAE

Treasuries QE1 (TR1)
Treasuries QE2 (TR2)
MBS (MBS)2.59958 ***1.11043 ***0.00463 ***-0.00840 ***0.01212 ***0.01897 ***1.38245 ***0.56895 ***-0.00511 ***0.000410.02258 ***0.01154 ***0.52458 **
0.41707 ***-0.00199-0.00389
0.01854 ***0.01901 ***Number of Observations
R-Squared

52170
0.01

QE I Announcements (AN)QE II Announcements (AN)Liquidity (LQ)

γEM E=0 γAE=0 γEM E-γAE=0
***
***
***
*
***

***
***
***

***
*

**
**

β

β + γEM E

β + γAE-0.14493 ***-0.01069 ***0.00029 *-0.00073 ***0.00031 ***-0.00027 ***-0.19244 *0.03509 *-0.00027
0.003270.00051-0.00053-0.20297 *0.004800.000170.00809
0.00082 **-0.00022

γEM E

γAE γEM E-γAE=0

**
***
**

*

*

45925
0.01

β

β + γEM E

β + γAE

Treasuries QE1 (TR1)
Treasuries QE2 (TR2)
MBS (MBS)-0.51600 ***-0.03725 ***0.00197 ***0.00187 ***-0.00196 ***-0.00172 **-0.25096 *-0.09583 **0.00238 ***0.00899 **-0.00097-0.00597 ***-1.51473 ***-0.25274 ***0.00374 ***-0.00082-0.00222-0.00392 ***Number of Observations
R-Squared

55382
0.07

Liquidity (LQ)

Change in 10 year bond yields

exchange rate return (positive means appreciation of the USD)

Dependent variable:

QE I Announcements (AN)QE II Announcements (AN)

***
***

γEM E

γAE γEM E-γAE=0
***
***
***

*

***
**
*

*

Note: See notes to Tables 4 to 6. LQ, TR1 and MBS are the unexpected part of Fed interventions, i.e. the residual of a regression where the size of operations is explained by predetermined
indicators of market tensions that are unaffected by the Fed interventions (see section 4.3 for details on the two stage approach); otherwise as in the benchmark model.

56

Figure A1
Cross-country heterogeneity in impact of Fed policy measures – portfolio flows & asset prices
A. Equity and Bond flows: total impact of QE I announcements
1

USA
GRC
ITA
AUT
FIN
IRL
FRA
GBR
NLD
BEL
NZLDNK
ESP
NOR
AUS
PRT

Total inflows in Bond Funds - % of AUM
-.5
0
.5

CAN
CZE
JPN

CHE

DEU

KOR

HKG
MYS

SGP IDNMEXISRPERTHAARG ZAF
VEN
PHL
RUSCHL
BRA
COL
KAZ
EGY
PAK

IND
POL

BGR

UKRNGA

CHN

TUR

HUN
ROM

-1

HRV

-1

0
1
Total inflows in Equity Funds - % of AUM

2

Total inflows in Bond Funds - % of AUM
-.5
0
.5

1

B. Equity and Bond flows: total impact of QE II announcements

GRC
CHN
IND
SGP
PAK
P
HL
THA
UKR
KAZ
EGY
MYS
VEN
ARG
CHLHKGTUR
RUS
IDN PER
ITA
MEX
FIN
AUT
KOR
IRL
ISR NLD
FRA
ZAF
HUN
SAU
ESP BRA POL
HRV
BEL
CAN
NOR
BGR
AUS
DNK
TWN JPNPRT
GBR
SWE
USA
CZE
ROM

VNM

MAR
KWT

DEU

-1

CHE

COL

NGA

-.5

0
.5
1
Total inflows in Equity Funds - % of AUM

57

1.5

10

C. Equity and Bond flows: Impact of treasury purchases at the maximum expansion of the
programme

IRL
GRC ITANLDGBR ESP
FRA
FIN
DNK
BEL

NOR

CHEPRT

ISR
SAU

SWE

AUT

AUS
JPN
CZE
POL
DEU
BRA HUN

KOR
MEX
CAN
MYS
ZAF
CHL
PER
ARG
VEN
IDN
RUS
COL
TUR
HKG
EGYKAZUSAPHLTWN PAK
THA
SGP

BGR
ROM
HRVUKR

NGA

IND
CHN
MAR

-20

Total inflows in Bond Funds - % of AUM
-10
0

NZL

KWT

-10

-5
0
5
Total inflows in Equity Funds - % of AUM

10

Note: Figures A to C show the total impact of QE instruments. The total impact is calculated by multiplying
the estimated impact coefficient of a QE instrument by the total size of the QE programme. Underlying
coefficients are estimated with modified version of the benchmark model, where the coefficients measuring
the impact of QE instruments are country-specific58

Figure A2
Cross-country heterogeneity in impact of Fed policy measures – asset prices.5

A. Equity prices and Bond yields: total impact of QE I announcements

CZE

TWN
CHN

POL

BGR
SGP

Total yield change
-.5
0

PHL
JPN THA

HKG
NZL

IND

CHE

KOR

MYS
ESP FIN
CAN
DEU
DNK
GBR
ISR
FRA
NOR
CHL
NLD
BEL
SWE ITA
RUS

IRL
GRC

COL

AUS

AUT

ZAF

MEX

PRT
USA

SAU

-1

BRA

-10

-5

0
Total equity price change

5

10

.4

B. Equity prices and Bond yields: total impact of QE II announcements

Total yield change
0
.2

BRA

GRC
IDN
HUN

HKG

AUS
CHE

CHN
ARG

IRL

MYS
JPN
SWE POL
ESP
ITA
IND
MEX
GBR
FIN RUS
ISR
KOR
TWN
NGA
SGP
DEU
NZL
BEL
CZE
DNK
FRA
AUT
ZAF
CAN
NLD
USA
HRV
PHL

-.2

BGR

PRT

TUR
VNM

COL

THA

VEN

-4

-2

0
2
Total equity price change

59

4

6

C. Equity prices and Bond yields: Impact of treasury purchases at the maximum expansion of
the programme
IRL

2

MEX
SGP
ESP
USA

Total yield change
-1
0

1

BRA

ISR

POL
ITA
DNK
TUR
HKG THANZLMYS
TWN
SWE
NOR
DEU
AUS
AUT HRV
NLD
BEL
CAN
FIN
PRT CHEGBR
KOR
RUS FRA JPN
PHL
GRC
COL

VEN
CHN
CHL
CZE
ZAF

IND
IDN

BGR

SAU
VNM

-2

NGA

-3

HUN

-20

0

20
Total equity price change

Note: See note to Figure A1.

60

40

60

AUS
CAN
CHE
DNK
EUR
GBR
ISR
JPN
NOR
NZL
SWE
USA
ARE
ARG
BGR
BRA
CHL
CHN
COL
CZE
ECU
EGY
HKG
HRV
HUN
IDN
IND
KAZ
KOR
KWT
MAR
MEX
MYS
NGA
PAK
PER
PHL
POL
QAT
ROM
RUS
SAU
SGP
SRB
THA
TUN
TUR
TWN
UKR
VEN
VNM
ZAF

-4

-2

0

2
AUS
CAN
CHE
DNK
EUR
GBR
ISR
JPN
NOR
NZL
SWE
USA
ARE
ARG
BGR
BRA
CHL
CHN
COL
CZE
ECU
EGY
HKG
HRV
HUN
IDN
IND
KAZ
KOR
KWT
MAR
MEX
MYS
NGA
PAK
PER
PHL
POL
QAT
ROM
RUS
SAU
SGP
SRB
THA
TUN
TUR
TWN
UKR
VEN
VNM
ZAF

-10

-5

0

5

10

Figure A3
Cross-country heterogeneity in impact of Fed policy measures – exchange rates

A. Exchange rate (total USD appreciation in %): total impact of QE I announcements

B. Exchange rate (total USD appreciation in %): total impact of QE II announcements

61

AUS
CAN
CHE
DNK
EUR
GBR
ISR
JPN
NOR
NZL
SWE
USA
ARE
ARG
BGR
BRA
CHL
CHN
COL
CZE
ECU
EGY
HKG
HRV
HUN
IDN
IND
KAZ
KOR
KWT
MAR
MEX
MYS
NGA
PAK
PER
PHL
POL
QAT
ROM
RUS
SAU
SGP
SRB
THA
TUN
TUR
TWN
UKR
VEN
VNM
ZAF

-20

-10

0

10

20
AUS
CAN
CHE
DNK
EUR
GBR
ISR
JPN
NOR
NZL
SWE
USA
ARE
ARG
BGR
BRA
CHL
CHN
COL
CZE
ECU
EGY
HKG
HRV
HUN
IDN
IND
KAZ
KOR
KWT
MAR
MEX
MYS
NGA
PAK
PER
PHL
POL
QAT
ROM
RUS
SAU
SGP
SRB
THA
TUN
TUR
TWN
UKR
VEN
VNM
ZAF

-10

0

10

20

30

40

C. Exchange rate (total USD appreciation in %): Impact of liquidity operations at the
maximum expansion of the programme

D. Exchange rate (total USD appreciation in %): Impact of treasury purchases at the
maximum expansion of the programme

Note: See note to Figure A1.

62