## 1.   Exchange Rate Volatility

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---

### Overview
- Objective: empirically unearth the “missing” cross-country determinants of exchange rate volatility by introducing “soft power” variables (demographic, institutional, political, social) alongside conventional macro-financial controls.
- Sample: balanced panel of 115 countries over the period 1996–2011.
- Soft power candidate universe: 20 variables across five categories (Governance, Population, Education, Financial Sector, Development); variable-reduction via VIF until VIF ≤ 5.0 yields nine retained variables.
- Main section begins on page "6".
- Empirical tables within the section:
  - "1.   Pooled Model (OLS)" — page "17"
  - "2.   Pooled Model (IV)" — page "19"
  - "3.   Fixed Effects Model (GMM): Exogenous Control Variables" — page "21"
  - "4.   Fixed Effects Model (GMM): Exogenous PCA Control Variables" — page "23"
  - "5.   Fixed Effects Model (GMM): Endogenous PCA Control Variables" — page "25"
- Relevant appendices:
  - "A1. Sample of Countries" — page "27"
  - "A2. Variable Definitions" — page "28"

### Measurement and data
- Dependent variable: annual volatility of the Real Effective Exchange Rate (REER), measured as ln(realized variance of monthly REER returns).
- Key summary statistics over 1995–2012:
  - Global average REER volatility declined from 7.7 percent (second half of the 1990s) to 5.5 percent (2000–07).
  - Exchange rate volatility surged to 8.2 percent in 2008; moderated toward pre-crisis level by 2012.
  - Cross-country range during 1995–2012: minimum 1 percent, maximum 86 percent.
- Control variables (10): CPII, TOTV, PRDV, GOVV, CBAL, OPEN, CONC, REGM, CRED, SCAP (definitions and sources reported in Panel A, Table A2).
- Soft power initial universe (20 variables): POLS, GOVE, REGQ, RLAW, CORR, VCEA; MAGE, FERT, MORT, LIFE, CHIL; PRIS, SECS, TERS; ZSCR, BCNC, FOPN; RGDP, AGRC, AGMS (Panel A, Table A1 and Panel B, Table A2).

### Variable reduction and retained soft-power predictors
- Variable reduction method: recursive variance inflation factor (VIF) screening; drop highest VIF each iteration until no VIF > 5.0.
- Final set retained (VIF cut-off 5.0): POLS, VCEA, LIFE, PRIS, TERS, ZSCR, BCNC, FOPN, AGMS (nine variables).
- Robustness checks:
  - Alternative VIF threshold 2.5 yields six variables; governance and development categories eliminated under this stricter cut.
  - Applying VIF procedure after demeaning by country and year yields fewer dropped variables; qualitative conclusions remain broadly similar.

### Econometric framework and estimation methods
- Benchmark pooled dynamic model: VOLi,t = α + ρ VOLi,t−1 + Δ′X i,t + Γ′S i,t + εi,t.
- Estimation approaches:
  - Pooled OLS (static and dynamic).
  - Instrumental Variables (IV) pooled model: control variables treated as pre-determined; first lags used as instruments.
  - Fixed effects dynamic panel via System GMM (Blundell and Bond, 1998): one-step GMM with robust standard errors; procedures to limit instrument proliferation include using only recent lags, collapsing instruments, and replacing controls with principal components.
- Diagnostic tests reported: Hansen J test p-values, Arellano-Bond AR2 test p-values; number of instruments reported to monitor instrument proliferation.

### Main empirical findings
- Persistence of volatility (lagged volatility coefficients):
  - Pooled dynamic OLS lagged volatility coefficients (All/Developed/Emerging): 0.485***, 0.346***, 0.439*** (Table 1 dynamic panel).
  - IV pooled dynamic: 0.551***, 0.354***, 0.521*** (Table 2 dynamic panel).
  - System GMM (Table 3, specification (1) with unrestricted instruments): lagged volatility = 0.391*** (standard error 0.035).
  - System GMM reduced-instrument specifications (Table 3): lagged volatility falls to 0.273***, 0.225***, 0.166*** across specifications (2)–(4).
- Control variables (consistent patterns):
  - Trade openness (OPEN): negative and highly significant in most specifications (example pooled OLS All static −0.156***; dynamic −0.071***).
  - FX regime flexibility (REGM): positive and significant (example pooled OLS dynamic All 0.135***).
  - Inflation (CPII) and productivity volatility (PRDV): positive coefficients generally; statistical significance varies by subsample.
  - Financial development: Credit/GDP (CRED) and Stock market cap/GDP (SCAP) often positive and significant in pooled estimates.
- Soft power variables (robust relationships across estimators):
  - Voice and accountability (VCEA): negative and significant (dampening volatility).
    - Examples: Pooled OLS static All −0.153***; dynamic −0.085***. IV dynamic All −0.074***. System GMM (Table 3 spec (1)) −0.098***.
  - Life expectancy (LIFE): negative and significant (dampening volatility).
    - Examples: Pooled OLS static All −0.018***; dynamic −0.009***. IV dynamic All −0.006**. System GMM spec (1) −0.011***.
  - Bank Z-score (ZSCR): negative and significant (higher Z-score → lower FX volatility).
    - Examples: Pooled OLS static All −0.006***; dynamic −0.004***. IV dynamic All −0.004***. System GMM spec (1) −0.005***.
  - Financial openness (FOPN): negative and often significant (dampening volatility).
    - Examples: Pooled OLS static All −0.038***; dynamic −0.025***. IV static All −0.045***; dynamic −0.032***. System GMM spec (1) −0.029**.
  - Primary schooling (PRIS): positive and significant (associated with greater volatility).
    - Examples: Pooled OLS static All 0.093***; dynamic 0.058***. IV dynamic All 0.055***. System GMM spec (1) 0.063***.
  - Tertiary schooling (TERS): heterogeneous effects; significant dampening in some subsamples (e.g., developed countries) but often statistically insignificant in full-sample GMM.
  - GDP agriculture minus services (AGMS): mixed signs; positive and significant in developed subsamples in pooled OLS, generally insignificant in GMM.
- Principal component (PCA) robustness:
  - Replacing controls with first four principal components yields similar coefficients on soft power variables (Table 4).
- Endogeneity checks:
  - Treating control variables as endogenous (PCA controls) and varying instrument sets yields broadly similar soft power coefficient magnitudes and signs (Table 5). Some coefficients lose significance in extreme instrument-reduction cases, but overall patterns persist.

### Key numeric points and model diagnostics
- Observations and sample sizes:
  - Pooled models: Observations All = 1840 (Table 1).
  - IV pooled Observations = 1725 (Table 2 reported 1725 in some specifications).
  - System GMM models: Observations = 1725 across GMM specifications (Tables 3–5).
- Model fit (pooled OLS adjusted R-squared):
  - Static All: 0.405; Developed: 0.645; Emerging: 0.354.
  - Dynamic All: 0.550; Developed: 0.697; Emerging: 0.479.
- Instrument counts and diagnostic test examples:
  - System GMM Table 3 spec (1): Number of instruments = 153; Hansen J test p-value = 0.960; AR2 p-value = 0.561.
  - System GMM Table 3 spec (4): Number of instruments = 37; Hansen J test p-value = 0.173; AR2 p-value = 0.441.
  - PCA GMM Table 4 spec (4): Number of instruments = 31; Hansen J test p-value = 0.213; AR2 p-value = 0.502.
  - Endogenous PCA GMM Table 5 various specs report Hansen J test p-values ranging from 0.074 to 1.000 and AR2 p-values from 0.738 to 0.950.

### Policy implications and conclusions
- Macro fundamentals remain important:
  - Inflation control, productivity stability, and trade openness are crucial for exchange rate stability.
- Institutional and structural reforms matter:
  - Higher “soft power” (voice and accountability, life expectancy, bank soundness, financial openness) are associated with lower exchange rate volatility.
- Human capital nuance:
  - Primary schooling (PRIS) is associated with higher volatility while tertiary schooling (TERS) can be dampening in some contexts — suggesting potential nonlinearities in human capital effects.
- Financial sector stability:
  - Higher bank Z-scores and prudent banking systems reduce exchange rate volatility; financial openness (Chinn-Ito index) is associated with lower volatility in many specifications.
- Research agenda:
  - The paper is a first step calling for further empirical and theoretical studies to unpack the causal channels linking quantitative measures of “soft power” and exchange rates.

*Source: _wp1563*

### 1.   Exchange Rate Volatility ..........................................................................................

### 1. Exchange Rate Volatility

### Overview
- Section title: "1.   Exchange Rate Volatility"
- Main section begins on page "6"

### Empirical Models and Tables
- Tables included in this section:
  - "1.   Pooled Model (OLS)" — page "17"
  - "2.   Pooled Model (IV)" — page "19"
  - "3.   Fixed Effects Model (GMM): Exogenous Control Variables" — page "21"
  - "4.   Fixed Effects Model (GMM): Exogenous PCA Control Variables" — page "23"
  - "5.   Fixed Effects Model (GMM): Endogenous PCA Control Variables" — page "25"

### Appendices Relevant to This Section
- Appendix Tables:
  - "A1. Sample of Countries" — page "27"
  - "A2. Variable Definitions" — page "28"

*Source: _wp1563 - 1.   Exchange Rate Volatility*

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

### _wp1563 - References

### Introduction and purpose
- Objective: empirically unearth the “missing” cross-country determinants of exchange rate volatility by introducing “soft power” variables (demographic, institutional, political, social) alongside conventional macro-financial controls.
- Sample: balanced panel of 115 countries over the period 1996–2011.
- Soft power concept: 20 candidate variables across five categories (Governance, Population, Education, Financial Sector, Development); variable-reduction via VIF until VIF ≤ 5.0 yields nine retained variables.

### Measurement and data
- Dependent variable: annual volatility of the Real Effective Exchange Rate (REER), measured as ln(realized variance of monthly REER returns).
- Key summary statistics over 1995–2012:
  - Global average REER volatility declined from 7.7 percent (second half of the 1990s) to 5.5 percent (2000–07).
  - Exchange rate volatility surged to 8.2 percent in 2008; moderated toward pre-crisis level by 2012.
  - Cross-country range during 1995–2012: minimum 1 percent, maximum 86 percent.
- Control variables (10): CPII, TOTV, PRDV, GOVV, CBAL, OPEN, CONC, REGM, CRED, SCAP (definitions and sources reported in Panel A, Table A2).
- Soft power initial universe (20 variables): POLS, GOVE, REGQ, RLAW, CORR, VCEA; MAGE, FERT, MORT, LIFE, CHIL; PRIS, SECS, TERS; ZSCR, BCNC, FOPN; RGDP, AGRC, AGMS (Panel A, Table A1 and Panel B, Table A2).

### Variable reduction and retained soft-power predictors
- Variable reduction method: recursive variance inflation factor (VIF) screening; drop highest VIF each iteration until no VIF > 5.0.
- Final set retained (VIF cut-off 5.0): POLS, VCEA, LIFE, PRIS, TERS, ZSCR, BCNC, FOPN, AGMS (nine variables).
- Robustness checks:
  - Alternative VIF threshold 2.5 yields six variables; governance and development categories eliminated under this stricter cut.
  - Applying VIF procedure after demeaning by country and year yields fewer dropped variables; qualitative conclusions remain broadly similar.

### Econometric framework and estimation methods
- Benchmark pooled dynamic model: VOLi,t = α + ρ VOLi,t−1 + Δ′X i,t + Γ′S i,t + εi,t (notation per text).
- Estimation approaches:
  - Pooled OLS (static and dynamic).
  - Instrumental Variables (IV) pooled model: control variables treated as pre-determined; first lags used as instruments.
  - Fixed effects dynamic panel via System GMM (Blundell and Bond, 1998): one-step GMM with robust standard errors; procedures to limit instrument proliferation include using only recent lags, collapsing instruments, and replacing controls with principal components.
- Tests reported: Hansen J test p-values, Arellano-Bond AR2 test p-values; attention to instrument proliferation (number of instruments reported).

### Main empirical findings (highlights from pooled, IV, and System GMM results)
- Persistence of volatility:
  - Pooled dynamic OLS lagged volatility coefficients (All/Developed/Emerging): 0.485***, 0.346***, 0.439*** (Table 1 dynamic panel).
  - IV pooled dynamic: 0.551***, 0.354***, 0.521*** (Table 2 dynamic panel).
  - System GMM (Table 3, specification (1) with unrestricted instruments): lagged volatility = 0.391*** (standard error 0.035).
  - System GMM reduced-instrument specifications (Table 3): lagged volatility falls to 0.273***, 0.225***, 0.166*** across specifications (2)–(4).
- Control variables (consistent patterns):
  - Trade openness (OPEN): negative and highly significant in most specifications (e.g., pooled OLS All static −0.156***; dynamic −0.071***).
  - FX regime flexibility (REGM): positive and significant (e.g., pooled OLS dynamic All 0.135***).
  - Inflation (CPII) and productivity volatility (PRDV): positive coefficients generally; statistical significance varies by subsample.
  - Financial development: Credit/GDP (CRED) and Stock market cap/GDP (SCAP) often positive and significant in pooled estimates.
- Soft power variables (robust relationships across estimators):
  - Voice and accountability (VCEA): negative and significant (dampening volatility). Examples:
    - Pooled OLS static All −0.153***; dynamic −0.085***.
    - IV dynamic All −0.074***.
    - System GMM (Table 3 spec (1)) −0.098***; remains negative and significant across GMM specs.
  - Life expectancy (LIFE): negative and significant (dampening volatility). Examples:
    - Pooled OLS static All −0.018***; dynamic −0.009***.
    - IV dynamic All −0.006**.
    - System GMM spec (1) −0.011***.
  - Bank Z-score (ZSCR): negative and significant (higher Z-score → lower FX volatility). Examples:
    - Pooled OLS static All −0.006***; dynamic −0.004***.
    - IV dynamic All −0.004***.
    - System GMM spec (1) −0.005***.
  - Financial openness (FOPN): negative and often significant (dampening volatility). Examples:
    - Pooled OLS static All −0.038***; dynamic −0.025***.
    - IV static All −0.045***; dynamic −0.032***.
    - System GMM spec (1) −0.029**.
  - Primary schooling (PRIS): positive and significant (associated with greater volatility). Examples:
    - Pooled OLS static All 0.093***; dynamic 0.058***.
    - IV dynamic All 0.055***.
    - System GMM spec (1) 0.063***.
  - Tertiary schooling (TERS): heterogeneous effects; significant dampening in some subsamples (e.g., developed countries) but often statistically insignificant in full-sample GMM.
  - GDP agriculture minus services (AGMS): mixed signs; positive and significant in developed subsamples in pooled OLS, generally insignificant in GMM.
- Principal component (PCA) robustness:
  - Replacing controls with first four principal components yields similar coefficients on soft power variables (Table 4).
- Endogeneity checks:
  - Treating control variables as endogenous (PCA controls) and varying instrument sets yields broadly similar soft power coefficient magnitudes and signs (Table 5). Some coefficients lose significance in extreme instrument-reduction cases, but overall patterns persist.

### Key numeric points and model diagnostics
- Observations and sample sizes:
  - Pooled models: Observations All = 1840 (Table 1), IV pooled Observations = 1725 (Table 2 reported 1725 in some specifications).
  - System GMM models: Observations = 1725 across GMM specifications (Tables 3–5).
- Model fit (pooled OLS adjusted R-squared):
  - Static All: 0.405; Developed: 0.645; Emerging: 0.354.
  - Dynamic All: 0.550; Developed: 0.697; Emerging: 0.479.
- Instrument counts and tests (examples):
  - System GMM Table 3 spec (1): Number of instruments = 153; Hansen J test p-value = 0.960; AR2 p-value = 0.561.
  - System GMM Table 3 spec (4): Number of instruments = 37; Hansen J test p-value = 0.173; AR2 p-value = 0.441.
  - PCA GMM Table 4 spec (4): Number of instruments = 31; Hansen J test p-value = 0.213; AR2 p-value = 0.502.
  - Endogenous PCA GMM Table 5 various specs report Hansen J test p-values ranging from 0.074 to 1.000 and AR2 p-values from 0.738 to 0.950.

### Policy implications and conclusions
- Macro fundamentals remain important: macroeconomic strength (inflation control, productivity stability, openness) is crucial for exchange rate stability.
- Institutional and structural reforms matter: higher “soft power” (voice and accountability, life expectancy, bank soundness, financial openness) are associated with lower exchange rate volatility.
- Human capital nuance: primary schooling (PRIS) is associated with higher volatility while tertiary schooling (TERS) can be dampening in some contexts — suggesting potential nonlinearities in human capital effects.
- Financial sector stability: higher bank Z-scores and prudent banking systems reduce exchange rate volatility; financial openness (Chinn-Ito index) is associated with lower volatility in many specifications.
- Research agenda: the paper is a first step calling for further empirical and theoretical studies to unpack the causal channels linking quantitative measures of “soft power” and exchange rates.

*Source: _wp1563 - References (PDF).*

### References

### _wp1563 - References

### Exchange rates, volatility, and macroeconomic implications
- Acemoglu, D., S. Johnson, and J. Robinson, 2001, “The Colonial Origins of Comparative Development,” American Economic Review, Vol. 91, pp. 1369–1401.  
- Acemoglu, D., S. Johnson, J. Robinson, and Y. Thaicharoen, 2003, “Institutional Causes, Macroeconomic Symptoms: Volatility, Crises and Growth,” Journal of Monetary Economics, Vol. 50, pp. 49–123.  
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- Baxter, M., and A. Stockman, 1989, “Business Cycles and the Exchange Rate Regime: Some International Evidence,” Journal of Monetary Economics, Vol. 23, pp. 377–400.  
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- Bleaney, M., and D. Greenway, 2001, “The Impact of Terms of Trade and Real Exchange Rate Volatility on Investment and Growth in Sub-Saharan Africa,” Journal of Development Economics, Vol. 65, pp. 491–500.  
- Brodsky, D., 1984, “Fixed Versus Flexible Exchange Rates and the Measurement of Exchange Rate Instability,” Journal of International Economics, Vol.16, pp.295–306.  
- Devereux, M., and C. Engel, 2003, “Monetary Policy in the Open Economy Revisited: Price Setting and Exchange Rate Flexibility,” Review of Economic Studies, Vol. 70, pp. 765–783.  
- Engle, C., and K. West, 2005, “Exchange Rate and Fundamentals,” Journal of Political Economy, Vol. 113, pp. 485–517.  
- Farrant, K., and G. Peersman, 2006, “Is the Exchange Rate a Shock Absorber or a Source of Shocks? New Empirical Evidence,” Journal of Money, Credit, and Banking, Vol. 38, pp. 939–961.  
- Flood, R., and A. Rose, 1995, “Fixing Exchange Rates: A Virtual Quest for Fundamentals,” Journal of Monetary Economics, Vol. 36, pp. 3–37.  
- Flood, R., and A. Rose, 1999, “Understanding Exchange Rate Volatility without the Contrivance of Macroeconomics,” Economic Journal, Vol. 109, pp. 660–672.  
- Hau, H., 2002, “Real Exchange Rate Volatility and Economic Openness: Theory and Evidence,” Journal of Money, Credit and Banking, Vol. 34, pp. 611–630.  
- Kenen, P., and D. Rodrik, 1986, “Measuring and Analyzing the Effects of Short-term Volatility in Real Exchange Rates,” Review of Economics and Statistics, Vol. 68, pp. 311–315.  
- Levy-Yeyati, E., and F. Sturzenegger, 2005, “Classifying Exchange Rate Regimes: Deeds vs. Words,” European Economic Review, Vol. 49, pp. 1603–1635.  
- Meese, R., and K. Rogoff, 1983, “Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?” Journal of International Economics, Vol. 14, pp. 3–24.  
- Morales-Zumaquero, A., and S. Sosvilla-Rivero, 2010, “Structural Breaks in Volatility: Evidence for the OECD and non-OECD Real Exchange Rates,” Journal of International Money and Finance, Vol. 29, pp. 139–168.  
- Mussa, M., 1986, “Nominal Exchange Rate Regimes and the Behavior of the Real Exchange Rates: Evidence and Implications,” Carnegie-Rochester Conference Series on Public Policy, Vol. 25, pp. 117–214.  
- Obstfeld, M., and K. Rogoff, 1998, “Risk and Exchange Rates,” NBER Working Paper, No. 6694 (Cambridge, MA: National Bureau of Economic Research).  
- Polak, J., 1988, “The Choice of an Exchange Rate Regime” in Development Issues in the Current International Monetary System: Essays in Honor of Byanti Khoswaman, edited by D. Sutalaksana (Singapore: Addison Wesley).  
- Rose, A., 1996, “Explaining Exchange Rate Volatility: An Empirical Analysis of the Holy Trinity of Monetary Independence, Fixed Exchange Rates, and Capital Mobility,” Journal of International Money and Finance, Vol. 15, pp. 925–945.  
- Rosenberg, M., 2004, “Firm Risk, Investment, and Employment Growth,” Journal of Economics and Finance, Vol. 28, pp. 164–285.  
- Sauer, C., and A. Bohara, 2001, “Exchange Rate Volatility and Exports: regional Differences between Developing and Industrialized Countries,” Review of International Economics, Vol. 9, pp. 133–152.  

### Investment, uncertainty, and firm/aggregate responses
- Caballero, R., and R. Pindyck, 1996, “Uncertainty, Investment and Industry Evolution,” International Economic Review, Vol. 37, pp. 641–662.  
- Chong, A., and M. Gradstein, 2009, “Volatility and Firm Growth,” Journal of Economic Growth, Vol. 14, pp. 1–25.  
- Dixit, A., and R. Pindyck, 1994, Investment Under Uncertainty (Princeton, NJ: Princeton University Press).  
- Federer, J., 1993, “The Impact of Uncertainty on Aggregate Investment Spending,” Journal of Money, Credit and Banking, Vol. 25, pp. 30–48.  
- Ramey, G. and V. Ramey, 1995, “Cross-Country Evidence on the Link Between Volatility and Growth,” American Economic Review, Vol. 85, pp. 1138–1151.  
- Rosenberg, M., 2004, “Firm Risk, Investment, and Employment Growth,” Journal of Economics and Finance, Vol. 28, pp. 164–285.  

### Trade, openness, and real economy links
- Baum, C., and M. Caglayan, 2010, “On the Sensitivity of the Volume and Variability of Trade Flows,” Journal of International Money and Finance, Vol. 29, pp. 79–93.  
- Chowdhury, A., 1993, “Does Exchange Rate Volatility Depress Trade Flows? Evidence from Error-Correction Models,” Review of Economics and Statistics, Vol. 75, pp. 700–706.  
- Dell’Ariccia, G., 1999, “Exchange Rate Fluctuations and Trade Flows: Evidence from the European Union,” IMF Staff Papers, Vol. 46, pp. 315–334.  
- Rose, A., 2000, “One Market, One Money: Estimating the Effect of Common Currencies on Trade,” Economic Policy, Vol. 30, pp. 7–45.  
- Sauer, C., and A. Bohara, 2001, “Exchange Rate Volatility and Exports: regional Differences between Developing and Industrialized Countries,” Review of International Economics, Vol. 9, pp. 133–152.  

### Econometric methods, panel data, and volatility modeling
- Arellano, M., and S. Bond, 1991, “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies, Vol. 58, pp. 277–297.  
- Blundell, R., and S. Bond, 1998, “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, Vol. 87, pp. 115–143.  
- Bollerslev, T., 1986, “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, Vol. 31, pp. 307–327.  
- Harris, R., and E. Tzavalis, 1999, “Inference for Unit Roots in Dynamic Panels Where the Time Dimension Is Fixed,” Journal of Econometrics, Vol. 91, pp. 201–226.  
- Im, K., M. Pesaran, and Y. Shin, 2003, “Testing for Unit Roots in Heterogeneous Panels,” Journal of Econometrics, Vol. 115, pp. 53–74.  
- Roodman, D., 2009, “A Note on the Theme of Too Many Instruments,” Oxford Bulletin of Economics and Statistics, Vol. 71, pp. 135–158.  
- Stock, J., and M. Watson, 2002, “Macroeconomic Forecasting Using Diffusion Indexes,” Journal of Business and Economic Statistics, Vol. 20, pp. 147–162.  

### Financial crises, fiscal stress, legal/institutional factors, and broader determinants
- Aizenman, J., and N. Marion, 1999, “Uncertainty and the Disappearance of International Credit,” NBER Working Paper, No. 7389 (Cambridge, MA: National Bureau of Economic Research).  
- Baldacci, E., I. Petrova, N. Belhocine, G. Dobrescu, and S. Mazraani, 2011, “Assessing Fiscal Stress,” IMF Working Paper, No. 11/100 (Washington: International Monetary Fund).  
- Canales-Kriljenko, J., and K. Habermeier, 2004, “Structural Factors Affecting Exchange Rate Volatility: A Cross-Section Study,” IMF Working Paper, No. 04/147 (Washington, DC: International Monetary Fund).  
- Laeven, L., and F. Valencia, 2008, “Systemic Banking Crises: A New Database,” IMF Working Paper, No. 08/224 (Washington: International Monetary Fund).  
- La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny, 1997, “Legal Determinants of External Finance,” Journal of Finance, Vol. 52, pp. 1131–1150.  
- North, D., and R. Thomas, 1973, The Rise of the Western World: A New Economic History (New York: Cambridge University Press).  
- Nye, J., 1990, “Soft Power,” Foreign Policy, Vol. 80, pp. 153–171.  
- Phillips, S., L. Catão, L. Ricci, R. Bems, M. Das, J. Di Giovanni, D. Unsal, M. Castillo, J. Lee, J. Rodriguez, and M. Vargas, 2013, “The External Balance Assessment (EBA) Methodology,” IMF Working Paper, No. 13/272 (Washington: International Monetary Fund).  

### Microstructure and theoretical foundations
- Lyons, R., 2001, The Microstructure Approach to Exchange Rates (Cambridge, Mass.: MIT Press).  
- Dixit, A., and R. Pindyck, 1994, Investment Under Uncertainty (Princeton, NJ: Princeton University Press).  
- Friedman, M., 1953, “The Case for Flexible Exchange Rates,” in Essays in Positive Economics (Chicago: University of Chicago Press).  

*Source: _wp1563 - References*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2015/_wp1563.pdf_