## _wp15287

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### I. INTRODUCTION — context and scope
- Central banks in several systemically important countries implemented unconventional monetary policy measures (UMPMs) over 2008–14, including large scale purchases of public and private debt securities and direct lending to banks, aimed to repair the monetary transmission mechanism and provide monetary accommodation at the zero lower bound.
- UMPMs (also referred to as quantitative easing (QE)) involve active use of the central bank balance sheet to influence market prices and conditions beyond a short-term policy interest rate.
- Over 2007–14 the cumulative balance sheet change of selected central banks was large: the Federal Reserve, the Bank of Japan, and the Bank of England experienced cumulative balance sheet changes amounting to 20 percent, 35 percent, and 17 percent of GDP, respectively; the European Central Bank’s net increase in assets was noted as modest over a similar period.
- UMPMs increased reserves and broad money via a “money multiplier,” and introduced negative interest rates for some policy instruments in some advanced countries.
- Portfolio rebalancing in the Systemic Four (S4: United States, United Kingdom, Euro area, and Japan) increased asset prices and led investors to acquire relatively more risky assets outside the S4, prompting capital outflows from the S4 and increased inflows and issuance of new securities in emerging market economies (EMEs).
- The overall effect of S4 UMPMs on the rest of the world (RoW) liquidity and monetary conditions is ambiguous: positive trade and capital spillovers can coexist with increased macro-financial vulnerabilities; empirical evidence on impacts on RoW banks’ balance sheets, liquidity, and money supply is limited.

### Major S4 programs analyzed
- U.S. Fed: large-scale assets purchase (LSAP) split by purchases of U.S. treasuries, mortgage backed securities (MBS), and securities of government sponsored enterprises (GSE).
- Bank of England (BoE): QE strategy implemented by the BoE.
- Bank of Japan (BoJ): asset purchase program(s).
- European Central Bank (ECB): government bond purchases (phases one and two), three-year long-term refinancing operation (LTRO), and the securities market program (SMP).

### Analytical framework and identification
- Focus on stock effects of UMPMs: how persistent shifts in long-term bond yields, arising from expansion of S4 central bank balance sheets, impacted global liquidity, monetary conditions, and financial sector balance sheets in other countries.
- Two-step empirical strategy:
  - First step: instrument long-term interest rates by S4 UMPM programs to extract variation in yields explained by implemented UMPMs over the period analyzed.
  - Second step: estimate S4 UMPM global and country-level impacts, controlling for global financial conditions and country-specific macroeconomic environment (policy interest rate, exchange rate, inflation, GDP growth, etc.).
- Outcome variables include broad money, banks’ balance sheet variables (non-core liabilities proxied by NFC deposits), and NFC issuance of securities.
- The analysis addresses endogeneity and the lack of a counterfactual by focusing on marginal cross-border spillovers conditional on global and domestic controls.

### Data sources, sample, and key definitions
- New quarterly dataset covering Q1:2002–Q2:2014 constructed combining SRFs MFS, IMF IFS and BoP, World Bank external debt, BIS international issuance, Dealogic issuance, and indicators: capital account openness, exchange rate regimes, institutional quality, banking structure and regulation.
- Sample: 131 countries (28 advanced, 103 emerging market and developing economies including low-income countries).
- Broad money series primarily derived from IMF SRFs.
- Funding liquidity measure: monetary aggregate L = sum of deposits of NFCs in the banking system (ODCs). NFC deposits include transferable and other deposits included in broad money as well as transferable and other deposits excluded from broad money, in both local and foreign currency.
- Core and non-core liabilities of banks computed using detailed SRF data reported confidentially to the IMF.

### Empirical specifications and estimation methods
- Time coverage: Q1:2002–Q2:2014 (UMPMS constructed Q1:2002–Q4:2013 for variable construction).
- First-stage identification: regressions of change in long-term bond yields on one-quarter-ahead change of stocks of nominal values of asset purchases (converted to USD and normalized by S4 nominal GDP).
- Estimation methods: OLS with bootstrapped regressions with 1,000 replications, GLS to account for serial autocorrelation; variables checked to be stationary. Bayesian VAR (BVAR) used as robustness check.
- Panel regression model: dependent variable = log difference of country-level liquidity measures (broad money supply, NFC deposits, issuance of securities); global and country-level controls included; model estimated with country fixed effects and standard errors clustered at the country level.
- Global controls include: contemporaneous log difference in the VIX, real global GDP growth, U.S. banks’ interoffice cross-border positions (one-period percentage change), global CPI change (percent per annum), quarterly log difference in US real effective exchange rate, and instrumented changes in long-term bond yields attributed to UMPMs.

### Descriptive empirical findings on transmission channels
- Primary channels identified:
  - Traditional interest rate channel: UMPMs reduced longer-term yields in S4 and encouraged investors to search for assets of similar maturities with higher risk-adjusted returns.
  - Portfolio rebalancing channel: central bank asset purchases changed relative demand and prices of securities, influencing investors’ portfolio decisions.
  - Global liquidity / banking channel: sizable UMPM programs boosted global liquidity and eased funding conditions supporting bank lending.
  - Exchange rate, reserves, and real channels: UMPMs impacted other countries via exchange rates, reserves accumulation, and real-economy effects.
- Empirical corroboration: S4 UMPMs are negatively correlated with nominal long-term interest rates, confirming compression of long-term interest rates in S4 and prompting rebounded private capital outflows from S4.
- Patterns in capital flows and asset issuance:
  - Gross private capital outflows after the crisis in the Euro Area and the United States rebounded but remain significantly below pre-crisis levels.
  - Private equity and other debt securities outflows (with exception of Japan) rebounded the most.
  - From 2009–13, the purchase of EMEs’ private securities tripled relative to the pre-crisis period of 2002–07.
  - Net public debt flows to EMEs (measured as total foreign assets less total foreign liabilities) became and remain significantly negative since 2009.

### Cross-country and sectoral balance-sheet dynamics
- Domestic liquidity injections and balance sheet expansions:
  - Central banks in AMs injected significant liquidity during 2008–09; since end-2010 AMs continued injecting liquidity as a substitute for banks’ liquidity provision.
  - Monetary authorities in EMEs and LICs expanded balance sheets significantly due to accumulation of foreign reserves and sterilization operations.
  - Banking systems in EMEs and LICs expanded total assets at about 15 percent on average during 2010–14.
- Real activity and credit-gap dynamics:
  - Real GDP growth of EMEs and LICs declined since 2010 by more than one percentage point (from 4.8 to 3.6 percent).
  - The BIS credit-to-GDP gap measure has significantly widened recently for some EMEs.
- Allocation of borrowed liquidity by RoW banks: increase lending to customers; invest in domestic or foreign securities; hold money as vault cash or reserves within the central bank.
- Funding vulnerabilities:
  - AEs significantly reduced reliance on wholesale funding (particularly from abroad); EMEs and LICs remain more vulnerable to interest rate/exchange rate shocks.
  - All groups increased reliance on credit to the central bank and the government.
  - Abundant global liquidity may increase vulnerabilities in LICs via higher share of wholesale and foreign funding in total bank funding.

### Country heterogeneity (selected examples)
- Turkey: share of non-core liabilities doubled since 2009; increased share of non-resident holdings in total funding; authorities adopted prudential measures in 2015 (reserve option mechanism and asymmetric reserve requirement increases for various deposit categories).
- Brazil: monetary tightening and capital controls helped maintain non-resident participation share of total ODC funding; decreased share of domestic deposits amid increased volatility and weaker growth prospects.

### First-stage and instrument construction for UMPM variables
- Independent variable: changes in net asset purchases by the S4 central banks during Q1:2002–Q4:2013.
- First-stage regression: change in long-term bond yields (percentage points) regressed on one quarter ahead change of stocks of nominal values of asset purchases by the concerned central bank converted to U.S. dollars and normalized by the S4 nominal GDP (percentage points).
- Key first-stage findings:
  - Coefficients on the changes in UMPMs are negative and statistically significant at the 1 percent level in the United States, the United Kingdom, and Japan.
  - 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.
- Selected reported correlations between UMPMs:
  - US LSAP MBS&GSE with US LSAP Treasuries: 0.17
  - US LSAP MBS&GSE with UK: 0.50
  - US LSAP Treasuries with UK: 0.23
  - UK with Japan: 0.30
  - ECB LTRO with UK: 0.50
  - ECB SMP correlations: with US LSAP MBS&GSE = -0.20; with US LSAP Treasuries = 0.35
  - ECB CBPP 1&2 correlation with US LSAP MBS&GSE: 0.48

### Main empirical results (selected, exact reported coefficients and findings)
- Association of S4 UMPMs with global liquidity and monetary aggregates:
  - Positive but not robustly significant association of S4 UMPMs with global liquidity measures defined by global growth of broad money, global NFC deposit growth, or global domestic credit growth.
  - Total S4 UMPMs have a positive impact on international banks’ cross-border credit growth, particularly extension of loans with longer maturity, when controlling for global banks’ inter-office lending.
  - Positive association between S4 UMPMs and international issuance of debt securities by non-S4 countries in FX.
- Cross-country panel regression highlights (selected exact reported coefficients and observations):
  - Table 3.1 USA LT IR, instrumented by UMPM in S4: example coefficients include 0.163, 0.199, 0.388, 0.382*, 0.088, 0.341, 0.377*, 0.510*, 0.403, 0.621**, 0.484, 0.557, 0.528, 0.066, 0.265, 0.507**, 0.168, 0.208**; Global banks interoffice assets: 0.781***, 0.663***, 0.774***, 0.493***.
  - Table 3.2 USA LT IR, instrumented by the UMPM: 0.297***, 0.305***, 0.294***, 0.318***, 0.215**, 0.208**, 0.379***, 0.113; Global banks interoffice claims: 0.656***, 0.655***, 0.662***, 0.682***, 0.990***, 0.942***, 0.628***, 0.660***. Number of observations reported across columns include 467, 345, 1745, 1744, 3693, 1095, 2900, 4517; R2 values reported exactly.
  - Table 3.3 UK LT IR instrumented by UMPM: -0.713***, -0.717***, -0.704***, -0.721***, -0.634**, -0.657***, -0.659***, -0.507***. Japan LT IR instrumented by UMPM: 0.719***, 0.740***, 0.702***, 0.717***, 0.379, 0.359*, 0.806***, 0.539***.
  - Table 3.4 USA LT IR instrumented by the UMPM = 0.434*** (All); Global banks interoffice claims = 0.624*** (All).
  - Table 3.5 USA LT IR instrumented by the UMPM = 0.084 (All); UK LT IR instrumented by the UMPM = -1.313*** (All); Japan LT IR instrumented by the UMPM = 1.132*** (All); EA LT IR instrumented by the UMPM = 0.600*** (All).
  - Table 3.6 USA LT IR instrumented by UMPM in S4 = -0.021 (All); Change in VIX = -0.017*** (All).
  - Table 3.7 UK LT IR instrumented by the UMPM = 0.279*** (All); Japan LT IR instrumented by the UMPM = -0.358*** (All); EA LT IR instrumented by the UMPM = 0.087* (All).
- Heterogeneity in effects:
  - BoJ asset purchase programs: positive impact on global liquidity and other countries’ monetary conditions, and a negative association with issuance of securities.
  - BoE QE program: strong negative association with global liquidity (broad money and NFC deposits) and positive impact on issuance of NFC securities.
  - U.S. Fed and ECB UMPMs: mixed results.
- Robust correlates:
  - Global banks’ interoffice claims are highly statistically significant and positively associated with global liquidity measures.
  - Appreciation of the dollar has a strong negative impact on global liquidity.
  - VIX (change) and REER (change) frequently enter with negative coefficients in regressions.

### Economic costs, vulnerabilities, and implications for normalization
- UMPM programs accompanied by economic costs: reallocation of resources on banks’ balance sheets; possible contribution to loosening fiscal discipline in EMEs.
- Macro-financial vulnerabilities identified:
  - Substitution of cross-border banking flows with portfolio flows of non-banks raises concerns about vulnerabilities.
  - Growing role of NFCs as de facto financial intermediaries may reduce macroprudential policy effectiveness.
  - Non-core liabilities of EME banks exhibit higher volatility than those of developed countries.
- Policy implications and recommendations (as presented):
  - Close monitoring of macro-financial vulnerabilities in EMEs and frequent debt sustainability analysis.
  - Need for better communication among central banks and with financial markets and strengthening of the global financial safety net.
  - As monetary policy begins to normalize in the S4, RoW liquidity and monetary conditions might tighten; the “taper tantrum” of 2013 illustrated extreme volatility from signaling changes in future policy.
  - Full assessment requires complete return to a normalized monetary policy; current analysis informs potential impacts on global monetary and liquidity conditions.

*Source: _wp15287 - References .............................................................................................................*

### References .............................................................................................................

### _wp15287 - References .............................................................................................................

### I. INTRODUCTION — context and scope
- Central banks in several systemically important countries implemented unconventional monetary policy measures (UMPMs) over 2008–14, including large scale purchases of public and private debt securities and direct lending to banks, aimed to repair the monetary transmission mechanism and provide monetary accommodation at the zero lower bound.
- UMPMs (also referred to as quantitative easing (QE)) involve active use of the central bank balance sheet to influence market prices and conditions beyond a short-term policy interest rate.
- Over 2007–14 the cumulative balance sheet change of selected central banks was large: the Federal Reserve, the Bank of Japan, and the Bank of England experienced cumulative balance sheet changes amounting to 20 percent, 35 percent, and 17 percent of GDP, respectively; the European Central Bank’s net increase in assets was noted as modest over a similar period.
- UMPMs increased reserves and broad money via a “money multiplier,” and introduced negative interest rates for some policy instruments in some advanced countries.
- Portfolio rebalancing in the Systemic Four (S4: United States, United Kingdom, Euro area, and Japan) increased asset prices and led investors to acquire relatively more risky assets outside the S4, prompting capital outflows from the S4 and increased inflows and issuance of new securities in emerging market economies (EMEs).
- The overall effect of S4 UMPMs on the rest of the world (RoW) liquidity and monetary conditions is ambiguous: positive trade and capital spillovers can coexist with increased macro-financial vulnerabilities; empirical evidence on impacts on RoW banks’ balance sheets, liquidity, and money supply is limited.

### Major S4 programs analyzed
- U.S. Fed: large-scale assets purchase (LSAP) split by purchases of U.S. treasuries, mortgage backed securities (MBS), and securities of government sponsored enterprises (GSE).
- Bank of England (BoE): QE strategy implemented by the BoE.
- Bank of Japan (BoJ): asset purchase program(s).
- European Central Bank (ECB): government bond purchases (phases one and two), three-year long-term refinancing operation (LTRO), and the securities market program (SMP).

### Key empirical findings (as reported)
- Positive and statistically significant relationships exist between UMPM implementation and global liquidity and monetary conditions measured by global NFC deposit growth (including China), banks’ cross-border flows, and issuance of securities (particularly in foreign currency).
- Heterogeneous effects across S4 central banks:
  - BoJ asset purchase programs: appear to have a positive impact on global liquidity and other countries’ monetary conditions, and a negative association with issuance of securities.
  - BoE QE program: strong negative association with global liquidity (broad money and NFC deposits) and positive impact on issuance of NFC securities.
  - U.S. Fed and ECB UMPMs: results are mixed.
- The substitution of cross-border banking flows with portfolio flows of non-banks raises new concerns about financial vulnerabilities.
- The growing role of non-financial corporations (NFCs) as de facto financial intermediaries may reduce macroprudential policy effectiveness and limit policymakers’ ability to respond to future shocks.
- UMPM programs may also encourage loosening of fiscal discipline and shifts in resource allocation.
- Impact of UMPM reversals (monetary policy normalization) on RoW could be varied and dependent on specific country policy frameworks and program design.

### II. ANALYTICAL FRAMEWORK — approach and identification
- Focus is on stock effects of UMPMs: how persistent shifts in long-term bond yields, arising from expansion of S4 central bank balance sheets, impacted global liquidity, monetary conditions, and financial sector balance sheets in other countries.
- The study does not focus on announcement/communication effects, given their evolving nature and the ex ante anticipation of some UMPMs.
- Two-step empirical strategy:
  - First step: instrument long-term interest rates by S4 UMPM programs to extract variation in yields explained by implemented UMPMs over the period analyzed.
  - Second step: estimate S4 UMPM global and country-level impacts, controlling for global financial conditions and country-specific macroeconomic environment (policy interest rate, exchange rate, inflation, GDP growth, etc.).
- Outcome variables include broad money, banks’ balance sheet variables (non-core liabilities proxied by NFC deposits), and NFC issuance of securities.
- The analysis addresses endogeneity and the lack of a counterfactual by focusing on marginal cross-border spillovers conditional on global and domestic controls.

### III. DATA AND DESCRIPTIVE ANALYSIS OF UMPM TRANSMISSION CHANNELS
- New quarterly dataset covering Q1:2002–Q2:2014 constructed, combining:
  - IMF standardized report forms (SRFs) monetary and financial statistics (MFS),
  - IMF’s International Financial Statistics (IFS) and Balance of Payments (BoP) databases,
  - World Bank external debt database,
  - BIS data on international issuance of securities,
  - Dealogic data on issuance of bonds by country of residence,
  - Additional indicators: capital account openness, exchange rate regimes, institutional environment quality, and banking system structure and regulations.
- Sample: 131 countries (28 advanced, 103 emerging market and developing economies including low-income countries).
- Broad money series primarily derived from IMF SRFs to ensure cross-country comparability per the IMF’s Monetary and Financial Statistics Manual.
- Funding liquidity measure: monetary aggregate L, defined as the sum of deposits of NFCs in the banking system (ODCs). NFC deposits include transferable and other deposits included in broad money as well as transferable and other deposits excluded from broad money, in both local and foreign currency.
- Additional variables: banking system foreign liabilities, BIS and Dealogic statistics on debt securities issuance.
- Core and non-core liabilities of banks computed using detailed SRF data reported confidentially to the IMF.

### IV. Empirical strategy and country groupings
- Descriptive analysis of transmission channels complemented by regression analysis.
- Countries analyzed in various groupings/aggregations based on income levels as appropriate for the analysis.
- Regression controls include global financial conditions and country-level macroeconomic variables.

### V. Conclusions (summary of empirical contribution)
- The paper provides new empirical evidence on the cross-border spillovers of S4 UMPMs to global liquidity, monetary aggregates, banks’ balance sheets (NFC deposits), and NFC securities issuance using a novel SRF-based dataset spanning Q1:2002–Q2:2014.
- Identifies heterogeneous spillover patterns across S4 UMPMs and highlights potential macro-financial vulnerabilities associated with capital flow substitution, NFCs’ expanded financial intermediation role, and the possible implications of policy normalization.

*Source: _wp15287 - References .............................................................................................................*

### Annex 1.

### Annex 1

### Descriptive analysis of UMPM transmission channels
- Scope of QE/UMPM measurement:
  - QE/UMPM variables defined as changes in actual net asset purchases by the S4 central banks during Q1:2002–Q4:2013 and used as an instrumental variable to isolate changes in long-term yields attributable to unconventional monetary policies in each S4 country and the S4 as a whole.
- Primary channels of cross-border spillovers identified:
  - Traditional interest rate channel: UMPMs reduced longer-term yields in S4 and encouraged investors to search for assets of similar maturities with higher risk-adjusted returns.
  - Portfolio rebalancing channel: central bank asset purchases changed relative demand and prices of securities, influencing investors’ portfolio decisions and causing changes in size and composition of private sector asset holdings.
  - Global liquidity / banking channel: sizable UMPM programs boosted global liquidity; persistently low interest rates and abundant liquidity incentivized financial institutions to search for yields and eased funding conditions supporting bank lending.
  - Exchange rate, reserves, and real channels: UMPMs impacted other countries via exchange rates, reserves accumulation, and real-economy effects.
- Empirical corroboration:
  - S4 UMPMs are negatively correlated with nominal long-term interest rates, confirming that UMPMs compressed long-term interest rates in S4 and prompted rebounded private capital outflows from S4 (figures referenced).
- Patterns in capital flows and asset issuance:
  - Gross private capital outflows after the crisis in the Euro Area and the United States rebounded but remain significantly below pre-crisis levels.
  - Private equity and other debt securities outflows (with exception of Japan) rebounded the most.
  - From 2009–13, the purchase of EMEs’ private securities tripled relative to the pre-crisis period of 2002–07.
  - Net public debt flows to EMEs (measured as total foreign assets less total foreign liabilities) became and remain significantly negative since 2009.

### UMPM programs and macro-financial vulnerabilities
- Domestic liquidity injections and balance sheet expansions:
  - Central banks in AMs injected significant liquidity during 2008–09; since end-2010 AMs continued injecting liquidity as a substitute for banks’ liquidity provision.
  - Monetary authorities in EMEs and LICs expanded balance sheets significantly due to accumulation of foreign reserves and sterilization operations.
  - Banking systems in EMEs and LICs expanded total assets at about 15 percent on average during 2010–14.
- Real activity and credit-gap dynamics:
  - Real GDP growth of EMEs and LICs declined since 2010 by more than one percentage point (from 4.8 to 3.6 percent).
  - The BIS credit-to-GDP gap measure has significantly widened recently for some EMEs, indicating increasing vulnerability and higher probability of future banking crises.
- Allocation of borrowed liquidity by RoW banks (threefold):
  - Increase lending to customers.
  - Invest in domestic or foreign securities.
  - Hold money as vault cash or reserves within the central bank.
- Banking-sector balance-sheet developments:
  - Since Q4:2008, AE banks’ leverage (excluding S4) decreased; banks in EMEs are slowly leveraging up by increasing exposure across domestic sectors.
  - Stock of credit to the private sector in EMEs and LICs fell immediately after the crisis but reversed in 2011; total banking credit in EMEs is currently higher than pre-crisis.
  - No visible changes to banks’ liquidity across AEs, EMEs, and LICs despite post-crisis liquidity regulation changes; LIC banks maintain highest liquidity and low leverage.
- Funding vulnerabilities:
  - AEs significantly reduced reliance on wholesale funding (particularly from abroad); EMEs and LICs remain more vulnerable to interest rate/exchange rate shocks.
  - All groups increased reliance on credit to the central bank and the government, potentially crowding out private investments.
  - Abundant global liquidity may increase vulnerabilities in LICs via higher share of wholesale and foreign funding in total bank funding, warranting careful monitoring.
- Country heterogeneity (examples):
  - Turkey: share of non-core liabilities doubled since 2009; increased share of non-resident holdings in total funding; authorities adopted prudential measures in 2015 (reserve option mechanism and asymmetric reserve requirement increases for various deposit categories).
  - Brazil: monetary tightening and capital controls helped maintain non-resident participation share of total ODC funding; decreased share of domestic deposits amid increased volatility and weaker growth prospects.
- Fiscal implications:
  - Debt ratios are generally moderate but higher than pre-crisis: about 42 percent of GDP at present (April 2015 Fiscal Monitor referenced).
  - Low oil prices add to fiscal pressures in commodity exporters.

### Empirical specifications (framework and estimation)
- Time coverage and methods:
  - First-stage identification: regressions of change in long-term bond yields on one-quarter-ahead change of stocks of nominal values of asset purchases (converted to USD and normalized by S4 nominal GDP), estimated for Q1:2002–Q2:2014.
  - Estimation methods include OLS with bootstrapped regressions with 1,000 replications, and GLS to account for serial autocorrelation; variables checked to be stationary.
  - Bayesian VAR (BVAR) with no restrictions used as robustness check for endogeneity.
- Core time-series model (conceptual description):
  - Dependent variable: quarterly growth of global liquidity measured as quarterly growth rate in global money supply, NFC deposits, global domestic or cross-border credit, and global issuance of international securities.
  - Global controls include contemporaneous log difference in the VIX, real global GDP growth, U.S. banks’ interoffice cross-border positions (one-period percentage change), global CPI change (percent per annum), quarterly log difference in US real effective exchange rate, and instrumented changes in long-term bond yields attributed to UMPMs.
- Panel regression model for cross-country analysis:
  - Dependent variable: log difference of country-level liquidity measures (broad money supply, NFC deposits, issuance of securities split by sector).
  - Global controls: contemporaneous log difference in the VIX, log difference of global banks’ interoffice cross-border positions, instrumented UMPM-induced changes in long-term yields.
  - Country-level controls (lagged and contemporaneous): real GDP growth, inflation, change in REER, spread between domestic and global interest rate, current account to GDP ratio, export growth, Chinn-Ito index, change in banks’ total assets, institutional and regulatory measures.
  - Model estimated for Q1:2002–Q2:2014 with country fixed effects and standard errors clustered at the country level.
  - Samples include different country groupings by income and versions that include and exclude S4 countries.

### Impact on global liquidity, global monetary aggregates, and cross-border credit (empirical results)
- Main empirical findings (Annex 3, Table 3.1 referenced):
  - There is a positive but not robustly significant association of S4 UMPMs with global liquidity measures defined by global growth of broad money (measured as M2 or alternative measures), global NFC deposit growth, or global domestic credit growth.
  - Total S4 UMPMs have a positive impact on international banks’ cross-border credit growth, particularly extension of loans with longer maturity, when controlling for global banks’ inter-office lending.
    - No statistically significant impact on cross-border credit when splitting the sample into banks, public sector, and nonbanks.
  - Positive association between S4 UMPMs and international issuance of debt securities by non-S4 countries in FX.
- Supplementary observations:
  - Banks and NFCs globally reduced nominal spending and appear to have been repairing balance sheets after the crisis.
  - QE in the U.K. is associated with deposits that are likely short-lived in a given bank (“flighty” deposits), potentially diminishing QE’s effect on bank lending.
  - Substitution observed from bank credit to non-bank loans (e.g., trade credit).
  - Targeted impacts found in specific market segments: MBS and GSEs in the United States; Japanese banks buying ABCP to cure balance sheets.
- Heterogeneity in UMPM effects:
  - Differential design of UMPMs can generate countervailing effects: e.g., U.S. Fed concentrated on asset side to support financial intermediation, BoJ targeted liabilities to increase excess reserves—reflecting market-based vs bank-based financial systems.
  - Faster government bond issuance relative to central bank purchases can reduce the net impact of QE.
  - ECB UMPMs had mixed and minimal impact on euro-area bank lending during 2010–13, possibly due to ongoing balance-sheet repair.
- Robust findings across controls:
  - Global banks’ interoffice claims are highly statistically significant and positively associated with global liquidity measures.
  - Appreciation of the dollar has a strong negative impact on global liquidity.

*Source: Annex 1 of the IMF working paper (content unit: _wp15287 - Annex 1).*

### Annex 3. Table 3.2–3.7).

### _wp15287 - Annex 3. Table 3.2–3.7).

### Main results of the cross-country panel regression model (2)
- Positive significant impact (association) of UMPMs on:
  - broad money growth,
  - growth of NFC deposits,
  - international issuance of debt securities.
- The positive association holds for broader measures of money supply defined as total holdings of ODCs’ deposits and securities and is primarily driven by EMEs—the coefficient on UMPMs in advanced economies sample is insignificant.
- The significance also disappears when we add dummies for foreign bank presence.
- When splitting total UMPM impact by country:
  - Statistically negative impact of U.S. and U.K. assets’ purchases on monetary and liquidity conditions in EMEs and LICs.
  - U.S. QE has a significantly positive association with the growth rate of broad money in advanced economies.
  - Introduction of QE in Japan has positive spillover to EMEs liquidity and monetary conditions; one possible channel is through increased Japanese banks foreign claims on EMEs. This result is robust in different samples and when controlling for institutional variables.
  - Positive association between EA UMPMs with other countries’ monetary and liquidity conditions is not robust to model and variable specifications.
- Results for NFCs deposit growth:
  - Similar and even more significant than broad money growth results.
  - United Kingdom QE program had significant negative impact on NFCs deposits growth globally.
  - Negative impact from U.K. QE was compensated by positive spillovers from Japan QE and EA UMPMs.
  - U.S. QE measures seem to have positive association with NFCs deposit growth in advanced economies.
- Issuance of non-financial sector securities:
  - Strong positive association between QE in the United Kingdom and issuance of non-financial sector securities globally.
  - Negative relationships between QE in Japan and issuance of NFC bonds in other countries.
  - Positive impact of the EA UMPMs on global issuance of NFCs’ securities, primarily driven by the impact on other advanced economies.
  - No statistically significant association between the UMPMs and issuance of public sector or financial sector securities.

### Drivers and channels
- Results are largely driven by EMEs, consistent with data showing increased debt and securities issuance and other capital flows into these countries.
- Capital inflows into EMEs likely led by:
  - search for higher yield by foreign investors,
  - “carry trade” by domestic NFCs borrowing in hard currencies abroad and parking proceeds in local banks.
- Portfolio rebalancing channel of QE/UMPM policies:
  - Led to redistribution and increased issuance of EMEs’ debt.
  - Increased the non-core liabilities in EME banking sectors.
- Non-core liabilities of EME banks exhibit higher volatility than those of developed countries, increasing vulnerability to S4 monetary policy reversals and unwinding of programs.
- Macro-prudential regulation in EMEs may become less effective due to increased significance of the non-banking sector as de facto “financial intermediary.”

### Heterogeneity across programs and systems
- Impacts differ depending on:
  - nature of each program,
  - initial macro-economic conditions,
  - countries’ policy response.
- U.S. QE programs might dominate in impact due to size and the relevance of the U.S. dollar as a global transaction currency.
- Differential program design can create countervailing effects on global liquidity:
  - U.S. Fed concentrated on asset side of balance sheet to support financial intermediary function.
  - BoJ targeted liability side to provide a buffer against funding liquidity by increasing private banks’ excess reserves.
  - Differences partly explained by financial system structures (market-based U.S. vs bank-based Japan).
  - Targeted asset purchase programs may help prevent excessive swings in asset prices.

### Economic costs, risks, and implications for normalization
- UMPM programs accompanied by economic costs:
  - Led to reallocation of resources on banks’ balance sheets.
  - Possibly contributed to loosening fiscal discipline in EMEs.
- Policy implications:
  - Close monitoring of macro-financial vulnerabilities in EMEs and frequent debt sustainability analysis recommended to head-off policy mistakes and maintain financial stability.
  - As monetary policy begins to normalize in the S4, RoW liquidity and monetary conditions might tighten.
  - The “taper tantrum” episode of 2013 showed that signaling a change in future monetary policy can create extreme volatility, influencing RoW exchange rates, flows and asset prices.
  - Need for better communication among central banks and with financial markets and strengthening of the global financial safety net.
- Full assessment requires complete return to a normalized monetary policy; current analysis sheds light on potential impacts on global monetary and liquidity conditions.

### ANNEX 1: Data description (selected variables and sources)
- Global liquidity: Measured as quarterly growth rate in global money supply, non-financial corporate (NFC) deposits, global domestic or cross-border credit, or global issuance of international securities. Data: IMF IFS, SRF, BIS.
- Broad money growth: Growth of broad money liabilities. Global money supply calculated by summing up broad money liabilities for all countries in sample. Data: Depository corporations survey 3SG for IFS.
- NFC deposits growth: Sum of transferable and other deposits of public and other (private) non-financial corporations to other depository corporations (ODCs), included in and excluded from broad money, national and foreign currency. Change over the period in percent to the stock of NFC deposits in a previous period. Data: Other Depository Corporations Survey 2SG for IFS, IMF.
- Real Effective Exchange Rate: Bank of International Settlements (BIS).
- VIX: Chicago Board Options exchange Market Volatility Index, the implied volatility of S&P 500 index options; average. Source: Bloomberg.
- Policy rate differential: Difference between domestic rate and Fed funds rate. Source: IMF IFS.
- Global banks interoffice claims: Growth in interoffice claims of the BIS reporting banks. Source: BIS.
- GDP growth: Real GDP growth, annual. Source: National Accounts Database, STA/IMF.
- Global GDP growth: Real GDP growth, annual. Source: WEO.
- Inflation: Annual percentage change of the CPI, end of period. Source: National accounts Database, STA/IMF.
- Global NFC deposits: Sum of the non-financial corporate deposits in U.S. dollars (EUR, JPY). Source: Other Depository Corporations Survey 2SG for IFS, IMF.
- Current Account to GDP: BoP data on current account divided by nominal GDP in U.S. dollars. Source: IMF IFS, BoP.
- Export growth: Exports of goods and non-factor services (in U.S. dollars). Source: Balance of Payments and international Investment Position, STA/IMF.
- Fixed exchange rate regime dummy: Equals one if country has fixed exchange rate, zero otherwise. Source: Ilzetzki, Reinhart, and Rogoff (2011).
- Foreign ownership dummy: Equals one if there are no restrictions on foreign banks owning domestic banks or entering the banking industry, zero otherwise. Source: World Bank surveys on bank regulation.
- Capital account openness: Chinn-Ito Index.
- U.S. long-term interest rate: 10 year U.S. Treasury yield. Source: IMF IFS.
- U.K. long-term interest rate: 10 year U.K. government securities yield. Source: IMF IFS.
- Japan long-term interest rate: 10 year Japan Treasury yield. Source: IMF IFS.
- ECB long-term interest rate: 10 year EA AAA Sovereign yield. Source: IMF IFS.

*Source: _wp15287 - Annex 3. Table 3.2–3.7).*

### ANNEX 2: DEFINITION OF THE UMPM VARIABLES

### ANNEX 2: DEFINITION OF THE UMPM VARIABLES

### Scope and data collected
- Collected information on asset purchases programs and UMPMs by BoE, BoJ, ECB, and the U.S. Fed.
- Sample period for variable construction: Q1:2002–Q4:2013.

### Country descriptions of UMPM programs (as reported)
- United States
  - 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 set a stage for second and third phases of QE (speech referenced).
  - 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 “Operation Twist”.
  - During the third phase of QE, Fed bought US$40 billion a month in mortgage securities, 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.
  - Definition conventions: purchases of the U.S. Treasury securities are referred as the first U.S. QE program, and purchases of agency debt plus mortgage-backed securities are referred to as the second QE program.
- United Kingdom
  - Principal unconventional measure: asset purchases financed by central bank money (QE).
  - January 2009: Chancellor authorized BoE to set up an Asset Purchase Facility (APF).
  - Between March 2009 and May 2012, the BoE purchased £325 billion worth of such asset.
  - Since then, the BoE expanded its APF by a further £50 billion.
  - Weekly outstanding amounts of BoE assets were used to calculate net actual purchases.
  - BoE also purchased high-quality commercial paper and corporate bonds at smaller scale than gilt purchases.
- Japan
  - QE1 announced in March 2001.
  - Bank increased outright purchases of long-term government bonds from 400 billion yen per month where necessary.
  - APP introduced in October 2010; on October 5, 2010, BoJ purchased JPY5 trillion in assets.
  - March, August, and October 2011: BoJ increased APP by JPY5 trillion to JPY20 trillion.
  - Combined with JPY35 trillion assigned to fixed-rate funds-supplying operation, APP reached JPY55 trillion.
  - APP covers private sector financial assets including commercial paper, corporate bonds, ETFs, REITs plus government securities.
  - February and April 2012: BoJ purchased additional JPY20 trillion.
  - July 2012: another purchase of JPY5 trillion.
  - September, October, and December 2012: BoJ purchased JPY5 trillion in JGB and JPY5 trillion in Treasury bills per month.
  - September 2013: BoJ expanded APP by JPY10 trillion, increasing overall size of stimulus program to JPY80 trillion.
- ECB
  - ECB’s UMPMs up to Q2 2014 described on the ECB website and in the ECB Monthly Bulletin (annex on Chronology of monetary policy measures published up to December 2014).

### Construction of UMPM variables and empirical approach
- Independent variable: changes in net asset purchases by the S4 central banks during Q1:2002–Q4:2013.
- Follow approach suggested by Ahmed and Zlate (2013).
- First-stage regression: change in long-term bond yields (in percentage points) regressed 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).
- Construction detail: subtract from the fitted value the estimated constant and error terms to form the variable (first-stage described; see equation (3) in source).
- For the total S4 UMPM measure: use 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).

### Key first-stage empirical findings (from description)
- Coefficients on the changes in UMPMs are negative and statistically significant at the 1 percent level in the United States, the United Kingdom, and Japan.
- 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.

### Correlations between UMPMs in S4 (selected reported correlations)
- US LSAP MBS&GSE with US LSAP Treasuries: 0.17
- US LSAP MBS&GSE with UK: 0.50
- US LSAP Treasuries with UK: 0.23
- UK with Japan: 0.30
- ECB LTRO with UK: 0.50
- ECB SMP correlations: with US LSAP MBS&GSE = -0.20; with US LSAP Treasuries = 0.35
- ECB CBPP 1&2 correlation with US LSAP MBS&GSE: 0.48

### Summary statistics and samples
- Two samples compared: Before crisis (2002:Q1–2008:Q2) and After crisis (2008:Q3–2013:Q3).
- Examples of summary statistics (reported exactly):
  - US slope of yield curve, percent — Before crisis: Obs 34, Mean 1.07, Median 0.98, Std. Dev. 1.51, Min -1.13, Max 3.30; After crisis: Obs 21, Mean 2.11, Median 2.13, Std. Dev. 0.84, Min 0.53, Max 3.46.
  - US long term IR, percent — Before crisis: Obs 34, Mean 4.65, Median 4.59, Std. Dev. 0.67, Min 3.62, Max 6.48; After crisis: Obs 21, Mean 2.76, Median 2.79, Std. Dev. 0.73, Min 1.64, Max 3.86.
  - CBOE VIX growth, pp — Before crisis: Obs 33, Mean -0.30, Median -1.47, Std. Dev. 18.86, Min -33.53, Max 49.10; After crisis: Obs 21, Mean -1.75, Median -7.40, Std. Dev. 30.38, Min -42.49, Max 89.69.
  - Global GDP growth — Before crisis: Obs 33, Mean 0.9%, Median 1.4%, Std. Dev. 1.8%, Min -2.7%, Max 3.5%; After crisis: Obs 19, Mean 0.5%, Median 1.0%, Std. Dev. 2.5%, Min -5.0%, Max 3.5%.
  - Global Inflation, annualized level — Before crisis: Obs 34, Mean 3.9%, Median 3.7%, Std. Dev. 0.7%, Min 3.1%, Max 6.3%; After crisis: Obs 21, Mean 3.6%, Median 3.4%, Std. Dev. 1.1%, Min 1.3%, Max 6.8%.
  - (Additional series and their statistics are reported in the source tables for global broad money growth, NFC deposits growth, credit growth, BIS reporting banks claims, etc.; see source tables for full lists and exact figures.)

### Regression results — main substantive findings (selected)
- Table 3.1 (Impact of S4 UMPMs on Global Liquidity Conditions): regressions with country fixed effects and clustered standard errors; USA LT IR instrumented by UMPM in S4:
  - Coefficients on USA LT IR, instrumented by UMPM in S4 range across specifications (examples): 0.163, 0.199, 0.388, 0.382*, 0.088, 0.341, 0.377*, 0.510*, 0.403, 0.621**, 0.484, 0.557, 0.528, 0.066, 0.265, 0.507**, 0.168, 0.208** (values reported exactly in table).
  - Global banks interoffice assets coefficient examples: 0.781***, 0.663***, 0.774***, 0.493*** (values reported exactly).
  - VIX and REER often have negative and statistically significant coefficients in multiple specifications (exact coefficients reported in table).
  - Observations: 48 in each specification shown; R2 ranges from 0.233 to 0.779 across columns (exact R2 values reported).
- Table 3.2 (Impact of UMPM Programs on Broad Money Growth — Total UMPM)
  - Global banks interoffice claims: 0.656*** (All), 0.655*** (Excl. S4), 0.662***, 0.682***, 0.990*** (AE, excl. S4), 0.942*** (AE), 0.628*** (EME), 0.660*** (Excl. S4 with controls).
  - USA LT IR, instrumented by the UMPM: 0.297***, 0.305***, 0.294***, 0.318***, 0.215**, 0.208**, 0.379***, 0.113 (column-specific values).
  - Change in VIX: -0.086*** etc. (exact values by column provided).
  - Number of observations and R2 by column: e.g., Number of observations 467, 345, 1745, 1744, 3693, 1095, 2900, 4517 (values reported exactly); R2 values: 0.101, 0.102, 0.104, 0.187, 0.175, 0.112, 0.104 (exact).
  - Dependent variable: Broad money growth, SRF reporters (USD).
- Table 3.3 (Impact on Broad Money Growth — Individual UMPM)
  - USA LT IR instrumented by UMPM: 0.08, 0.074, 0.033, 0.091, 0.533***, 0.503***, 0.024, -0.197 (column values).
  - UK LT IR instrumented by UMPM: -0.713***, -0.717***, -0.704***, -0.721***, -0.634**, -0.657***, -0.659***, -0.507***.
  - Japan LT IR instrumented by UMPM: 0.719***, 0.740***, 0.702***, 0.717***, 0.379, 0.359*, 0.806***, 0.539***.
  - EA LT IR instrumented by UMPM: 0.230**, 0.230*, 0.300***, 0.247**, -0.272, -0.176, 0.285**, 0.230*.
  - Number of observations and R2 by column reported (e.g., Number of observations 467, 345, 1745, ...; R2 values 0.105, 0.106, 0.107, 0.193, 0.180, 0.117, 0.109).
- Tables 3.4–3.7 report analogous panel-regression results (Total and Individual UMPM) for NFC deposits growth, NFC securities’ issuance, and NFC securities’ issuance (individual), with reported coefficients, significance levels (***, **, *), number of observations, and R2 values. Selected example coefficients (exactly as reported):
  - Table 3.4: USA LT IR instrumented by the UMPM = 0.434*** (All); Global banks interoffice claims = 0.624*** (All).
  - Table 3.5: USA LT IR instrumented by the UMPM = 0.084 (All); UK LT IR instrumented by the UMPM = -1.313*** (All); Japan LT IR instrumented by the UMPM = 1.132*** (All); EA LT IR instrumented by the UMPM = 0.600*** (All).
  - Table 3.6: USA LT IR instrumented by UMPM in S4 = -0.021 (All); Change in VIX = -0.017*** (All).
  - Table 3.7: UK LT IR instrumented by the UMPM = 0.279*** (All); Japan LT IR instrumented by the UMPM = -0.358*** (All); EA LT IR instrumented by the UMPM = 0.087* (All).

### Main analytical conclusions (as presented)
- UMPMs by S4 central banks materially affected long-term yields in the United States, the United Kingdom, and Japan (negative first-stage coefficients significant at 1 percent).
- UMPM measures in the euro area showed no statistically significant impact on the compression of euro area long-term yields over the period analyzed.
- Instrumented changes in long-term interest rates (from S4 UMPM activity) enter significantly in panel regressions explaining global liquidity measures (broad money growth, NFC deposits growth, NFC securities issuance) across various samples and specifications; signs and magnitudes vary by country/program (USA, UK, Japan, EA) and by dependent variable.
- VIX (change) and REER (change) frequently enter with negative coefficients in regressions explaining broad money growth, NFC deposits growth, and NFC securities issuance (coefficients and significance levels reported in tables).

*Source: _wp15287 - ANNEX 2: DEFINITION OF THE UMPM VARIABLES (PDF content provided).*

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### Miscellaneous relevant studies and speeches
- Burns, Andrew, Mizuho Kida, Jamus Jerome Lim, Sanket Mohapatra, and Marc Stocker, 2014, “Unconventional Monetary Policy Normalization in High-Income Countries: Implications for Emerging Market Capital Flows and Crisis risks,” the World Bank, Policy Research Working Paper No. 6830.
- Chung, Kyuil, Hail Park, and Hyun Song Shin, 2012, “Mitigating Spillover Effects from Currency Hedging,” National Institute Economic Review, NIESR, London.
- Hattori, Masazumi, Hyun Song Shin, and Wataru Takahashi, 2009, “A Financial System Perspective on Japan’s Experience in the Late 1980s,” paper presented at the 16th Bank of Japan International Conference, May 2009, Bank of Japan IMES discussion paper, http://www.imes.boj.or.jp/english/publication/edps/2009/09-E-19.pdf
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- Shin, Hyun Song and Laura Yi Zhao, 2013, “Non-financial Firms as Surrogate Intermediaries” working paper, Princeton University.
- Swanson, Eric T., 2011, “Let’s Twist Again: A High-Frequency Event-Study Analysis of Operation Twist and Its Implications for QE2,” Brookings Papers on Economic Activity, vol. 42 (Spring), pp. 151–207.

*References as listed in _wp15287 - REFERENCES*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2015/_wp15287.pdf_