Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

Annex 1: Data Description

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Annex 2: Definition of the UMPM Variables

For the purpose of this research we collected the following information on asset purchases programs and UMPMs by BoE, BoJ, ECB, and the U.S. Fed:

The United States

For U.S. QE measure Fed’s data on stock of agency—and GSE-backed securities’ assets are used. During first phase of QE Fed buys US$1.24 trillion in mortgage securities. On the 27 of August 2014, Ben Bernanke, the Chairman of the Federal Reserve at the time, gave a speech in which he set a stage for second and third phases of QE. During second phase of QE Fed buys US$600 billion in the U.S. treasury securities. From September 9, 2011, until December 31, 2012, Fed engaged in the co-called “Operation Twist” transactions. During the third phase of QE, Fed bought US$40 billion a month in mortgage securities (to infinity and beyond), and starting from 2013 Fed has been buying additional US$45 billion a month in the U.S. Treasuries until unemployment rate falls to 6.5 percent. From December 2013, Fed officially announced tapering of QE. The purchase of the U.S. Treasury securities are referred as the first U.S. QE program, and the purchases of agency debt plus mortgage-backed securities are referred to as the second QE program.

The United Kingdom

In the United Kingdom, the principal element of the unconventional measures was the policy of asset purchases financed by central bank money, so-called quantitative easing (QE). In January 2009, the Chancellor of the Exchequer authorized the BoE to set up an Asset Purchase Facility (APF) to buy high-quality assets financed by the issue of Treasury bills and the DMO’s cash management operations.1 When assets are purchased from non-banks (either directly or indirectly via intermediate transactions), the banking sector gains both new reserves at the Bank of England and a corresponding increase in customer deposits. Between March 2009 and May 2012, the BoE purchased £325 billion worth of such asset. Since then, the BoE has expanded its APF by a further £50 billion. Weekly outstanding amounts of BoE assets were used to calculate the net actual purchases. The Bank also pursued a number of activities targeted to improve the functioning of specific financial markets, such as purchases of high-quality commercial paper and corporate bonds. The scale of these operations was much less than for the gilt purchases, consistent with the Bank acting as a backstop purchaser/seller with the intention of improving market functioning.

Japan

Japan announced its QE1 in March 2001.2 The Bank increased the amount of its outright purchase of long-term government bonds from 400 billion yen per month, in cases where it was considered necessary for providing liquidity smoothly. The outright purchases were subject to the limitation that the outstanding amount of long-term government bonds effectively held by the Bank were kept below the outstanding balance of banknotes issued. The Assets Purchases Program (APP) was first introduced in October 2010 to promote economic growth and price stability. On October 5, 2010, the BoJ purchased JPY5 trillion in assets. In March, August, and October 2011, the BoJ increased the size of the APP by JPY5 trillion to JPY20 trillion to facilitate purchases of Japan government bonds (JGBs). This, along with the JPY35 trillion assigned to the fixed-rate funds-supplying operation, puts the APP at JPY55 trillion. The BOJ’s APP also covers private sector financial assets, including commercial paper, corporate bonds, exchange-traded funds (ETFs), and real estate investment trusts (REITs) in addition to government securities. In February and April 2012, the BoJ purchased additional JPY20 trillion in assets. In July 2012, the BoJ conducted another purchase of JPY5 trillion. During September, October, and December 2012, the BoJ purchased JPY5 trillion in the JGB and JPY5 trillion in Treasury bills per month. In September 2013, the BoJ has expanded APP by JPY10 trillion, increasing overall size of the stimulus program to JPY80 trillion.

ECB

The ECB’s UMPMs up to Q2 2014 are well described on the ECB website and in the ECB Monthly Bulletin.3

Definition of the UMPM Variables

We use changes in net asset purchases by the S4 central banks during Q1:2002–Q4:2013 as an independent variable to isolate more directly the change in long-term yields that could be attributed to unconventional monetary policies in each of the S4 countries individually and the S4 as a whole. In this we follow the approach suggested by Ahmed and Zlate (2013). First, we regress the change in long-term bond yields (in percentage points) on one quarter ahead change of stocks of nominal values of assets purchases by the concerned central bank converted to U.S. dollars and normalized by the S4 nominal GDP (in percentage points). To construct the variable, we subtract from the fitted value the estimated constant and error terms. In the first-stage regression (see below equation (3)), the coefficients on the changes in UMPMs is negative and statistically significant at the 1 percent level, with about 20 to 40 percent of the variation in yields explained by implemented UMPMs over the period analyzed in the United States, the United Kingdom, and Japan. The UMPM measures in the euro area showed no statistically significant impact on the compression of the euro area long-term yields over the period. To construct the total S4 UMPM measure we use the change in the U.S. long-term bond yield (as a proxy for global interest rate) and regress it on one quarter ahead change in actual stocks of assets purchases by the S4 central banks (normalized by the S4 nominal GDP).

ΔLTIRc,t=βc+β1ΔUMPMGDPc,t+1+εc,t(3)
Table 2.1.

Correlations between UMPMs in S4

(Changes in assets purchases in percent of S4 GDP)

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Source: Authors’ estimates.
Table 2.2.

Summary Statistics for Two Samples

(Before 2002:Q1-2008:Q2 and after 2008:Q3-2013:Q3 the GFC)

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Annex 3: Regression Results

Table 3.1.

Impact of S4 UMPMs on Global Liquidity Conditions

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Notes: The table reports the estimates of panel regressions with country fixed effects and clustered standard errors at the borrower country level. *** indicate significance at 1 percent, ** at 5 percent, and * at 10 percent, respectively.
Table 3.2.

Impact of UMPM Programs on Broad Money Growth

(Total UMPM)

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Notes: The table reports the estimates of panel regressions with country and time fixed effects and clustered standard errors at the country level. The dependent variable is the growth of broad money (from SRF forms). Column 1 represents results for the whole sample of 131 countries. Columns 2 and 3 show the results for the whole sample, excluding the S4 countries, using fixed (column 2) and random (column 3) effects models. In column 4, the lag of the independent variable is added to the model. Columns 5 and 6 show the results for advanced economies, first excluding S4 countries from the sample (column 5) and then including them (column 6). Column 7 represents the results for EME countries. And column 8 represents the result for the whole sample, excluding the S4, controlling for two institutional variables exchange rate regime (Ilzetzki, Reinhart and Rogoff (2008)) and foreign ownership in banking sector (World Bank surveys on bank regulation). *** indicate significance at 1 percent, ** at 5 percent, and * at 10 percent, respectively.
Table 3.3.

Impact of UMPM Programs on Broad Money Growth

(Individual UMPM)

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Notes: The table reports the estimates of panel regressions with country and time fixed effects and clustered standard errors at the country level. The dependent variable is the growth of broad money (from SRF forms). Column 1 represents results for the whole sample of 131 countries. Columns 2 and 3 show the results for the whole sample, excluding the S4 countries, using fixed (column 2) and random (column 3) effects models. In column 4, the lag of the independent variable is added to the model. Columns 5 and 6 show the results for advanced economies, first excluding S4 countries from the sample (column 5) and then including them (column 6). Column 7 represents the results for EME countries. And column 8 represents the result for the whole sample, excluding the S4, controlling for two institutional variables exchange rate regime (Ilzetzki, Reinhart and Rogoff (2008)) and foreign ownership in banking sector (World Bank surveys on bank regulation). *** indicate significance at 1 percent, ** at 5 percent, and * at 10 percent, respectively.
Table 3.4.

Impact of UMPM programs on NFC Deposits Growth

(Total UMPM)

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Notes: The table reports the estimates of panel regressions with country and time fixed effects and clustered standard errors at the country level. The dependent variable is the growth of NFC deposits (from SRF forms). Column 1 represents results for the whole sample of 131 countries. Columns 2 and 3 show the results for the whole sample, excluding the S4 countries, using fixed (column 2) and random (column 3) effects models. In column 4, the lag of the independent variable is added to the model. Columns 5 and 6 show the results for advanced economies, first excluding S4 countries from the sample (column 5) and then including them (column 6). Column 7 represents the results for EME countries. And column 8 represents the result for the whole sample, excluding the S4, controlling for two institutional variables exchange rate regime (Ilzetzki, Reinhart and Rogoff (2008)) and foreign ownership in banking sector (World Bank surveys on bank regulation). *** indicate significance at 1 percent, ** at 5 percent, and * at 10 percent, respectively.
Table 3.5.

Impact of UMPM Programs on NFC Deposits Growth

(Individual UMPM)

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Notes: The table reports the estimates of panel regressions with country and time fixed effects and clustered standard errors at the country level. The dependent variable is the growth of NFC deposits (from SRF forms). Column 1 represents results for the whole sample of 131 countries. Columns 2 and 3 show the results for the whole sample, excluding the S4 countries, using fixed (column 2) and random (column 3) effects models. In column 4, the lag of the independent variable is added to the model. Columns 5 and 6 show the results for advanced economies, first excluding S4 countries from the sample (column 5) and then including them (column 6). Column 7 represents the results for EME countries. And column 8 represents the result for the whole sample, excluding the S4, controlling for two institutional variables exchange rate regime (Ilzetzki, Reinhart and Rogoff (2008)) and foreign ownership in banking sector (World Bank surveys on bank regulation). *** indicate significance at 1 percent, ** at 5 percent, and * at 10 percent, respectively.
Table 3.6.

Impact of UMPM Programs on NFC Securities’ Issuance

(Total UMPM)

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Notes: The table reports the estimates of panel regressions with country and time fixed effects and clustered standard errors at the country level. The dependent variable is the growth of NFC securities issuance. Column 1 represents results for the whole sample of 131 countries. Columns 2 and 3 show the results for the whole sample, excluding the S4 countries, using fixed (column 2) and random (column 3) effects models. In column 4, the lag of the independent variable is added to the model. Columns 5 and 6 show the results for advanced economies, first excluding S4 countries from the sample (column 5) and then including them (column 6). Column 7 represents the results for EME countries. And column 8 represents the result for the whole sample, excluding the S4, controlling for two institutional variables exchange rate regime (Ilzetzki, Reinhart and Rogoff (2008)) and foreign ownership in banking sector (World Bank surveys on bank regulation). *** indicate significance at 1 percent, ** at 5 percent, and * at 10 percent, respectively.
Table 3.7.

Impact of UMPM Programs on NFC Securities’ Issuance

(Individual UMPM)

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Notes: The table reports the estimates of panel regressions with country and time fixed effects and clustered standard errors at the country level. The dependent variable is the growth of NFC securities issuance. Column 1 represents results for the whole sample of 131 countries. Columns 2 and 3 show the results for the whole sample, excluding the S4 countries, using fixed (column 2) and random (column 3) effects models. In column 4, the lag of the independent variable is added to the model. Columns 5 and 6 show the results for advanced economies, first excluding S4 countries from the sample (column 5) and then including them (column 6). Column 7 represents the results for EME countries. And column 8 represents the result for the whole sample, excluding the S4, controlling for two institutional variables exchange rate regime (Ilzetzki, Reinhart and Rogoff (2008)) and foreign ownership in banking sector (World Bank surveys on bank regulation). *** indicate significance at 1 percent, ** at 5 percent, and * at 10 percent, respectively.