The impacts of economic sanctions on exchange rate volatility

Economic Modelling 82 (2019) 58–65

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Economic Modelling journal homepage: www.journals.elsevier.com/economic-modelling

The impacts of economic sanctions on exchange rate volatility Yiwei Wang a, Ke Wang a, Chun-Ping Chang *, b a b

School of Economics and Finance, Xi’an Jiao Tong University, Xi’an, Shaanxi, China Shih Chien University, Kaohsiung, Taiwan

A R T I C L E I N F O

A B S T R A C T

Jel Codes: F51 F31 C33

This research empirically analyzes the impact of various instruments of economic sanctions on official exchange rate volatility by employing data from a panel of 23 target countries covering the period 1996–2015 and using the Least Squares Dummy Variable Corrected (LSDVC) model. Our findings suggest that economic sanctions do significantly influence the target countries’ exchange rate volatility. Specifically, we are able to see different sanction present its different effects on exchange rate volatility. Furthermore, the robustness evidence of the eliminating country as Iran, eliminating variable of political ideology, intercepting time period, cross-sectional regression analysis, using real exchange rate volatility as proxy variable and a new sanctions database, are basically consistent with the previous finding. Overall, our empirical findings offer implications for those sanctioned countries about how to stabilize their exchange rate when facing sanctions.

Keywords: Economic sanctions Exchange rate volatility LSDVC

1. Introduction A stable exchange rate guarantees foreign trade and provides a good external environment for economic development, while in contrast, an unstable exchange rate will increase future income and financial uncertainty, increase the risk of domestic and foreign investments, and consequently reduce social welfare (Devereux, 2004; Byrne and Davis, 2005). Previous literature shows that greater exchange rate volatility usually leads to greater risk of domestic and foreign direct investments, especially in developing countries (Urata and Kawai, 2000; Serv
en, 2003; Byrne and Davis, 2005). From these viewpoints, the influencing factors of exchange rate volatility are mostly discussed from the economic and financial aspects, such as the impact of oil prices and international capital flows on exchange rate (Basher et al., 2012; Jongwanich and Kohpaiboon, 2013), while a few studies in the literature have discussed the impact of political factors on exchange rate (Steinberg and Shih, 2012). Once launched, economic sanctions inevitably impact international oil prices and international capital flows and also cause changes in the political policies of both sides. However, there is no exploration between the relationship of economic sanctions and exchange rate, and this research looks to fill this gap in the literature. Economic sanctions are a coercive measure between pure diplomatic pressure and extreme military intervention. Current literature on economic sanctions is limited to case studies of single-target countries. Economic sanctions imposed by the EU on Russia in 2014 led to a series

of economic crises in that country. The collapse of the exchange rate of Russia’s ruble led to serious inflation. Russia’s economic growth depends partly on energy exports and trade surpluses, and economic sanctions have restricted the export of petroleum energy fuels. Moreover, significant changes have taken place in the structure of foreign trade, the economy has been seriously affected, foreign investment has been declining, the investment environment has deteriorated, the ruble has depreciated substantially, and exchange rate volatility has increased. Economic sanctions can also lead to reduced imports, lower supplies, higher inflation, and exchange rate volatility (Gurvich and Prilepskiy, 2015; Dreger et al., 2016). Taking Iran as another example, the comprehensive trade embargo and financial restrictions imposed by the EU and the U.S. have strengthened economic sanctions against the country, basically cutting off financial and trade channels between Iran and the international community and forcing relevant countries to stop or significantly reduce their import of Iranian oil. Many foreign oil companies have withdrawn from Iran, resulting in a rapid decline in Iranian oil production and exports, a sharp decline in oil revenues, a sharp devaluation of its currency (the riyal), and higher inflation and unemployment, thus resulting in exchange rate volatility (Dudl
ak, 2018). Our hypothesis is that economic sanctions do increase exchange rate volatility, which may come from the following two channels: the first is the import and export trade channel. Economic sanctions, especially those restricting the import and export trade of the target country, impact the trade structure of the target country. On the one hand, the reduction

* Corresponding author. E-mail address: [email protected] (C.-P. Chang). https://doi.org/10.1016/j.econmod.2019.07.004 Received 17 April 2019; Received in revised form 5 July 2019; Accepted 10 July 2019 Available online 10 July 2019 0264-9993/© 2019 Elsevier B.V. All rights reserved.

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Economic Modelling 82 (2019) 58–65

of exports leads to financial difficulties from the country that depends on this part of income, rapid inflation of its currency, and increasing exchange rate volatility; on the other hand, the reduction of imports leads to lower commodity supplies in the target country and an absence of demand. Under the condition of constant demand, the domestic currency rapidly depreciates and exchange rate volatility increases. Taking Iran as an example, the EU and the U.S. imposed a comprehensive trade embargo on Iran, basically cutting off the financial and trade channels between Iran and the international community, resulting in a rapid decline in Iran’s oil production and export volume, a sharp decline in oil revenue, a sharp devaluation of the Iranian currency, and rising unemployment, which put the Iranian economy heavily dependent on oil exports in a predicament (Xiong and Tian, 2015). The second channel is the financial channel. When the target country is subject to sanctions including investment ban, financial transaction ban, assets being frozen, and export credit ban, the financing of its domestic enterprises can only be carried out through its central bank, thus generating a large amount of money, triggering inflation, and increasing exchange rate volatility (Gurvich and Prilepskiy, 2015). We contribute to the findings in the literature in several aspects. First, we investigate the potential relationship between economic sanctions and exchange rate volatility. We not only estimated the volatility of the official exchange rate, but also compared the volatility of the real exchange rate, thus enriching the research on exchange rate volatility and expanding the area of research concerning economic sanctions. Second, we analyze the impact of economic sanctions on exchange rate volatility in 23 target countries from 1996 to 2015 where the U.S. and the EU were the initiators of economic sanctions in the selected sample, by using the Least Squares Dummy Variable Corrected (LSDVC) model to estimate the relationship between economic sanctions and exchange rate volatility. Third, the results show that for 23 target countries, the plurilateral sanctions, EU sanctions, and sanctions intensity have a positive impact on exchange rate volatility, while U.S. and unilateral sanctions present weak impact on exchange rate volatility. We offering a representative case for countries impacted by economic sanctions and the resultant exchange rate volatility. Fourth, in the robustness test, we use four ways: eliminating Iranian, eliminating political variables, 12-year time window subsamples and cross-sectional regression sub-samples. The results of this part are basically consistent with those of the whole samples, thus proving the robustness of the model and also solving the problem of endogeneity. Finally, we use the real exchange rate and other sanction indicators from the Targeted Sanctions Consortium database (proposed by Hud
akov
a et al., 2013) to improve the reliability of the conclusions. In summary, we find that the impact of economic sanction on the two exchange rate volatility is clearly.1 The rest of the paper is structured as follows. Section 2 discusses the method and describes the data. Section 3 carries out empirical research and provides the results. Section 4 summarizes some conclusions and policy recommendations.

2. Data and methodology 2.1. Data 2.1.1. Data source Compared with traditional cross-sectional data, panel data increase the degree of freedom and decrease collinearity between explanatory variables, thus improving the effectiveness of empirical estimation (Hassan et al., 2011). Based on panel data of 23 target countries initiated by the U.S. and the EU, we analyze the impact of economic sanctions on exchange rate volatility in these target countries. Most data come from the World Bank’s World Development Indicators System (WDIS) and German Institute of Global and Area Studies’ Sanctions Dataset (GIGA). Table 1 shows 23 countries and related information, which including the Measures: aid sanctions (AS); arms embargo (AE); financial sanctions (FS); interruption of military (IM); visa ban (VB); comprehensive trade embargo (CT); diplomatic sanctions (DS); commodity embargo (CE); flight ban (FB); freezing of financial assets (AF) of sending country and sending country of each target country, Table 2 gives data sources and detailed descriptions of all variables.23

Table 1 List 23 target countries and sender. (1)

(2)

(3)

(4)

(5)

Target Countries

Sender (U.S.)

Sender (EU)

Measures (U.S.)

Measures (EU)

Azerbaijan Belarus

✓ ✓

Cameroon China Colombia Cote d’Ivoire Croatia Congo (Dem. Rep.) Haiti Honduras Indonesia Iran (Islamic Rep.) Jordan Niger Nigeria

✓ ✓ ✓ ✓

Pakistan Peru Sri Lanka Sudan Togo Venezuela (RB) Yemen (Rep.) Zimbabwe

○

○

○

AS VB, AF, AS AS, AE AE FS, AS, AE, IM AE, AS

○

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

○

IM, AS, VB, AE

○

CT, FS, AS, AE AS AE CT, FS, AE

○ ○

○ ○

○

○

AE AS, IM, FS, VB IM, AS, DS, FB, VB AE AS, IM AE AS, DS, FS, CE

○

✓ ✓ ✓

○

AE AS, AE VB, FS

VB, AF, DS, AE AE AS AE AE, AS, VB AS AS, DS IM, AS, FS

AS AS, AE, DS, VB AS AE, AS AS

VB, AF, AE, AS

Notes: “√” Listed in column (2) indicates that the sanctions were issued by the United States, “○” Listed in column (3) indicates that the sanctions were issued by the European Union. “Measures” reflects the instruments used by the country that initiated the sanctions: aid sanctions (AS); arms embargo (AE); financial sanctions (FS); interruption of military (IM); visa ban (VB); comprehensive trade embargo (CT); diplomatic sanctions (DS); commodity embargo (CE); flight ban (FB) and the freezing of financial assets (AF) of sending country.

1 In different GMM, when the variable is persistent, its lag level performs poorly as an instrument for its differenced series, then the system GMM may have suffered from too many instrument problems, causing the Hansen tests to perform very poorly (Sleaman et al., 2015). Hence, Bun and Kiviet (2003) and Bruno (2005), who proposed a methodology to approximate the small sample bias of the LSDV estimator, constructed the LSDVC estimator and demonstrated that this estimator is more efficient and robust compared to numerous instrumental variable estimators in dynamic panel data models (Chang and Berdiev, 2011).

2 Our data period is up to 2015 because of the availability of data is limited, due to a variety of sanctions data has been considered and are available until 2015, and most sanctions events occurred in the 20 years covering 1996–2015. After comprehensive consideration, we choose the data of this period as the research object. 3 If necessary, we are willing to share our datasets upon request.

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Table 2 Definition of variables and data sources.

Table 3 The descriptive statistics.

Variable

Definition

Source

Variable

Observations

Mean

Std. Dev

Min

Max

Official Exchange Rate EU

Official exchange rate (local currency units per US$, period average)

WDI

Economic sanctions initiated by the European Union Economic sanctions initiated by the United States Whether the sanctions were imposed by the EU and U.S. jointly Whether the sanctions were imposed by either the EU or the U.S. only The (formal) intensity of sanctions in ascending order The sanctions’ effect on the target country’s economy or a section thereof The sanctions do not affect the target country’s economy or a section thereof GDP per capita constant at 2010 US dollars Price of imported crude oil (US$ per barrel) right ¼ 1; left ¼ 3; center ¼ 2; no information ¼ 0; no executive ¼ NA Current account balance (% of GDP) Lending interest rate (%) Inflation, consumer prices (annual, %) Gross domestic savings (% of GDP)

GIGA

EX HV EU U.S. Plurilateral Unilateral Intensity Eco Noneco GDP Oil Ideology Current Interest Inflation Saving Reserves Bank Crisis

448 447 460 460 460 460 460 460 460 459 460 460 404 393 412 458 378 431 382

1074.846 0.913 0.280 0.363 0.189 0.228 2.224 0.448 0.011 7.568 3.819 0.126 2.546 22.259 10.548 18.953 22.187 26.662 0.077

3099.487 12.879 0.450 0.481 0.392 0.420 2.992 0.498 0.104 1.003 0.686 0.332 8.097 45.570 20.945 13.987 2.251 24.961 0.267

0.0013 0 0 0 0 0 0 0 0 5.621 2.543 0 30.688 4.35 8.525 24.000 16.073 0.194 0

29011.49 270.429 1 1 1 1 11 1 1 9.601 4.715 1 33.679 578.958 293.679 58.069 28.992 342.116 1

U.S. Plurilateral Unilateral Intensity Eco Noneco GDP Oil Ideology Current Interest Inflation Saving Reserves Bank Crisis

Total reserves (includes gold, current US$) Bank deposits to GDP (%) Banking crisis dummy (1 ¼ banking crisis, 0 ¼ none)

GIGA GIGA GIGA GIGA GIGA GIGA WDI IEA Statistics Beck et al. (2000) database of political WDI WDI WDI World Bank and OECD national accounts data IMF, IFS

Table 4 Panel unit root tests.

IMF, IFS Systemic Banking Crises Database: IMF

Notes: WDI: World Development Indicators; GIGA: German Institute of Global and Area Studies’ Sanctions Dataset; IMF: International Monetary Fund; IFS: International Financial Statistics.

Variable

ADF

PP

Official Exchange Rate HV GDP Oil Current Interest Inflation Saving Reserves Bank

2.213** 2.116** 2.235** 7.624*** 7.106*** 2.056** 9.928*** 6.273*** 6.802*** 6.451***

4.393 35.629*** 6.251 4.795 1.483* 6.398*** 6.479*** 2.120** 2.483*** 3.905***

Notes: “*” indicates significance at 10%. “**” indicates significance at 5%. “***” indicates signEificance at 1%.

2.1.2. Variables we use the logarithmic form of GDP per capita to evaluate the economic growth of sanctioned countries. Oil price: The change of international Oil price affects an exchange rate. Ghosh (2011) used daily data from July 2, 2007 to November 28, 2008 to explore India’s crude oil price-exchange rate relationship. Research shows that the increase in oil price returns has led to the devaluation of the Indian currency against the US dollar. Therefore, we choose Oil price as a control variable. Political ideology: Political ideology is a group of ideas put forward by the ruling class to all members of society. Chang and Lee (2017) note that political formation influences the choice of exchange rate regime, exchange rate volatility is due to some kind of political incentive. Therefore, we choose Political ideology to measure the influence of the ruling class on the exchange rate. Current account balance: Balance of payments refers to the income and expenditure generated by a country’s economic exchanges with other countries in the world over a certain period of time. Müller-Plantenberg (2010) expounds how the balance of payments flow caused by the balance of payments imbalance affects the demand for different currencies in the foreign exchange market over time, and ultimately the exchange rate. It is also found that the way in which this effect occurs depends on the exchange rate regime and the ease of capital inflow and outflow. We use Current account balance to assess balance of payments. Interest rate: There is a clear transmission mechanism between Interest rate and exchange rate. Hacker et al. (2012) uses wavelet analysis to investigate the relationship between the spot exchange rate and interest rate differential for seven pairs of countries. Showing a two-way and long-term negative relationship between the two. Moreover, the influence of interest rate on the exchange rate is more significant. In order to avoid collinearity, this paper chooses the loan interest rate for estimation.

(1) Dependent variable Because exchange rate volatility cannot be observed directly in the market, we only choose alternative variables to express it. According to Ichiue and Koyama (2011), we take the historical volatility of an exchange rate as an index for evaluation. Because actual exchange rate data are incomplete, we choose the official exchange rate for estimation. We use HV to represent the historical exchange rate volatility. For the calculation of exchange rate volatility, we follow Ichiue and Koyama (2011) and Wang and Wu (2012) by adopting the GARCH (1,1) model to fit the exchange rate volatility. Before fitting, we test the ADF unit root of the official exchange rate. The result shows in Table 4 that there is evidence of stationarity of the official exchange rate that is stable in sequence, proving that the GARCH (1,1) model is effective. (2) Explanatory variables As this paper studies the impact of economic sanctions on exchange rate volatility, there are seven explanatory variables to measure economic sanctions. They are Unilateral sanctions, Plurilateral sanctions, U.S. sanctions, EU sanctions, Sanctions Intensity, the sanctions’ effect on the target country’s economy or a section thereof, and the sanctions do not affect the target country’s economy or a section thereof. For ease of description, we abbreviate these explanatory variables as Unilateral, Plurilateral, U.S., EU, Intensity, Eco, and Noneco. In addition, this study also includes the following control variables. GDP per capita: Gross domestic product (GDP) is an indicator of the overall level of economic growth of a country. GDP is closely related to exchange rate and exchange rate volatility (Belloumi, 2010). Therefore, 60

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can deal with heterogeneity and sequence correlation problems. In addition, in order to solve potential endogeneity and deviation estimation caused by variables, we introduce dynamics and employ the LSDVC model as the main estimation technique. The estimation specification is:

Inflation: Inflation is the average rate of price increase in an index of goods and services. Ito and Sato (2008) used the Auto-Vector regression method to study the effect of Indonesia’s inflation on the exchange rate. When Inflation occurs, the domestic currency depreciates, the trade structure changes, and exchange rate volatility arises. Therefore, we choose Inflation rate to evaluate the impact of currency volume on exchange rate volatility. Gross domestic savings: Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). Some scholars believe that domestic savings have a negative impact on exchange rate (Ito and Krueger, 2009; Montiel and Serv
en, 2008). We choose domestic total savings as one of the controlling variables of exchange rate volatility. Total reserve: Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. Cady and Gonzalez-Garcia (2007) and Aizenman et al. (2012) conduct an empirical analysis of the relationship between Total reserves and exchange rate, believing that positive reserves reduce exchange rate volatility. We use the logarithmic form of Total reserves (including gold, current US$) to represent Total reserves. Bank deposit: There are few studies on the relationship between Bank deposit and exchange rate. Frenkel and Rapetti (2008) studies the relationship between Bank deposit and exchange rate from the perspective of bank operation, finding that the former may have a negative impact on the latter. Therefore, we choose Bank deposit to GDP (%) as one of the control variables. Banking crisis: There are few studies in the literature about the correlation between bank crisis and exchange rate. Singh et al. (2018) studies the relationship between exchange rate and Banking crisis through the exchange rate of US dollar-Indian rupee and stock price of PNB Bank, showing results that there is a significant negative relationship between them. Therefore, we choose the fictitious variable bank crisis as a replacement of this variable.4

yit ¼ αyi;t1 þ βXit þ μi þ ηt þ εit ; i ¼ 1; ……Nt ¼ 1; ……T

(1)

where yit denotes the dependent variable exchange rate volatility, Xit corresponds to a set of independent explanatory variables, μi is the unobserved country-specific effect, ηt is the time-specific effect, εit is an error term, and i and t represent countries and periods, respectively. 3. Empirical results 3.1. Estimated results To test for stationarity of the variables, Table 4 shows the results of the panel unit root test. Since the panel data we use are unbalanced, we use the ADF and PP methods in the Fisher test for comparison. Most variables are significant at least at the 10% level under the two methods. Therefore, we reject the original assumption that the panel has no unit root and is a smooth panel. As shown in Table 5, the dependent variable used herein is exchange rate volatility, and the explanatory variables are EU, U.S., Plurilateral, Unilateral, Intensity, Eco and Noneco. The first is listed as the result of EU, the results show that the coefficients of EU is significant and positive at least at the 1% level. It proves that with the increase of EU sanctions on target countries, the exchange rate volatility in target countries increases. In a sense, this confirms the view that economic sanctions affect the economy of the target country (Neuenkirch and Neumeier, 2015; Dylan, 2017). Since most EU sanctions are imposed by restricting imports (aid sanctions, arms embargo, visa ban), this tests the assumption of import and export channels. The second column reports estimates of U.S.. The results show that the coefficients of U.S. is insignificant. This means that no matter if the number of U.S. sanctions increases or decreases, there will be a weak impact on exchange rate volatility in the target countries. We speculate that there are three possible reasons for the impact of EU sanctions on exchange rate volatility in target countries, while U.S. sanctions have a weak impact on exchange rate volatility. First, as shown in Fig. 1, there are ten sanctions by the U.S. (AS, AE, FS, IM, VB, CT, DS, CE, FB, AF), while only five sanctions by the EU (AS, AE, VB, DS, AF), and EU sanctions are more concentrated than those of the U.S. This means that the impact of EU sanctions on target countries is more concentrated, and the impact of U.S. sanctions on target countries is more decentralized, and so the impact of EU sanctions on target countries is significant. Second, the target countries that have been sanctioned by the U.S. for more than five years account for 70% of the total sample, while the target countries that have been sanctioned by the EU for more than five years account for 50% of the total sample. According to Neuenkirch and Neumeier (2015), we see that the impact of sanctions on target countries is often short term. In the long run, the target country typically takes countermeasures or seeks other trading partners to reduce the impact of sanctions. As can be seen, EU sanctions take less time than the U.S., which may lead to the effect of U.S. sanctions being weaker than that of EU sanctions. Therefore, the impact of U.S. sanctions on exchange rate volatility in target countries is not as significant as that of EU sanctions. Finally, according to Neuenkirch and Neumeier (2015), the distance between the sending country and the target country of sanctions has a negative impact on the effect of sanctions. The farther the distance is, the weaker the effect of sanctions is, while the closer the distance is, the stronger the effect of sanctions is. This view is in line with the intuitive impression that trade with neighboring countries usually has a shorter cycle and a larger amount, and that sanctions from neighboring countries have a greater impact on target countries. The target countries of U.S. sanctions are located in Asia, Africa, and Europe (long-range sanctions).

2.2. Data description statistics Table 3 reports descriptive statistics of the variables. The average value of HV is 0.913, and the standard deviation is very large, indicating that there are great differences in exchange rate volatility in different regions in different years. The maximum and minimum values of Unilateral, Plurilateral, U.S., and EU are 1 and 0, respectively. The average and standard deviation are large, illustrating that different countries have great differences in economic sanctions. The difference between the maximum and minimum Intensity shows great variations in the degree of economic sanctions among the sanctioned countries. The average value and standard deviation of Eco are significantly higher than those of Noneco, indicating that economic sanctions inevitably affect an economy. 2.3. Empirical method Following the classical literature on exchange rate volatility and economic sanctions, this paper adopts the panel data method, as according to Wooldridge (2012) panel data are better than the cross-sectional method, because the former has more observations and

4 Banking crisis dummy (1 ¼ banking crisis, 0 ¼ none): A banking crisis is defined as systemic if two conditions are met: a. Significant signs of financial distress in the banking system (as indicated by significant bank runs, losses in the banking system, and/or bank liquidations); and b. Significant banking policy intervention measures in response to significant losses in the banking system. The first year that both criteria are met is considered as the year when the crisis starts becoming systemic. The end of a crisis is defined by the year before both real GDP growth and real credit growth are positive for at least two consecutive years.

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Table 5 LSDVC official exchange rate historical volatility. Variables

(1)

(2) ***

0.639 (0.000) 0.265*** (0.001)

L.log (HV) EU U.S. Plurilateral Unilateral Intensity Eco Noneco GDP Oil Ideology Current Interest Inflation Saving Reserves Bank Crisis Observations Long-run effect

0.619

(3) ***

(0.000)

0.663

(4) ***

(0.000)

0.609

(5) ***

(0.000)

0.653

(6) ***

(0.000)

(7) ***

0.626

(0.000)

0.598*** (0.000)

0.035 (0.485) 0.207** (0.030) 0.014 (0.778) 0.026** (0.036) 0.132*** (0.009) 0.503*** (0.009) 0.013 (0.810) 0.041 (0.616) 0.002 (0.476) 0.002 (0.556) 0.005*** (0.000) 0.013*** (0.000) 0.059* (0.089) 0.008** (0.028) 0.005 (0.948) 437 0.737*** (0.006)

0.434** (0.029) 0.015 (0.788) 0.025 (0.769) 0.001 (0.604) 0.001 (0.937) 0.005*** (0.000) 0.009** (0.010) 0.044 (0.255) 0.006* (0.088) 0.005 (0.951) 437 0.092 (0.495)

0.501** (0.011) 0.005 (0.918) 0.013 (0.878) 0.001 (0.632) 0.001 (0.748) 0.006*** (0.000) 0.010*** (0.005) 0.055 (0.127) 0.005 (0.206) 0.017 (0.844) 437 0.617* (0.081)

0.417** (0.034) 0.020 (0.725) 0.014 (0.867) 0.001 (0.575) 0.0001 (0.984) 0.005*** (0.000) 0.009** (0.013) 0.039 (0.306) 0.006* (0.094) 0.013 (0.886) 437 0.036 (0.778)

0.467** (0.017) 0.010 (0.854) 0.027 (0.753) 0.001 (0.607) 0.002 (0.544) 0.006*** (0.000) 0.011*** (0.004) 0.061* (0.087) 0.005 (0.165) 0.007 (0.938) 437 0.076* (0.065)

0.466** (0.019) 0.014 (0.804) 0.044 (0.608) 0.001 (0.642) 0.002 (0.620) 0.006*** (0.000) 0.011*** (0.002) 0.061 (0.106) 0.007* (0.054) 0.025 (0.787) 437 0.353** (0.021)

0.252* (0.082) 0.412** (0.033) 0.050 (0.425) 0.004 (0.958) 0.001 (0.540) 0.001 (0.795) 0.006*** (0.000) 0.009** (0.011) 0.048 (0.208) 0.007* (0.052) 0.004 (0.961) 437 0.629 (0.113)

Notes: The values in parentheses denote the p-value. “*” indicates significance at 10%. “**” indicates significance at 5%. “***” indicates significance at 1%.

U.S.

EU

AS

AE

FS

IM

VB

CT

DS

CE

FB

AF

AS

AE

VB

DS

exchange rate volatility of target countries, and U.S. sanctions account for the majority; thus, the impact of unilateral sanctions on the exchange rate volatility of target countries is not significant. The definition of plurilateral sanctions refers to the simultaneous occurrence of U.S. sanctions and EU sanctions, and so plurilateral sanctions are mainly affected by EU sanctions and therefore have a significant impact on the exchange rate volatility of target countries.6 The fifth column shows estimates of Intensity. It can be seen that the coefficients of Intensity are significant and positive at least at the level of 5%. It proves that sanctions intensity has a positive impact on the exchange rate volatility of the target country. With the increase of sanctions intensity, the exchange rate volatility of the target country increases. Because sanctions intensity is classified according to the method of sanctions; the less the relationship is between the two countries, the higher is sanctions intensity, which includes trade, finance, personnel exchanges, and so on. Bahmani-Oskooee and Aftab (2017) find that trade flows between the two countries have a significant impact on exchange rate volatility; the greater the change in trade flows is in the short term, the greater is exchange rate volatility. Therefore, the higher sanctions intensity is, the greater is the impact on trade flows, and the greater the impact is on exchange rate volatility, which is consistent with previous studies.7 Columns 6 and 7 show estimates of Eco and Noneco, respectively. It can be seen that the coefficients of Eco are significant and positive at least at the 1% level, which shows that Eco has a positive impact on the exchange rate volatility of target countries; the coefficients of Noneco are significant at least at the 10% level, which shows that the less the sanctions that do not affect the economy, the less the impact on the exchange rate volatility of target countries. This result conforms to the definitions of Eco and Noneco, whereby if the method of sanctions is related to the economy of the target country, then it will inevitably affect the exchange rate volatility of the target country. If the method of sanction is not related to the economy of the target country, then it will inevitably not affect the exchange rate volatility of the target country. Also, the long-run estimates of the relationship between economic sanctions and exchange rate volatility, except for U.S., Unilateral and

AF

Fig. 1. Methods of U.S. and EU sanctions.

Furthermore, the number of them accounts for 75% of total sanctions, while the target countries of EU sanctions are located in the Americas (remote sanctions), accounting for 20% of total sanctions. This means that EU sanctions may be more effective than U.S. sanctions and have a more significant impact on exchange rate volatility in target countries. The third column shows estimates of Plurilateral. It can be seen that the coefficients of Plurilateral are significant and positive at least at the 5% level. It proves that plurilateral sanctions have a positive impact on the exchange rate volatility of the target country. With the increase in the number of plurilateral sanctions, the exchange rate volatility of the target country increases.5 The fourth column shows estimates of Unilateral. Interestingly, compared with the Plurilateral in the third column, the coefficients of Unilateral are insignificant. This means that no matter whether the number of unilateral sanctions increases or decreases, it will not affect the exchange rate volatility of target countries. We believe that the main reason for the difference between the two results is that the impacts of U.S. sanctions and EU sanctions on the exchange rate volatility of target countries are different, and the number of them is also different. The number of sanctions imposed by the U.S. is 24 and that by the EU is 16; the number of sanctions imposed by the U.S. in the sample countries during 1996–2015 was 53% higher than that imposed by the EU. The definition of Unilateral sanctions refers to the occurrence of U.S. sanctions or EU sanctions. However, U.S. sanctions have no effect on the

6 Unilateral sanction: When the sanctions are imposed by either the EU or the U.S. only. 7 Sanction intensity: The (formal) intensity of sanctions in ascending order.

5

Plurilateral sanction: When the sanctions are imposed by the EU and U.S. jointly. 62

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Table 6 LSDVC robustness analysis. Panel A: Eliminating Iran Variables

(1)

L.log (HV) 0.608*** (0.000) EU 0.263*** (0.000) U.S. Plurilateral Unilateral Intensity Eco Noneco Observations 417 Panel B: Eliminating Ideology L.log (HV) 0.637*** (0.000) EU 0.261*** (0.001) U.S. Plurilateral Unilateral Intensity Eco Noneco Observations 437

(2)

(3)

(4)

(5)

(6)

(7)

0.587*** (0.000)

0.627*** (0.000)

0.581*** (0.000)

0.618*** (0.000)

0.596*** (0.000)

0.573*** (0.000)

0.028 (0.570) 0.196** (0.035) 0.012 (0.819) 0.024** (0.033) 0.129** (0.010) 417 0.615*** (0.000)

417 0.659*** (0.000)

417

417

417

0.254* (0.095) 417

0.606*** (0.000)

0.650*** (0.000)

0.623*** (0.000)

0.594*** (0.000)

0.032 (0.512) 0.206** (0.030) 0.016 (0.747) 0.025** (0.036) 0.126** (0.012) 437

437

437

437

437

0.251* (0.080) 437

Panel C: Twelve-year Dummy (1996–2007) Variables

(1)

L.log (HV) 0.551*** (0.000) EU 0.285*** (0.002) U.S. Plurilateral Unilateral Intensity Eco Noneco Observations 276 Panel D: Cross Sectional Regressions L.log (HV) 0.624*** (0.000) EU 0,492*** (0.003) U.S. Plurilateral Unilateral Intensity Eco Noneco Observations 158

(2)

(3)

(4)

(5)

(6)

(7)

0.523*** (0.000)

0.571*** (0.000)

0.525*** (0.000)

0.588*** (0.000)

0.555*** (0.000)

0.506*** (0.000)

0.009 (0.869) 0.203** (0.045) 0.031 (0.648) 0.027 (0.115) 0.149* (0.059) 276

276

276

276

276

0.258* (0.089) 276

0.604*** (0.000)

0.645*** (0.000)

0.594*** (0.000)

0.654*** (0.000)

0.646*** (0.000)

0.578*** (0.000)

0.026 (0.843) 0.325** (0.045) 0.039 (0.727) 0.046 (0.104) 0.252** (0.034) 158

158

158

158

158

0.343 (0.158) 158

Notes: The control variable are not reported, but available upon request. The values in parentheses denote the P-value. “*” indicates significance at 10%. “**” indicates significance at 5%. “***” indicates significance at 1%.

Iran may bring to different results. Second, sanctions against Iran are a matter of international concern as most of the literature on sanctions refers to Iran (Dizaji and Bergeijk, 2013; Haidar, 2017). As seen from the results of the relationship between economic sanctions and exchange rate volatility shown in Table 6 Panel A, except for the U.S. and Unilateral, other variables are significant at least at the level of 10%, which means the results are consistent with our earlier finding as in Table 5. Thus, our robust evidence is consistent, and even after removing a country that is often subject to sanctions, the results do not change. The second test is a sub-sample regression after eliminating the political variables of political ideology. Therefore, we exclude this variable from the model. As shown in Table 6, Panel B, except for U.S., Unilateral and Noneco other sanctions’ variables are positively statistically significant at least at the 10% level, which is basically similar to the results in Table 5. The third test used in this paper is to intercept part of the time period as a sub-sample. We choose the sub-sample data from 1996 to 2007, because the exchange rate of each country changed greatly after the 2008 financial crisis. We believe that the data before the financial crisis can better reflect the impact of economic sanctions on exchange rate volatility (Vermeulen and Grammatikos, 2011; Coudert et al., 2011). From Table 6 Panel C, we find that except for Intensity and Noneco, the other explanatory variables are consistent with the results of the whole sample.

Noneco, other variables are significant at a level of at least 10%, indicating that this effect is sustained into the long run.8

3.2. Robustness To further test the robustness of the results, we use four different methods in Table 6: eliminating Iran, eliminating political variables, 12year sub-samples and sub-samples obtained by cross-sectional regression. The first test is sub-sample regression after eliminating Iran. We consider two main reasons for eliminating Iran. First, Iran is mainly subject to the UN and the U.S. sanctions; however, the earlier evidence shows that the impact of the U.S. sanctions on exchange rate volatility is weak, and the UN sanctions are not the focus of our study, the removal of

8 The long-run effect of xit on yit is both a cumulative process as well as a total adjustment of the above N-term effects (Bruno, 2005). Specifically, β is the immediate effect of xit on yit in t, which will pass on its influence through the dynamics of yit in the future. Similarly, the effect of xit on yi;tþ1 in tþ 1 is written as α  β, while on yi;tþ2 in t þ 2 is α2  β; thus, yi;tþn in t þ n is αn  β. The total

α Þ adjustment can then be written as U ¼ β þ αβ þ α2 β þ … þ αn β ¼ βð1 1α . When n → ∞, αn → 0, because of autoregressive coefficient α < 1, and the long-run n

effect is calculated as U ¼

β 1α.

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Economic Modelling 82 (2019) 58–65

Table 7 The SYS-GMM using real exchange rate with historical volatility. Variables L.log (HV) EU U.S. Plurilateral Unilateral Intensity Eco GDP Oil Ideology Current Interest Inflation Saving Reserves Bank Crisis Observations AR (1) (P-value) AR (2) (P-value) Sargan (P-value) Hansen (P-value)

(1)

(2) ***

0.538 (0.001) 3.062*** (0.000)

0.335

(3) **

(0.025)

(4) **

0.340

(0.014)

0.498

(5) **

(0.001)

(6) ***

0.501

(0.001)

0.425*** (0.008)

0.345 (0.518) 1.390 (0.254) 1.812*** (0.002) 1.039*** (0.000) 0.030 (0.990) 0.123 (0.760) 0.251 (0.665) 0.005 (0.853) 0.048 (0.175) 0.026 (0.332) 0.059** (0.036) 0.014 (0.909) 0.006 (0.850) 0.130 (0.758) 182 0.030 0.362 0.396 0.900

0.266 (0.289) 0.829** (0.027) 0.338 (0.566) 0.034 (0.184) 0.046 (0.209) 0.062** (0.041) 0.083*** (0.003) 0.235** (0.044) 0.042 (0.221) 0.228 (0.597) 182 0.056 0.280 0.184 0.287

0.309 (0.168) 0.866** (0.014) 0.335 (0.541) 0.036 (0.136) 0.042 (0.225) 0.055** (0.028) 0.077*** (0.003) 0.263** (0.014) 0.045 (0.147) 0.185 (0.643) 182 0.073 0.139 0.079 0.212

0.362 (0.113) 0.374 (0.381) 0.310 (0.577) 0.011 (0.659) 0.047 (0.171) 0.007 (0.812) 0.045 (0.113) 0.225** (0.037) 0.035 (0.268) 0.053 (0.897) 182 0.033 0.144 0.286 0.401

0.418 (0.238) 0.446 (0.181) 0.158 (0.771) 0.013 (0.582) 0.029 (0.488) 0.018 (0.527) 0.026 (0.346) 0.254* (0.059) 0.064* (0.095) 0.036 (0.928) 182 0.039 0.177 0.323 0.403

0.905* (0.068) 0.340 (0.171) 0.525 (0.196) 0.338 (0.577) 0.020 (0.464) 0.053 (0.155) 0.021 (0.510) 0.066** (0.026) 0.230* (0.051) 0.039 (0.264) 0.073 (0.869) 182 0.032 0.162 0.315 0.449

Notes: The values in parentheses denote the P-value. “*” indicates significance at 10%. “**” indicates significance at 5%. “***” indicates significance at 1%.

the World Bank’s World Development Indicators (WDI), and the results are reported in Table 7.9 We see that EU, Unilateral, Intensity, and Eco still have a statistically significant impact on real exchange rate volatility, which is basically consistent with the results of the official exchange rate. For resolving the problem of endogeneity, we fix it by using the panel system GMM method with lagged dependent variable as instrumental variables. As seen in the bottom of Table 7, the Arellano–Bond test of first-order autocorrelation rejects the null hypothesis (p-value<0.10) in all equations, showing that considering the dynamics of exchange rate volatility is necessary. Next, the Arellano–Bond test of second-order autocorrelation does not reject the null hypothesis (p-value>0.10) in all specifications, suggesting that the estimated residuals do not generate second-order serial correlation and the results are consistent. The p-value of the Hansen test is larger than 0.10, indicating that the Hansen test cannot reject the null hypothesis and the instrumental variables are valid. Overall, the results of these tests confirm the reliability of the system GMM method. In order to make our results more convincing, we use another dataset, which is the Targeted Sanctions Consortium (TSC) database to test the result of UN sanctions. The results are shown in Table 8. We select three countries and use the two variables of “UN sanctions” and “whether the sanctions belong to commodity sanctions” to analyze exchange rate volatility.10 As shown in Table 8, both UN sanctions and Commodity sanctions have a significant positive impact on exchange rate volatility. With the increase of sanctions, the range of exchange rate volatility in the target countries increases. This is consistent with the results of earlier finding, which shows that UN sanctions have a similar influence as EU sanctions.

Table 8 SYS-GMM TSC database. Variables

(1)

UN commodity sanctions (CS) GDP Oil Ideology Current Inflation Saving Bank Observations AR1 (P-value) AR2 (P-value) Sargan (P-value) Hansen (P-value)

0.279* (0.084) 0.003 (0.898) 0.101* (0.051) 0.025 (0.581) 0.001 (0.590) 0.004* (0.057) 0.0003 (0.962) 0.010** (0.041) 42 0.001 0.769 0.710 0.998

(2) 0.338*** (0.001) 0.025 (0.229) 0.007 (0.852) 0.140** (0.024) 0.007 (0.131) 0.005*** (0.002) 0.0004 (0.947) 0.017*** (0.001) 42 0.000 0.686 0.478 0.999

Notes: same as Table 7. Because Interest and Reserves have some missing data, and the value of Crisis of each country is 0, we delete the three variables.

EU, Plurilateral, and Eco are significant at least at the 10% level, showing that these 3 factors have a positive impact on the exchange rate volatility of target countries during the 12-year period 1996–2007. Finally, because of the frequent sanctions imposed by the U.S., and to seek more evidence, we thus calculate the cross-sectional regression coefficients of 23 countries and selected nine countries with negative coefficients in US sanctions as a set of samples for a robustness test, which include Azerbaijan, Belarus, Colombia, Cote d’Ivoire, Indonesia, Nigeria, Pakistan, Peru and Venezuela (RB), respectively. The empirical results in Table 6 Panel D are basically consistent with Table 5. The above four methods can basically conclude that economic sanctions have a positive impact on the volatility of official exchange rate, which has passed the robustness test. But we also wonder whether economic sanctions have the same impact on the real exchange rate, or whether sanction data from other databases have the same impact on exchange rate volatility. Tables 7 and 8 show the results of these two concerns, respectively. We hence analyze 10 countries using real exchange rates taken from

4. Conclusions and policy implications In order to analyze the relationship between economic sanctions and exchange rate volatility, we use panel data of 23 target countries sanctioned by the U.S. and the EU from 1996 to 2015, with EU, U.S., Plurilateral, Unilateral, Intensity, Eco, and Noneco as explanatory variables, while exchange rate volatility is the dependent variable, in order to establish the LSDVC panel data model. The results show that for 23 target countries, plurilateral sanctions, EU sanctions, and sanctions intensity have a positive impact on exchange rate volatility, while U.S. sanctions

9 They include Cameroon, China, Colombia, Cote d’Ivoire, Congo (Dem. Rep.), Iran (Islamic Rep.), Nigeria, Pakistan, Togo, and Venezuela (RB). There is no case of “Noneco” in the panel.

10

64

Including Cote d’Ivoire, Iran (Islamic Rep.) and Sudan.

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Economic Modelling 82 (2019) 58–65

and unilateral sanctions have no impact on exchange rate volatility. We believe that the main reason for this situation is the difference between U.S. as well as EU sanctions. The various methods of sanctions imposed by the U.S., the long duration of sanctions, and the long distance to the target countries all lead to the weakening of the impact of sanctions imposed by the U.S. on the exchange rate volatility of the target countries. Because the number of U.S. sanctions is larger than EU sanctions, resulting in Unilateral being insignificant. Considering this characteristic, we suggest that when the target government is faced with short-term sanctions of close proximity, high intensity and single mode, it should pay more attention to the volatility of its exchange rate, which may lead to considerable changes in exchange rate in the future, thus creating a series of economic problems. For the sender of sanctions, if the potential goal of sanctions is to volatility the exchange rate of the sanctioned country, long-term sanctions with long-distance, low intensity and complex ways should be avoided as far as possible, as this may lead to the failure of sanctions. We put forward two hypotheses about the relationship between economic sanctions and exchange rate volatility: one is the import channel, and the other is the export channel. The sanctioned countries can use these two channels to reduce exchange rate volatility in three ways (Thaver and Ekanayake, 2011; Haidar, 2017). First, the sanctioned countries can establish good cooperative relations with neighboring countries and regions through various channels and establish new trade modes. For example, after the outbreak of the Ukrainian crisis in 2014, European and American countries imposed sanctions on Russia, resulting in the decline of the Russian ruble and domestic economic turmoil there. If Russia actively seeks to cooperate with China and establish new trade patterns in energy, high-speed rail, and agriculture, then it may ease the domestic pressure. Second, stimulating domestic demand is also an important strategy that can reduce the dependence on international trade relations to a certain extent. Third, sanction countries can promote the upgrading of industrial structure, adjust their domestic supply-side structure, and form a new economic development mechanism, and then once new development mechanisms and industries are formed, other countries will become highly dependent on these new products with competitive advantages, thus breaking the impact of sanctions on them (Hosseinzadeh et al., 2013). This paper further tests the robustness. The results are basically consistent with the baseline equation, which proves the robustness of the model and further illustrates the impact of economic sanctions on exchange rate volatility in target countries.

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