## 6. Correlation of Financial and Macroeconomic Variables in Advanced Countries

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### I. Introduction — framing and research question
- Research focus: whether exchange rate regimes affect the transmission of global financial conditions (proxied by the VXO index) to domestic financial conditions, capital flows, and macroeconomic outcomes in 43 EMEs over 1986–2013.
- Exchange rate regime classifications:
  - Aggregate: Fixed, Intermediate, Float.
  - Disaggregated: Hard pegs, Single-currency pegs, Basket pegs, Bands, Crawling arrangements, Managed floats, Free floats.
- Empirical proxy: VXO index (log); mean and standard deviation of (log) VXO in the sample are about 3 and 0.4, respectively.

### II. Stylized facts (data and unconditional correlations)
- Sample and data:
  - 43 EMEs, quarterly data over 1986–2013.
  - De facto exchange rate classification: Ghosh et al. (2015).
- Regime snapshot (2013):
  - About 26 percent of sample countries have a fixed exchange rate regime.
  - About 63 percent are intermediate regimes.
  - Remaining countries are identified as free floats.
- Co-movement observations:
  - Net capital flows surge when U.S. interest rates and global risk aversion (VXO) are low, and recede when they are high.
  - Liability flows (nonresident net domestic asset acquisitions) largely drive the negative correlation between net flows and global risk aversion; asset flows show a positive raw correlation with U.S. interest rates and VXO.
  - Domestic private sector credit, house prices, and equity returns in EMEs generally move with net capital flows and are negatively correlated with VXO.
- Regime-differentiated unconditional correlations:
  - The negative correlation between VXO and net capital flows, domestic credit growth, house and stock prices, and leverage growth tends to be larger (in absolute value) for fixed exchange rate regimes compared to intermediate regimes and free floats.
  - Real GDP growth’s negative association with VXO is stronger in fixed exchange rate regimes.
  - No systematic pattern across regimes emerges for advanced economies (AEs).

### III. Empirical approach
- Baseline specification:
  - Regress financial variable f(i,t) (credit growth; house price growth; stock return; change in loan-to-deposit ratio) on log(VXO), regime dummies (Fixed, Int), interactions (Fixed×log(VXO), Int×log(VXO)), control vector z, country fixed effects, and time effects (preferred specification includes quarter-year effects ηt).
- Estimation details:
  - Quarterly OLS with country-clustered standard errors.
  - Sample restricted to at least partially open countries (Quinn and Toyoda index above 25th percentile).
  - Crisis observations dropped in main estimations to mitigate endogeneity; robustness checks retain them.

### III.A Domestic financial conditions — empirical findings
- Credit growth
  - VXO effect: a one standard deviation increase in the VXO (log) lowers credit growth by about 0.6 percentage points (sample mean quarterly domestic credit growth = 2 percent).
  - Fixed regimes have about 3 percentage points higher credit growth than floats, and about 2 percentage points higher than intermediate regimes (examples: Fixed regime 3.007*** (1.011); Fixed x log (VXO) -1.981* (1.003)).
  - Interaction: a one standard deviation shock to the VXO implies about 1 percentage point lower real credit growth in fixed regimes relative to more flexible regimes.
  - Disaggregated regimes: negative VXO sensitivity concentrated in hard pegs and conventional single-currency pegs.
  - Pooled sample including AEs: differences across regimes vanish; EME-only sample drives results.
- House prices
  - Fixed regimes: faster average real house price growth than intermediate regimes or floats.
  - Interaction: a one standard deviation increase in the VXO implies about 1½-2 percentage points larger reduction in quarterly real house price growth in fixed rate regimes relative to floats (example coefficient: Fixed regime x log (VXO) -3.815** (1.653)).
  - House prices positively associated with real GDP growth and domestic credit growth.
  - Disaggregated regimes: higher VXO sensitivity concentrated in hard and single-currency pegs.
- Equity returns
  - Strong negative relation to log(VXO) and to U.S. real interest rates (examples: Log (VXO) -6.469*** (0.937); further columns up to -9.376*** (1.219)).
  - No significant difference in VXO-sensitivity across exchange rate regimes in preferred specifications.
  - Equity returns linked to portfolio inflows; portfolio flows move with VXO but are not regime-sensitive.
- Banking system leverage (change in loan-to-deposit ratio)
  - Fixed regimes experience faster average leverage growth; deleveraging after a global shock is larger in fixed regimes.
  - A one standard deviation increase in log(VXO) implies about 1 percentage point decrease in leverage growth in fixed regimes compared to floats (examples: Fixed x log (VXO) coefficients ranging from -1.142 to -1.567** across specifications).
  - Other determinants accelerating leverage growth: higher output growth, larger capital flows, and lower policy rates.

Summary implication for financial conditions:
- Fixed exchange rate regimes are systematically more sensitive to global market volatility and investor risk aversion in transmission to domestic credit, house prices, and bank leverage; managed floats and other flexible intermediate regimes provide insulation largely similar to pure floats.

### III.B Capital flows — differential responses by regime and flow type
- Net private flows (percent of GDP)
  - EMEs with fixed and intermediate regimes attract more net flows (in percent of GDP) than floats on average.
  - Interaction: a one standard deviation shock to log(VXO) implies about 2 percent and ½ percent of GDP lower net flows in fixed and intermediate regimes, respectively, relative to floats (mean quarterly net flows ≈ 4 percent of GDP in fixed and intermediate regimes). Example interaction: Fixed x log (VXO) -4.698** (table scale units consistent with flow measures).
- Liability vs. asset flows
  - Liability flows differ markedly across regimes; asset flows do not.
  - Foreign investors display stronger herding and greater volatility in pegged regimes (hard pegs, single-currency pegs, basket pegs).
- By liability type
  - Portfolio and other investment (mainly cross-border bank) flows react strongly and negatively to log(VXO).
  - FDI flows remain stable with respect to log(VXO).
  - Sensitivity of portfolio flows to log(VXO) is not affected by exchange rate regime.
  - Declines in both FDI and other investment liability flows to EMEs are larger in fixed regimes compared to intermediate regimes or floats.
- Asset flows (resident net foreign asset acquisitions)
  - log(VXO) effect on asset flows is strongly positive (residents retrench from abroad when global risk aversion rises).
  - Retrenchment is smaller for intermediate regimes than for other regimes (examples: Log (VXO) coefficients 2.118***; Intermediate x log (VXO) -2.463*** in some specs).

Policy-relevant implication for flows:
- Pegged regimes experience larger inward liability flows in benign global conditions but also more pronounced reversals when global risk appetite falls; managed or flexible regimes moderate these swings.

### III.C Effects on the real economy — macroeconomic relevance
- Real GDP growth regressions (specification with controls)
  - Real output growth in EMEs declines as log(VXO) rises (examples: Log (VXO) coefficients -0.459***, -0.479***, -0.492*** across specs).
  - Interaction: Fixed x log (VXO) coefficients about -0.756** to -0.856** across specifications—implying larger adverse real GDP impacts under fixed regimes.
  - Quantitative example from paper summaries:
    - A rise in the VXO is associated with a 0.2 percentage point decline in the growth rate (against a mean quarterly growth rate of 1 percent across exchange rate regimes).
    - The decline is about double for fixed exchange rate regimes: a one standard deviation shock to the VXO lowers the growth rate by about 0.4 percentage points in fixed regimes relative to the rest.
  - Fixed rate regimes experience greater output volatility than floats (measured by year-on-year growth rates or rolling standard deviations).
- Mechanisms:
  - Transmission operates via amplified changes in domestic credit growth, house prices, bank leverage and liability inflows—channels that are regime-sensitive.
  - Portfolio flows and stock returns transmit global shocks but show limited regime differentiation.

Policy implication:
- Flexible exchange rates act as a partial buffer for the real economy—reducing sensitivity of real GDP growth to global financial shocks—but insulation is imperfect in practice.

### IV. Robustness, sensitivity, and endogeneity checks
- Controls and specifications
  - Additional controls: Quinn and Toyoda capital account openness index; changes in reserve requirements; institutional quality; Δlog(VXO).
  - Main findings: fixed×log(VXO) interaction remains negative and significant in credit, house price, and leverage regressions when including openness index; exceptions noted (e.g., fixed×log(VXO) marginally insignificant in one credit specification, p-value=0.11).
  - Change in log(VXO) itself generally not statistically important in core specifications.
- Sample composition checks
  - Restricting to major EMEs or fully open EMEs produces similar results; fully open EMEs sample (size drops to 22) still shows larger declines under fixed regimes.
- Outlier and smoothing checks
  - Excluding observations in bottom and top 0.25th percentile produces no dramatic change for real domestic credit, house price, and stock price growth regressions; leverage interaction loses significance in one check (p-value=0.23).
  - Results robust to using a three-quarter centered moving average and to using a four-quarter moving average.
- Alternative regime classifications
  - Reinhart and Rogoff (RR) coarse classification: Hard pegs and conventional pegs (coarse classification 1) experience rapid credit, house price, and leverage growth and sharper reversals when log(VXO) rises (RR coarse 1 × log(VXO) significantly negative).
  - IMF de jure classification: Similar broad pattern; some differences (e.g., leverage effects less pronounced under de jure classification).
- Endogeneity and simultaneity
  - Exchange rate regimes are persistent while quarterly financial variables are volatile; reverse causality considered less likely.
  - Crisis-year observations and regime-switch years dropped with results unchanged.
  - Orthogonalization of regime variables and log(VXO) with respect to correlated regressors yields similar interactions (some loss of significance for house price growth in one orthogonalized check).
  - Regression of log(VXO) on EME country-specific financial variables yields coefficients close to zero and statistically insignificant.

### V. Synthesis and policy takeaways
- Empirical summary (EME sample, 1986–2013):
  - Fixed exchange rate regimes: more prone to rapid domestic credit and house price growth, larger bank leverage, larger net and liability flows in benign times, and larger contractions in these variables and in real GDP when global risk aversion rises.
  - Managed floats and other flexible intermediate regimes: insulation properties largely similar to free floats; rigid pegs (hard pegs, single-currency pegs) show the weakest insulation.
  - Stock returns and portfolio flows: sensitive to global conditions but not significantly regime-differentiated.
- Quantitative magnitudes highlighted:
  - log(VXO) (one standard deviation) → ≈0.6 percentage points lower credit growth (mean quarterly credit growth = 2 percent).
  - log(VXO) (one standard deviation) → ≈1 percentage point larger reduction in quarterly credit growth in fixed regimes relative to floats.
  - log(VXO) (one standard deviation) → ≈1½-2 percentage points larger reduction in quarterly real house price growth in fixed regimes relative to floats.
  - log(VXO) (one standard deviation) → ≈1 percentage point larger decrease in leverage growth in fixed regimes relative to floats.
  - log(VXO) (one standard deviation) → ≈2 percent of GDP lower net flows in fixed regimes, and ½ percent of GDP lower in intermediate regimes relative to floats (mean quarterly net flows ≈ 4 percent of GDP).
- Policy guidance:
  - Exchange rate flexibility materially cushions EMEs from global financial shocks, especially for credit, housing, and bank leverage dynamics, and thereby mitigates adverse real GDP impacts.
  - Full-market-determined floats are not strictly necessary to obtain insulation benefits; managed floats and other flexible intermediate arrangements can deliver much of the buffering advantage without pure floating volatility.
  - Because insulation is partial, policymakers should complement exchange rate flexibility with additional tools (macroprudential measures, capital-flow management measures, monetary and fiscal policy adjustments) to preserve macro-financial stability amid volatile global financial conditions.

*Source: IMF Working Paper wp17130 — section 6 (References, figures, tables, appendix excerpts provided).*

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

### References

### Tables
- 1. Real Domestic Credit Growth in EMEs, 1986Q1–2013Q4 .................................................30
- 2. Real House Price Growth in EMEs, 1986Q1–2013Q4 ........................................................31
- 3. Real Stock Returns in EMEs, 1986Q1–2013Q4 ..................................................................32
- 4. Change in Loan-to-Deposit Ratio in EMEs, 1986Q1–2013Q4 ...........................................33
- 5. Net Capital Flows in EMEs, 1986Q1–2013Q4....................................................................34
- 6. Liability Flows in EMEs, 1986Q1–2013Q4 ........................................................................35
- 7. Asset Flows in EMEs, 1986Q1–2013Q4 .............................................................................36
- 8. FDI, Portfolio, and Other Investment Liability Flows in EMEs, 1986Q1–2013Q4 ............37
- 9. Real GDP Growth in EMEs, 1986Q1–2013Q4 ...................................................................38
- 10. Robustness Analysis: Alternate Specifications ..................................................................39
- 11. Robustness Analysis: Further Checks ................................................................................40

### Appendix
- A1. List of countries in the sample ..........................................................................................41
- A2. Variable description and data sources ...............................................................................41
- A3. Financial Conditions and Disaggregated Exchange Rate Regimes ..................................42
- A4. Domestic Credit Growth and Exchange Rate Regimes in Advanced Economies ............43
- A5. Capital Flows and Disaggregated Exchange Rate Regimes .............................................44

### Figures
- 1. Net Capital Flows and Domestic Credit in EMEs, 2010–13 ...............................................25
- 2. De Facto Exchange Rate Regimes in EMEs, 1986–2013 ....................................................25
- 3. Global Factors and Capital Flows to EMEs, 1986Q1-2013Q4 ............................................26
- 4. Global Risk Aversion, Capital Flows and Financial Variables in EMEs ............................27
- 5. Correlation of Financial and Macroeconomic Variables in EMEs ......................................28

*Source: wp17130 - References (PDF).*

### 6. Correlation of Financial and Macroeconomic Variables in Advanced Countries ...............29

### 6. Correlation of Financial and Macroeconomic Variables in Advanced Countries

### I. Introduction — framing and research question
- Research focus: whether exchange rate regimes affect the transmission of global financial conditions (proxied by the VXO index) to domestic financial conditions, capital flows, and macroeconomic outcomes in 43 EMEs over 1986–2013.
- Motivation: recent debate on cross-border financial spillovers, the “global financial cycle,” and whether floating exchange rates still provide monetary autonomy and insulation in financially integrated economies.
- Exchange rate regime classifications used: aggregate (fixed, intermediate, float) and a finer disaggregated classification (hard pegs, single-currency pegs, basket pegs, bands, crawling arrangements, managed floats, free floats).
- Key empirical proxy: VXO index (log), chosen for longer data coverage (available from 1986 onward); mean and standard deviation of (log) VXO in the sample are about 3 and 0.4, respectively.

### II. Stylized facts (data and unconditional correlations)
- Sample and data sources:
  - 43 EMEs, quarterly data over 1986–2013.
  - Exchange rate regimes: de facto classification from Ghosh et al. (2015).
- Regime snapshot (2013):
  - About 26 percent of sample countries have a fixed exchange rate regime.
  - About 63 percent are intermediate regimes.
  - Remaining countries are identified as free floats.
- Co-movement observations:
  - Net capital flows surge when U.S. interest rates and global risk aversion (VXO) are low, and recede when they are high.
  - Liability flows (nonresident net domestic asset acquisitions) largely drive the negative correlation between net flows and global risk aversion; asset flows (resident net foreign asset acquisitions) show a positive raw correlation with U.S. interest rates and VXO.
  - Domestic private sector credit, house prices, and equity returns in EMEs generally move with net capital flows and are negatively correlated with VXO.
- Regime-differentiated unconditional correlations:
  - The negative correlation between VXO and net capital flows, domestic credit growth, house and stock prices, and leverage growth tends to be larger (in absolute value) for fixed exchange rate regimes compared to intermediate regimes and free floats.
  - Real GDP growth’s negative association with VXO is stronger in fixed exchange rate regimes.
  - No systematic pattern across regimes emerges for advanced economies (AEs), partly due to Eurozone regime interpretation and safe-haven dynamics.

### III. Does the exchange rate regime matter? — empirical approach
- Baseline empirical specification:
  - Financial variable f(i,t) (credit growth; house price growth; stock return; change in loan-to-deposit ratio) regressed on VXO (log), regime dummies (Fixed, Int), interactions (Fixed×VXO, Int×VXO), control vector z, country fixed effects, and time effects in preferred specifications.
  - Preferred specification includes quarter-year effects (ηt) to control for global factors more broadly.
- Estimation details:
  - Quarterly OLS with country-clustered standard errors; sample restricted to at least partially open countries (Quinn and Toyoda index above 25th percentile).
  - Crisis observations dropped in main estimations to mitigate endogeneity; robustness checks retain them.

### III.A Domestic financial conditions — key empirical findings
- Credit growth:
  - VXO effect: a one standard deviation increase in the VXO (log) lowers credit growth by about 0.6 percentage points (sample mean quarterly domestic credit growth = 2 percent).
  - Fixed regimes have, on average, about 3 percentage points higher credit growth than floats, and about 2 percentage points higher than intermediate regimes.
  - Interaction effect: a one standard deviation shock to the VXO implies about 1 percentage point lower real credit growth in fixed regimes relative to more flexible regimes.
  - U.S. real interest rate (T-bill or shadow federal funds rate) coefficients and interactions are generally statistically insignificant.
  - Disaggregated regimes: negative VXO sensitivity of credit growth is concentrated in hard pegs and conventional single-currency pegs; basket pegs, bands, crawling arrangements, and managed floats statistically similar to free floats.
  - Pooled sample including AEs: differences across regimes vanish; results driven by EME-only sample (excluding eurozone AEs restores EME findings in pooled sample).
- House prices:
  - Fixed regimes: faster average real house price growth than intermediate regimes or floats.
  - Interaction effect: a one standard deviation increase in the VXO implies about 1½-2 percentage points larger reduction in quarterly real house price growth in fixed rate regimes relative to floats.
  - House prices positively associated with real GDP growth and domestic credit growth; not systematically related to net capital flows once controlling for those channels.
  - Disaggregated regimes: higher VXO sensitivity concentrated in hard and single-currency pegs.
- Equity returns:
  - Equity returns in EMEs are strongly negatively related to VXO and to U.S. real interest rates.
  - No significant difference in the sensitivity of equity returns to VXO across exchange rate regimes in preferred specifications.
  - Equity returns are strongly linked to portfolio inflows, which move with VXO but are not regime-sensitive—consistent with limited differential regime effect on stock returns.
- Banking system leverage (loan-to-deposit ratio change):
  - Fixed regimes experience faster average leverage growth; but deleveraging in response to a negative global financial shock is also larger in fixed regimes.
  - A one standard deviation increase in the VXO implies a decrease in leverage growth by about 1 percentage point in fixed rate regimes compared to floats.
  - Other determinants: higher output growth, larger capital flows, and lower policy rates accelerate leverage growth.

Summary implication for financial conditions:
- Fixed exchange rate regimes are systematically more sensitive to global market volatility and investor risk aversion in the transmission to domestic credit, house prices, and bank leverage; managed floats and other flexible intermediate regimes provide insulation largely similar to pure floats.

### III.B Capital flows — differential responses by regime and flow type
- Net private flows (percent of GDP):
  - EMEs with fixed and intermediate regimes attract more net flows (in percent of GDP) than floats on average.
  - Interaction effect: a one standard deviation shock to VXO implies about 2 percent and ½ percent of GDP lower net flows in fixed and intermediate regimes, respectively, relative to floats (mean quarterly net flows ≈ 4 percent of GDP in fixed and intermediate regimes).
  - U.S. real interest rate and its interactions generally statistically insignificant.
- Liability (nonresident) flows vs. asset (resident) flows:
  - Liability flows differ markedly across regimes; asset flows do not.
  - Foreign investors display stronger herding and greater volatility in pegged regimes (hard pegs, single-currency pegs, basket pegs).
- By type of liability flow:
  - Portfolio and other investment (mainly cross-border bank) flows react strongly and negatively to VXO in EMEs.
  - Foreign direct investment (FDI) flows remain stable with respect to VXO.
  - The sensitivity of portfolio flows to VXO is not affected by the exchange rate regime; this helps explain why equity returns do not show strong regime-dependent differences.
  - Declines in both FDI and other investment liability flows to EMEs are larger in fixed regimes compared to intermediate regimes or floats.
- Asset flows (resident net foreign asset acquisitions):
  - VXO effect on asset flows is strongly positive (residents retrench from abroad when global risk aversion rises).
  - Retrenchment is smaller for intermediate regimes than for other regimes.

Policy-relevant implication for flows:
- Pegged regimes experience larger inward liability flows in benign global conditions but also more pronounced reversals when global risk appetite falls; managed or flexible regimes moderate these swings.

### III.C Effects on the real economy — macroeconomic relevance
- Real GDP growth regressions (specification (2) with controls):
  - Real output growth in EMEs declines as VXO rises.
  - The analysis establishes that the financial-channel amplification observed under fixed regimes translates into larger adverse real GDP impacts when global financial conditions tighten (empirical estimates and exact marginal effects presented in the paper tables).
- Policy implication:
  - Flexible exchange rates act as a partial buffer for the real economy—reducing sensitivity of real GDP growth to global financial shocks.
  - Insulation is not perfect in practice; policymakers may need additional tools (beyond exchange rate flexibility) to achieve macro-financial stability amid volatile capital flows and global financial spillovers.

### IV. Synthesis and policy takeaways
- Empirical findings (EME sample, 1986–2013):
  - Fixed exchange rate regimes: more prone to rapid domestic credit and house price growth, larger bank leverage, larger net and liability flows in benign times, and larger contractions in these variables and in real GDP when global risk aversion rises.
  - Managed floats and other flexible intermediate regimes: exhibit insulation properties largely similar to free floats; rigid pegs (hard pegs, single-currency pegs) show the weakest insulation.
  - Stock returns and portfolio flows: sensitive to global conditions but not significantly regime-differentiated.
- Quantitative magnitudes highlighted in the analysis:
  - VXO (one standard deviation) → ≈0.6 percentage points lower credit growth (mean quarterly credit growth = 2 percent).
  - VXO (one standard deviation) → about 1 percentage point larger reduction in quarterly credit growth in fixed regimes relative to floats.
  - VXO (one standard deviation) → about 1½-2 percentage points larger reduction in quarterly real house price growth in fixed regimes relative to floats.
  - VXO (one standard deviation) → about 1 percentage point larger decrease in leverage growth in fixed regimes relative to floats.
  - VXO (one standard deviation) → about 2 percent of GDP lower net flows in fixed regimes, and ½ percent of GDP lower in intermediate regimes relative to floats (mean quarterly net flows ≈ 4 percent of GDP).
- Policy guidance:
  - Exchange rate flexibility materially cushions EMEs from global financial shocks, especially for credit, housing, and bank leverage dynamics, and thereby mitigates adverse real GDP impacts.
  - Full-market-determined floats are not strictly necessary to obtain insulation benefits; managed floats and other flexible intermediate arrangements can deliver much of the buffering advantage without pure floating volatility.
  - Because insulation is partial, policymakers should complement exchange rate flexibility with additional tools (macroprudential measures, capital-flow management measures, monetary and fiscal policy adjustments) to preserve macro-financial stability in the face of volatile global financial conditions.

*Source: IMF Working Paper — 6. Correlation of Financial and Macroeconomic Variables in Advanced Countries (section content provided).*

### 0.2 percentage point decline in the growth rate (against a mean quarterly growth rate of 1

### wp17130 - 0.2 percentage point decline in the growth rate (against a mean quarterly growth rate of 1

### Key empirical findings
- A rise in the VXO is associated with a 0.2 percentage point decline in the growth rate (against a mean quarterly growth rate of 1 percent across exchange rate regimes).
- The decline in output is about double for fixed exchange rate regimes compared to both intermediate regimes and floats:
  - A one standard deviation shock to the VXO lowers the growth rate by about 0.4 percentage points in fixed regimes relative to the rest.
- Fixed rate regimes experience significantly greater output volatility than floats when VXO rises, whether measured by year-on-year real output growth rates or the volatility of quarterly growth rates (defined as the 3 or 5-quarter rolling standard deviation of real GDP growth rates).
- Global financial conditions (VXO) and the U.S. real interest rate have significantly negative coefficients in the regressions, indicating that flexible exchange rates provide imperfect insulation; global financial conditions still transmit to domestic macroeconomic conditions under flexible regimes.

### Mechanisms and transmission
- Exchange rate flexibility reduces the magnitude of cross-border transmission of global financial shocks to:
  - Domestic credit growth
  - Real estate (house) prices
  - Financial sector leverage
- Transmission is driven mainly by private liability flows, particularly:
  - Other investment (mainly banking) flows
  - To some extent foreign direct investment
- Portfolio flows are highly sensitive to global financial shocks but are not materially affected by exchange rate flexibility.

### Sensitivity analysis (specifications, samples, controls)
- Additional control variables considered: capital account openness (Quinn and Toyoda index), changes in reserve requirements (proxy for macroprudential policy), institutional quality, and change in the (log) VXO index.
- Main robustness results:
  - Including the Quinn and Toyoda capital account openness index: the interaction term between fixed regime and VXO remains negative and statistically significant in credit, house price, and leverage growth regressions (cols. [1], [6], [16]); insignificant in equity return regression (col. [11]).
  - Controlling for changes in reserve requirements or institutional quality does not materially affect results; tightening reserve requirements appears to dampen credit growth and stock price increases (cols. [2], [12]).
  - Including the change in the (log) VXO and its interactions leaves VXO-in-levels results largely unchanged; exception: domestic credit growth regression where the fixed×VXO interaction becomes marginally statistically insignificant (p-value=0.11; col. [3]).
  - The change in the (log) VXO itself is not statistically important in the specifications (cols. [3], [8], [13], [18]).
- Sample composition robustness:
  - Restricting sample to major EMEs (Passari and Rey, 2015) or to fully open EMEs (maximum Quinn-Toyoda openness) produces largely similar results.
  - In the fully open EMEs sample (sample size drops to 22 countries), fixed regimes still experience larger declines in credit, house price, and leverage growth as VXO rises (Table 10, cols. [4], [9], [14], [19]).
- Outlier exclusion:
  - Excluding observations in bottom and top 0.25th percentile of domestic financial variables produces no dramatic change for real domestic credit, house price, and stock price growth regressions (Table 10; cols. [5],[10],[15]).
  - Interaction term fixed×VXO loses statistical significance in the leverage growth regression when outliers removed (p-value=0.23; col. [20]).
- Moving-average smoothing:
  - Results use a three-quarter centered moving average; results robust to using a four-quarter moving average instead.

### Alternative exchange rate classifications
- Using Reinhart and Rogoff’s (RR) de facto coarse classification (lower numbers = more rigid):
  - Hard pegs and conventional pegs (coarse classification 1) are more likely to experience rapid credit, house price, and leverage growth (Table 11, cols. [1], [4], [10]).
  - These rigid regimes are more likely to experience sharper reversals in domestic conditions when global sentiment changes (significantly negative coefficient on RR coarse 1 × VXO).
  - Other flexible regimes (coarse classifications 2 and 3) are generally at least as insulated as pure floats.
- Using IMF de jure classification:
  - Similar broad pattern; main difference is for leverage where de jure classification does not show a significantly different effect of fixed or intermediate regimes (col. [11]).
  - Less pronounced economic differences across regimes in de jure classification may reflect de jure floats that are de facto fixed regimes.

### Endogeneity checks
- Omitted variables:
  - Benchmarks include a range of time-varying domestic variables, country-fixed effects, and time effects.
  - Additional approach: include interaction terms between regime variables and domestic/global variables (institutional quality; credit to GDP; capital account openness; commodity prices); results unchanged.
  - Orthogonalization: exchange rate regime variables and VXO were orthogonalized with respect to possibly correlated regressors (real GDP growth, net capital flows to GDP, domestic credit to GDP, institutional quality, capital account openness index, country-specific effects, quarter-year effects; and VXO on U.S. real short-term interest rates and commodity prices). Interaction terms built from residuals produce results similar to main estimates, except house price growth where fixed×VXO turns statistically insignificant (p-value=0.16; Table 11, cols. [3], [6], [9], [12]).
- Simultaneity / reverse causality:
  - Exchange rate regimes are persistent while quarterly financial variables are volatile; reverse causality is less likely.
  - Observations from financial crisis years (when regime switches may occur) were dropped; results unchanged if excluding all years with regime switches.
  - Sample restricted to countries with no exchange rate regime switch over the sample period, using a shorter horizon 2005–13 (owing to scarcity of such countries): despite reduced sample size, findings remain robust.
  - Regression of VXO on EME country-specific financial variables finds coefficients close to zero and wholly statistically insignificant, reducing concern that EMEs materially affected VXO.

### Policy implications and conclusions
- Exchange rate regime matters for EME exposure to global financial shocks:
  - Flexible exchange rates materially reduce costs to EMEs from global financial shocks but do not provide perfect insulation.
  - The most inflexible regimes (hard pegs and conventional pegs) drive the differential sensitivity; regimes toward the flexible end (bands, crawls, managed floats) behave similarly to pure floats from an insulation perspective.
- Policy toolkit:
  - Exchange rate regime choice is an important lever for managing domestic financial and macroeconomic outcomes amid volatile global financial conditions.
  - Complementary policies (capital controls and macroprudential policy, including reserve requirement changes) are useful given imperfect insulation under flexible exchange rates.
- Macroeconomic relevance:
  - Output sensitivity to global financial shocks is economically sizable, with output almost twice as sensitive in fixed exchange rate regimes compared to intermediates and floats.

*Source: IMF working paper wp17130 (content unit provided).*

### References

### References

### Key Bibliographic Entries
- Ahearne, A., J. Ammer, B. Doyle, L. Kole, and R. Martin, 2005, “House Prices and Monetary Policy: A Cross-Country Study,” International Finance Discussion Papers No. 841 (Washington, D.C.: Board of Governors of the Federal Reserve System).
- Ahmed, S., and A. Zlate, 2014, “Capital Flows to Emerging Market Economies: A Brave New World?” Journal of International Money and Finance, 48(PB): 221-248.
- Aizenman, J., M. Chinn, and H. Ito, 2015, “Monetary Policy Spillovers and the Trilemma in the New Normal: Periphery Country Sensitivity to Core Country Conditions,” NBER Working Papers 21128 (Cambridge, MA: National Bureau of Economic Research).
- Balli, H., and B. Sorensen, 2013, “Interaction Effects in Econometrics,” Empirical Economics, 45(1): 583-603.
- Baxter, M., and A. Stockman, 1989, “Business Cycles and the Exchange-Rate Regime: Some International Evidence,” Journal of Monetary Economics, 23(3): 377-400.
- Bekaert, G., M. Ehrmann, M. Fratzscher, and A. Mehl, 2014, “The Global Crisis and Equity Market Contagion,” The Journal of Finance, 69(6): 2597-2649.
- Bekaert, G., and A. Mehl, 2017, “On the Global Financial Market Integration ‘Swoosh’ and the Trilemma,” NBER Working Paper 23124 (Cambridge, MA: NBER).
- Bluedorn, J., and C. Bowdler, 2010, “The Empirics of International Monetary Transmission: Identification and the Impossible Trinity,” Journal of Money, Credit and Banking, 42(4): 679-713.
- Borensztein, E., J. Zettelmeyer, and T. Philippon, 2001, “Monetary Independence in Emerging Markets: Does the Exchange Rate Regime Make a Difference?” IMF Working Paper WP/10/11 (Washington DC: International Monetary Fund).
- Bruno, V., and H. Shin, 2015a, “Capital Flows and the Risk-Taking Channel of Monetary Policy,” Journal of Monetary Economics, 71(April): 119-132.
- Bruno, V., and H. Shin, 2015b, “Cross-Border Banking and Global Liquidity,” Review of Economic Studies, 82(2): 535-564.
- Caceres, C., Y. Carriere-Swallow, and B. Gruss, 2016, “Global Financial Conditions and Monetary Policy Autonomy,” IMF Working Paper WP/16/108.
- Calvo, G., and C. Reinhart, 2002, “Fear of Floating,” Quarterly Journal of Economics, 117(2): 379-408.
- Calvo, G., L. Leiderman, and C. Reinhart, 1993, “Capital Inflows and Real Exchange Rate Appreciation in Latin America: The Role of External Factors,” IMF Staff Papers, 40(1): 108-151.
- Claessens, S., A. Kose, and M. Terrones, 2011, “Financial Cycles: What? How? When?” NBER International Seminar on Macroeconomics, 7(1): 303-344.
- di Giovanni, J., and J. Shambaugh, 2008, “The Impact of Foreign Interest rates on the Economy: The Role of the Exchange Rate Regime,” Journal of International Economics, 74(2): 341-361.
- Edwards, S. (2015), “Monetary Policy Independence under Flexible Exchange Rates: An Illusion?” World Economy, 38(5): 773-787.
- Fernandez-Arias, E., 1996, “The New Wave of Private Capital Inflows: Push or Pull?” Journal of Development Economics, 38(2): 389-418.
- Fischer, S., 2001, “Exchange Rate Regimes: Is the Bipolar View Correct?” Remarks made at the American Economic Association Meeting, New Orleans, January 6.
- Frankel, J., S. Schmukler, and L. Serven, Luis, 2004, “Global Transmission of Interest Rates: Monetary Independence and Currency Regime,” Journal of International Money and Finance, 23(5): 701-733.
- Georgiadis, G., and A. Mehl, 2015, “Trilemma, Not Dilemma: Financial Globalisation and Monetary Policy Effectiveness,” Globalization and Monetary Policy Institute Working Paper 222 (Dallas: Federal Reserve Bank of Dallas).
- Gertler, M., and P. Karadi, 2011, “A Model of Unconventional Monetary Policy,” Journal of Monetary Economics, 58(January): 17-34.
- Ghosh, A., J. Ostry, and C. Tsangarides, 2010, “Exchange Rate Regimes and the Stability of the International Monetary System,” IMF Occasional Paper 270 (Washington D.C.: IMF).
- Ghosh, A., J. Ostry, and M. Qureshi, 2015, “Exchange Rate Management and Crisis Susceptibility: A Reassessment,” IMF Economic Review, 63(1): 238-276.
- Ghosh, A., J. Ostry, and M. Qureshi, 2017, “Managing the Tide: How Do Emerging Markets Respond to Capital Flows?” IMF Working Paper, forthcoming.
- Ghosh, A., M. Qureshi, J. Kim, J. Zalduendo, 2014, “Surges,” Journal of International Economics, 92(2): 266-285.
- Goldberg, L., 2013, “Banking Globalization, Transmission, and Monetary Policy Autonomy,” NBER Working Paper 19497 (Cambridge, MA: National Bureau of Economic Research).
- Goodhart, C., and P. Turner, 2014, “Rate Rise Pattern is Different This Time,” Financial Times, April 2, 2014 (https://www.ft.com/content/2e28e3fc-b984-11e3-b74f-00144feabdc0).
- Hofmann, B., and E. Takatas, 2015, “International Monetary Spillovers,” BIS Quarterly Review, September: 105-118.
- Jeanne, O., and A. Rose, 2002, “Noise Trading and Exchange Rate Regimes,” Quarterly Journal of Economics, 117 (2): 537-569.
- Klein, M., and J. Shambaugh, 2015, “Rounding the Corners of the Policy Trilemma: Sources of Monetary Policy Autonomy,” American Economic Journal: Macroeconomics, 7(4): 33-66.
- Krippner, L., 2013, “Measuring the Stance of Monetary Policy in Zero Lower Bound Environments,” Economics Letters, 118(1): 135–138.
- Laeven, L., and F. Valencia, 2013, “Systemic Banking Crises Database,” IMF Economic Review, 61(2): 225-270.
- Levy-Yeyati, E., and F. Sturzenegger, 2005, “Classifying Exchange Rate Regimes: Deeds vs. Words,” European Economic Review, 49(6), 1603-1635.
- Magud, N., C. Reinhart, and E.Vesperoni, 2014, “Capital Inflows, Exchange Rate Flexibility and Credit Booms,” Review of Development Economics, 18(3): 415-430.
- Miniane, J., and J. Rogers, 2007, “Capital Controls and the International Transmission of U.S. Monetary Shocks,” Journal of Money, Credit and Banking, 39(5): 1003-1035.
- Miranda-Agrippino, S., and H. Rey, 2015, “World Asset Markets and the Global Financial Cycle,” NBER Working Paper 21722 (Cambridge, MA: NBER).
- Mundell, R., 1963, “Capital Mobility and Stabilization Policy under Fixed and Flexible Exchange Rates,” The Canadian Journal of Economics and Political Science, 29(4): 475-485.
- Obstfeld, M., 2014, “Trilemmas and Trade-offs: Living with Financial Globalization,” Paper presented at the Inaugural Asian Monetary Policy Forum, Singapore, May 2014 (http://abfer.org/docs/ampf/2014/Trilemmas_ampf-paper.pdf).
- Obstfeld, M., J. Shambaugh, and A. Taylor, 2005, “The Trilemma in History: Tradeoffs among Exchange Rates, Monetary Policies, and Capital Mobility,” Review of Economics and Statistics, 87(3): 423-438.
- Obstfeld, M., and A. Taylor, 1998, “The Great Depression as a Watershed: International Capital Mobility Over the Long Run,” in M. Bordo, C. Goldin, and E. White (eds.), The Defining Moment: The Great Depression and the American Economy in the Twentieth Century, 353–402 (Chicago: University of Chicago Press).
- Ostry, J., 2014, “Comment on Trilemmas and Tradeoffs by Maurice Obstfeld,” Remarks made at the Inaugural Asian Monetary Policy Forum, Singapore, May 2014 (http://abfer.org/docs/ampf/2014/AMPFDiscussion-discussant-jonathan.pdf).
- Ostry, J., A. Ghosh, K. Habermeier, M. Chamon, M. Qureshi, and D. Reinhardt, 2010, “Capital Inflows: The Role of Controls,” IMF Staff Position Notes 2010/04 (Washington D.C.: International Monetary Fund).
- Ostry, J., A. Ghosh, M. Chamon, and M. Qureshi, 2011, “Capital Controls: When and Why?” IMF Economic Review, 59(3): 562-580.
- Ostry, J., A. Ghosh, M. Chamon, and M. Qureshi, 2012, “Tools for Managing Financial Stability Risks,” Journal of International Economics, 88(2): 407-421.
- Passari, E., and H. Rey, 2015, “Financial Flows and the International Monetary System,” Economic Journal, 125(584): 675–698.
- Quinn, D., and A. Toyoda, 2008, “Does Capital Account Liberalization Lead to Economic Growth?” Review of Financial Studies, 21(3):1403-1449.
- Rajan, R., 2014, “Competitive Monetary Easing: Is It Yesterday Once More?” Remarks made at the Brooking Institution, Washington DC, April 10, 2014 (https://rbi.org.in/Scripts/BS_SpeechesView.aspx?Id=886).
- Reinhart, C., and K. Rogoff, 2004, “The Modern History of Exchange Rate Arrangements: A Reinterpretation,” Quarterly Journal of Economics, 119(1): 1-48.
- Reinhart, C., and V. Reinhart, 2008, “Capital Flow Bonanzas: An Encompassing View of the Past and Present,” NBER Working Paper 14321 (Cambridge, MA: NBER).
- Rey, H., 2015, “Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence,” NBER Working Paper 21162 (Cambridge, MA: NBER).
- Rey, H., 2016, “International Channels of Transmission of Monetary Policy and the Mundellian Trilemma,” IMF Economic Review, 64(1): 6-35.
- Ricci, L., and W. Shi, 2016, “Trilemma or Dilemma: Inspecting the Heterogeneous Response of Local Currency Interest Rates to Foreign Rates,” IMF Working Paper No. 16/75.
- Rogoff, K., A. Husain, A. Mody, R. Brooks, and N. Oomes, 2004, “Evolution and Performance of Exchange Rate Regimes,” IMF Occasional Paper 229 (Washington D.C.: International Monetary Fund).
- Rose, A., 2011, “Exchange Rate Regimes in the Modern Era: Fixed, Floating, and Flaky,” Journal of Economic Literature, 49(3): 652-72.
- Shambaugh, J., 2004, “The Effect of Fixed Exchange Rates on Monetary Policy,” The Quarterly Journal of Economics, 119(1): 301-352.s
- Shin, H., 2016, “The Bank/Capital Markets Nexus Goes Global,” Remarks made at the London School of Economics and Political Science, November 15, 2016 (http://www.bis.org/speeches/sp161115.pdf).
- Volcker, P., 1978, “The Political Economy of the Dollar,” Federal Reserve Bank of New York Quarterly Review, Winter (1978-79): 1-12.

### Figures — Sources and Notes
- Figure 1. Net Capital Flows and Domestic Credit in EMEs, 2010–13
  - Source: Authors calculations based on IMF’s IFS database.
  - Notes: Net capital flows exclude other investment liabilities of the general government and reserve assets. Change in domestic credit to GDP ratio is 3-year cumulative change.
- Figure 2. De Facto Exchange Rate Regimes in EMEs, 1986–2013
  - Source: Ghosh, Ostry, and Qureshi (2015).
  - Notes: Panels show aggregate classification (Fixed, Intermediate, Float) and fine classification (Hard peg, Single currency peg, Basket peg, Horizontal band, Crawling peg, Managed float, Float) across 1986–2013.
- Figure 3. Global Factors and Capital Flows to EMEs, 1986Q1–2013Q4
  - Sources: IFS database, Bloomberg, and Krippner (2013).
  - Notes: Figures present three quarter moving average of flows. Net capital flows exclude other investment liabilities of the general government and reserve assets. Flows are presented in BPM5 terms with positive (negative) numbers indicating inflows (outflows).
  - Panels and axes referenced include: Net capital flows (in USD bln.), VXO (in logs; right-axis), Shadow short rate (in pct.; right-axis), US 10-y govt. bond yield (in pct.; right-axis), US 3-m T-bill rate (in pct.; right-axis), Liability flows (in USD bln.), Asset flows (in USD bln.).
- Figure 4. Global Risk Aversion, Capital Flows and Financial Variables in EMEs
  - Sources: Authors’ calculations based on IFS database, and Bloomberg.
  - Notes: Three-quarter moving average of net capital flows, real domestic credit growth, real house price growth, and real stock price growth is presented. Net capital flows exclude other investment liabilities of the general government and reserve assets. Flows are presented in BPM5 terms with positive (negative) numbers indicating inflows (outflows).
  - Panels reference VXO (in logs; right-axis), real stock price growth (in pct.; right-axis), real house price growth (in pct.; right-axis), real private sector domestic credit growth (in pct.).
- Figure 5. Correlation of Financial and Macroeconomic Variables in EMEs
  - Source: Authors’ calculations.
  - Note: Panel [a] shows the unconditional correlation across countries between the (log) VXO index and three-quarter moving average of real domestic private sector credit growth, real house price growth, real stock price growth, change in loan-to-deposit ratio, net capital flows to GDP, and real GDP growth. Panel [b] shows the unconditional correlation between three-quarter moving average of net and liability flows, and the same set of variables. *,**, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively.
- Figure 6. Correlation of Financial and Macroeconomic Variables in Advanced Countries
  - Source: Authors’ calculations.
  - Note: Panel [a] shows the unconditional correlation across countries between the (log) VXO index and three-quarter moving average of the same set of variables as in Figure 5. Panel [b] shows unconditional correlation between three-quarter moving average of net and liability flows and the same set of variables. The sample comprises Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland, France, Germany, Greece, Hong Kong SAR, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Malta, Netherlands, New Zealand, Norway, Portugal, Singapore, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom. Including the United States in the sample has no significant impact on the correlations. *,**, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively.

### Tables — Dependent Variables, Samples, and Key Estimates
- Table 1. Real Domestic Credit Growth in EMEs, 1986Q1–2013Q4
  - Dependent variable: three-quarter moving average of quarterly real domestic private sector credit growth rate (in percent).
  - Sample: open countries (above 25th sample percentile of the Quinn-Toyoda capital account openness index) and non-financial crisis years.
  - Key model/sample facts:
    - Observations: 2,555; 2,555; 2,555; 2,555; 2,552; 2,551; 1,844; 1,598 (across columns).
    - No. of countries: 43; 43; 43; 43; 43; 42; 35.
    - Adjusted R2: 0.235; 0.240; 0.240; 0.240; 0.253; 0.434; 0.421 (across columns).
    - Fixed regime coefficient examples:
      - Fixed regime 3.007*** (1.011)
      - Fixed x log (VXO) -1.981* (1.003)
    - Intermediate regime coefficient examples:
      - Intermediate regime 1.141 (0.726)
      - Intermediate x log (VXO) 0.237 (0.726)
    - Other reported coefficients:
      - Lagged real GDP growth 1.006*** (0.164)
      - Lagged private credit/GDP -0.090*** (0.013)
      - Lagged net capital flows/GDP 0.050*** (0.017) (in the specification where included)
      - Lagged central bank policy rate -0.238** (0.113) (where included)
    - Linear trend and Global financial crisis: Linear trend 0.016 (0.010); Global financial crisis 1.619*** (0.552).
  - Notes: Fixed exchange rate regime is binary (=1 for hard and single currency pegs). Intermediate regime is binary (=1 for basket pegs, horizontal bands, crawling pegs, and managed float). Reference category: independent floats. Domestic control variables are lagged two periods. Global financial crisis binary equals one for 2008Q4 and 2009Q1. Clustered standard errors (by country) in parentheses. ***, **, * indicate significance at 1, 5, 10 percent.
- Table 2. Real House Price Growth in EMEs, 1986Q1–2013Q4
  - Dependent variable: three-quarter moving average of quarterly real house price growth rate (in percent).
  - Sample: open countries (above 25th sample percentile of the Quinn-Toyoda capital account openness index) and non-financial crisis years.
  - Key model/sample facts:
    - Observations: 1,090; 1,090; 1,090; 1,090; 1,090; 950; 849 (across columns).
    - No. of countries: 25; 25; 25; 25; 25; 22.
    - Adjusted R2: 0.226; 0.293; 0.293; 0.295; 0.367; 0.416; 0.408 (across columns).
    - Fixed regime coefficient examples:
      - Fixed regime -0.7971 (1.486)
      - Fixed regime x log (VXO) -3.815** (1.653)
    - Intermediate regime coefficient examples:
      - Intermediate regime -0.943 (0.856)
      - Intermediate regime x log (VXO) 2.293** (0.869)
    - Other reported coefficients:
      - Lagged real GDP growth 0.890*** (0.190)
      - Lagged domestic credit growth 0.124*** (0.041)
      - Lagged central bank policy rate 0.078*** (0.023) (where included)
    - Global financial crisis -1.546** (0.698) (where reported).
  - Notes: Domestic control variables lagged two periods. Constant included. Clustered standard errors (by country) in parentheses. ***, **, * indicate significance at 1, 5, 10 percent.
- Table 3. Real Stock Returns in EMEs, 1986Q1–2013Q4
  - Dependent variable: three-quarter moving average of quarterly real stock price growth rate (in percent).
  - Sample and model facts:
    - Observations: 2,011; 2,011; 2,011; 2,011; 2,011; 1,531; 1,412 (across columns).
    - No. of countries: 37; 37; 37; 37; 37; 36; 32.
    - Adjusted R2: 0.112; 0.115; 0.154; 0.138; 0.437; 0.512; 0.518 (across columns).
    - Key coefficients:
      - Log (VXO) -6.469*** (0.937) and further columns -8.583*** (1.171), -8.886*** (1.171), -9.376*** (1.219)
      - Intermediate regime x log (VXO) 3.744** (1.791); 4.145** (1.798); 4.536** (1.869)
      - Lagged real GDP growth -1.170*** (0.380)
      - Real US T-bill rate -0.804*** (0.193) (where included)
      - Real shadow federal funds rate -0.554*** (0.155) (where included)
      - Lagged portfolio liability flows/GDP 0.065** (0.025) (where included)
      - Global financial crisis -0.035*** (0.012); -0.039*** (0.012); -0.088*** (0.012); -0.102*** (0.015) (across specifications)
  - Notes: See notes of Table 2 for sample construction and estimation conventions. Clustered standard errors (by country) in parentheses. ***, **, * indicate significance at 1, 5, 10 percent.

*Content unit: wp17130 - References (figures, tables, and bibliography as provided).*

### appendix fo r descriptio n o f o ther variables. Sample co mprises o pen co untries (i.e., tho se abo ve the 25th sample

### wp17130 - appendix for description of other variables (sample comprises open countries above 25th Quinn-Toyoda percentile; non-financial crisis years)

### Change in Loan-to-Deposit (LTD) Ratio in EMEs, 1986Q1–2013Q4 (Table 4)
- Fixed regime coefficients (cols. (1)-(7)): 2.405*, 5.845**, 5.816**, 6.195***, 6.540***, 6.436**, 3.631
- Fixed regime clustered s.e. examples: (1.343), (2.219), (2.259), (2.253), (2.102), (2.472), (2.428)
- Intermediate regime coefficients (cols. (1)-(7)): 0.640, 2.240, 2.293, 2.637, 3.201, 3.591, 2.960
- Log (VXO) coefficients (examples): -0.468, 0.092, 0.092, 0.182
- Fixed x log (VXO) coefficients (examples): -1.142, -1.130, -1.238*, -1.477**, -1.567**, -1.478*
- Intermediate x log (VXO) coefficients (examples): -0.526, -0.542, -0.637, -0.924, -1.259*, -1.366**
- Lagged real GDP growth coefficients (cols. shown): 0.482***, 0.481***, 0.477***, 0.463***, 0.361**, 0.328, 0.304 (clustered s.e. e.g., (0.152), (0.146), (0.204), (0.202))
- Lagged LTD ratio coefficients: -0.060***, -0.060***, -0.061***, -0.060***, -0.060***, -0.084***, -0.058*** (clustered s.e. e.g., (0.012), (0.020), (0.017))
- Other reported items: Country fixed effects = Yes (all cols.); Quarter-year effects = No for early cols., Yes in some later cols.
- Observations (cols.): 2,561; 2,561; 2,561; 2,561; 2,561; 2,561; 1,844; 1,598 (table shows multiple counts)
- Adjusted R2 examples: 0.169, 0.169, 0.169, 0.170, 0.218, 0.275, 0.295
- No. of countries examples: 43, 43, 43, 43, 42, 35
- Note: Dependent variable = three-quarter moving average of change in LTD ratio; lagged LTD ratio = two-quarter lagged LTD ratio.

### Net Capital Flows in EMEs, 1986Q1–2013Q4 (Table 5)
- Fixed regime coefficients: 0.457, 14.612**, 15.148**, 16.122**, 15.222**, 16.862*** (clustered s.e. e.g., (1.482), (5.999), (6.059), (6.212), (6.178), (6.138))
- Intermediate regime coefficients: 1.488**, 6.799*, 6.743*, 7.397*, 7.679**, 10.094***
- Log (VXO) examples: -1.432**, 0.588, 0.601, 0.774 (clustered s.e. e.g., (0.571), (0.816))
- Fixed x log (VXO) coefficients: -4.698**, -4.891**, -5.046***, -4.795**, -4.780**
- Intermediate x log (VXO) examples: -1.742, -1.732, -1.904, -1.869, -2.508**
- Lagged real GDP growth examples: 0.396***, 0.386***, 0.383***, 0.378***, 0.361***, 0.427** (clustered s.e. e.g., (0.095), (0.161))
- Lagged institutional quality examples: 19.579***, 20.419***, 19.705***, 17.465***, 13.716**, 24.141**
- Lagged domestic credit/GDP examples: -0.042**, -0.045**, -0.042**, -0.038*, -0.044*, -0.094***
- Global financial crisis coefficients: -2.196**, -1.877*, -1.898**, -2.312** (clustered s.e. e.g., (0.919), (0.955), (0.936), (0.896))
- Observations examples: 2,093; 2,093; 2,093; 2,093; 2,093; 2,093; 1,625
- Adjusted R2 examples: 0.362, 0.370, 0.373, 0.381, 0.404, 0.450
- No. of countries = 38 (cols. shown)

### Liability Flows in EMEs, 1986Q1–2013Q4 (Table 6)
- Fixed regime coefficients: 1.047, 14.560**, 15.282**, 16.632**, 17.855**, 18.243**
- Intermediate regime examples: 0.230, -1.895, -1.665, -0.483, 2.970, 3.499
- Log (VXO) coefficients: -3.550***, -2.864***, -2.764***, -2.477***
- Fixed x log (VXO) coefficients: -4.498**, -4.764**, -4.983**, -5.157**, -4.660*
- Lagged real GDP growth examples: 0.459***, 0.443***, 0.438***, 0.431***, 0.377***, 0.459**
- Lagged institutional quality examples: 23.532***, 24.768***, 23.775***, 20.170***, 15.277**, 29.990***
- Global financial crisis coefficients: -2.971**, -2.789**, -2.907**, -3.608*** (clustered s.e. e.g., (1.170), (1.232), (1.184), (1.098))
- Observations: 2,093 (cols.); 1,625 (some cols.)
- Adjusted R2 examples: 0.363, 0.373, 0.376, 0.388, 0.431, 0.463
- No. of countries = 38

### Asset Flows in EMEs, 1986Q1–2013Q4 (Table 7)
- Fixed regime coefficients (examples): -0.590, 0.052, -0.134, -0.510, -2.633, -1.381
- Intermediate regime coefficients (examples): 1.259*, 8.694***, 8.408***, 7.880**, 4.709, 6.594*
- Log (VXO) coefficients (examples): 2.118***, 3.453***, 3.365***, 3.251***
- Intermediate regime x log (VXO) coefficients (examples): -2.463***, -2.354**, -2.246**, -1.483, -2.123*
- Lagged real GDP growth examples: -0.063*, -0.057, -0.055, -0.053, -0.016, -0.032
- Global financial crisis examples: 0.776, 0.912, 1.009, 1.296* (clustered s.e. e.g., (0.829), (0.826), (0.784), (0.734))
- Observations: 2,093 (cols.); 1,625 in later cols.
- Adjusted R2 examples: 0.259, 0.266, 0.266, 0.270, 0.290, 0.314
- No. of countries = 38

### FDI, Portfolio, and Other Investment Liability Flows in EMEs, 1986Q1–2013Q4 (Table 8)
- Fixed regime coefficients across columns (selected): 6.197*, 6.348*, 7.081*, 8.761**, 1.909, 1.768, 1.371, 0.458, 8.580, 9.359*, 10.483*, 11.060*
- Fixed x log (VXO) coefficients (examples): -2.155*, -2.202*, -2.362*, -2.673*, -0.302, -0.263, -0.182, 0.031, -2.796, -3.077*, -3.233*, -3.386*
- Log (VXO) examples (cols. for portfolio/other): -0.023, 0.025, 0.232, -0.837**, -0.833**, -0.949**, -1.259**, -1.229**, -1.041*
- Lagged real GDP growth examples: 0.114***, 0.111***, 0.108**, 0.050, 0.009, 0.012, 0.013, 0.062*, 0.314***, 0.310***, 0.304***, 0.251*** (clustered s.e. examples in table)
- Lagged institutional quality examples: 5.210***, 4.608**, 2.455, 1.586, 5.075**, 5.633***, 6.865***, 5.870**, 13.622***, 12.593***, 9.861***, 7.875*
- Real US T-bill rate and shadow federal funds rate included in some specifications (examples: 0.091***, -0.108, -0.108*)
- Observations: 2,093 (multiple cols.)
- Adjusted R2 examples: 0.325, 0.329, 0.340, 0.366, 0.184, 0.189, 0.199, 0.219, 0.317, 0.329, 0.349, 0.384
- No. of countries = 38 across columns
- Note: Columns [1]-[4] = FDI liability flows (pct. of GDP); [5]-[8] = portfolio liability flows; [9]-[12] = other investment liability flows.

### Real GDP Growth in EMEs, 1986Q1–2013Q4 (Table 9)
- Fixed regime coefficients: 2.521**, 2.511**, 2.518**, 2.879***, 2.564**
- Log (VXO) coefficients: -0.459***, -0.479***, -0.492*** (examples)
- Fixed x log (VXO) coefficients: -0.756**, -0.756**, -0.753**, -0.856**, -0.758**
- Lagged net capital flows/GDP coefficients: 0.013***, 0.014***, 0.014***, 0.010**, 0.008*
- Lagged private credit/GDP coefficients: -0.018*** (repeated)
- Lagged real GDP per capita coefficients: -1.947***, -1.872***, -1.920***, -2.022***, -1.541*
- Linear trend examples: 0.014**, 0.011**, 0.011**
- Global financial crisis coefficient examples: -1.462***, -1.419***, -1.411***
- Observations examples: 2,121; 2,121; 2,121; 2,121; 2,111; 635 (some cols.)
- Adjusted R2 examples: 0.345, 0.351, 0.347, 0.421, 0.497
- No. of countries examples: 38

### Robustness Analysis: Alternate Specifications (Table 10)
- Wide set of alternate specifications reported for dependent variables: real private sector credit growth, real house price growth, real stock price growth, change in LTD ratio.
- Example Fixed regime coefficients across robustness cols: 8.929***, 9.787**, 7.650**, 8.923, 7.961**, 15.500***, 23.541***, 16.027***, 15.796***, 12.970***, 7.941, 3.038, 7.860, 23.163**, 6.755, 6.054***, 5.692***, 5.874**, 6.971, 4.220**
- Example Fixed x log (VXO) coefficients across robustness cols: -2.108**, -2.714*, -1.785, -2.311*, -1.865*, -4.770***, -7.096***, -4.885***, -6.223***, -3.890***, -1.657, -0.560, -1.707, -5.339, -1.583, -1.313*, -1.712*, -1.330*, -1.996*, -0.776
- Example Intermediate x log (VXO) coefficients showing significance in some specs: -1.446**, -1.826**, -1.730**, -1.007, -1.279**
- Lagged real GDP growth examples across specs: 0.999***, 1.094**, 0.989***, 0.351, 0.996***, 0.809***, 0.837***, 0.784***, 1.220***, 0.746***, -0.365, -0.429*, -0.385, -0.654*, -0.429*, 0.347**, 0.476**, 0.328**, 0.233, 0.440**
- Lagged net capital flows/GDP examples across specs: 0.079***, 0.076**, 0.083***, 0.058**, 0.068***; negative and significant in other specifications (e.g., -0.115**, -0.097*, -0.115**, -0.121*, -0.112**)
- Lagged private credit/GDP and lagged credit growth, lagged LTD ratio reported with coefficients and significance across specifications (examples: lagged private credit/GDP -0.092***, -0.097***, -0.085***; lagged LTD ratio -0.069***, -0.060***, -0.068***, -0.055***, -0.054***)
- Observations and Adjusted R2 vary by specification; No. of countries reported per column (examples: 39, 29, 22, 24, 33)
- Note: KA = Quinn-Toyoda capital account openness index; Open = fully open EMEs (index=100); ΔRR = change in average reserve requirement; Outlier = excludes top and bottom 0.25th percentiles; ΔLog(VXO) = log difference of VXO.

### Robustness Analysis: Further Checks (Table 11)
- Multiple further robustness checks reported (RR coarse classification, DJ, Endog. approaches, sample restrictions).
- Selected Fixed regime and interaction examples: Fixed regime 5.429*, 13.598**, 11.470***, 16.3855.822, 11.039, 2.482, 10.335** (clustered s.e. examples shown)
- Fixed x log (VXO) examples: -1.647*, -3.250**, -2.331**, -4.941***, -4.205, -4.059***, -1.800, -2.338, -7.745, -0.654, -2.495*, -1.175
- RR coarse classification coefficients examples: 15.310*, 25.569***, 3.880, 6.861***; other coarse classification rows include negative and positive coefficients (examples given)
- Lagged real GDP growth examples across checks: 0.755***, 1.009***, 1.001***, 0.368, 0.754***, 0.806***, 0.993***, 0.836** (clustered s.e. examples)
- Lagged net flows/GDP examples across checks: 0.062*, 0.087***, 0.082***, 0.079***, -0.001, 0.006, -0.008, -0.027, -0.108*, -0.106**, -0.104***, -0.164***, 0.060***, 0.079***, 0.078***, 0.089*** (clustered s.e. examples)
- Lagged credit/GDP examples: -0.078**, -0.084**, -0.091***, -0.125*** (clustered s.e. examples)
- Lagged LTD ratio examples: -0.060***, -0.064***, -0.077***, -0.063*** (clustered s.e. examples)
- Observations and Adjusted R2 vary by check; No. of countries reported per column (examples: 37, 39, 38, 18, 24, 33)

### Appendix: Sample, Variable Descriptions, Data Sources, and Additional Tables (Tables A1–A5, A3–A5, A4)
- Sample list: EMEs and advanced economies listed (examples include Argentina, Brazil, China, India, Mexico, Russia, South Africa; advanced examples include Australia, Canada, Japan, Sweden, United Kingdom, etc.). (Table A1)
- Variable descriptions and sources (Table A2) — selected entries (preserve wording and numeric units exactly as in source):
  - Capital account openness: Index (high=liberalized, low=closed). Source: Quinn and Toyoda (2008)
  - Capital flows: In USD billions (BPM5 presentation). Net financial flows exclude financing items and other investment liabilities of general government, i.e., the difference between IFS series codes “...4995W.9” and “...4753ZB9.” Liability flows and other investment liability flows also exclude other investment liabilities of the general government. Source: IMF's IFS database
  - Capital flows/GDP: In percent. Capital flows scaled by (1/4)*annual GDP. Variable smoothed by taking 3-quarter moving average. Source: Authors' calculations
  - Consumer price index (CPI): Index. Source: IMF's INS database
  - Domestic private sector credit: In local currency (LC). Source: IMF's IFS database
  - Exchange rate regime: De facto, de jure. Sources: Ghosh et al. (2015); Reinhart and Rogoff (2004) updated data (URL omitted per instructions)
  - GDP current/constant prices: In billions of USD (or LC). Seasonally adjusted observations for quarterly data. Sources: IMF's WEO and IFS databases; Haver analytics
  - Real GDP growth: Quarter-on-quarter percentage change in real GDP. Variable smoothed by taking three-quarter moving average. Source: Authors' calculations
  - Global financial crisis (GFC): Binary variable equal to 1 for 2008Q4/2009Q1, 0 otherwise. Source: Authors' calculations
  - House prices: Index (in real terms). Source: IMF's Macrofinancial Unit database
  - Institutional quality: Index (average of ICRG's 12 political risk components). Source: Political Risk Group
  - Loan to deposit (LTD) ratio: In percent. Source: IMF's IFS database
  - Change in LTD ratio: In percentage points. Variable smoothed by taking three-quarter moving average. Source: Authors' calculations
  - Policy rate: Policy rate or discount rate (in percent). Source: IMF's IFS database
  - Reserve requirements: Average of reserve requirements on local currency demand, saving, and term deposits (in percent). Authors' calculations based on data from Federico et al. (2014)
  - Shadow federal funds rate: In percent. In real terms computed as [(1+nominal interest rate)/(1+expected inflation)]- 1, where expected inflation is one-period ahead inflation. Authors' calculations based on Krippner (2013) and IMF's WEO database
  - Stock prices (in real terms): Stock price index deflated by quarterly CPI. Source: Bloomberg and authors' calculations
  - Real stock price growth: Quarter-on-quarter percentage change in real stock prices. Variable smoothed by taking three-quarter moving average. Source: Authors' calculations
  - U.S. interest rate: U.S. 3-month Treasury bill rate, and 10-year government bond yield (in percent). Sources: IMF's IFS database and Bloomberg
  - VXO/VIX index: Chicago Board Options Exchange Market Volatility Index. Source: Bloomberg
- Additional appendix tables (A3–A5) provide estimates for financial conditions and disaggregated exchange rate regimes, domestic credit growth and exchange rate regimes in advanced economies, and capital flows by disaggregated exchange rate regimes. Selected coefficients and interactions (Hard peg, Conventional peg, Basket peg, Horizontal band, Crawling peg, Managed float) and their interactions with log(VXO) are reported across dependent variables (credit growth, house price growth, stock price growth, change in LTD ratio, net capital flows, liability flows, asset flows, FDI liability flows, portfolio liability flows, other investment liability flows). Examples include:
  - Hard peg coefficients in Table A3 examples: 6.983**, 8.086**; interactions Hard peg x log (VXO) examples: -1.609, -1.946*
  - Basket peg x log (VXO) in Table A5 examples: -8.875***, -9.865***, -6.242**, -8.426***
  - Lagged institutional quality and lagged credit/GDP frequently reported with large positive institutional coefficients in capital flow regressions (examples: 19.688***, 24.977***; clustered s.e. examples provided)

*Source: IMF Working Paper wp17130 appendix (tables and variable descriptions as provided in the supplied content).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17130.pdf_