







































 
 

 

78 
© 2025 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 12, No. 2, 78-89, 2025 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/economy.v12i2.6840 

© 2025 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Multivariate GARCH estimations of volatility spillover amongst oil prices, 
exchange rates and news-based uncertainty in the CEE - 3 countries 

 
David Umoru1   

Timothy Igbafe Aliu2   

Shehu S. Umar3   

Beauty Igbinovia4   

 

 
( Corresponding Author) 

 
1,2Department of Economics, Edo State University Uzairue, Iyamho, Nigeria. 
1Email: david.umoru@yahoo.com  
2Email: timothyigbafe@gmail.com  
3Department of Statistics, Auchi Polytechnic Auchi, Nigeria. 
3Email: shehu.umar@gmail.com  
4Department of Economics, Edo State University Uzairue, Iyamho, Nigeria. 
4Email: beauty.igbinovia@edouniversity.edu.ng  

 
Abstract 

This study empirically examined the comparative difference in the outcomes of multivariate 
GARCH estimations in the volatility transmission amongst oil prices, new-based policy 
uncertainty and exchange rates of the CEE-3 countries. The methodological scope is restricted to 
BEKK-GARCH, Constant CCC-GARCH and VEC-GARCH. The results of this research indicate 
significant transfer of volatility from the HUF/EUR, PLN/EUR, and CZK/EUR exchange rates 
to the price of Brent oil. The BEKK-GARCH results uphold the co-volatility with relation to 
exchange rates and oil prices in the CEE-3 countries and this was found as highly reciprocating 
and interdependent. The research also established a reciprocating transmission of volatility 
between the fluctuating price of oil and news based economic policy uncertainties in Hungary and 
Czech. The CCC-GARCH model sufficiently estimated oil price volatility spillover on currency 
rate and its volatility spillover on oil price fluctuation in Czech while VEC-GARCH model 
estimations sufficiently estimated oil price volatility transmission to exchange rates. The Polish 
and Czech news-based policy uncertainties were significant in influencing the PLN/EUR and 
CZK/EUR exchange rates respectively. The BEKK-GARCH and VECH-GARCH model 
estimations are efficient and hence highly recommended for ascertaining the volatility 
transmission within the financial markets in the CEE-3 countries. 

 
Keywords: BEKK-GARCH model, CCC-GARCH model, CZK/EUR exchange rate, High-Income countries, HUF/EUR exchange rate, 
NEWS-based uncertainty, Oil price variation, PLN/EUR exchange rate, VEC-GARCH model, Volatility transmission. 

 
Citation | Umoru, D., Aliu, T. I., Umar, S. S., & Igbinovia, B. 
(2025). Multivariate GARCH estimations of volatility spillover 
amongst oil prices, exchange rates and news-based uncertainty in 
the CEE - 3 countries. Economy, 12(2), 78–89. 
10.20448/economy.v12i2.6840 
History:  
Received: 12 May 2025 
Revised: 6 June 2025 
Accepted: 9 June 2025 
Published: 27 June 2025 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 

Funding:   This study received no specific financial support. 
Institutional Review Board Statement: Not Applicable 
Transparency: The   authors   confirm   that   the   manuscript   is   an   
honest, accurate, and transparent account of the study; that no vital features of 
the study have been omitted; and that any discrepancies from the study as 
planned have been explained. This study followed all ethical practices during 
writing. 
Competing Interests: The authors have no competing interests to declare 
Authors’ Contributions: All authors contributed equally to the conception, 
methodology, discussion, writing, and supervision of the manuscript. All 
authors have read and agreed to the published version of the manuscript. 
 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 79 
2. Earlier Scientific Findings ............................................................................................................................................................. 80 
3. Econometric Methodology ............................................................................................................................................................ 80 
4. Results and Discussions .................................................................................................................................................................. 81 
5. Conclusion ......................................................................................................................................................................................... 88 
References .............................................................................................................................................................................................. 88 
 

 

 

mailto:david.umoru@yahoo.com
mailto:timothyigbafe@gmail.com
mailto:shehu.umar@gmail.com
mailto:beauty.igbinovia@edouniversity.edu.ng
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/economy.v12i2.6840
https://orcid.org/0000-0002-1198-299X
https://orcid.org/0000-0002-1920-6901
https://orcid.org/0009-0001-5615-9833
https://orcid.org/0009-0005-6820-1110


Economy, 2025, 12(2): 78-89 

79 
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Contribution of this paper to the literature 
The study advances our understanding of the modeling volatility spread amongst news-based 
uncertainty, exchange rate returns, and shocks to the price of oil of the CEE-3 countries.  The discovery 
of a reciprocal distribution of volatility between news-based economic policy uncertainty and fluctuating 
oil prices in Hungary and the Czech Republic adds to the body of empirical research.  For future financial 
economics scientists who could be intrigued by the fields of market volatility modeling, this could serve 
as a resource. 

 
1. Introduction 

The three nations that make up Central and Eastern Europe (CEE) are the subject of this study is Hungary, the 
Czech Republic, and Poland. The prolonged instability brought on by Russia's war has exposed the CEE region to 
longer-term vulnerabilities, including high inflation and an economic downturn. Along with increased uncertainty, 
pandemics, and shocks to energy prices, these economies have also seen slower short-term growth and long-term 
macroeconomic prospects that are affected. Significant disruptions have been transmitted to Poland, Hungary, and 
the Czech Republic by the combined effect of all these variables. In July 2024, the positive trade balance with EU 
Member States increased by CZK 5.8 billion, year over year, according to the (Ikwuagwu & Yagboyaju, 2023). The 
amount of the trade imbalance with non-EU nations rose by CZK 3.4 billion. The primary cause of the negative 
impact on the overall trade balance was a greater trade deficit of CZK 3.7 billion in basic metals and CZK 1.9 
billion in "refined petroleum products," among other items. The Hungarian economy is extremely susceptible to 
shocks from without. The threats are defined by poor macroeconomic growth and significant uncertainty about the 
strength of the macroeconomy.  The selection of these countries stems from the fact that, in terms of economic 
growth and development, they are transition economies, becoming market economies. These nations also use the 
same currency.  

The vulnerabilities are typified by large budget deficit expenditure, poor macroeconomic activity, and 
significant concerns about the health of the macroeconomy. For instance, the GDP growth rate in 2022 was 4.6%, 
but in 2023 it fell by 0.9%. Capital inflows that generate debt are used to finance the external deficit. In July 2024, 
the USD to HUF exchange rate saw a 90-day high of 371.5440 and a 90-day low of 351.6660. This suggests that 
the USD/HUF changed by -3.92 and the 90-day average was 359.5148. Poland is home to a thriving market that is 
situated in the center of Central Europe. In 2022, the GDP expanded by 4.9% after declining by 2.7% in 2020. 
Nonetheless, there has been a noticeable decline in economic activities of Poland as a result of Russia's invasion of 
Ukraine. The exchange rate between the euro and Polish zloty on September 19, 2024, was 4.27. In June 2023, 
Poland's exchange rate versus the US dollar averaged 4.1 (USD/PLN). In comparison to April 2024, the daily 
average foreign exchange market turnover grew by 276 million USD (3.1%) to USD 9,145 million, according to the 
(Nabila, Usman, Indryani, & Kurniasari, 2021).  

The CEE countries are major consumers of oil, rather than producers. When oil prices rise, these countries 
experience higher import costs, leading to inflation and a weaker currency (Abiad & Qureshi, 2023). Conversely, 
when oil prices fall, their economies benefit from lower fuel costs, leading to decreased inflation and a stronger 
currency. Aside oil price shocks, news-based uncertainty is another crucial factor that may also play significant role 
in the dynamics of financial markets in Africa and Europe (Adeosun, Adeosun, Tabash, & Anagreh, 2023). In recent 
years, the rise of globalization has interconnected economies and financial systems across the globe, making news 
events and uncertainty in one region affect foreign exchange rates in another. Hence, news events such as political 
unsteadiness, economic downturns, natural disasters, or geopolitical tensions in one region can have ripple effects 
on foreign exchange rates in other regions (Bush & Noria, 2021). Investors and traders closely monitor news 
events to assess risks and make informed decisions on foreign exchange trading. 

In Europe, news-based uncertainty can impact foreign exchange rates, especially during times of economic 

instability or political turmoil (Olasehinde‐Williams & Olanipekun, 2022). The Eurozone, which comprises 19 
nations that use the euro as their currency, is particularly sensitive to news events that can impact the stability of 
the euro. For instance, news of a potential breakup of the Eurozone or a major economic crisis in one of its member 
countries can lead to fluctuations in foreign exchange rates across the region. Moreover, news-based uncertainty in 
Europe can also be driven by external factors such as global economic trends, trade policies, or changes in interest 
rates by central banks. For example, news of a trade war between major economies like the US and China can 
impact foreign exchange rates in Europe as investors and traders adjust their positions based on the potential risks 
and opportunities arising from such news events (Aftab, Naeem, Tahir, & Ismail, 2024). 

In Europe, the oil price uncertainty and exchange rate problems also pose significant challenges to the 
economy. This is because high income nations in Europe is highly dependent on oil imports to meet its energy 
needs, and fluctuations in oil prices can lead to increased costs for consumers and businesses. This can impact 
inflation rates, consumer spending, and overall economic growth in the region. Additionally, exchange rate 
problems resulting from oil price uncertainty can create instability in the financial markets, leading to fluctuations 

in stock prices, currency values, and investment decisions (Olasehinde‐Williams & Olanipekun, 2022). Despite the 
fact that some studies have been carried out for the BRICS nations (Umoru et al., 2023; Wang, Cheng, & Cao, 
2022) no recent study utilized a combination of multivariate GARCH estimation methods like BEKK-GARCH, 
CCC-GARCH and VEC-GARCH from 2019Q1 to 2023Q4. This study sought to fill this research gap. 
Accordingly, the study aims at ascertaining whether or not there is a significant difference in the outcomes of 
volatility spillover amongst oil prices, exchange rates and uncertainty basing analysis on Multivariate GARCH 
Estimations in High Income Countries. The findings of the study contribute to knowledge and add up to empirical 
literature on oil price shocks, news-based uncertainty and exchange rate returns modeling. This could become a 
reference point for prospective researchers in financial economics who may be interested in the areas of market 
volatility modeling. The following section provides a synopsis of related studies. The study's methodology, results, 
and discussions come next. The final section of the paper is the concluding remarks. 
 
 



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2. Earlier Scientific Findings  
The dynamic volatility spillover between the BRICS nations' gold, exchange, and oil prices was examined by 

Oladeji and Musa (2022). The analysis discovered a relationship between China's exchange rate and oil prices, 

but not between Brazil and India's volatility spillover toward gold. Yusufu, Yusufu, and Abdullahi (2022) 
worked on the association that exists between foreign direct investment and volatility in rates of exchange in 
Turkey. Expected gain from FDI is at risk as the oscillations in the rate of exchange express or show instability. 
Nevertheless, FDI has crucially influenced how many investments take place. We examine the relationship 
between the volatility of the exchange rate and foreign direct investment (FDI) in Turkey from the fourth quarter 
of 2005 to the first quarter of 2018. With the use of the Toda-Yamamoto causality test, the oscillations in the rate 
of exchange were ascertained. 

Using copulas, Wang et al. (2022) investigated the potential of oil price shocks to predict the CNY/USD 
exchange rate. The bivariate copula outperformed the univariate in terms of predicting abilities, according to the 
results. The influence of both domestic and international oil prices on the Chinese currency rate was shown to be 
considerable by Kutu, Alorı, and Ngalawa (2021). Chang and Tan (2008) investigated how the dollar's value 
fluctuated in relation to the exchange rates of OPEC nations. The dynamic factor technique was used by Camanho, 
Hau, and Rey (2022) to assess the degree of uncertainty in the Colombian economy. The market rate of the peso 
and the prices of gas and oil were utilized by the author. The created index demonstrates the rise in 
unpredictability that coincided with the 2008 global and COVID-19 crises. 

In a study by Abdullahi, Abubarkar, Fakunmoju, and Giwa (2016) which analyzed the causes of instability in 
rates of exchange, it was pointed out that change in foreign reserves adversely caused fluctuations in currency rates 
in Bangladesh, Malaysia, and China, while India's exchange rate fluctuated wildly. Nonetheless, changes in 
government spending had a positive and significant impact on currency rate fluctuations in China, Malaysia, 
Pakistan, Indonesia, and Bangladesh. The fluctuation in terms of trade required a significant reduction in the rate 
of exchange fluctuations in Bangladesh and Pakistan. But in China, Malaysia, Pakistan, Indonesia, and India, it 
causes an increase in that. On its own side, variations in gold price added advantageously and in a momentous way 
to the rate of exchange volatility in Indonesia, Bangladesh, and Malaysia.’ The precariousness in the rate of 
exchange of Pakistan and Indonesia was majorly influenced by production. 

By using the wavelet analysis approach, Engle (2002) highlighted the time-frequency connection between oil 
prices, stock market returns, and currency rates. The results demonstrate a strong relationship between foreign 
exchange rates and oil prices, that is, the Indian stock market. The association between the Indian market's 
exposure to developed markets' volatility and a few macroeconomic events was identified by the author. Seguino 
(2020) examines the link between the G10 exchange rate and the OPEC newspaper count index using a Bayesian 
inference approach and multiple vector autoregressive models. 

Salimi, Saeedi, Heidarzadeh, and Emamverdi (2023) investigated the influence the instruments of monetary 
policy had on the volatility of the rate of exchange from 1997 to 2017 in Sudan. To ascertain the influence in the 
short-term period, they made use of co-integration analysis. Proceeding from this, they established that the 
variables were stable at their first difference. To analyze the long-term link, VECM was estimated. The findings 
showed that there has been instability in the currency rate of Sudan throughout the time frame under investigation. 
The variations in the money supply and variables of the rate of profit margin that experienced involvement of the 
Central Bank of Sudan from time to time explained the volatility in the short term. 

 

3. Econometric Methodology 
The research evaluates transmission of volatility amongst the CEE-3 countries' exchange rates. The CEE- 3 

countries considered in this research are Hungary, Poland and Czech Republic. Accordingly, the HUF/EUR, 
PLN/EUR and CZK/EUR serve as the CEE-3's currency exchange rates, respectively. Each of the CEE-3's 
monthly currency exchange rate returns was utilized. Also, monthly volatility in Brent crude oil prices was utilized 
for estimation. The news-based economic policy uncertainty index uncertainty was constructed by evaluating the 
frequency of words associated with news, policy, and uncertainty appearing together in the English-language main 
newspapers of Hungary, Poland, and the Czech Republic. This methodology of obtaining economic country-specific 
uncertainty was pioneered by Baker, Bloom, and Davis (2016). It has also been adopted by Kilic and Balli (2024).  

The econometrics estimations covered the sample period of January 2009 to June 2024. The choice of these 
CEE- 3 countries stems from the fact that they are transition economies. These countries are on transition to 
market economies as against being communist nations. The econometric methodological scope is restricted to three 
(3) multivariate-GARCH estimation techniques. These include: BEKK-GARCH, CCC-GARCH and VECH-
GARCH. The E-views 13 econometric software was utilized in the estimation process. The output of each was 
compared to determine the best fit for volatility spillover estimation for developing and developed countries. We 
measure the reliability of news-based uncertainty by examining their track record of accuracy. This was done by 
looking at the history of the news outlet and checking for any past instances of false reporting or bias. Additionally, 
analyzing the sources of the news outlet provided insight into the credibility of news outlets. According to Bush 
and Noria (2021) when a news source consistently relies on reputable sources and fact-checks their information, it 
is likely to be more reliable.  

The BEKK-GARCH, CCC-GARCH and VEC-GARCH models were estimated. The justification for estimating 
these set of equations is rooted on the fact that BEKK-GARCH equation is a potent tool for measuring volatility in 
financial markets. Its capacity to measure cross-correlations between variables as well as time-varying volatility 
makes it a useful model for risk control and portfolio enhancement (Rastogi & Kanoujiya, 2024). Similarly, the 
CCC-GARCH equation has become a valuable tool for financial analysts and researchers in modeling and 
forecasting asset or rate returns in volatile markets (Xiao, Xu, Liu, & Liu, 2020). By incorporating both the CCC 
assumption and the GARCH process, this equation provides a more comprehensive and flexible framework for 
capturing the dynamics of volatility (Salimi et al., 2023). As financial markets continue to evolve and become more 
complex, the CCC-GARCH equation remains a key tool for understanding and managing risk in portfolios. In line 



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with the preceding, the BEKK-GARCH model was effectively applied in this research to the analysis of financial 
data to show interdependencies.  The mean and variance equations listed below define the model: 

𝐻𝑈𝐹/𝐸𝑈𝑅𝑖𝑡 = 𝛿𝑖 +∑𝛾𝑖𝑘𝐻𝑈𝐹/𝐸𝑈𝑅𝑡−𝑗 +∑𝛽𝑒𝑡−𝑗,𝑖

𝑞

𝑗=1

𝑝

𝑖=1

 

𝜎𝐻𝑈𝐹/𝐸𝑈𝑅𝑖𝑡
2 = 𝐵0𝐵0

′ 𝛿𝑖 + ∑ 𝐺𝑖𝑘𝑒𝑡−𝑖𝑒𝑡−𝑖
′ 𝐺𝑖

′𝑝
𝑖=1 + ∑ 𝐶𝑖𝜎𝐻𝑈𝐹/𝐸𝑈𝑅𝑖𝑡𝑡−𝑖

2𝑞
𝑗=1 𝐶𝑖

′              (1) 

𝑃𝐿𝑁/𝐸𝑈𝑅𝑖𝑡 = 𝜑𝑖 +∑𝛼𝑖𝑘𝑃𝐿𝑁/𝐸𝑈𝑅𝑡−𝑗 +∑𝜌𝑒𝑡−𝑗,𝑖

𝑞

𝑗=1

𝑝

𝑖=1

 

𝜎𝑃𝐿𝑁/𝐸𝑈𝑅𝑖𝑡
2 = 𝐵0𝐵0

′ + ∑ 𝐺𝑖𝑘𝑒𝑡−𝑖𝑒𝑡−𝑖
′ 𝐺𝑖

′𝑝
𝑖=1 + ∑ 𝐶𝜎𝑃𝐿𝑁/𝐸𝑈𝑅𝑖𝑡𝑡−𝑖

2𝑞
𝑗=1 𝐶𝑖

′               (2) 

𝐶𝑍𝐾/𝐸𝑈𝑅𝑖𝑡 = 𝜏𝑖 +∑𝜛𝑖𝑘𝐶𝑍𝐾/𝐸𝑈𝑅𝑡−𝑗 +∑𝜇𝑒𝑡−𝑗,𝑖

𝑞

𝑗=1

𝑝

𝑖=1

 

𝜎𝐶𝑍𝐾/𝐸𝑈𝑅𝑖𝑡
2 = 𝐵0𝐵0

′ + ∑ 𝐺𝑖𝑘𝑒𝑡−𝑖𝑒𝑡−𝑖
′ 𝐺𝑖

′𝑝
𝑖=1 + ∑ 𝐶𝜎𝐶𝑍𝐾/𝐸𝑈𝑅𝑖𝑡𝑡−𝑖

2𝑞
𝑗=1 𝐶𝑖

′               (3) 

where: i 𝜑𝑖𝜏𝑖 are the mean of HUF/EUR, PLN/EUR and CZK/EUR exchange rates; 𝛾 and 𝛽; 𝛼 and 𝜌; and 

𝜛 and 𝜇are the autoregressive and moving average coefficients of the aforementioned exchange rates respectively;   

𝜎𝐻𝑈𝐹/𝐸𝑈𝑅𝑖𝑡
2  , 𝜎𝑃𝐿𝑁/𝐸𝑈𝑅𝑖𝑡

2  and 𝜎𝐶𝑍𝐾/𝐸𝑈𝑅𝑖𝑡
2  are the conditional covariance matrices of HUF/EUR, PLN/EUR and 

CZK/EUR at time t; 𝐵0 is a matrix of constants; 𝐺𝑖𝑘 and 𝐶𝑖 are coefficient matrices; 𝑒𝑡 is an array of standardized 

regression errors; p and q are the lag orders. The breaking down of the conditional covariance matrix into 
conditional correlation is the methodological applicability of the CCC-GARCH model. Therefore, a considering 
serially uncorrelated vectors of zero-mean, the variables are modeled using the following Equation 4. 

𝑒𝑡 = 𝑋𝑡 − 𝜇       (4) 
The covariance of the study's variables was used to express the contemporaneous correlation, so that: 

∑ 𝛦𝑡−1[(𝑋𝑡 − 𝜇)(𝑋𝑡 − 𝜇)′]𝑡       (5) 
To correct for conditional heteroskedasticity, we estimated for each variable, the conditional 

volatility 
2i

t  using a GARCH model and the standardized residuals are given by: 

𝜐𝑡 = 𝐶𝑡
−1(𝑋𝑡 − 𝜇),       (6) 

{
𝐶𝑡
𝑖,𝑖 = 𝜎𝑡

𝑖∀𝑖 = 𝑗

𝐶𝑡
𝑖,𝑖 = 0∀𝑖 ≠ 𝑗

      (7) 

where tC  represents conditional fluctuations contained in a diagonal matrix as expressed in Equation 7, so that 

the CCC estimator matrix of (Bollerslev, 1990) becomes:  

𝑟 = 1/𝑇∑ [𝐶𝑡
−1(𝑋𝑡 − 𝜇)]𝑇

𝑡=1 [𝐶𝑡
−1(𝑋𝑡 − 𝜇)]𝑡

′     (8) 
The dynamic conditional correlations (DCC) were estimated based on Equation 9. 

𝜌𝐶𝐶𝐶 = 𝑟 + 𝜑[𝜐𝑡−1𝜐𝑡−1
′ − 𝑟] + 𝜂[𝜌𝑡−1 − 𝑟]     (9) 

𝜌𝐶𝐶𝐶 = 𝑟 + 𝜑
1

𝐶𝑡−1
([(𝑋𝑡−1 − 𝜇)][(𝑋𝑡−1 − 𝜇)]𝑡

′ − 𝑟) + 𝜂[𝜌𝑡−1 − 𝑟]  (10) 

The GARCH representation of the DCC-GARCH model is given correspondingly in Equation 11: 

𝜌𝐶𝐶𝐶 = 𝛷 + 𝜑𝜐𝑡−1𝜐𝑡−1
′ + 𝜂𝜌𝑡−1               (11) 

𝜌𝐶𝐶𝐶 = 𝑟 + ∑ 𝜑𝑖[𝜐𝑡−1𝜐𝑡−1
′ − 𝑟] + ∑ 𝜂𝑗[𝜌𝑡−1 − 𝑟

𝑞
𝑗=1

𝑝
𝑖=1 ]            (12) 

Where  is a matrix with 2 ( 1/ 2)n n+ + parameters. In effect, the unconditional relationship represented by 

the DCC-GARCH model according to Engle (2002) and  becomes Equation 13 accordingly: 

𝑟 = 𝛷/1 − 𝜑 − 𝜂               (13) 
Therefore, while estimating time-varying covariance and correlations between variables, the DCC-GARCH 

methodology targets variance. We used the quasi-maximum likelihood estimation (QMLE) approach to estimate 
the BEKK-GARCH model having assumed that the regression residuals obey a standardized distribution that is 
normally distributed. The QMLE was adopted because, even in cases when the error distribution deviates from 
normality, the method yields consistent and asymptotically Gaussian regression estimates (Bollerslev, 1990). The 
main source that provides data for the study was the IMF dataset. 

 

4. Results and Discussions 
The pooled data for the CEE-3 countries of Europe are descriptively presented in Table 1. Table 1 show that 

mean oil price is 1.6. The standard deviation of news-based policy uncertainty in the CEE -3 countries are 
0.637554, 1.3267, and 1.32038 are moderately low. The HUF/EUR, PLN/EUR, and CZK/EUR currency 
exchange rates had standard deviation values given by 153.1, 67.4 and 112.5 respectively. These values signify 
instability in the exchanger rates of the CEE-3 countries. The series are not normal in distribution, according to 
the Jarque-Bera test (p<0.05). 

 
 
 
 
 
 
 
 
 
 
 
 



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Table 1. Preliminary results. 

Statistics OILP HUNNEWS POLNEWS CZENEWS HUF/EUR PLN/EUR  CZK/EUR 

Mean 1.600 3.209 1.293 1.288 17.081 19.386 187.349 
Median 1.592 3.087 3.871 1.568 10.913 184.267 148.487 
Maximum 2.530 5.100 0.187 1.289 372.596 426.481 790.374 
Minimum 0.590 2.000 1.139 2.489 0.501 32.489 134.086 
Std. dev. 0.271 0.638 1.327 1.320 153.182 167.389 112.549 
Skewness -0.152 0.521 1.732 1.371 4.294 13.489 18.487 
Kurtosis 3.923 2.716 1.389 1.380 22.159 19.743 18.034 
Jarque-Bera 37.086 21.062 79.560 1.230 13.546 14.571 123.809 
Probability 0.000 0.000 0.000 0.000 0.000 0.000 0.000 
Source: Authors’ Eviews 13 results (2024). 

 
From Figures 1 and 2 the trend behavior of oil price, NEWS-based uncertainty and they exchange rates of the 

CEE-3 countries of Europe showed high volatility clustering. This is evident as high rise shocks are followed with 
higher rise values while low drops are followed with further drops. 

 

 
Figure 1. Oil price, news-based policy uncertainty and exchange rates of the CEE-3 countries. 

Source: Authors’ plot (2024) with Eviews 13 results. 

 



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Figure 2. % change in oil price, news-based policy uncertainty and exchange rates of the CEE-3 countries. 

Source: Authors’ plot (2024) with Eviews 13 results. 

 
This section analyzes the results of estimations of the three (3) multivariate-GARCH (M-GARCH) models. 

Tables 2, 3, and 4 are the BEKK-GARCH estimates for Hungary, Poland and Czech. According to Tables 2, 3 and 
4 the mean equations of the BEKK-GARCH for Hungary, Poland and Czech Republic shows that oil price volatility 
spillover on HUF/EUR was significant (p=0.0001) while volatility spillover of the HUF/EUR on oil prices was 
also insignificant (p=0.0745). Similar results were obtained for Poland and Czech Republic. Accordingly, oil price 
volatility transmissions on the PLN/EUR and CZK/EUR exchange rates are both substantial at the 1% level. 
Also, the volatility transmission from the HUF/EUR, PLN/EUR and CZK/EUR exchange rates to Brent oil 
prices is substantial at the 1% level. Thus, the volatility vectors change overtime, the co-volatility between 



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currency rates and oil prices in the CEE-3 countries is highly reciprocating and interdependent. Also, the volatility 
spillover of Hungary news based policy uncertainty on the HUF/EUR exchange rate was positively significant 
(p=0.000) as revealed by the coefficient 0.027813. Conversely, the HUF/EUR exchange rate volatility transmission 
on news economic policy uncertainty of the Hungarian economy was insignificant (p=0.6689) with a negative 
coefficient. However, the Polish and Czech news based economic policy uncertainties were significant in influencing 
the PLN/EUR and CZK/EUR exchange rates respectively. The coefficients of all the variance equations and 
covariance specifications of the diagonal BEKK-GARCH are all significant for Hungary, Poland and Czech. This 
shows that BEKK-GARCH does sufficiently estimate impact of fluctuations in oil prices on the currency rates of 
the CEE-3 countries of Europe. 

 
Table 2. BEKK- GARCH estimates for Hungary. 

Variables Coefficient P-value Variables Coefficient P-value 

Constant 0.808*** 0.000 Constant 0.785*** 0.000 
OILVOL 0.045** 0.000 HUNNEWS 0.028*** 0.000 
Constant 1.533*** 0.000 Constant 4.109*** 0.000 
HUF/EUR 0.039** 0.001 HUF/EUR -0.001 0.669 
Variance equation coefficients 
Variables Coefficient P-value Variables Coefficient P-value 
C(5) 0.069 0.000 C(5) 1.185 0.000 
C(6) 0.036 0.000 C(6) 3.975 0.004 
C(7) 2.043 0.000 C(7) -0.071 0.000 

C(8) 1.844 0.000 C(8) 0.011 0.000 
C(9) -0.218 0.999 C(9) 0.879 0.844 
C(10) 0.131 0.028 C(10) -0.041 0.000 
Covariance specification: Diagonal BEKK 
Covariance structure Coefficient P-value  Covariance structure Coefficient P-value 
M(1,1) 0.007 0.000 M(1,1) 0.010 0.000 
M(2,2) 0.026 0.000 M(2,2) 0.008 0.004 
A1(1,1) 1.013 0.000 M(1,1) 2.145 0.000 
A1(2,2) 1.844 0.000 M(2,2) 2.136 0.000 
B1(1,1) -2.182 0.991 M(1,1) -0.013 0.844 
B1(2,2) 0.139 0.028 M(2,2) 0.181 0.000 

Note: Significance is indicated at 1%, 5%, respectively, by ***, **. 
Source: Authors’ Eviews 13 results (2024) 

 
Table 3. BEKK-GARCH estimates for Poland. 

Variables Coefficient P-value Variables Coefficient P-value 

Constant 1.038** 0.003 Constant 0.171*** 0.000 
OILVOL 13.488*** 0.000 POLNEWS 1.047** 0.002 

Constant 1.093* 0.000 Constant 1.039*** 0.000 
PLN/EUR 1.029*** 0.000 PLN/EUR 1.095 0.061 
Variance equation coefficients 
Variables Coefficient P-value Variables Coefficient P-value 
C(5) 1.388 0.000 C(5) 1.185 0.000 
C(6) 3.104 0.000 C(6) 3.102 0.000 
C(7) -0.194 0.000 C(7) -0.123 0.000 
C(8) 1.079 0.000 C(8) 0.062 0.000 
C(9) 0.943 0.000 C(9) 0.103 0.000 
C(10) -7.044 0.000 C(10) -6.021 0.000 
Covariance specification: Diagonal BEKK 
Covariance structure Coefficient P-value Covariance structure Coefficient P-value 
M(1,1) 2.479 0.000 M(1,1) 1.289 0.000 
M(2,2) 3.175 0.000 M(2,2) 3.913 0.000 
A1(1,1) -0.194 0.000 M(1,1) -0.287 0.000 
A1(2,2) 1.016 0.000 M(2,2) 0.017 0.000 
B1(1,1) 0.239 0.000 M(1,1) 0.103 0.000 
B1(2,2) -0.158 0.000 M(2,2) -6.041 0.000 

Note: Significance is indicated at 1%, 5%, and 10%, respectively, by ***, **, and *. 
Source: Authors’ Eviews 13 results (2024) 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



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Table 4. BEKK- GARCH estimates for Czech Republic. 

Variables Coefficient P-value Variables Coefficient P-value 

Constant 0.436* 0.000 Constant 0.179*** 0.000 
OILVOL 2.481*** 0.000 CZENEWS 0.161*** 0.000 
Constant 1.209 0.225 Constant 1.190*** 0.000 
CZK/EUR 0.104** 0.003 CZK/EUR 2.102 0.061 
Variance equation coefficients 
Variables Coefficient P-value Variables Coefficient P-value 
C(5) 7.361*** 0.000 C(5) 1.156 0.000 
C(6) 0.287*** 0.000 C(6) 0.194 0.000 
C(7) -0.029*** 0.000 C(7) -0.588 0.000 
C(8) 0.287*** 0.000 C(8) 0.206 0.000 
C(9) 0.188*** 0.000 C(9) 0.039 0.000 
C(10) -0.023*** 0.000 C(10) -0.561 0.000 
C(11) 5.192*** 0.000 C(11) 9.476 0.000 
Covariance specification: Constant conditional corr. 
Covariance structure Coefficient P-value  Covariance structure Coefficient P-value 
M(1,1) 5.239 0.000 M(1,1) 1.089 0.000 
M(2,2) 1.075 0.000 M(2,2) 0.191 0.000 
A1(1,1) -0.156 0.000 M(1,1) -0.587 0.000 
A1(2,2) 2.011 0.000 M(2,2) 0.271 0.000 

B1(1,1) 0.879 0.000 M(1,1) 0.066 0.000 
B1(2,2) -8.236 0.000 M(2,2) -9.561 0.000 

Note: Significance is indicated at 1%, 5%, and 10%, respectively, by ***, **, and *. 
Source: Authors’ Eviews 13 results (2024) 

 
Tables 5, 6 and 7 are the CCC-GARCH estimates for Hungary, Poland and Czech respectively. The 

significance of oil price volatility transmission on HUF/EUR, PLN/EUR, and CZK/EUR at the 1% level with a p-
value of 0.000 was identified. The volatility transmission from HUF/EUR, and PLN/EUR on oil price volatility 
was insignificant with the p-values, 0.8679, and 0.1567 respectively. Only the CZK/EUR exchange rate volatility 
spillover had significant influence on oil prices. The news based policy uncertainty of Hungary had insignificant 
effect on HUF/EUR exchange rate as reported by high p-value of 0.6121. Nonetheless, the effects of the Polish and 
Czech news based economic policy uncertainty indices on the PLN/EUR, and CZK/EUR exchange rates are 
considerable given the zero p-value for each currency. The HUF/EUR exchange rate volatility spillover on 
Hungary news based uncertainty was significant with a negative value. Similarly, CZK/EUR rate had a significant 
coefficient given by 0.00395**. This shows significant volatility transmission from the CZK/EUR to the Czech 
policy uncertainty. Hence, the price of oil fluctuates in tandem with economic policy in the Hungary. The 
coefficients of the variance equations and covariance specification of the CCC-GARCH model were all significant 
with p-value less than 0.05. This shows that CCC-GARCH sufficiently estimate oil price volatility spillover on 
currency rate and currency rate volatility spillover transmission on oil price in the CEE-3 countries. 

 
Table 5. CCC-GARCH estimates for Hungary. 

Variables Coefficient P-value Variables Coefficient P-value 

Constant 26.037*** 0.000 Constant 0.862*** 0.000 
OILVOL -11.964** 0.002 HUNNEWS 0.002 0.612 
Constant 1.569*** 0.000 Constant 3.062*** 0.000 
HUF/EUR 6.187 0.868 HUF/EUR 0.005*** 0.000 
Variance equation coefficients 
Variables Coefficient P-value  Variables Coefficient P-value  
C(5) 1.1854 0.224 C(5) 0.002 0.017 
C(6) 3.975 0.000 C(6) 2.929 0.000 
C(7) -0.071 0.948 C(7) 1.061 0.991 
C(8) 0.011 0.000 C(8) 0.002 0.059 
C(9) 0.879 0.000 C(9) 2.772 0.000 
C(10) -0.049 0.477 C(10) 0.019 0.627 
C(11) 0.122 0.252 C(11) -0.054 0.416 
Covariance specification: Constant conditional correlation 
Covariance structure Coefficient P-value  Covariance structure Coefficient P-value 

M(1) 1.182 0.000 M(1) 2.383 0.000 
A1(1) 1.089 0.000 A1(1) 5.001 0.000 
B1(1) -0.002 0.000 B1(1) 0.011 0.000 
M(2) 0.011 0.000 M(2) 1.885 0.000 
A1(2) 0.879 0.000 A1(2) 3.569 0.000 
B1(2) -0.011 0.000 B1(2) 0.485 0.000 
R(1,2) 0.158 0.000 R(1,2) -0.813 0.0000 

Note: At 1%, 5%, respectively, ***, ** indicate significance. 
Source: Authors’ Eviews 13 results (2024) 

 
 
 
 
 
 



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Table 6. CCC-GARCH estimates for Poland. 

Variables Coefficient P-value  Variables Coefficient P-value  

Constant 0.157*** 0.000 Constant 0.807 0.625 
OILVOL 0.014*** 0.000 POLNEWS 0.047*** 0.000 
Constant 1.797 0.234 Constant 1.532*** 0.000 
PLN/EUR 0.001 0.157 PLN/EUR 0.039 0.069 
Variance equation coefficients 
Variables Coefficient P-value  Variables Coefficient P-value  
C(5) 0.975*** 0.000 C(5) 1.216** 0.019 
C(6) 0.219*** 0.001 C(6) 18.155*** 0.007 
C(7) 0.011** 0.000 C(7) -0.065** 0.010 
C(8) 0.003*** 0.000 C(8) 3.962** 0.004 
C(9) 0.165** 0.000 C(9) 5.332*** 0.000 
C(10) 0.069** 0.000 C(10) -0.717*** 0.000 
C(11) 0.107*** 0.000 C(11) 1.145*** 0.000 
Covariance specification: Constant conditional correlation 
Covariance structure Coefficient P-value  Covariance structure Coefficient P-value 
M(1) 0.157*** 0.004 M(1) 1.0158 0.011 
A1(1) 0.076*** 0.000 A1(1) 1.005*** 0.007 
B1(1) 0.011*** 0.000 B1(1) -0.065** 0.000 
M(2) 0.003*** 0.000 M(2) 3.961*** 0.004 

A1(2) 0.002*** 0.000 A1(2) 5.332*** 0.000 
B1(2) 0.002*** 0.007 B1(2) -0.712** 0.000 
R(1,2) 0.101*** 0.002 R(1,2) 1.146*** 0.000 

Note: At 1%, 5%, respectively, ***, ** indicate significance. 
Source: Authors’ Eviews 13 results (2024) 

 
Table 7. CCC-GARCH estimates for Czech Republic. 

Variables Coefficient P-value Variables Coefficient P-value 

Constant 0.561** 0.000 Constant 0.808*** 0.000 
OILVOL 0.021*** 0.000 CZENEWS 0.047*** 0.000 
Constant 1.451*** 0.000 Constant 1.532 0.143 
CZK/EUR 0.176*** 0.000 CZK/EUR 0.004** 0.001 
Variance equation coefficients 
Variables Coefficient P-value Variables Coefficient P-value 
C(5) 2.383 0.001 C(5) 0.002 0.002 
C(6) 5.001 0.008 C(6) 2.925 0.008 
C(7) 0.011 0.793 C(7) 1.868 0.005 
C(8) 1.853 0.000 C(8) 0.003 0.000 
C(9) 3.698 0.000 C(9) 1.722 0.000 

C(10) 0.458 0.000 C(10) 0.019 0.000 
C(11) -0.829 0.000 C(11) -0.054 0.000 
Covariance specification: Constant conditional correlation 
Covariance structure Coefficient P-value  Covariance structure Coefficient P-value 
M(1) 0.975 0.000 M(1) 1.185 0.000 
A1(1) 0.219 0.000 A1(1) 3.975 0.000 
B1(1) 0.013 0.000 B1(1) -0.007 0.000 
M(2) 0.046 0.000 M(2) 0.011 0.000 
A1(2) 0.164 0.000 A1(2) 0.894 0.000 
B1(2) 0.098 0.000 B1(2) -0.047 0.000 
R(1,2) 0.107 0.000 R(1,2) 0.125 0.000 

Note: At 1%, 5% respectively, ***, ** indicate significance. 
Source: Authors’ Eviews 13 results (2024) 

 
From Tables 8, 9 and 10 which report the VECH-GARCH estimates for Hungary, Poland and Czech Republic, 

the mean equation of the VECH-GARCH shows that oil price volatility spillover on the HUF/EUR, PLN/EUR, 
and CZK/EUR was significant (p=0.000) while the HUF/EUR and PLN/EUR volatility effect on oil price 
volatility were insignificant. Also, the influence of HUNNEWS uncertainty on the HUF/EUR exchange rate was 
significant (p=0.0072) while HUF/EUR exchange rate volatility spillover on news based HUNNEWS uncertainty 
was also significant (p=0.0000). This shows significance of the mean vector coefficient of the GARCH equation. 
Same results were obtained for the Czech Republic. The coefficients of the variance equations and covariance 
specification of the VECH-GARCH coefficients are all significant. This shows that VECH-GARCH only 
sufficiently estimated oil price volatility spillover on currency rate, but not the reverse. However, the reciprocal 
volatility spillover exists between oil price variation and news based uncertainties in high income nations in 
Europe. 

 
 
 
 
 
 
 
 
 



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Table 8. VECH-GARCH estimates for Hungary. 

Variables Coefficient P-value Variables Coefficient P-value 

Constant 0.436*** 0.000 Constant 0.104*** 0.000 
OILVOL 0.018*** 0.000 HUNNEWS 0.059** 0.007 
Constant 1.395** 0.000 Constant 1.023** 0.006 
HUF/EUR 1.953 0.661 HUF/EUR -0.002*** 0.000 
Variance equation coefficients 
Variables Coefficient P-value Variables Coefficient P-value 
C(5) 0.067*** 0.000 C(5) 0.058*** 0.000 
C(6) 0.393*** 0.000 C(6) 0.104*** 0.000 
C(7) 3.195*** 0.000 C(7) 2.811*** 0.000 
C(8) -1.529*** 0.000 C(8) -1.791** 0.000 
Transformed variance coefficients 
Covariance structure Coefficient P-value Covariance structure Coefficient P-value 
M(1,1) 0.023*** 0.000 M(1,1) 0.008*** 0.000 
M(2,2) 0.039*** 0.000 M(2,2) 0.010*** 0.000 
A1 3.195** 0.000 A1 2.811** 0.002 
B1 -2.035*** 0.852 B1 -1.095*** 0.000 

Note: At 1%, 5% respectively, ***, ** indicate significance. 
Source: Authors’ Eviews 13 results (2024) 

 
Table 9. VECH-GARCH estimates for Poland. 

Variables Coefficient P-value Variables Coefficient P-value 

Constant 0.808*** 0.000 Constant 0.808** 0.000 
OILVOL 0.045*** 0.000 POLNEWS 0.047*** 0.000 
Constant 1.533 0.286 Constant 1.533** 0.000 
PLN/EUR 0.033 0.061 PLN/EUR 0.004 0.061 
Variance equation coefficients 
Variables Coefficient P-value Variables Coefficient P-value 
C(5) 0.187** 0.000 C(5) 0.010*** 0.001 
C(6) 1.603*** 0.003 C(6) 0.089*** 0.000 
C(7) 5.109*** 0.000 C(7) 1.587*** 0.000 
C(8) 3.025*** 0.000 C(8) 2.131*** 0.000 
Transformed variance coefficients 
Covariance 
structure 

Coefficient 
 

P-value 
 

Covariance 
structure 

Coefficient P-value 

M(1,1) 1.349** 0.000 M(1,1) 0.007*** 0.001 
M(2,2) 7.467** 0.008 M(2,2) 0.039 0.002 
A1 1.210*** 0.000 A1 0.195*** 0.000 
B1 5.039*** 0.000 B1 -9.467*** 0.000 

Note: At 1%, 5%, respectively, ***, ** indicate significance. 
Source: Authors’ Eviews 13 results (2024) 

 
Table10. VECH-GARCH estimates for Czech Republic. 

Variables Coefficient P-value Variables Coefficient P-value 

Constant 0.153 0.327 Constant 0.088*** 0.000 
OILVOL 5.019*** 0.000 CZENEWS 0.011*** 0.000 
Constant 1.133*** 0.000 Constant 1.099*** 0.000 

CZK/EUR 5.044 0.356 CZK/EUR 1.568*** 0.000 
Variance equation coefficients 

Variables Coefficient P-value Variables Coefficient P-value 
C(5) 0.897*** 0.000 C(5) 0.103*** 0.000 
C(6) 6.011*** 0.000 C(6) 0.879*** 0.000 
C(7) 3.098*** 0.000 C(7) 2.091*** 0.000 
C(8) -2.568*** 0.000 C(8) 5.879*** 0.000 

Transformed variance coefficients 
Covariance 
structure 

Coefficient 
 

P-value 
 

Covariance 
structure 

Coefficient P-value 

M(1,1) 0.567*** 0.000 M(1,1) 0.103*** 0.000 

M(2,2) 6.009*** 0.000 M(2,2) 0.239*** 0.000 
A1 3.115*** 0.000 A1 2.099*** 0.000 
B1 -2.016*** 0.000 B1 5.898*** 0.000 

Note: At 1% respectively, *** indicate significance. 
Source: Authors’ Eviews 13 results (2024) 

 

4.1. Discussion  
The results demonstrate considerable volatility transmission from HUF/EUR, PLN/EUR and CZK/EUR 

exchange rates to Brent oil prices. The BEKK-GARCH results uphold an extremely interconnected co-variability 
between currency rates and oil prices in the CEE-3 countries. The findings of this study are consistent with those 
of Li and Chen (2023) and Ding, Zheng, Cui, and Du (2023) respectively. The research results of Li and Chen 
(2023) confirm that there are transmission intensity changes between energy costs and the RMB exchange rate. 
The covariance between fluctuations in energy prices and those in exchange rates and oil prices was 
econometrically clarified by Ding et al. (2023). The implication associated with the research was that a continuous 
reduction in the co-movement between the fluctuations of oil prices and currency rates in developing economies is 
indispensable. 



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The findings of the resent research agrees with those of Adi, Adda, and Wobilor (2022) where it was 
established that that there was a non-reciprocating volatility transmission from the price of Brent oil to the 
effective exchange rate market, but a considerable reversible volatility transmission between the energy price 
(Brent oil) and the dollar-naira exchange rate. These authors established that Nigeria had loosened the fixed 
exchange rate regime during the post-structural adjustment programme era, which had a substantial impact on the 
naira's strong depreciation against other currencies, particularly the US dollar. Also, the present research outcome 
aligns with the outcome of Balcilar and Usman (2021) empirical research which reveals significant volatility in both 
the price of oil and the currency rate. Our results also agree with Chang and Tan (2008) who identified a 
remarkable link between the world oil prices and the stock performance of the BRICS countries. Throughout the 
whole study period, the relationship's degree fluctuates depending on which countries import and export oil. 
Countries that export oil typically have a stronger and more positive correlation with changes in oil prices than 
those relying on imported oil, which typically have a negative correlation.  

The findings are consistent with the research of Seguino (2020) which discovered significant correlations at 
truncated frequencies, indicating a significant long-term impact on currency rates and equity market returns of G7 
countries. In addition, the result agrees with the work of Salisu, Rufai, and Nsonwu (2025) who found that the 
typical market index went straight down by 35 per cent both within the group of nations using the hit associated 
with the pandemic. The BRICS and Group 7 countries' market indices went straight down as well because the lives 
of individuals are greatly impacted during this period. Also, the result corroborated the findings of Salimi et al. 
(2023) who discovered robust connectivity at low frequencies shows that Covid-19 cases have a major long-term 
influence on the stock market and exchange rate returns of the most afflicted countries under consideration. 

 

5. Conclusion 
The study evaluates whether or not there is a notable variation in the outcomes of volatility spillover amongst 

oil prices, exchange rates and uncertainty basing analysis on multivariate GARCH estimations in for the CEE-3 
countries, namely Czech Republic, Hungary and Poland. The summary of research findings are as follows: The 
BEKK-GARCH model does not sufficiently estimate oil price volatility spillover on currency rate and its volatility 
spillover on oil price in the CEE-3 countries. The CCC-GARCH sufficiently estimated fluctuation of the oil price 
that affects the exchange rate and volatility transmission in exchange rate on oil price in the CEE-3 countries. On 
its part, the VEC-GARCH only sufficiently estimated oil price volatility spillover on exchange rate. However, the 
reciprocal volatility spillover exists between oil price and news-based uncertainties in high income nations in 
Europe. We found evidence of transmission of oil price fluctuations on exchange rate in high income nations in 
Europe.  

The foreign currency market's significance cannot be overestimated in an open economy. Based on findings, it 
is concluded that increase in volatility spillovers in oil price and news-based uncertainties leads to dynamic changes 
in foreign exchange returns in in the CEE-3 countries. The national currency's value should be stabilized by the 
government through the implementation of a flexible currency regime that is sufficiently adaptable to be adjusted 
at whim. In any economy, the balance of payments will improve with policies that promote the normalization of 
currency rates. The government could execute a discretionary policy on exchange rate management. Other 
econometrics multivariate modeling approaches like O-GARCH are suggested in further studies.  

 

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https://doi.org/10.14293/s2199-1006.1.sor-.ppbl3hy.v1
https://doi.org/10.1002/pa.2278
https://doi.org/10.1108/JEAS-08-2021-0167
https://doi.org/10.1108/ijhma-10-2023-0137
https://doi.org/10.1080/13545701.2019.1609691
https://doi.org/10.22495/jgrv12i1art17
https://doi.org/10.1016/j.resourpol.2022.103025

