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Economic Policy Uncertainty and Exchange Rate Volatility
By
Robert Krol*
Professor
Department of Economics
California State University, Northridge
Northridge, CA 91330-8374
[email protected]
October 30, 2013
(Revised May 9, 2014)
Abstract
This paper investigates the impact of general economic and economic policy uncertainty
on exchange rate volatility for ten industrial and emerging economies since 1990. The
results suggest home and U.S. economic policy uncertainty directly increases exchange
rate volatility for some of the currencies examined. For the more integrated industrial
economies, there is strong evidence that both home country and U.S. economic policy
uncertainty increases currency volatility during bad economic times. For the less
integrated emerging economies, only home country economic policy uncertainty
increases exchange rate volatility during bad economic times. General economic
uncertainty also increases exchange rate volatility, but the size of the impact is generally
smaller than economic policy uncertainty.
* Financial support for this project was provided by the Charles Koch Foundation. I want
to thank Jonathan Brogaard for providing the economic policy uncertainty data. Helpful
comments were provided by Shirley Svorny, the referees, and editor. I also want to thank
Dillon Perera and Darren Mayward for excellent research assistance.
1. Introduction
In the wake of the recent global recession and political fights over government
budget deficits, uncertainty over economic policy has been high. Recent research by
economists has resulted in new measures of economic policy uncertainty. These new
measures of economic policy uncertainty have been used to quantify its impact on the
economy. This paper explores the idea that economic policy uncertainty influences
exchange rate volatility.
Exchange rate volatility increases the risk associated with international
transactions. When exchange rate volatility is high, businesses and international
investors are more likely to use costly hedging instruments in an attempt to manage this
risk. As a result, greater exchange rate volatility can have a negative impact on
international trade and financial flows between nations, reducing the country welfare
gains from these transactions.
The value of an exchange rate is determined by expectations of the economic
fundamentals and policies of each country. If a high level of economic policy uncertainty
leads to more revisions in an agent’s expectations of the fundamental factors that
determine the value of an exchange rate, the result would be greater exchange rate
volatility. Using a sample of ten industrial and emerging economies over 20 years, I find
that economic policy uncertainty, from both home and abroad, directly increases
exchange rate volatility for some of the countries in the sample. For industrial
economies, I find strong evidence that both home country and United States economic
policy uncertainty increases exchange rate volatility when their economies are doing
poorly. For emerging economies, only home country economic policy uncertainty
1
increases volatility when their economies perform poorly. This difference reflects the
higher degree of integration between the industrial economies. For comparison, I
examine the impact of general economic uncertainty on exchange rate volatility. General
economic uncertainty also raises currency volatility, but the magnitude of the effect is
small relative to the impact of economic policy uncertainty.
In the past, economic policy uncertainty was measured using political indicators
such as uncertainty surrounding elections or legislative outcomes. Leblang and Bernhard
(2006) use these measures to examine the impact of policy uncertainty on exchange rate
volatility. Using an empirical method that differs from the approach used in this paper,
they find evidence that political indicators influence exchange rate volatility. 1 Additional
studies that use elections as indicators of economic policy uncertainty find significant
affects on the economy. Bialkowski, et. al. (2008) and Boutchkova, et. al. (2012) find
increased equity market volatility around elections. Julio and Yook (2012) find firms
reduce investment around elections.
Recently, Baker, Bloom, and Davis (2013) and Brogaard and Detzel (2012) have
constructed indices of economic policy uncertainty for the United States and other
countries. These indices measure the continuous evolution of economic policy
uncertainty through time. Baker, Bloom, and Davis (2013) find that increased economic
policy uncertainty associated with the recent recession significantly reduced real GDP,
investment, and employment in the United States. Because it increases risk, greater
economic policy uncertainty can be expected to reduce investment and hiring in the
economy. Brogaard and Detzel (2012) find economic policy uncertainty reduced stock
1
Their approach uses generalized autoregressive conditional heteroscedasticity or GARCH to model
volatility.
2
market returns, increased market volatility, and raised equity risk premiums in a panel of
21 countries. Economic policy uncertainty makes purchasing stocks more risky.
Columbo (2013) finds that economic policy uncertainty in the United States has a
greater impact on Euro Area prices and output than economic policy uncertainty in the
Euro Area itself. It is likely that there are significant spillover effects surrounding
economic policy uncertainty in the global economy.
Pástor and Veronesi (2013) develop a general equilibrium model in which stock
prices are influenced by government actions. In this model, uncertainty over future
actions increases the risk premium associated with holding stocks. This effect is greater
when the economy is weak, as governments are more likely to act -- correctly or
incorrectly -- when the economy is doing poorly. Empirically Pástor and Veronesi find
evidence to support their model using data from the United States equity market. I find
support for their hypothesis in currency markets.
The remainder of the paper is organized in the following fashion. The next
section discusses how economic policy uncertainty has been measured. The third section
discusses the empirical model and the fourth section presents the results. The paper ends
with a brief conclusion.
2. Measuring Economic Policy Uncertainty
Economists and political scientists are interested in quantifying the impact
economic policy uncertainty has on the economy. Two measures have been used, either
the date of an event, such as an election, or more recently a constructed index.
Early research on how politics and economic policy uncertainty influence
financial markets and the economy focused on events such as an election or the passage
3
of a legislative bill [see Bernhard and Leblang (2006)]. These types of events are likely
to generate economic policy uncertainty and influence the behavior of individuals in the
economy. In this approach, economic policy uncertainty is measured in a discrete
fashion. Events occur at a particular point in time. An advantage of this approach is that
elections tend to be more exogenous with respect to current economic conditions.
However, in parliamentary political systems, the timing of an election may be tied to
economic conditions. The incumbent government has an incentive to call for an election
when the economy is doing well. Alesina, Cohen, and Roubini (1992) and Heckelman
and Berument (1998) provide evidence supporting this idea for OECD countries. So this
approach to measure economic policy uncertainty may not completely resolve the
endogeneity issue.
A disadvantage associated with using election dates to measure economic policy
uncertainty is that it is an imperfect measure of how a new government’s policy will be
implemented over time. An index approach would offers continuous measure of
economic policy uncertainty. It may do a better job capturing the evolution of economic
policy uncertainty over time. A drawback to the index approach is there is a greater
chance it is correlated with current economic conditions and is not exogenous [see
Hatzious, Phillips, Stehn, and Wu (2012)].
Because this paper uses an economic policy index in the analysis, I conduct a
Hausman test developed by Spencer and Berk (1981) to determine if endogeneity is an
issue for the sample of countries in this paper. Based on these tests, when the economic
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policy uncertainty index is not exogenous with respect to the foreign exchange market,
the regression model is estimated using instrumental variables.2
Baker, Bloom, and Davis (2013) develop an index of economic policy
uncertainty. The index for the United States is a weighted average of information from
three sources. First, an internet search counts articles in major newspapers using key
words associated with economic policy uncertainty. Second, as a measure of tax code
uncertainty, a Congressional Budget Office compilation of the value of tax code
expirations ten years forward is included in the index. The third measure is the
dispersion in forecasts of inflation and government spending taken from the Philadelphia
Federal Reserve Bank’s Survey of Professional Forecasters. Indices for the Euro Area
and Canada are constructed in a similar manner excluding the CBO data. These indices
begin in 1985. Brogaard and Detzel (2012) construct indices for 21 countries beginning
in 1990. Due to data limitations, the data series begin at a later date for some of the
emerging economies in the sample. Their indices only include data from an internet
search and count of articles that use key words associated with economic policy
uncertainty in these countries. The Access World News database serves as the source.
3. Empirical Model
The models estimated in this paper view the exchange rate as an asset price. The
current value of the exchange rate reflects expectations about economic fundamentals,
such as, monetary and fiscal policies. Higher levels of economic policy uncertainty cause
agents to adjust expectations about policy and the economy causing the exchange rate to
fluctuate [Obstfeld and Rogoff ( 1996) or Engel, Mark, and West (2007)]. Engel, Mark,
and West (2007) discuss the importance of how policy is made for understanding
2
I follow the same procedure for the measure of general economic uncertainty as well.
5
exchange rate movements and volatility. This suggests uncertainty, in terms of both
general economic and policy uncertainty will influence the path of the exchange rate.3
To understand asset price volatility, the finance literature often uses Brownian
motion. It is a useful way to characterize movements in an asset price, like an exchange
rate, over time. Exchange rate volatility is represented by equation one (Baker, Bloom,
and Davis 2013 and Brogaard and Detzel 2012).4
σi,t = βdt + σEPUUS t-1 dzEPUUS t + σEPU* t-1 dzEPU* t + σE t-1 dzE t
(1)
In equation one, σi,t measures exchange rate volatility (defined below), dt is a
long-run trend or drift term which captures any long-run predictability (e.g. movement
toward purchasing power parity), dzEPUUS t, dzEPU* t, and dzE t measure U.S. economic
policy uncertainty shocks, foreign economic policy uncertainty shocks, and economic
uncertainty shocks respectively. σEPUUS t-1, σEPU* t-1, and σE t-1 measures the stochastic
volatility of the economic policy and economic shocks respectively to the foreign
exchange market. High values for the parameter σEPUUS t-1 (or σE t-1 ) suggests positive
shocks to economic policy uncertainty (or economic uncertainty) result in high exchange
rate volatility. This paper investigates the relative importance of each type of uncertainty
on exchange rate volatility. This means that in times of high economic policy uncertainty
(or economic uncertainty), the exchange rate is expected to be more volatile. For
example, an increase in economic policy uncertainty in Brazil cause risk adverse home
3
The literature on exchange rate forecasting indicates that it is difficult for standard models to predict
future exchange rates better than a random walk model. Standard exchange rate models have had some
success in long-run forecasting (Rogoff and Stavrakeva 2008). Related to this paper, Blomberg and Hess
(1997) found political variables, such as party, election, or candidate characteristics improve forecasts.
4
Brownian motion is a continuous time representation of a random walk. This paper assumes a geometric
Brownian motion with stochastic volatility as a way to model the path of an exchange rate over time.
6
and foreign investors to reduce their holdings of Brazilian assets, resulting in a
depreciation of the Brazilian currency.
The first empirical exchange rate model estimated in this paper is represented by
equation two. Each exchange rate is quoted relative to the United States dollar. This
reflects the fact that the United States economy represents a large share of the global
economy and that the United States dollar is the primary vehicle currency for
international financial transactions. Because of the significant global role of the United
States economy, exchange rate volatility is explained by the level of economic policy
uncertainty in the home country and the United States. The regression contains a set of
control variables that capture general economic conditions in each country and the
foreign exchange market. All economic policy effects on the volatility of the exchange
rate are capture through the economic policy uncertainty indices.
σi,t = α + β1 EPUi,t + β2 EPUus,t + γ X + εt
(2)
In equation two, σi,t equals the standard deviation of the percentage change (logged first
differences) of the daily exchange rate within each month. EPUi,t and EPUus,t are the
economic policy uncertainty indices for country i and the United States respectively. X
represents a set of control variables. In order to insure economic policy uncertainty
comparability as much as possible, I use the Brogaard and Detzel (2012) economic policy
uncertainty index for all countries except the Euro Area. I use Baker, Bloom, and
Davis’s economic policy uncertainty index for the entire Euro Area since Brogaard and
Detzel only construct indices on an individual country basis.
The control variables (X) include inflation as measured by the percentage change
in the consumer price index for each country. Economic activity is measured by the
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percentage change in the total industrial production index for each country. Foreign
exchange market conditions are measured by the real exchange rate which captures
deviations away from purchasing power parity.5 Because higher economic policy
uncertainty is expected to increase exchange rate volatility, the estimated coefficients β1
and β2 are both expected to be positive.
One complication with the regression model is that economic policy uncertainty
may also be measuring general economic uncertainty in the economy. This means
general economic uncertainty must be controlled for in any attempt to quantify the impact
of economic policy uncertainty on exchange rate volatility.6 I accomplish this by
including the Chicago Board of Options 30 day volatility index or VIX in each
regression. The VIX index is a standard measure of general economic uncertainty in
applied research. This index is based on S & P 500 options and measures investor
sentiment and implied market volatility.7 It is a weighed combination of different put and
call option prices that are out of the money in a given time period. Since option prices
are a positive function of volatility, higher option prices imply greater volatility in the
market. Greater economic uncertainty would be associated with a higher option prices
and a higher VIX index.
While the index is constructed for the U.S. market, it is clearly driven by both
domestic and international factors. Most of the VIX peaks over the last 20 years occurred
5
The exchange rate, consumer price index, and industrial production index were downloaded from FRED2
at the Federal Reserve Bank of St. Louis (http://research.stlouisfed.org/fred2/). The economic policy
uncertainty indices were provided by Jonathan Brogaard. The Euro Area economic policy index was
downloaded from Steven Davis’s webpage (http://faculty.chicagobooth.edu/steven.davis/). Data used in
the regressions are monthly. The data is either seasonally adjusted at the source or adjusted by running a
regression of the unadjusted data on twelve monthly dummy variables using the residuals as the seasonally
adjusted series.
6
See Baker, Bloom, and Davis (2013) for more details on this issue.
7
The Black-Scholes option pricing model is used to solve for implied volatility given market prices.
8
during Middle East wars, the 1987 speculative attack on the Hong Kong dollar, the 1997
Asia crisis, and the 1998 Russia default. Bloom (2009), Baker, Bloom, and Davis (2013),
and Bloom (2013) use it to measure economic uncertainty in the United States. Forbes
and Warnock (2012) and Rey (2013) use it to measure economic uncertainty in the global
economy.
Pástor and Veronesi (2013) argue that governments are more likely to change
economic policies when the economy is doing poorly. As a result, economic policy
uncertainty should be higher when the economy performs poorly. In order to test the
hypothesis that economic policy uncertainty is higher in a weak economy, I estimate
equation three.
σi,t = α + β1 EPUi,t + β2 EPUus,t + β3 EPUi,t * ∆IPIi,t + β4 EPUus,t * ∆IPIus,t + γ X + εt (3)
Equation three adds two interaction terms to regression two. Each economic policy
uncertainty index is multipled by the change in industrial production. The coefficients β3
and β4 are expected to be negative. If there is greater economic policy uncertainty when
growth in industrial production is negative, the result should be higher exchange rate
volatility.
4. Empirical Results
Regression results are reported in Table One for industrialized economies and
Table Two for emerging economies. The industrial and emerging countries examined are
Canada, the Euro Area, Japan, Sweden, the United Kingdom, Brazil, India, Mexico,
South Africa, and South Korea. All exchange rates are quoted relative to the United
States dollar. The sample includes the major industrial trading partners of the United
States and emerging economies with flexible exchange rate regimes. For industrial
9
countries, the model is estimated for the period from June 1990 to February 2012 except
for the Euro Area, because the euro was introduced and began floating in January 1999.
For emerging countries, the sample period is the same except for Mexico, which began
floating its currency in November 1993. The sample starting date for Brazil is January
1995 because of data limitations. Regression models are estimated using either ordinary
least squares or instrumental variables estimators. Instrumental variables are used
whenever the Spencer-Berk test indicates that the economic policy uncertainty or the VIX
index variables are not exogenous with respect to the foreign exchange market. I use two
lags of each independent variable as instruments. High predictive content of the variables
used in this test and in model estimation is needed in order for the instrumental variable
approach to correct any bias (Bound, Jaeger, and Baker 1995). Each first stage
regression had a high F statistic (usually greater than ten) that was significant at less than
the one percent level indicating the instruments are valid. For Table One, all regressions
are estimated using instrumental variables. In Table Two, all regressions are estimated
using instrumental variables except Brazil. Newey-West standard errors with lags set at
five or six depending on the sample size are used to correct for autocorrelation.
When regression one is estimated for the industrial economies, the economic
policy uncertainty variable for the home country has a positive and significant impact on
volatility at the 10 percent level or less for the Euro Area. The coefficient for Japan is
also significant but has the wrong sign. The United States economic policy uncertainty
variable is positive and significant for Canada and Japan.
For the emerging economies, home economic policy uncertainty significantly
increases volatility for India. The coefficient for South Africa is significant but negative.
10
United States economic policy uncertainty significantly increases volatility only for
South Africa. In both Tables One and Two, the VIX index that measures general
economic uncertainty is significantly positive for all countries except the Euro Area and
Japan for industrial countries and India for the emerging economies. However, the size
of the coefficient is generally smaller compared to the economic policy uncertainty
coefficient indicating the impact is modest relative to economic policy uncertainty.
I next examine Pástor and Veronesi’s hypothesis that policy uncertainty is greater
when economies are doing poorly and find strong supporting results. Poor economic
performance increases policy activism and possibly uncertainty. Focusing on the
interaction term between economic policy uncertainty and growth in industrial
production, for the industrial economies, the home country interaction term is negative
and significant at the ten percent level or less for Canada, the Euro Area, Sweden, and the
United Kingdom. The United States interaction term is of the correct sign and also
significant for the Euro Area and Sweden. In the case of Japan, the coefficient sign is
incorrect. The economic policy uncertainty variable results change very little.
Looking at the emerging economies, the home country interaction variable is
negative and significant at the ten percent level or less for Brazil, India, and South Korea.
It is negative and significant at the 11 percent level for Mexico. The United States
interaction variable is never statistically significant. The economic uncertainty variables
change very little.8 The VIX index is positive and significant for all countries except the
Euro Area, Japan, and India.
88
When each index is measured in first differences, there are 63 percent fewer significant coefficients of
the correct sign for economic policy uncertainty. This suggests it is the level rather than the change in
uncertainty that influences exchange rate volatility. The change in the VIX index remains positive and
significant, but the size of the coefficient is smaller, suggesting it now has less of an impact.
11
Looking at the other control variables, faster home country economic growth
reduces exchange rate volatility. This may reflect the decline in market interventions
when the economy is doing well. United States economic growth rate has no significant
impact on exchange rate volatility for emerging economies, and an ambiguous impact on
industrial economies currencies. Higher home country inflation increases exchange rate
volatility, especially for emerging economies. This makes sense since central banks have
less credibility in these economies. Larger deviations from purchasing power parity, as
measured by the real exchange rate, increases exchange rate volatility. This suggests
greater long run currency market disequilibrium can destabilize currency markets in the
short run.
These results indicate that economic policy uncertainty from the United States
and the home country increase exchange rate volatility. This is particularly true when
economies are performing poorly supporting Pástor and Veronesi’s hypothesis. For
industrial countries in bad economic times, home country and United States economic
policy uncertainty significantly increase exchange rate volatility. The transmission of
economic policy uncertainty from the United States to other industrial countries
represents the high degree of financial openness between these countries over the sample
period. For emerging economies in bad economic times, home country economic policy
uncertainty, not United States economic policy uncertainty, significantly increases
exchange rate volatility. The volatility in the foreign exchange markets generated by
economic policy uncertainty is primarily home grown. This result also reflects the lower
degree of financial openness between these countries and the United States during the
12
sample period.9 These results show the while general economic uncertainty influences
exchange rate volatility, it is quantitatively smaller than economic policy uncertainty.
5. Conclusions and Policy Implications
This paper investigates the impact of home and United States economic policy
uncertainty on exchange rate volatility for ten countries over the last twenty years. The
empirical results suggest home and United States economic policy uncertainty
significantly increase volatility in many of the countries examined in this paper. This is
especially true for industrial countries when their economies are doing poorly. For
emerging economies, exchange rate volatility is generated from economic policy
uncertainty only from home. General economic uncertainty increases exchange rate
volatility, but is generally of a smaller magnitude than economic policy uncertainty.
Greater exchange rate volatility, measured in terms of standard deviations, makes
it more likely and worthwhile for individuals who are engaged in international
transactions to use costly options to hedge exposures. As a result, risk adverse
individuals may be less inclined to carry out international transactions because of the
higher costs associated with trading. This suggests that economic policy uncertainty can
have a negative impact on the international economy.
Politicians and policymakers first want to get a country’s economic policies right.
However, they should be aware that uncertainty in the policy arena negatively affects the
performance of the economy and increases volatility in financial markets. Decisiveness
Chinn and Ito (2006) construct an index of financial openness using data from the IMF’s Annual Report
on Exchange Arrangements and Exchange Restrictions. It combines data on exchange rate arrangements
and rules. It also measures whether there are current and capital account restrictions on a country basis.
Looking at the countries in this sample for 2011, the Euro Area, Canada, the U.K., Japan, Sweden, and the
U.S. all have the highest index scores (2.44), indicating a high degree of openness. The emerging
economies scores are significantly lower indicating they are less open. India and South Africa have scores
of -1.17, while Brazil’s score is -.11, South Korea’s score is .94, and Mexico’s score is 1.12. The country
index comparison indicates the industrial economies are far more open that the emerging economies.
9
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and clarity in economic policy decision making, especially in bad economic times, can
lead to better economic performance and more stable financial markets.
References
Alesina, Alberto, Gerald D. Cohen, and Nouriel Roubini (1992) “ Electorial Business
Cycles in Industrial Democracies,” European Journal of Political Economy, 1-23.
Baker, Scott, Nicholas Bloom, and Steven Davis (2013) “Measuring Economic Policy
Uncertainty,” Working Paper University of Chicago Booth School of Business.
Bernhard, William and David Leblang (2006) Democratic Processes and Financial
Markets: Pricing Politics, Cambridge University Press, New York, NY.
Bialkowski, Jedrzej, Katrin Gottschalk, and Tomaz Wisniewski (2008) “Stock Market
Volatility around National Elections,” Journal of Banking and Finance, 1941-1953.
Bloom, Nicholas (2009) “The Impact of Uncertainty Shocks,” Econometrica, 623 – 685.
Bloom, Nicholas (2013) “Fluctuations in Uncertainty,” N.B.E.R. Working Paper #19714.
Blomberg, S. Brock and Gregory D. Hess (1997) “ Politics and Exchange Rates,” Journal
of International Economics, 189-205.
Brogaard, Jonathan and Andrew Detzel (2012) “The Asset Pricing Implications of
Government Economic Policy Uncertainty,” Working Paper Foster School of Business,
University of Washington.
Bound, John, David Jaeger, and Regina Baker (1995) “Problems with Instrumental
Variables Estimation When the Correlation Between the Instruments and Endogeneous
Explanatory Variable is Weak,” Journal of the American Statistical Association, 443450.
Boutchkova, Maria, Hitesh Doshi, Art Durnev, and Alexander Molchanov (2012)
“Precarious Politics and Return Volatility,” Review of Financial Studies, 1111-1154.
Chinn, Menze and Hiro Ito (2006) “What Matters for Financial Development? Capital
Controls, Institutions, and Interactions,” Journal of Development Economics, 163-192.
Columbo, Valentina (2013) “Economic Policy Uncertainty in the US: Does it Matter for
the Euro Area?” Economics Letters, 39-42.
Engel, Charles, Nelson Mark, and Kenneth West (2007) “Exchange Rate Models Are Not
as Bad as You Think,” NBER Macroeconomics Annual, 381-441.
14
Forbes, Kristin and Francis Warnock (2012) “Capital Flow Waves: Surges, Stops, Flight,
and Retrenchment,” Journal of International Economics, 235-251.
Hatzius, Jan, Alec Phillips, Jari Stehn, and Shuyan Wu (2012) “Policy Uncertainty: Is
Now the Time?” Goldman Sachs US Economics Analyst, 1-11.
Heckelman, Jac C. and Hakan Berument (1998) “ Political Business Cycles and
Endogenous Elections,” Southern Economic Journal, 987-1000.
Julio, Brandon and Youngsuk Yook (2012) “Political Uncertainty and Corporate
Investment Cycles,” Journal of Finance, 45-83.
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Exchange Markets: The World According to GARCH,” International Studies Quarterly,
69-92.
Luboš Pástor and Pietro Veronesi (2013) “Political Uncertainty and Risk Premia,”
Journal of Financial Economics, 520-545.
Newey, Whitney and Kenneth West (1987) “A Simple Positive-Definite Heteroskedastic
and Autocorrelation Consistent Covariance Matrix,” Econometrica, 703-708.
Obstfeld, Maurice and Kenneth Rogoff (1996), Foundations of International
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Policy Independence,” Global Dimensions of Unconventional Monetary Policy 2013
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Rogoff, Kenneth and Vania Stavrakeva (2008) “The Continuing Puzzle of Short Horizon
Exchange Rate Forecasting,” NBER Working Paper # 14071.
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Econometrica, 1079-1085.
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Table 1 – Industrial Economies
Dependent Variable is Exchange Rate Volatility
Country
Variable
Constant
EPUi
EPUUS
CAN
CAN
EUR
EUR
JPN
JPN
SWE
SWE
UK
UK
-.05
(.00)
-.00
(.62)
.013
(.01)
-.05
(.00)
-.00
(.62)
.014
(.02)
-270
(.01)
-.052
(.78)
.003
(.01)
.731
(.01)
.166
(.77)
-.006
(.12)
.002
(.71)
.014
(.002)
.58
.001
-.01
(.24)
.002
(.02)
.002
(.59)
-.01
(.44)
.003
(.04)
-.001
(.89)
-.160
(.00)
-.214
(.06)
.001
(.28)
.724
(.00)
.608
(.08)
-.003
(.47)
.004
(.38)
.005
(.10)
.52
.002
-.09
(.00)
-.01
(.03)
.033
(.00)
-.08
(.00)
.001
(.94)
.023
(.01)
-.05
(.75)
1.76
(.00)
.003
(.12)
.148
(.74)
-5.35
(.00)
.001
(.01)
.004
(.51)
.001
(.11)
.48
.002
-.01
(.54)
.002
(.24)
-.000
(.97)
-.01
(.53)
.002
(.27)
.000
(.98)
-.078
(.07)
-.337
(.03)
.005
(.00)
.186
(.07)
.949
(.04)
-.003
(.50)
.001
(.81)
-.000
(.51)
.49
.003
-.01
(.44)
.003
(.41)
-.00
(.91)
-.01
(.33)
.004
(.20)
-.00
(.95)
-.84
(.00)
-.06
(.70)
.002
(.01)
2.14
(.00)
.134
(.79)
-.00
(.69)
.002
(.57)
.001
(.31)
.50
.002
EPUiRES
EPUUSRES
VIX
∆IPIi
∆IPIUS
∆CPIi
∆CPIUS
REALEX
Rbarsq
SEE
.003
(.00)
.008
(.64)
.006
(.74)
-.006
(.09)
.002
(.74)
.015
(.00)
.56
.002
.001
(.13)
-.043
(.02)
-.039
(.03)
-.001
(.69)
.002
(.66)
.003
(.33)
.46
.002
.004
(.03)
-.003
(.85)
-.024
(.40)
.001
(.01)
.012
(.02)
.002
(.01)
.24
.003
.005
(.00)
-.011
(.32)
-.060
(.11)
-.003
(.44)
.001
(.87)
-.000
(.57)
.47
.003
.003
(.01)
-.07
(.10)
-.07
(.03)
.001
(.74)
-.00
(.71)
.001
(,47)
.43
.002
Notes: CAN equals Canada, EUR equals Euro Area, JPN equals Japan, SWE equals
Sweden, and UK equals the United Kingdom. EPUi equals the economic policy
uncertainty index for country i. EPUiRES equals the interaction variable of the economic
policy uncertainty index for country i multiplied by the growth in industrial production
for country i. VIX equals the CBOE volatility index. ∆IPIi equals the growth rate in the
industrial production index for country i. ∆CPIi equals the growth rate in the consumer
price index for country i. REALEX equals the real exchange rate. Rbarsq equals the R
bar squared for the regression. SEE equals the standard error of the regression.
Coefficient p-values are reported in parentheses. All regressions are estimated using
instrumental variables. Bold and italicized entries indicate the variable has a p-value of
10 percent or less.
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Table 2 – Emerging Economies
Dependent Variable is Exchange Rate Volatility
Country
Variable
Constant
EPUi
EPUUS
BRZ
BRZ
IND
IND
MEX
MEX
SAF
SAF
SKO
SKO
-.011
(.66)
-.000
(.89)
-.003
(.77)
-.012
(.62)
-.001
(.80)
-.002
(.83)
-.163
(.02)
-.040
(.94)
.009
(.00)
.399
(.04)
.143
(.92)
.009
(.03)
-.004
(.25)
.001
(.43)
.41
.005
-.011
(.17)
.008
(.01)
-.001
(.60)
-.012
(.16)
.008
(.01)
-.001
(.69)
-.074
(.09)
-.239
(.15)
.001
(.56)
.178
(.08)
.671
(.17)
.001
(.23)
-.004
(.10)
-.001
(.14)
.46
.002
-.051
(.04)
.005
(.36)
.008
(.13)
-.051
(.03)
.006
(.27)
..008
(.15)
-.258
(.11)
.431
(.16)
.005
(.00)
.695
(.00)
-1.23
(.17)
.014
(.21)
-.003
(.48)
.001
(.02)
.49
.003
-.027
(.21)
-.003
(.08)
.012
(.08)
.007
(.38)
-.005
(.09)
-.005
(.03)
-.103
(.39)
.501
(.15)
.010
(.00)
.269
(.41)
-1.51
(.13)
.008
(.06)
-.009
(.07)
-.001
(.65)
.28
.004
-.038
(.12)
.000
(.50)
.001
(.57)
-.039
(.10)
.003
(.36)
.005
(.58)
-.159
(.09)
.350
(.55)
.006
(.10)
.472
(.10)
-1.06
(.54)
.010
(.05)
-.010
(.04)
.000
(.20)
.38
.004
EPUiRES
EPUUSRES
VIX
∆IPIi
∆IPIUS
∆CPIi
∆CPIUS
REALEX
Rbarsq
SEE
.009
(.00)
.068
(.00)
.024
(.58)
.011
(.023)
-.004
(.28)
.001
(.42)
.40
.005
.001
(.54)
-.002
(.76)
-.039
(.06)
.001
(.24)
-.005
(.09)
-.001
(.20)
.45
.002
.005
(.00)
-.007
(.67)
.051
(.21)
.015
(.18)
-.003
(.02)
.001
(.02)
.24
.003
.004
(.08)
-.015
(.23)
-.016
(.76)
.008
(.02)
-.008
(.10)
-.001
(.56)
.18
.004
.001
(.01)
-.012
(.09)
-.034
(.46)
.001
(.04)
-.012
(.02)
.000
(.24)
.38
.004
Notes: BRZ equals Brazil, IND equals India, MEX equals Mexico, SAF equals South
Africa, and SKO equals South Korea. EPUi equals the economic policy uncertainty
index for country i. EPUiRES equals the interaction variable of the economic policy
uncertainty index for country i multiplied by the growth in industrial production for
country i. VIX equals the CBOE volatility index. ∆IPIi equals the growth rate in the
industrial production index for country i. ∆CPIi equals the growth rate in the consumer
price index for country i. REALEX equals the real exchange rate. Rbarsq equals the R
bar squared for the regression. SEE equals the standard error of the regression.
Coefficient p-values are reported in parentheses. All regressions are estimated using
instrumental variables except BRZ. Bold and italicized entries indicate the variable has a
p-value of 10 percent or less.
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