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ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 18, No. 2 (2024), pp. 202-211 

 

202 

 

EXCHANGE RATE VOLATILITY AND INWARD FDI: A PANEL 

DATA ANALYSIS OF BALKAN COUNTRIES 

 

K. MORINA, N. MERA 

 

Kevin Morina¹, Nertil Mera² 

¹ ² EPOKA University, Albania 

¹ https://orcid.org/0009-0005-8575-4518, E-mail: kmorina18@epoka.edu.al  

² https://orcid.org/0009-0009-5593-2327, E-mail: nmera@epoka.edu.al 

 

Abstract This study investigates the relationship between exchange rate volatility and 

foreign direct investment (FDI) inflows in Balkan countries, a region known for its political 

instability and significant economic and institutional challenges. Using secondary data from 

2011 to 2021, this study employs a fixed-effects model with a dynamic specification to analyze 

the impact of exchange rate volatility on FDI, alongside other key determinants such as 

inflation, crude oil prices and corruption perception index. The results indicate that exchange 

rate volatility negatively impacts the FDI inflows, while crude oil prices are found to have a 

significant positive effect. The impact of inflation and corruption perception index is found 

insignificant in explaining FDI inflows, suggesting that these factors do not substantially 

influence investment decisions in the Balkan region. These findings contribute to the literature 

on FDI in the Balkans and provide valuable insights for policymakers and investors. The 

results suggest that reducing exchange rate volatility could improve the investment climate and 

enhance the region’s attractiveness for foreign investment.    

Keywords: Exchange Rate Volatility, Foreign Direct Investment, Panel Data Analysis, 

Balkan Countries. 

 

INTRODUCTION 

Foreign direct investment (FDI) plays an important role in improving critical sectors 

such as technology, infrastructure, employment as well as serving as a key indicator of 

economic growth especially in developing economies. They are crucial for bridging the capital 

gap in emerging and transition economies, fostering development, and promoting economic 

diversification (Görg & Greenaway, 2004). On the other hand, developed countries tend to rely 

less on FDI as a source of economic stimulus due to having more robust financial systems and 

greater access to domestic sources of funds (Mallampally and Sauvant, 1999).  

The attraction of FDI relies on a variety of factors, with exchange rate stability being a 

significant consideration for foreign investors, particularly in those economies where 

currencies are volatile. Investors use different risk management strategies to mitigate the 

uncertainty coming through the exchange rate volatility (Adler and Dumas, 1984). In this 

context, the investment decision is often influenced by the degree of exchange rate risk, where 

countries with more stable currencies typically attract higher FDI inflows (Froot and Stein, 

1991). As exchange rate volatility increases, so does the risk of potential returns, thus shifting 

the investments towards markets perceived as less risky (Bénassy-Quéré et al., 2007). 

https://orcid.org/0009-0005-8575-4518
mailto:kmorina18@epoka.edu.al
https://orcid.org/0009-0009-5593-2327
mailto:nmera@epoka.edu.al


 

 

 

Kevin MORINA, Nertil MERA 

203  

Being characterized by economies in transition, the Balkan countries present an 

interesting case for examining the relationship between exchange rate volatility and FDI 

inflows. Over the past few decades, the region has become attractive for investments, 

particularly in sectors such as services, renewable energy, real estate, oil and gas, and food 

processing. However, the role of exchange rate volatility in shaping these investment patterns 

remains underexplored in regional literature. 

The objective of this study is to contribute to the existing literature by offering an 

empirical investigation of how exchange rate fluctuations influence FDI in the region, while 

controlling for other key factors that may affect investment decisions. Moreover, it aims to 

provide valuable insights for policymakers and investors to further enhance the region's appeal 

to foreign investments. Additionally, the findings may provide a valuable reference for future 

research in international business and economic development. The increasing importance of 

attracting foreign capital to stimulate economic growth in transition economies further 

highlights the relevance of this study. 

The next section presents a review of existing literature on this topic and provides the 

theoretical foundation for the study. The methodology section includes econometric modeling 

and quantifies the relationship between exchange rate volatility and FDI, followed by the 

empirical results and the conclusion section. 

 

LITERATURE REVIEW 

Foreign direct investment (FDI) has long been a focal point of research due to its 

significant impact on a country's economy, business environment, and financial systems. This 

study aims to explore how exchange rate fluctuations impact FDI inflows in the Balkan region, 

accounting also for other explanatory variables. Understanding the factors that influence FDI 

is crucial, particularly in the context of exchange rate volatility, as it can affect investment 

decisions. Hanusch et al. (2018) found a negative impact of exchange rate volatility on FDI 

inflows through a sample of 80 countries including Bulgaria, Romania and Serbia. Benassy-

Quéré et al. (2001) used panel data analysis to investigate 42 developing countries for the 

period from 1984 to 1996. The results highlight the significant role of exchange rate volatility 

in influencing FDI, demonstrating its negative impact on inflows. Ullah et al (2012) showed 

similar results for Pakistan from 1980 to 2010. The exchange rate volatility deterred FDI 

inflows while the currency stability was found to be promoting more inflows in Pakistan. 

Furthermore, the same findings are confirmed for Nigeria, Iran and South Asian countries 

(Osinubi & Amaghionyeodiwe, 2009; Sharifi-Renani & Mirfatah, 2012; Azhar et al., 2015). 

Other authors have used fixed effects models to capture the impact of exchange rate volatility 

on FDI inflows. Brzozowski (2006) used fixed effects model in 32 developing countries while 

the same model was applied in Dal Bianco and Loan (2017) across 10 Latin American countries 

for the period from 1990 to 2012. Both studies suggest that there is a negative relationship 

between exchange rate volatility and investment inflows, reinforcing the robustness of this 

correlation across different countries.  

Different from the previous studies, Balaban et al. (2019) found mixed results using 

System-GMM methodology among a group of 16 countries in transition economies. While 

exchange rate volatility had a negative impact on FDI inflows in the manufacturing industry, 

quite the opposite was found for the financial intermediation industry. Furceri and Borelli 



 

 

 

EXCHANGE RATE VOLATILITY AND INWARD FDI: A PANEL DATA ANALYSIS OF 

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204 

 

(2008) investigated 35 EMU countries stating that in countries with a closed economy, 

exchange rate volatility had a negative influence on FDI, opposite to countries with open 

economies, where FDI inflows were positively influenced by exchange rate volatility. 

Surprisingly, Dhakal et al. (2010) found a positive relationship among investment inflows and 

exchange rate volatility in a group of 6 southeastern Asian countries, where mostly were 

developing economies. Similar findings are shown in Chowdhury and Wheeler (2008) through 

a VAR model in 4 developed countries for the period from 1972 to 2005.  

Among other variables, inflation was also found to affect FDI inflows. Demirhan and 

Yilmaz (2015) used RE and FE models in a sample of 7 Balkan countries for two different time 

intervals. They found a significant negative impact of inflation on FDI inflows in both models. 

The impact of inflation was found to be insignificant in Kurtovic et al. (2014) which 

investigated the determinants of FDI in 6 Balkan countries for a period from 1994 to 2012 

using fixed effects model. Agudze and Ibhagui (2021) investigated a sample of 74 countries 

including both industrialized and non-industrialized countries and concluded that inflation had 

a negative impact in both groups of countries. The same findings are confirmed in a larger 

sample of 148 countries comprised of low income, middle income, and high-income countries 

for the period from 1996 to 2016 through a GMM methodology (Sabir et al., 2019). Contrary 

to the theory and other studies, Jaiblai and Shenai (2019) found a positive impact of inflation 

on FDI in Sub-Saharan countries using an ARDL approach. Insignificant results about inflation 

are found in Africa and Eastern Europe (Asiedu, 2002; Rathnayaka et al., 2021). 

Another important factor in explaining FDI inflows is corruption given its negative 

effect due to perceived risk and cost of investment. Skabic and Orlic (2007) found a significant 

negative impact of corruption on FDI inflows even in the presence of EU membership while 

investigating a sample comprised of 7 Central European countries and 6 Western Balkan 

countries for the period from 1993 to 2005. Corruption is found to have a significant negative 

impact on FDI in the Asian region through a sample of 24 countries for the period from 1980 

to 2000 (Mathur & Singh, 2013). Al-Sadig (2009) found this negative relationship in a larger 

sample of 171 countries during the period of 1984 to 2004.  In contrast to studies that report a 

negative impact, corruption is found to positively affect the FDI inflows in Middle East and 

North Africa as shown in Helmy (2013) and Sub-Saharan Africa according to Gossel (2018). 

As noted in these studies, there is a lack of consensus on the effect of corruption on FDI as 

evidenced in different studies through conflicting conclusions particularly with regards to 

different regions. 

Along with other factors, fluctuations in crude oil prices can influence FDI. The impact 

may be both positive and negative, depending on the sector and country context. Rogmans and 

Ebbers (2013) included crude oil prices to determine the FDI inflows in a sample of 16 

countries in Middle East and North Africa and found a positive relationship among the said 

variables. Similar patterns have been identified in a sample of six GCC countries (Eissa and 

Elgammal, 2020). Unlike this study, Mina (2007) found a negative impact of oil prices in FDI 

for the GCC countries. Earlier studies such as Gastanaga et al. (1998) have found similar results 

through a sample of 49 developing countries. Diminishing oil prices have contributed to the 

slowdown of the FDI inflows in the Balkan region (Marjanovic et al. 2021). 

In conclusion, the literature shows strong consensus on the negative impact of exchange 



 

 

 

Kevin MORINA, Nertil MERA 

205  

rate volatility on FDI, though some studies, particularly those focused in developed economies 

or specific industries, present contradictory findings. In addition to exchange rate volatility, 

considerable research is focused on using inflation, corruption and oil prices to explain the 

movements in FDI inflows. The evidence is mixed or inconclusive especially regarding the 

Balkan region. This study aims to address the limited research on the relationship between 

exchange rate volatility and FDI, with a primary focus on the Balkan countries. 

 

METHODOLOGY 

Data and Data Sources 

The data sample for this study consists of 7 Balkan countries, including Albania, Bosnia 

and Herzegovina, Bulgaria, Croatia, North Macedonia, Romania, and Serbia. The data span 

from 2011 to 2021, providing an optimal sample for analyzing the variables under 

investigation. Table 1 outlines a short description of the variables including the name, 

calculation method, abbreviations used in the study and the expected impact of explanatory 

variables on the dependent variable.  

       

   Table 1. Description of the Variables 

Variables      Proxy Abbreviation Expected effect 

Foreign Direct Investment FDI as a % of GDP FDI N/A 

Exchange Rate Volatility SD of % change of local 

currencies to EUR 

EXRV Negative 

Inflation % change among CPIs INF Negative 

Corruption Perception 

Index 

The corruption index CORR Negative 

Crude Oil Prices Historical gasoline prices COP Ambiguous 

    Source: Prepared by the authors. 

 

The calculation method of FDI, which is the dependent variable in this study, involves 

dividing the total FDI volume by the GDP of each respective country. Exchange rate volatility 

was measured as the standard deviation of the percentage changes in national currencies` value 

relative to the Euro. The inflation rate was calculated using the standard method of percentage 

change on Consumer Price Index (CPI) for consecutive years. The data related to FDI, and 

inflation were sourced from the World Bank database while the data regarding exchange rate 

volatility were sourced from investing.com, a financial data platform. The Corruption 

Perception Index was measured on a scale of 0 (most corrupt) to 100 (least corrupt), obtained 

from Trading Economies, a global economic and financial platform providing historical data 

on a wide range of indicators. Given that crude oil price indexes were the only aggregator of 

the underlying data and the countries in the study were grouped together, historical gasoline 

prices for each respective country were used as a proxy to represent this variable. 

 

Research Model 

http://www.investing.com/


 

 

 

EXCHANGE RATE VOLATILITY AND INWARD FDI: A PANEL DATA ANALYSIS OF 

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206 

 

This study uses panel data regression analysis with a dynamic specification to assess 

the impact of exchange rate volatility, along with other explanatory variables such as inflation 

rate, corruption, and oil prices, on Foreign Direct Investment (FDI). The choice between the 

random and fixed effects models was made based on the results of the Hausman test. Given the 

nature of the study, balanced panel data with no missing observation were selected for the 

analysis. Some preliminary tests were conducted to ensure the stationarity of the data and the 

robustness of the model.  

One of the key considerations when employing panel data models is the stationarity of 

the variables. The unit root hypothesis states that variables should be stationary, meaning they 

do not exhibit persistent trending patterns. When variables have a unit root, it becomes difficult 

to accurately capture their isolated impact leading to unreliable and spurious results. The 

stationarity of the variables in this study was assessed using Philips-Perron Fisher unit root test. 

The null hypothesis assumes the presence of a unit root while the alternative hypothesis is in favor 

of stationarity. As shown in Table 2, the unit root test results suggest the rejection of the null 

hypothesis indicating the I(0) stationarity of the variables at 5% significance level. 

 

   Table 2. Unit Root Test Summary (Stationarity) 

Phillips-Perron Fisher Unit Root Test 

 t-Stat Prob.* 

FDI 49.9461 0.0000* 

EXRV 39.5809 0.0003* 

INF 38.4422 0.0004* 

CORR 53.9646 0.0000* 

COP 24.4991 0.0398* 

   *Rejects the null hypothesis at 5% significance level. 

    Source: Generated by the authors using E-Views. 

 

Another important assumption is the absence of a perfect correlation among the 

explanatory variables. Multicollinearity analysis was conducted to verify the degree of 

correlation among the independent variables. A correlation coefficient above 0.8 indicates a 

high correlation, suggesting the removal of one of the variables, thus avoiding spurious results. 

As shown in Table 3, the correlation coefficients among the dependent variables in this study 

are all below 0.8 supporting the inclusion of all variables in the model. 

 

  Table 3. Multicollinearity Analysis 

 Correlation Matrix 

 EXRV INF CORR COP 

EXRV 1    

INF 0.4592 1   

CORR -0.0621 -0.2078 1  

COP 0.3974 0.3828 -0.1198 1 

  Source: Generated by the authors using E-Views. 



 

 

 

Kevin MORINA, Nertil MERA 

207  

In addition to multi-collinearity assumption, the expected value of the error terms needs 

to be zero given the explanatory variables. The violation of the zero conditional mean 

assumption results in an endogeneity problem, leading to biased and inefficient estimation of 

coefficients. The results presented in Table 4 show that the residuals mean is close to zero, 

satisfying the zero conditional mean assumption, which is essential for the model validity.  

 

  Table 4. Zero Conditional Mean Assumption 

Residuals Mean 

EXRV 1.62E-15 

INF 1.64E-15 

CORR 1.17E-15 

COP 3.14E-15 

  Source: Generated by the authors using E-Views. 

 

After ensuring that the regressors are not correlated with the error terms, it is important 

to verify that the error terms exhibit homoscedasticity. The presence of heteroskedasticity can 

lead to inefficient estimators and biased statistical inferences. The assumption of 

homoskedasticity was examined using the Breusch-Pagan test. The null hypothesis assumes 

homoscedasticity while the alternative hypothesis is in favor of heteroscedasticity. Referring to 

the F-statistic from the Breusch-Pagan test results in Table 5, the null hypothesis was not rejected 

at 5% significance level, implying that the variance of the error terms is constant.  

 

  Table 5. Heteroskedasticity Assumption Test 

Dependent Variable: RESID01^2 

Method: Least Squares 

Variable Coefficient Std. Error t-Statistic Prob. 

CORR -1.16E-05 1.08E-05 -1.0682 0.2894 

EXRV -0.0077 0.0120 -0.6361 0.5270 

COP -7.63E-05 0.0004 -0.1862 0.8529 

INF -0.0030 0.0030 -0.9705 0.3354 

C 0.0010 0.0007 1.4592 0.1494 

EXRV (-1) 0.0021 0.0111 0.1903 0.8497 

R-squared 0.0513 Adjusted R-squared -0.0228 

F-statistic 0.6925 Prob (F-statistic) 0.6309 

  Source: Generated by the authors using E-Views. 

 

Regarding the normality of the error terms, given that the sample size exceeds 30 

observations, the Central Limit Theorem ensures the approximation of normality in the 

sampling distribution of the estimators. Furthermore, autocorrelation robust standard errors 

were employed in the final estimation of the regression equation to account for potential serial 

correlation in the error terms. After ensuring that all the relevant assumptions were fulfilled, it 

was important to determine whether a random effects or fixed effects model was more 

appropriate. Hausman test was used to determine whether the unique error terms had a 

correlation with the explanatory variables.  



 

 

 

EXCHANGE RATE VOLATILITY AND INWARD FDI: A PANEL DATA ANALYSIS OF 

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208 

 

    Table 6. Hausman Test 

Correlated Random Effects - Hausman Test 

Test cross-section random effects    

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob. 

Cross-section random 101.7000 5.0000 0.0000 

  Source: Generated by the authors using E-Views. 

 

The null hypothesis in the Hausman test states the appropriateness of the random effects 

model while the alternative hypothesis suggests the fixed effects model. The test statistics 

presented in Table 6 suggest the rejection of the null hypothesis at 5% significance level. This 

implies that the fixed effects model is more appropriate given the presence of a correlation 

among individual effects and the regressors.  

 

 EMPIRICAL RESULTS 

This section presents the empirical results of this study, showing the significance of the 

impact of exchange rate volatility, inflation rate, corruption index, and oil prices on FDI inflows 

in the Balkan countries. After the fulfillment of all the relevant assumptions, a panel data fixed 

effects model was employed to analyze this relationship. The equation below represents the 

model specification of the regression equation. 

𝐹𝐷𝐼𝑖,𝑡 = 𝑎 + β1𝐸𝑋𝑅𝑉𝑖,𝑡 +  β2𝐼𝑁𝐹𝑖,𝑡 +  β3𝐶𝑂𝑅𝑅𝑖,𝑡 +  β4𝐶𝑂𝑃𝑖,𝑡 +  β5𝐸𝑋𝑅𝑉𝑖,𝑡−1

+ 𝑢                (1) 

Where: (α) stands for the constant term of the regression, (i) stands for the country, (t) 

stands for period, and (u) stands for the serially uncorrelated error term. 

 

Estimation Output 

The empirical results derived from the panel least squares model are summarized in 

Table 7. The estimation output presents the model fit and diagnostic statistics, outlining the 

relationship between FDI inflows and exchange rate volatility, inflation rate, corruption index, 

and oil prices in the Balkan countries.  

 

  Table 7. Estimation Output 

Dependent Variable: FDI 

Method: Panel Least Squares 

Sample (adjusted): 2012 2021 

Variable Coefficient Std. Error t-Statistic Prob. 

C 0.0267 0.0401 0.6645 0.5090 

EXRV* -0.6956 0.2706 -2.5698 0.0128 

INF 0.0353 0.0548 0.6448 0.5216 

CORR -0.0003 0.0008 -0.3951 0.6942 

COP* 0.0323 0.0147 2.1908 0.0325 

EXRV (-1) * -1.4131 0.2617 -5.3982 0.0000 

R-squared 0.7885 Adjusted R-squared 0.7484 

F-statistic 19.6595 Prob(F-statistic) 0.0000 



 

 

 

Kevin MORINA, Nertil MERA 

209  

 * Significant at 5% significance level 

  Source: Generated by the authors using E-Views. 

 

The regression results displayed in Table 7 suggest that exchange rate volatility and oil 

prices are statistically significant in explaining the variations in FDI inflows. A significant 

negative relationship is observed among exchange rate volatility and its first lag with respect 

to FDI inflows. Similar findings were observed in Benassy-Quéré et al. (2001), Brzozowski 

(2006), and Hanusch et al. (2018). While literature generally suggests a negative relationship 

among oil prices and FDI, some studies report a positive relationship.  This study reveals a 

positive relationship between oil prices and FDI. These findings are in line with those of 

Marjanovic et al. (2021), who suggest a slowdown in FDI inflows due to decreasing oil prices 

in the Balkan countries. In contrast to exchange rate volatility and oil prices, inflation rate and 

corruption were found to be statistically insignificant in explaining the FDI inflows in this 

study. In line with this results, Kurtovic et. al. (2014) also found inflation to be insignificant 

for the Balkan countries. Following this results, the significance of the overall model is 

examined, demonstrating the reliability of the factors influencing FDI in the region. The 

adjusted R-squared statistics show the explanatory power of the model indicating that 75 

percent of the variation in the FDI inflows is explained by the movements in the explanatory 

variables. This is further supported by the F-statistic value showing the significance of the 

model in explaining the variations in FDI inflows.      

 

CONCLUSIONS 

The main aim of this study was to investigate the relationship between exchange rate 

volatility and FDI inflows with a particular focus in the Balkan countries. Other control 

variables such as inflation rate, corruption, and oil prices, were used alongside the exchange 

rate volatility to explain the movements in FDI inflows. The empirical results from the panel 

data regression analysis indicate a significant negative relationship between exchange rate 

volatility and FDI inflows in the Balkan countries. The findings align with most of the literature 

reviewed in this study, confirming the importance of currency stability in attracting FDI. 

Fluctuations in the oil prices, on the other hand, were found to have a positive relationship with 

FDI inflows. However, literature presents mixed results regarding this relationship, and the 

positive relationship is particularly evident when FDI targets the energy or oil sector. Unlike 

the other variables, inflation rate and corruption were found to be statistically insignificant in 

explaining FDI movements. Kurtovic et. al. (2014) also found inflation to be insignificant in 

the Balkan countries, suggesting that this relationship has remained consistent over time.  

While this study provides valuable contributions to the existing literature, it also has 

limitations. The data used in the analysis is limited to a specific time period and may not fully 

capture the long-term dynamics of FDI. Future studies could expand the analysis by 

incorporating additional variables, exploring cross-country comparisons, or applying 

alternative methodologies, to better understand the complexities of FDI inflows.  

 

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