Bulletin of Monetary Economics and Banking, Vol. 21, No. 2 (2018), pp. 251  268
CAN ECONOMIC POLICY UNCERTAINTY PREDICT
EXCHANGE RATE AND ITS VOLATILITY? EVIDENCE FROM ASEAN COUNTRIES
Solikin M. Juhro1 and Dinh Hoang Bach Phan2
1Bank Indonesia Institute, Bank Indonesia, Jakarta, Indonesia. Email: solikin@bi.go.id
2Taylor’s Business School, Taylor’s University, Malaysia. Email: dinhphan325@gmail.com
ABSTRACT
This paper examines whether global Economic Policy Uncertainty (EPU) predicts exchange rates and their volatility in ten ASEAN countries using monthly data from January 1997 to December 2017. Applying the predictive regression model of Westerlund and Narayan (2012, 2015), we find that EPU positively and statistically significantly predicts the exchange rates of six out of ten currencies. A one standard deviation increase in the EPU index leads to a depreciation of between 0.050% and 2.047% in these currencies. Moreover, EPU predicts exchange rate volatility for all ten ASEAN countries. Their exchange rate volatilities increase by between 0.107% and 0.645% as a result of a one standard deviation increase in the EPU index. These results are robust to different forecasting horizons and subsample periods, and after controlling for the Global Financial Crisis.
Keywords: Economic policy uncertainty; Predictability; Exchange rate; ASEAN.
JEL Classification: G12; G17.
Article history: 

Received 
: July 1, 2018 
Revised 
: October 22, 2018 
Accepted 
: October 23, 2018 
Available online 
: October 31, 2018 
https://doi.org/10.21098/bemp.v21i2.974
252Bulletin of Monetary Economics and Banking, Volume 21, Number 2, October 2018
I. INTRODUCTION
We investigate whether global Economic Policy Uncertainty (EPU) can predict exchange rates and their volatility in ten ASEAN countries. The foreign exchange market is regarded as the most liquid and largest financial market (Record, 2003). Exchange rate stability is important for building and maintaining a robust economy. Increased exchange rate volatility, for instance, can have negative effects on an economy, including: (1) greater uncertainty on future consumption (Obstfeld and Rogoff, 1998; Devereux, 2004); (2) increased volatility of business profitability (Braun and Larrain, 2005; Aghion, Bacchetta, and Rancière, 2009);
(3)increased risk for domestic and foreign direct investment (Campa, 1993; Darby,
Hallett, Ireland, and Piscitelli, 1999; Urata and Kawai, 2000; Servén, 2003; Byrne and Davis, 2005); (4) increased inflation uncertainty and higher interest rates along with reduced investment and consumption (Grier and Grier, 2006); and
(5)changes in production cost and increased international transaction risk (Baum and Caglayan, 2006). Given these issues, predicting exchange rate and its volatility are of direct interest to central bank policymaking. Therefore, understanding what predicts exchange rate and its volatility is important.
The literature on exchange rate prediction is rich and quite extensive. Various predictors have been examined, such as exchange rates themselves (Meese and
Rogoff, 1983; Engel, Mark, and West, 2014), monetary fundamental variables (Giacomini and Rossi, 2010; Molodtsova,
EPU on forecasting macroeconomic and financial variables, including studies on predicting inflation (Colombo, 2013; Jones and Olson, 2013; Balcilar, Gupta, Jooste, 2017) recession (Karnizova and Li, 2014), GDP (Stockhammar and Österholm,
2016), and stock returns (Phan, Sharma, and Tran, 2018).
However, the literature on using EPU to forecast exchange rate and its volatility is limited.3 Balcilar et al. (2016) test whether EPU predicts exchange rate in 16 countries and find no evidence of predictability, except the Brazilian Real. Dai, Zhang, Yu, and Li (2016) examine the Chinese market and find a causal relationship from EPU to the exchange rate in China when EPU is high.
Krol (2014) finds that domestic and US EPU increase exchange rate volatility for a number of currencies.4 That EPU is a global index implies that it should affect both
3The popular EPU index used in the literature was developed by Baker et al. (2016) and is found to be a good proxy to show the development of
Federal Reserve System). Baker et al. (2016) show that this measure significantly impacts financial and macro variables such as stock price volatility, investment, output, and employment.
4Previous studies (Balcilar et al., 2016; Dai et al., 2016) explain the effect of EPU on exchange rate through two channels: (1) EPU as an additional risk factor in the market, and (2) an indirect channel via other macro variables.
Can Economic Policy Uncertainty Predict Exchange Rate and Its Volatility? 
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Evidence from ASEAN Countries 
developing and emerging markets. Nevertheless, nothing is known about how the EPU performs in predicting exchange rate and its volatility in the ASEAN context. The present paper, therefore, addresses this research gap.
Our approach is as follows. First, we collect data for the ten ASEAN currencies and compute their return and volatility. For EPU index data, we use the Baker, Bloom, and Davis (2016) measure. Next, we employ the Feasible Generalized Least Squares (FGLS) model of Westerlund and Narayan (2012, 2015), which accounts for predictor persistency and endogeneity, and model heteroskedasticity to predict exchange rate and its volatility (using the EPU as a predictor). Finally, we test the robustness of our findings through forecasting horizons, subsamples of data, and controlling for the Global Financial Crisis (GFC).
Our empirical findings are threefold. First, we find that EPU predicts exchange rate for six out of ten ASEAN currencies. This result suggests that EPU can predict exchange rate but is
This paper proceeds as follows. We describe our data sample and predictive regression model in Section II. Next, Section III discusses our main findings and robustness test results. Finally, our conclusions are set forth in Section IV.
II. DATA AND METHODOLOGY
A. Data
Our dataset consists of exchange rate series of ten ASEAN countries
volatilities are calculated as , where rt is daily exchange rate return.
5Brunei Dollar (BND), Cambodian Riel (KHR), Indonesian Rupiah (IDR), Lao Kip (LAK), Malaysian Ringgit (MYR), Myanmar Kyat (MMK), Philippine Peso (PHP), Singapore Dollar (SGD), Thai Baht
(THB), and Vietnam Dong (VND).
254Bulletin of Monetary Economics and Banking, Volume 21, Number 2, October 2018
The second dataset is EPU. Following previous studies (Wang, Chen, and Huang, 2014; Ajmi, Aye, Balcilar, El Montasser, and Gupta, 2015; Li, Balcilar, Gupta, and Chang, 2016; Li and Peng, 2017; Phan, Sharma, and Tran, 2018), we use the Baker et al. (2016) EPU measure. Greater uncertainty manifests as a higher value of the index. EPU data are available on Baker’s website.6 Our sample period and data frequency are chosen based on EPU data availability. We use monthly data from January 1997 to December 2017, comprising 252 observations. Figure I plots EPU and exchange rates of ten ASEAN countries.
Figure 1. Plots of Economic Policy Uncertainty and Exchange Rates
This figure plots the Economic Policy Uncertainty (EPU) and exchange rates of 10 ASEAN countries. These countries are: Brunei Dollar (BND), Cambodian Riel (KHR), Indonesian Rupiah (IDR), Lao Kip (LAK), Malaysian Ringgit (MYR), Myanmar Kyat (MMK), Philippine Peso (PHP), Singapore Dollar (SGD), Thai Baht (THB), and Vietnam Dong (VND). The data are
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Can Economic Policy Uncertainty Predict Exchange Rate and Its Volatility? 
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Evidence from ASEAN Countries 
Figure 1. Plots of Economic Policy Uncertainty and Exchange Rates (Continued)
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256Bulletin of Monetary Economics and Banking, Volume 21, Number 2, October 2018
Figure 1. Plots of Economic Policy Uncertainty and Exchange Rates (Continued)
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Can Economic Policy Uncertainty Predict Exchange Rate and Its Volatility? 
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Evidence from ASEAN Countries 
Figure 1. Plots of Economic Policy Uncertainty and Exchange Rates (Continued)
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258Bulletin of Monetary Economics and Banking, Volume 21, Number 2, October 2018
B. Methodology
Following the predictability literature, the predictive regression model can be written as:
(1)
where xt is exchange rate return or its volatility in month t for one of ten ASEAN countries, and the predictor
(2)
(3)
where ρ ≤ 1. ϵt and εt are expected to be uncorrelated and have mean zero. If this assumption is violated, the predictor is endogenous and leads to a biased
βusing OLS estimation. The predictor EPU can also be highly persistent. A
2017), commodities (Sharma, 2016; Han, Lv, and Yin, 2017), inflation (Salisu and
Isah, 2018), economic growth (Narayan, Sharma, Poon,, Westerlund, 2014), and carbon emissions (Narayan and Sharma, 2015).
III. EMPIRICAL FINDINGS
A. Preliminary Results
Table 1 reports common descriptive statistics of exchange rate return and its volatility for ten ASEAN countries (Panels A & B) and the predictor, EPU, (Panel C). Considering the exchange rate returns in Panel A, we note that the monthly average in the first column varies from
Can Economic Policy Uncertainty Predict Exchange Rate and Its Volatility? 
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Evidence from ASEAN Countries 
Table 1.
Descriptive Statistic
This table reports selective descriptive statistics for exchange rate returns (Panel A), exchange rate return volatility (Panel B) and the predictor, EPU (Panel C). The statistics include the mean value, standard deviation (SD), AR(1), and ARCH(1). AR(1) refers to the autoregressive coefficient of order 1, while ARCH (1) refers to a Lagrange multiplier test of the zero slope restriction in an ARCH regression of order 1 and the

Panel A: Exchange Rate Returns 





Mean 
SD 
Skewness 
Kurtosis 
JB 
AR(1) 
ARCH 
Brunei Dollar (BND) 
2.922 
4.622 
43.618 
0.000 
0.049 
0.840 

Cambodian Riel (KHR) 
0.226 
1.616 
4.370 
39.099 
0.000 
0.215 
0.574 
Indonesian Rupiah (IDR) 
0.693 
7.135 
2.760 
28.584 
0.000 
0.227 
0.000 
Lao Kip (LAK) 
0.873 
5.118 
5.262 
35.290 
0.000 
0.221 
0.402 
Malaysian Ringgit (MYR) 
0.187 
2.454 
0.543 
10.186 
0.000 
0.190 
0.000 
Myanmar Kyat (MMK) 
2.156 
30.597 
15.734 
249.035 
0.000 
0.000 
0.949 
Philippine Peso (PHP) 
0.255 
2.453 
1.578 
10.857 
0.000 
0.109 
0.035 
Singapore Dollar (SGD) 
1.773 
0.387 
5.428 
0.000 
0.001 
0.011 

Thai Baht (THB) 
0.095 
3.329 
1.487 
24.328 
0.000 
0.158 
0.002 
Vietnam Dong (VND) 
0.284 
0.945 
4.280 
25.562 
0.000 
0.611 


Panel B: Exchange Rate Return Volatility 





Mean 
SD 
Skewness 
Kurtosis 
JB 
AR(1) 
ARCH 
Brunei Dollar (BND) 
2.799 
5.123 
4.387 
22.255 
0.000 
0.637 
0.000 
Cambodian Riel (KHR) 
1.677 
3.728 
7.443 
68.681 
0.000 
0.179 
0.991 
Indonesian Rupiah (IDR) 
3.595 
5.619 
5.243 
40.213 
0.000 
0.793 
0.000 
Lao Kip (LAK) 
1.676 
4.960 
5.076 
32.076 
0.000 
0.241 
0.390 
Malaysian Ringgit (MYR) 
1.510 
2.003 
3.536 
21.199 
0.000 
0.822 
0.000 
Myanmar Kyat (MMK) 
4.755 
43.066 
11.076 
123.781 
0.000 
0.495 
0.000 
Philippine Peso (PHP) 
1.943 
1.744 
4.184 
27.833 
0.000 
0.504 
0.045 
Singapore Dollar (SGD) 
1.551 
0.840 
2.696 
14.580 
0.000 
0.701 
0.000 
Thai Baht (THB) 
2.092 
2.509 
3.797 
19.339 
0.000 
0.655 
0.000 
Vietnam Dong (VND) 
0.617 
1.431 
5.786 
43.961 
0.000 
0.447 
0.000 


Panel C: Predictor 






Mean 
SD 
Skewness 
Kurtosis 
JB 
AR(1) 
ARCH 
EPU 
109.082 
43.617 
1.304 
5.057 
0.000 
0.839 
0.000 
We now examine the exchange rate volatilities of ASEAN countries in Panel B. The results suggest that MMK has the highest volatility (mean value, 4.755%) while VND has the lowest volatility (mean value, 0.617%). Five currencies (BND, IDR, MYR, SGD, and THB) have
We test for endogeneity for the predictor EPU and report the results in Table 2. We examine the coefficient γ in Equation (3), which is a regression of residuals from Equation (1) on residuals from the AR(1) predictor regression model Equation (2). We observe limited evidence for endogeneity, as the coefficient γ is statistically insignificant in all cases (except the predictive regression model of SGD volatility).
260Bulletin of Monetary Economics and Banking, Volume 21, Number 2, October 2018
Table 2.
Endogeneity Test
This table reports the results for the endogeneity test in the predictive regression model. The endogeneity test is based on a regression of residuals from the predictive regression model on residuals from the
statistical significance at the 5% level.

Exchange Rate Returns 
Exchange Rate Return Volatility 


γ 
γ 

Brunei Dollar (BND) 
0.006 
0.421 
0.010 
0.483 
Cambodian Riel (KHR) 
0.723 
0.359 

Indonesian Rupiah (IDR) 
0.020 
0.296 
0.469 

Lao Kip (LAK) 
0.637 
0.780 

Malaysian Ringgit (MYR) 
0.008 
0.205 
0.007 
0.188 
Myanmar Kyat (MMK) 
0.709 
0.102 
0.376 

Philippine Peso (PHP) 
0.008 
0.242 
0.002 
0.636 
Singapore Dollar (SGD) 
0.005 
0.314 
0.005** 
0.018 
Thai Baht (THB) 
0.004 
0.673 
0.451 

Vietnam Dong (VND) 
0.000 
0.894 
0.755 
In summary, the preliminary results suggest strong evidence for persistency and heteroskedasticity, and weak evidence for endogeneity. Therefore, it is rational to use the FGLS estimator of WN (2012, 2015) to eliminate biases and inefficiency.
B. Baseline Results
We report the results for the prediction test in Table 3. The coefficient (columns 2 and 4) and the
Can Economic Policy Uncertainty Predict Exchange Rate and Its Volatility? 
261 
Evidence from ASEAN Countries 
Table 3.
Exchange Rate and Its Volatility Predictability
This table reports results on the predictability of exchange rate and its volatility, where the predictor is EPU. The predictive regression model is the
denote significance at the 10%, 5% and 1% levels, respectively.

Exchange Rate Returns 
Exchange Rate Return Volatility 


Coefficient 
Coefficient 

Brunei Dollar (BND) 
0.224 
0.011*** 
0.000 

Cambodian Riel (KHR) 
0.003*** 
0.009 
0.008*** 
0.000 
Indonesian Rupiah (IDR) 
0.010*** 
0.000 
0.015*** 
0.000 
Lao Kip (LAK) 
0.007*** 
0.000 
0.009*** 
0.000 
Malaysian Ringgit (MYR) 
0.001** 
0.012 
0.012*** 
0.000 
Myanmar Kyat (MMK) 
0.047*** 
0.000 
0.008*** 
0.000 
Philippine Peso (PHP) 
0.005* 
0.071 
0.011*** 
0.000 
Singapore Dollar (SGD) 
0.003 
0.198 
0.013*** 
0.000 
Thai Baht (THB) 
0.465 
0.010*** 
0.000 

Vietnam Dong (VND) 
0.001 
0.259 
0.002*** 
0.000 
We also observe that an EPU increase leads to an increase in the volatility of all ASEAN country currencies. The coefficients of EPU in the exchange rate volatility predictive regression models are statistically significantly positive in all currencies. The most affected currency is the IDR and the least affected currency is the VND. A one index point increase in the EPU index leads to an increase of 0.015% in IDR volatility and 0.002% in VND volatility. If we consider a one standard deviation increase in the EPU index, the increases are 0.107% and 0.645%, respectively.
C. Robustness Tests
We utilize three robustness test analyses for our baseline results. First, we consider predictability at longer forecasting horizons. We use EPU to predict exchange rate and its volatility two months and three months ahead. The results are reported in Table 4. When we consider a
262Bulletin of Monetary Economics and Banking, Volume 21, Number 2, October 2018
Table 4.
Robustness Test Using Different Forecasting Horizons
This table reports results on

Panel A: 



Exchange Rate Returns 
Exchange Rate Return Volatility 


Coefficient 
Coefficient 

Brunei Dollar (BND) 
0.000 
0.852 
0.019*** 
0.000 
Cambodian Riel (KHR) 
0.003*** 
0.001 
0.008*** 
0.000 
Indonesian Rupiah (IDR) 
0.010*** 
0.000 
0.013*** 
0.000 
Lao Kip (LAK) 
0.015*** 
0.000 
0.002*** 
0.000 
Malaysian Ringgit (MYR) 
0.246 
0.002*** 
0.000 

Myanmar Kyat (MMK) 
0.063* 
0.075 
0.022*** 
0.000 
Philippine Peso (PHP) 
0.005** 
0.021 
0.011*** 
0.000 
Singapore Dollar (SGD) 
0.001 
0.719 
0.013*** 
0.000 
Thai Baht (THB) 
0.198 
0.009*** 
0.000 

Vietnam Dong (VND) 
0.002 
0.153 
0.003*** 
0.000 

Panel B: 



Exchange Rate Returns 
Exchange Rate Return Volatility 


Coefficient 
Coefficient 

Brunei Dollar (BND) 
0.002 
0.519 
0.010*** 
0.000 
Cambodian Riel (KHR) 
0.000 
0.842 
0.008*** 
0.000 
Indonesian Rupiah (IDR) 
0.009*** 
0.000 
0.016*** 
0.000 
Lao Kip (LAK) 
0.010*** 
0.000 
0.009*** 
0.000 
Malaysian Ringgit (MYR) 
0.001 
0.217 
0.002*** 
0.000 
Myanmar Kyat (MMK) 
0.040*** 
0.000 
0.010*** 
0.000 
Philippine Peso (PHP) 
0.005* 
0.051 
0.011*** 
0.000 
Singapore Dollar (SGD) 
0.001 
0.667 
0.013*** 
0.000 
Thai Baht (THB) 
0.513 
0.010*** 
0.000 

Vietnam Dong (VND) 
0.002 
0.192 
0.003*** 
0.000 
Our second robustness test uses subsample periods. Our sample is monthly data over 21 years, which is sufficient for this robustness test. We divide our sample into two subsample periods: January
Can Economic Policy Uncertainty Predict Exchange Rate and Its Volatility? 
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Evidence from ASEAN Countries 
Table 5.
Robustness Test Using
This table reports results on the predictability of exchange rate returns and its volatility using EPU as the predictor for two sub sample periods. The predictive regression model is the

Panel A: 



Exchange Rate Returns 
Exchange Rate Return Volatility 


Coefficient 
Coefficient 

Brunei Dollar (BND) 
0.003 
0.259 
0.014*** 
0.000 
Cambodian Riel (KHR) 
0.009*** 
0.000 
0.008*** 
0.000 
Indonesian Rupiah (IDR) 
0.623 
0.031*** 
0.000 

Lao Kip (LAK) 
0.024*** 
0.000 
0.005*** 
0.005 
Malaysian Ringgit (MYR) 
0.001*** 
0.000 
0.000*** 
0.000 
Myanmar Kyat (MMK) 
0.000** 
0.033 
0.014*** 
0.000 
Philippine Peso (PHP) 
0.019*** 
0.000 
0.016*** 
0.000 
Singapore Dollar (SGD) 
0.004 
0.236 
0.014*** 
0.000 
Thai Baht (THB) 
0.006 
0.223 
0.016*** 
0.000 
Vietnam Dong (VND) 
0.000 
0.001*** 
0.000 


Panel B: 



Exchange Rate Returns 
Exchange Rate Return Volatility 


Coefficient 
Coefficient 

Brunei Dollar (BND) 
0.847 
0.014*** 
0.000 

Cambodian Riel (KHR) 
0.204 
0.008*** 
0.000 

Indonesian Rupiah (IDR) 
0.007** 
0.018 
0.007*** 
0.000 
Lao Kip (LAK) 
0.469 
0.002*** 
0.000 

Malaysian Ringgit (MYR) 
0.010*** 
0.001 
0.014*** 
0.000 
Myanmar Kyat (MMK) 
0.034*** 
0.000 
0.031*** 
0.000 
Philippine Peso (PHP) 
0.005* 
0.073 
0.010*** 
0.000 
Singapore Dollar (SGD) 
0.753 
0.013*** 
0.000 

Thai Baht (THB) 
0.007 
0.008*** 
0.000 

Vietnam Dong (VND) 
0.003*** 
0.000 
0.003*** 
0.000 
Our final robustness test controls for the GFC.7 We add a dummy variable that equals 1 during the GFC period July
7We also have a robustness test that controls for the ASIAN Financial Crisis and we find robust results. The results are available up on request.
264Bulletin of Monetary Economics and Banking, Volume 21, Number 2, October 2018
Table 6.
Robustness Test Controlling for the Global Financial Crisis
This table reports results on the predictability of exchange rate returns and its volatility using EPU as the predictor after controlling for the global financial crisis effect. The predictive regression model is the

Exchange Rate Returns 
Exchange Rate Return Volatility 


Coefficient 
Coefficient 

Brunei Dollar (BND) 
0.174 
0.014*** 
0.000 

Cambodian Riel (KHR) 
0.538 
0.008*** 
0.000 

Indonesian Rupiah (IDR) 
0.005* 
0.097 
0.023*** 
0.000 
Lao Kip (LAK) 
0.007*** 
0.000 
0.011* 
0.089 
Malaysian Ringgit (MYR) 
0.001*** 
0.001 
0.000*** 
0.000 
Myanmar Kyat (MMK) 
0.024 
0.793 
0.003*** 
0.000 
Philippine Peso (PHP) 
0.004* 
0.090 
0.015*** 
0.000 
Singapore Dollar (SGD) 
0.003 
0.251 
0.015*** 
0.000 
Thai Baht (THB) 
0.435 
0.016*** 
0.000 

Vietnam Dong (VND) 
0.001 
0.001*** 
0.000 
IV. CONCLUSION
This paper investigates whether EPU can predict ASEAN exchange rates and their volatilities. Our analysis is based on monthly data over the period January 1997 to December 2017 for ten ASEAN countries. We apply the FGLS model of Westerlund and Narayan (2012, 2015), which accounts for persistency, endogeneity, and heteroskedasticity issues.
Our results suggest strong evidence for the predictability of exchange rates and their volatility using EPU as the predictor. Our baseline findings show that EPU positively and statistically significantly predicts the exchange rate of six out of ten currencies in our sample: KHR, IDR, LAK, MYR, MMK, and PHP. Exchange rate predictability is
We test the consistency of our baseline empirical analysis via two robustness tests. First, we use EPU to predict exchange rate and its volatility for two months and three months ahead. Second, we split our sample into two subsample periods: January
Can Economic Policy Uncertainty Predict Exchange Rate and Its Volatility? 
265 
Evidence from ASEAN Countries 
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