Evaluating BRICS as an optimum currency area: insights from SVAR modeling

Nach, Marida; Ncwadi, Ronney Article Evaluating BRICS as an optimum currency area: insights from SVAR modeling Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Nach, Marida; Ncwadi, Ronney (2024) : Evaluating BRICS as an optimum currency area: insights from SVAR modeling, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-25, https://doi.org/10.1080/23322039.2024.2399321 This Version is available at: https://hdl.handle.net/10419/321588 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Evaluating BRICS as an optimum currency area: insights from SVAR modeling Marida Nach & Ronney Ncwadi To cite this article: Marida Nach & Ronney Ncwadi (2024) Evaluating BRICS as an optimum currency area: insights from SVAR modeling, Cogent Economics & Finance, 12:1, 2399321, DOI: 10.1080/23322039.2024.2399321 To link to this article: https://doi.org/10.1080/23322039.2024.2399321 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 06 Sep 2024. Submit your article to this journal Article views: 1251 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Evaluating BRICS as an optimum currency area: insights from SVAR modeling Marida Nach and Ronney Ncwadi Development and Tourism, School of Economics, Nelson Mandela University, Gqeberha, South Africa ABSTRACT The study evaluates the feasibility of BRICS (Brazil, Russia, India, China, and South Africa) countries to form an Optimum Currency Area (OCA) through the analysis of shock correlation within the OCA framework. Employing a structural vector auto regression (SVAR) model proposed by Blanchard and Quah, the analysis encompasses both external and domestic shocks affecting individual countries. The study employs four variables: world real GDP, domestic real GDP as a proxy for output, the real effective exchange rate, and the inflation rate as a proxy for price level, to estimate the shocks. Subsequent analysis includes ANOVA, impulse response function (IRF), and variance decomposition to discern shock magnitudes, adjustment dynamics, and underlying determinants of variability. Empirical results show that BRICS countries display symmetric responses to external shock, however, with overall different sources of variation in the responses to domestic supply, demand, and monetary shocks. The results of the analysis using the ANOVA test, IRF, and variance decomposition also highlight the nuanced disparities across BRICS countries in terms of demand, and monetary shocks, indicative of differences in transmission mechanisms and policy responses. The study underscores the implication of aligning exchange rate mechanisms and monetary policies in facilitating the convergence of shock levels, thereby fostering economic stability among BRICS countries. IMPACT STATEMENT This study provides a critical analysis of the feasibility of BRICS countries forming an Optimum Currency Area (OCA) through the application of Structural Vector Auto Regression (SVAR) modeling. By analyzing the correlation and dynamics of both external and domestic macroeconomic shocks, the research highlights significant disparities in how these shocks affect BRICS countries. Despite these differences, the study identifies a strong symmetric response to external shocks. These findings underscore the challenges of aligning exchange rate mechanisms and monetary policies among these diverse economies, yet also reveal the potential for economic stability through coordinated policy responses to symmetric external shocks. This work is pivotal in informing policy decisions on deeper economic integration within the BRICS bloc. It also suggests that with careful policy design, particularly focusing on strengthening economic interdependencies and mitigating asymmetric shock, the BRICS bloc could enhance its economic stability and global influence. Therefore, the study also offers valuable insights into the feasibility of a common currency or enhanced economic integration for emerging economies considering monetary unions in a complex global financial landscape. The study’s recommendations provide a roadmap for BRICS countries to navigate the complexities of economic integration, making it a significant contribution to the field of international economics and regional development strategies. ARTICLE HISTORY Received 5 March 2024 Revised 22 July 2024 Accepted 23 August 2024 KEYWORDS Optimum currency area (OCA); structural vector auto regression (SVAR); shock dynamics; BRICS SUBJECTS Development Economics; International Economics; International Finance; Economic Forecasting; Econometrics; Regional Development; Economics; Economics and Development 1. Introduction The acronym BRICS represents a coalition of emerging economies encompassing Brazil, Russia, India, China, and South Africa. These nations have garnered significant attention due to their combined influence on the global financial landscape. There is a growing recognition of the need for a new global CONTACT Marida Nach [email protected] Development and Tourism, Faculty of Business and Economics Sciences, School of Economics, Nelson Mandela University, University Way, Gqeberha, Summerstrand 6001, South Africa ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2399321 https://doi.org/10.1080/23322039.2024.2399321 financial framework that is both more inclusive and adaptable to the needs of emerging and developing economies. The leaders of the BRICS nations have addressed concerns such as increasing debt burden, financial crises, and the sluggish pace of reform in the international monetary system. As discussions on the necessity of reforms arise against the backdrop of shortcomings in the global financial system, the BRICS alliance emerges as a key player in shaping the direction of international financing (Cynthia et al., 2017; Kirton & Larionova, 2022; Larionova & Kirton, 2018). BRICS is serving as a model for emerging and developing economies seeking to establish themselves in the global financial arena. BRICS alliance presents of a hopeful direction in confronting these challenges, given it compromises diverse economies with significant growth prospects, thereby positioning itself as a central point of discussion on the global financial landscape (Lowe, 2016; Xing, 2016). Although BRICS nations experience different growth paths, their collective influence on the global economy remains undeniable. To underscore their significance in the global economy, it’s essential to highlight their share of global GDP, trade, and investment activities. Together, BRICS nations contribute to over 25% of the global economic output and represent 42% of the global population (Richter, 2023; United Nations Conference on Trade and Development, 2023). As a group, they make up over 17% of global imports and exports. This combined trade volume reflects their increasing participation in the global supply chain and their importance as major trading partners in various regions. For instance, China’s rapid rise, driven by its manufacturing prowess and innovation, has transformed the global supply chains and investment patterns. India, with its thriving technology sector and large population, stands as the another economic driving force within BRICS. BRICS nations’trade have also experienced expansion due to factors like complementary production capabilities, infrastructure development, and initiatives to boost trade (Iqbal, 2022; Yarygina et al., 2020). Moreover, the collective importance of the BRICS nations can be further exemplified by their increasing involvement as recipients and providers of foreign direct investment (FDI). These countries have progressively emerged as appealing recipients for investment and have been actively engaging in overseas investments, seeking to access to new untapped markets, and technology transfer (Hooijmaaijers, 2021a, 2021b). Additionally, over the past two decades, the FDI to and from these nations have grown significantly, thereby playing a substantial role in bolstering the overall gross fixed capital formation (United Nations Conference on Trade and Development, 2023). Furthermore, the BRICS nations are actively asserting their influence in global economic governance forums and institutions. Apart from their yearly summits, they have set up significant institutions such as the New Development Bank (NDB) and the Contingent Reserve Arrangement (CRA), exemplifying their commitment to fostering economic collaboration. The NDB, which a financing capacity of $32.8 billion, mainly focuses on backing sustainable development projects that are in line with the objectives of the Paris Agreement. Its main goal is to provide financial support for projects on climate changes, that are resilient to disasters, technological advancements focus, and socially inclusive (Abdenur & Folly, 2015; Cooper, 2017; Dixon, 2015). The CRA, supported by a dedicated resource pool of $100 billion, plays an important role in enhancing financial stability through the provision of liquidity and precautionary instruments to address the short-term balance of payments challenges (Cattaneo et al., 2015; UNFCCC, n.d.). These initiatives highlight the BRICS’commitment to shaping the global economy and addressing global challenges. With a growing share of global GDP, trade, and investment, BRICS nations collectively represent a significant force in the global economy, with their influence extending across various sectors. Given the foregoing discourse on the noteworthy position of BRICS in the global economy landscape, it is important to delve into the fiscal and monetary strategies prevalent in these countries. The BRICS nations exhibit diverse approaches toward fiscal and monetary policies, reflecting their distinct economic conditions, institutional structures, and policy objectives (Duggan et al., 2022). Brazil and South Africa have faced challenges associated with fiscal deficits and public debt, prompting them to implement measures such as austerity programs and fiscal reforms to stabilize their economies. In contrast, Russia has opted to utilize monetary policy tools to manage inflation and stabilize its currency, often relying on interest rate adjustments and foreign exchange interventions. India, on the other hand, has pursued a combination of fiscal stimulus and monetary easing to support economic growth, with a particular focus on investing in infrastructure and expanding credit. China, with its centrally planned economy, employs 2 M. NACH AND R. NCWADI a blend of fiscal policies aimed at targeted investment and monetary policies geared towards maintaining stability in financial markets and controlling inflation (Jawadi et al., 2014;Tendengu et al., 2022). Monetary policies of these nations have increasingly shifted towards the use of interest rates as the primary monetary instrument, accompanied by the adoption of inflation targets as a key objective of monetary policy. Both fiscal and monetary policies in the BRICS nations are flexible and tailored to address their specific economic needs and challenges (Jawadi et al., 2011;Mbangata & Kanayo, 2017). Despite these differences, BRICS nations have increased efforts to collaborate through platforms such as the BRICS Summits and initiatives like the CRA and the NDB, as stated above (Akalpler & Hove, 2022). Rationale for BRICS as an OCA In the context of continuous discussions about global financial reform, the 15th BRICS summit held in Johannesburg in 2023 has sparked growing interest in establishing a common currency for the BRICS nations (Council of Councils, n.d.). Progressing towards this goal, BRICS countries have initiated the use of their national currencies in trade through currency swaps and bilateral payment agreements. This strategy aims to improve the efficiency of international trade and investment activities, both within the BRICS bloc and with external partners, thus helping to address trade deficits and other economic challenges faced by these and similar countries (Karataev et al., 2017; Kregel, 2015). A common currency for BRICS nations presents a unique opportunity to enhance economic integration and global influence. By eliminating the need for currency exchange, businesses and consumers would experience reduced transaction costs, facilitating smoother and more efficient trade across the bloc. This increase in trade efficiency would lead to greater price transparency, fostering competition and potentially lowering costs for goods and services (De Grauwe, 2020, pp. 101–110). Moreover, a unified monetary policy could stabilize exchange rates among member countries, creating a more predictable economic environment that is attractive to foreign investors (Frankel & Rose, 1998). Therefore, the collective economic power of a unified BRICS currency would amplify the bloc’s influence in international financial institutions, enhancing its ability to negotiate favourable terms and advocate for policies that benefit its member countries. Despite the geographic and economic diversity among BRICS nations, a common currency could provide a framework for mitigating external economic shocks. Mundell (1961) expresses a view that by pooling economic resources and diversifying risks, member countries could achieve greater economic stability and resilience (Alessina et al., 2002; Barro & Tenreyro, 2007; Mundell, 1961). It is against this background that a collaborative approach would also enable the BRICS countries to share best practices and support each other in times of economic difficulty. The potential benefits of enhanced trade, investment, and global influence make the pursuit of a common currency a worthwhile endeavour for BRICS, promising a more integrated and robust economic future for its member nations. The establishment of a BRICS currency for global settlements could bring benefits such as mitigating the effects of currency fluctuations, lessening currency risks for participating nations, and improving the effectiveness of international transactions (De Grauwe, 2020, pp. 55–70). These microeconomic benefits of a unified currency need to be compared with the macroeconomic challenges of managing shocks that affect individual countries differently. Evaluating the shock alignment among BRICS countries is essential for determining the feasibility of a unified currency system and formulating effective policy measures for the future. This discussion provides a robust basis for the empirical analysis. The theory of optimum currency area (OCA), introduced by Mundell (1961) posits that if nations face symmetric shocks, implementing a uniform monetary policy will be more suitable. However, if there are structural disparities and variations in shocks, a uniform monetary policy may not be suitable (Mundell, 1961). According to De Grauwe (1996,2020), a currency union can enhance the stability of monetary policy, leading to deeper integration in the global economy (De Grauwe, 1996,2020). The distinctive features of the BRICS nations including their economic heterogeneity, exposure to various economic shocks, intra-regional trade and investment linkages, policy mechanisms, and geopolitical considerations, make the group a compelling case study of an OCA analysis. Therefore, this paper aims to evaluate the feasibility of BRICS countries as an Optimum Currency Area through the correlation and dynamics of both external and domestic macroeconomic (supply, demand COGENT ECONOMICS & FINANCE 3 and monetary) shocks. Our analysis draws on prior empirical studies that underscore the relevance of shock correlation within the OCA framework. The structure of the paper is as follows: Section 2 presents in the literature review a comprehensive analysis of shock symmetry within the OCA framework, along with a review of prior studies exploring this context. Section 3 describes the data and the methodology employed in this study. The empirical results are presented in Section 4.Section 5 offers a discussion on the results and their implications. Lastly, Section 6 concludes the paper. 2. Literature review 2.1. The OCA theory and the shocks symmetry Macroeconomic shocks are unexpected occurrences that have substantial effects on an economy. These shocks can arise from various sources, such as fluctuations in commodity prices, changes in exchange rates, shifts in interest rates, or alterations in economic growth patterns. They can be classified into two main types: short-term shocks, which quickly return to a state of balance with limited consequences, and persistent shocks, which stem from underlying vulnerabilities within an economy (Nchor, 2020). Evaluating whether these shocks exhibit asymmetry or symmetry plays a crucial role in influencing the formation of an OCA. The Optimum Currency Area (OCA) theory provides a theoretical framework for comprehending currency unifications and elucidates the rationale behind their establishment on a global scale, encompassing various regions such as Europe, Asia, Africa, and Latin America. By examining the asymmetry and symmetry of macroeconomic shocks, the OCA theory aids in evaluating the efficiency of currency unions, the appropriateness of a common currency, and the monetary policy for participating nations. Initially introduced by Mundell (1961), further developed by McKinnon (1963) and (Kenen, 1969), the OCA theory outlines the different conditions under which countries should adopt a unified currency to enhance economic integration. The main difference between symmetric and asymmetric shocks lies in their impact. Symmetric shocks have a uniform or synchronized effect on all regions or sectors, while asymmetric shocks have a varying impact, affecting certain regions or sectors more than others (Apazidis & Eliasson, 2019). In cases where exchange rate adjustments, which serve as a mechanism for mitigating shocks, are not available due to the adoption of a common currency, asymmetric shocks become particularly problematic as they pose challenges to monetary unions. The inability of the nominal exchange rate to fluctuate in a monetary union hinders adjustment to specific shocks, increasing the costs of surrendering control over monetary policy. The absence of a nominal exchange rate as a means of adapting to shocks amplifies the costs linked to surrendering autonomy over monetary policy (Bayoumi, 1994; Bayoumi & Eichengreen, 1992; Jager & Hafner, 2013). Therefore, shock symmetry is important within the Optimum Currency Area (OCA) framework, indicating synchronized economic fluctuations among member countries, which is favorable for a unified currency and monetary policy. Conversely, asymmetric shocks pose challenges to currency unions, potentially leading to economic imbalances and difficulties for specific member countries (Campos & Macchiarelli, 2018; Omotor & Niringiye, 2011). Moreover, literature highlights the economic consequences of shocks within monetary unions, as demonstrated by historical events like the oil price shocks of the 1970s and the 2009 financial (Baas & Belke, 2023; Filippidis et al., 2020; Jones & Leiby, 1996; Kilian & Lewis, 2011). Moreover, the lack of flexibility in adjusting exchange rates within such unions exacerbates the challenges in responding to asymmetric shocks, thereby escalating the costs associated with relinquishing control over monetary policy autonomy. Conversely, a flexible exchange rate system allows for adjustments in exchange rates, facilitating the absorption of asymmetric shocks and enabling adaptation to country-specific economic disturbances. This adaptability can mitigate the costs related to asymmetric shocks and strengthen individual countries’ability to effectively tackle economic challenges (Khatat et al., 2020). Therefore, understanding the nature of economic shocks is imperative in evaluating the feasibility and effectiveness of currency unions. This comprehension informs the efficacy of a unified monetary policy in addressing diverse economic circumstances and the challenges faced by member states. 4 M. NACH AND R. NCWADI 2.2. Empirical literature review Supply shocks and demand shocks are both important factors in shaping economic outcomes, as highlighted in economic literature (Shapiro, 1987). Early studies by Blanchard and Quah (1989) as well as (Bayoumi & Eichengreen, 1992) and (Bayoumi, 1994) emphasized the significant impact of symmetric shocks on output, particularly in assessing the suitability of countries for monetary unions. These studies have laid the groundwork for extensive research on monetary unions, guiding decisions on country selection, monetary policy, and related initiatives. In the context of the BRICS countries, Saji (2019) used the Markov Regime-Switching Model (MRSM) to examine the possibility of a currency union among BRICS countries, focusing on with a specific focus on comparing the behavior of their real exchange rate markets. The findings revealed disparities in market behavior prior to the formation of the BRICS. suggesting that strengthening policy cooperation in the region, particularly in terms of monetary management, could enhance the prospects of a robust currency union among BRICS members (Saji, 2019). Quah (2016)Quad’s study assessed the feasibility of monetary integration within BRICS applying different OCA theory criteria. While the results were inconclusive, the study hinted at China’s potential role in monetary integration (Quah, 2016). These studies have sparked further exploration in the field, emphasizing the need for more rigorous econometric analyses as advocated in this study to understand unique data characteristics in BRICS. The examination of the macroeconomic effects of shocks is just as crucial as understanding the nature of these shocks. In a study by BenMabrouk and HadjMohamed (2022), they used the SVAR model to analyze the effects of oil shocks on the volatility of BRICS and G7 markets. Their research showed that oil shocks had a noticeable influence on both markets, but the impact varied depending on the type of shock and the specific market being studied. They found that the response to market volatilities was more pronounced in the case of demand shocks compared to supply shocks (BenMabrouk & HadjMohamed, 2022). Another study by Naeem et al. (2021) found positive and long-lasting relationships between oil demand shocks and BRIC markets (Brazil, Russia, India, and China). Through their Cross-quantile dependence analysis, they demonstrated a positive and significant correlation between oil demand shocks and BRIC stock returns (Naeem et al., 2021). Li and Guo (2022)focusedontheshockimpacts of oil price and component shock on inflation in the BRICS countries. They used a novel multiple threshold nonlinear autoregressive distributed lag model (MTNARDL) and discovered significant asymmetries between oil price and inflation, particularly in China in the short term. They also observed that demand shocks had a temporary impact during periods of extreme (Li & Guo, 2022). The following studies delved into the concept of shock symmetry within the OCA framework to assess the feasibility of currency union in different regions. Dongfack and Ouyang (2020) evaluated the performance of the Central African Economic and Monetary Community (CAEMC) as a currency union, focusing on member countries’vulnerability to asymmetric shocks. Using a SVAR model, they identified structural macroeconomic shocks and observed output growth and inflation impulse responses. Their findings highlighted the asymmetric effects of aggregate demand and supply shocks, highlighting potential adjustment costs due to the absence of monetary sovereignty (Dongfack & Ouyang, 2020). Khezri et al. (2019) examined the responses of supply, demand, and external shocks on the Shanghai Cooperation Organization (SCO) countries. They employed a global vector autoregression (GVAR) approach, with quarterly data of price levels, interest rates, exchange rates, and GDP. Their finding revealed asymmetric responses of central banks and variations in macroeconomic indicators depending on the type of shock (Khezri et al., 2019). Moreover, Basnet and Pradhan (2017) investigated the economic interdependence within MERCOSUR countries. Employing the serial correlation common features test, their finding revealed a strong longterm co-movement in key macroeconomic variables namely, exchange rate, real output, investment, and intra-regional trade, indicating strong macroeconomic interdependence. These findings support the idea of deepening integration within Mercosur countries (Basnet & Pradhan, 2017). In another study, Kazerooni and Razzaghi (2014) employed a three-variable SVAR model to assess the possibility of a common currency area among members of the D-8 group. Their findings reveal significant and symmetrical shocks, suggesting a currency union formation within subgroups of Malaysia, Turkey, Nigeria, and Pakistan (Kazerooni & Razzaghi, 2014). Lastly, Sheikh et al. (2013) evaluated the shocks nature and business cycle synchronization among East African Community (EAC) countries to assess its OCA suitability. COGENT ECONOMICS & FINANCE 5 Using the SVAR model, their findings showed that the EAC was mainly influenced by external supply shock and domestic demand shock, with symmetry between domestic supply and monetary shocks (Sheikh et al., 2013). The foregoing theoretical and empirical literature review presented the basis for the evaluation of the feasibility of BRICS as an OCA. The literature has provided insights into the appropriate models and crucial variables to consider when analyzing shock within the BRICS framework. 3. Methodology 3.1. Data The dataset utilized in this study was obtained from the World Development Indicators (WDI), the International Financial Statistics (IFS), and the Federal Reserve of ST Louis Economic Data, provided by the World Bank, and the International Monetary Fund (IMF) and the Federal Reserve of ST Louis Economic Data, respectively. The dataset spans annually from 1993 to 2022. The selection of this timeframe ensures the availability of a robust dataset with ample observations for all variables for all BRICS countries required for econometric modelling. The dataset consists of four variables: world real GDP which serves as a proxy for world output, each country’s domestic real GDP, a proxy for domestic output, inflation rate as presenting the price level, and real effective exchange rate for the five BRICS countries. 3.2. Structural vector auto regression (SVAR) The SVAR model, originally introduced by Sims (1980), serves as a tool for analyzing macroeconomic shocks dynamics. It is widely in assessing economic theories and understanding the transmission of various shocks and provide flexibility by incorporating contemporaneous variables, conducting individual shock analysis, and estimating unobservable parameters, thus overcoming the limitations of non-structural VAR models (Gottschalk, 2001). Built upon economic theory, SVAR integrates short-term (Bayoumi & Eichengreen, 1992) and long-term constraints (Blanchard & Quah, 1989), allowing for a deeper understanding of economic connections. Furthermore, Blanchard and Quah (1989) model, rooted in Aggregate Demand and Aggregate Supply (AD-AS) theory, converts reduced-form VAR equations into structural equations, enabling the extraction of economic structure (Fern andez-Villaverde & Rubio-Ram ırez, 2010; Ouliaris & Pagan, 2016; Rubio-Ram ırez et al., 2010; Stock & Watson, 2016). However, despite its effectiveness, the SVAR model is not without limitations. Implicit constraints and assumptions within the model can potentially result in misinterpretations and restricted assumptions (Gottschalk, 2001; Lippi & Reichlin, 1993). But its capacity to analyze monetary transmissions and nonpolicy shocks makes it a valuable tool in economic research. Therefore, this model remains widely utilized in empirical analyses of monetary and fiscal policy and the study OCAs. In this study, the SVAR model employs the following AD-AS economic theory to impose long-run restrictions: Aggregate supply shocks are the sole source of permanent movement in output in the long run, Aggregate supply and demand shocks both can affect the real exchange rates in the long run, and finally, Monetary shocks have no real effect on both output and real exchange rate. Whenever there is no indication of restrictions according to the AD-AS theory framework, the structure shocks do not affect a variable. Consequently, the absence of long-run restrictions on price level reflects the silent stance of the AD-AS theory framework on this variable. 3.3. Model specification The study uses a multivariate structural vector autoregression (SVAR) model. Moreover, we refine previous methodologies by incorporating four macroeconomic variables instead of two variables. This study 6 M. NACH AND R. NCWADI has used E-Views13 to conduct the SVAR modelling. The model is expressed in both reduced and matrix forms, enabling the expression of each endogenous variable solely as a function of predetermined variables. Comprising one external shock, represented as the global supply shock, and three domestic shocks (supply, demand and monetary), the model encapsulates the dynamics of the economy. Upon identifying these shocks, assumptions are imposed on variables to enable the measurement the shock symmetry or asymmetry. This assessment is conducted through four analytical approaches: a. Correlation Analysis: Examining the correlation of external global supply shock, domestic supply, demand, and monetary shocks. b. One-Way ANOVA and Coefficient of Variance Analysis: using statistical methods to analyze the nonlinear dependence of underlying these macroeconomic shocks c. Impulse Response Analysis: Examining the response of these variables to identified shocks over time. d. Variance Decomposition Analysis: Assessing the relative contribution of each shock to the variability of these variables. The applicability of these analytical techniques relies on the stationarity and logarithmic transformation of global real GDP (y t ), domestic real GDP (y t ), the inflation rate (inf t ), and real effective exchange rate (ex t ). By ensuring the stationarity of these variables and their log transformation, the analysis maintains robustness and enhances the interpretability of results. The outlined analytical process above is symbolically represented as follows: Consider a K-dimension time series: yt,t¼1, ...,T: ytcan be a vector auto-regression of finite order p: yt¼x1t,x2t,x3t,x4t ðÞ (1) The Equation (1) be a vector of the four endogenous variables where: x 1t is the world economic growth rate, (y t ), x 2t is the domestic growth rate (y t ), x 3t is the domestic inflation rate (inf t ), and x 3t is the domestic real effective exchange rate (ex t ). The dynamic structural of the model is represented as following: AL ðÞ yt¼utor Ayt¼uþX p i¼1 Atyt−1þvt(2) Ais a 44 matrix of Structural coefficients among endogenous variables. utis a zero mean serial uncorrelated error term, also called structural shocks. vtis a vector of orthogonal structural shocks to the system so at: i¼1, ...p(3) The variance co-variance matrix of the structural error-term is: Xue¼ðvtvt0Þ¼ðutut0Þ¼Ik (4) (k) the number of variables This means that: There are as many structured shocks as variables in the model, structural shocks are uncorrelated, implying Puis diagonal, variance of all structural shocks are normalized to one. The next step is to derive a reduced for of Equation (2), meaning expressing ytas a function of logged ytonly. The reduced form of Equation (2) is: yt¼A1yt−1þ...þApyt−pþet(5) COGENT ECONOMICS & FINANCE 7 shocks across all BRICS countries at a 5% significance level. This finding implies the presence of a structural symmetric shock and similarity in the response to external shocks among BRICS. The highest coefficient (0.9218) is observed between India and Brazil, followed by (0.8789) between Russia and Brazil, Table 5. Unit root test-South Africa. Phillps-Perron Stationary (PP) Test WRGDP RGDP EX INF With Constant t-Statistic −5.9001 −4.3327 −0.0251 −3.1506 Prob. 0.0000 0.0020 0.9486 0.0338   no  With Constant & Trend t-Statistic −6.1974 −4.9594 −2.0421 −3.4134 Prob. 0.0001 0.0021 0.5548 0.0691   no  Without Constant & Trend t-Statistic −1.7375 −2.4076 2.5214 −1.1178 Prob. 0.0780 0.0179 0.9961 0.2332  no no At First Difference d(WRGDP) d(RGDP) d(EX) d(INF) With Constant t-Statistic −25.5705 −20.9381 −4.8648 −7.2146 Prob. 0.0001 0.0001 0.0005 0.0000     With Constant & Trend t-Statistic −24.7886 −20.0926 −5.0173 −9.1523 Prob. 0.0000 0.0000 0.0020 0.0000     Without Constant & Trend t-Statistic −25.3336 −21.9680 −4.2804 −7.0620 Prob. 0.0000 0.0000 0.0001 0.0000     Dicky-Fulller (ADF) Test WRGDP RGDP EX INF With Constant t-Statistic −5.8147 −4.3154 −0.3400 −3.8431 Prob. 0.0000 0.0021 0.9069 0.0069   n0  With Constant & Trend t-Statistic −5.8268 −4.9671 −1.9273 −4.2974 Prob. 0.0003 0.0021 0.6147 0.0106   n0  Without Constant & Trend t-Statistic −0.6215 −2.5869 1.6814 −1.5491 Prob. 0.4387 0.0116 0.9746 0.1119 no  no no At First Difference d(WRGDP) d(RGDP) d(EX) d(INF) With Constant t-Statistic −6.2491 −5.4751 −4.7905 −5.5360 Prob. 0.0000 0.0001 0.0007 0.0001     With Constant & Trend t-Statistic −6.1331 −5.3064 −4.7296 −5.6619 Prob. 0.0002 0.0011 0.0039 0.0006     Without Constant & Trend t-Statistic −6.3843 −5.5791 −4.3028 −5.4108 Prob. 0.0000 0.0000 0.0001 0.0000     () Significant at the 10%; () Significant at the 5%; () Significant at the 1%. and (no) Not Significant (MacKinnon, 1996) one-sided p-values. Source: Authors ‘own computation using EViews 13. Table 6. Lag length-Brazil. Lag LogL LR FPE AIC SC HQ 0−206.9349 NA 71.76760 15.62481 15.81678 15.68189 1−154.0031 86.25913 a 4.734638 a 12.88912 13.84900 a 13.17454 a 2−137.1338 22.49240 4.850793 12.82473 a 14.55251 13.33849 a Indicates lag order selected by the criterion: AIC: Akaike information criterion; SC: Schwarz information criterion; HQ: Hannan-Quinn information criterion. Source: Authors ‘own computation using EViews 13. 14 M. NACH AND R. NCWADI and India and Russia (0.8292). On the other hand, the lowest correlation coefficient (0.5288) is found between South Africa and China. This result highlights the potential for mutual benefits through symmetric adjustments in relation to external shocks. The symmetric nature of external shock across the BRICS countries suggests that adopting a single currency would mitigate bilateral exchange rate distortions caused by this type of shocks. 5.1.2. Domestic supply shock Table 12 displays a predominantly negative and statistically insignificant correlation for domestic real output, suggesting that there are asymmetric responses to domestic supply shock. These findings underscore the presence of asymmetric domestic supply shocks within the BRICS nations. The dissimilarity observed in the degree of these shocks may be attributed to the unique nature of their exports, which necessitate requiring different adjustment strategies. 5.1.3. Monetary shock The result presented in Table 13 indicates that only two out of ten country pairings exhibit a positive and statistically significant correlation in response to monetary shock. Specifically, Russia and Brazil (0.3981) and South Africa and Russia (0.4834) demonstrate such a relationship. Conversely, the other pairs show no evidence of correlation to domestic demand shock, as indicated by p-values exceeding 5%. This underscores an overall asymmetric adjustment to monetary shock at the exception of Russia, Brazil, and South Africa. Table 8. Lag length-India. Lag LogL LR FPE AIC SC HQ 0−145.4820 NA 0.756817 11.07274 11.26472 a 11.12982 1−122.1810 37.97203 0.448300 10.53192 11.49180 10.81735 2−99.39497 30.38133 a 0.296316 a 10.02926 a 11.75704 10.54302 a a Indicates lag order selected by the criterion: AIC: Akaike information criterion; SC: Schwarz information criterion; HQ: Hannan-Quinn information criterion. Source: Authors ‘own computation using EViews 13. Table 9. Lag length-China. Lag LogL LR FPE AIC SC HQ 0−186.3587 NA 91.09525 15.86322 16.05956 15.91531 1−158.6100 43.93531 a 35.07267 14.88417 15.86588 a 15.14462 a 2−140.6111 22.49861 33.78161 a 14.71760 a 16.48468 15.18640 a Indicates lag order selected by the criterion: AIC: Akaike information criterion; SC: Schwarz information criterion; HQ: Hannan-Quinn information criterion. Source: Authors ‘own computation using EViews 13. Table 10. Lag length-South Africa. Lag LogL LR FPE AIC SC HQ 0−191.6284 NA 23.09505 14.49099 14.68297 a 14.54808 a 1−174.6198 27.71773 a 21.80334 a 14.41628 a 15.37616 14.70170 2−159.5080 20.14902 25.44421 14.48207 16.20986 14.99583 a Indicates lag order selected by the criterion: AIC: Akaike information criterion; SC: Schwarz information criterion; HQ: Hannan-Quinn information criterion. Source: Authors ‘own computation using EViews 13. Table 7. Lag length-Russia. Lag LogL LR FPE AIC SC HQ 0−354.6428 NA 4052155 26.56613 26.75811 a 26.62322 1−335.1746 31.72598 a 3188341 a 26.30923 a 27.26911 26.59465 a 2−321.1338 18.72105 4027949 26.45435 28.18214 26.96811 a Indicates lag order selected by the criterion: AIC: Akaike information criterion; SC: Schwarz information criterion; HQ: Hannan-Quinn information criterion. Source: Authors ‘own computation using EViews 13. COGENT ECONOMICS & FINANCE 15 Table 11. Correlation of the external (supply) shock. Correlation t-Statistic Probability BRAZIL BRAZIL 1.000000 ——— ——— RUSSIA BRAZIL 0.878965 9.215657 0.0000 RUSSIA RUSSIA 1.000000 ——— ——— INDIA BRAZIL 0.921839 11.89245 0.0000 INDIA RUSSIA 0.829286 7.419914 0.0000 INDIA INDIA 1.000000 ——— ——— CHINA BRAZIL 0.715823 4.808248 0.0001 CHINA RUSSIA 0.655945 4.076068 0.0005 CHINA INDIA 0.739831 5.157781 0.0000 CHINA CHINA 1.000000 ——— ——— SOUTH_ AFRICA BRAZIL 0.610511 3.854202 0.0007 SOUTH_ AFRICA RUSSIA 0.792997 6.637127 0.0000 SOUTH_ AFRICA INDIA 0.681839 4.660535 0.0001 SOUTH_ AFRICA CHINA 0.528894 2.923021 0.0079 SOUTH_ AFRICA SOUTH_AFRICA 1.000000 ——— ——— Source: Authors ‘own computation using EViews 13. Table 12. Correlation of the domestic supply shock. Correlation t-Statistic Probability BRAZIL BRAZIL 1.000000 ——— ——— RUSSIA BRAZIL 0.060611 0.303611 0.7639 RUSSIA RUSSIA 1.000000 ——— ——— INDIA BRAZIL −0.496408 −2.859198 0.0084 INDIA RUSSIA −0.344849 −1.836923 0.0781 INDIA INDIA 1.000000 ——— ——— CHINA BRAZIL −0.088469 −0.416588 0.6810 CHINA RUSSIA −0.140080 −0.663574 0.5139 CHINA INDIA 0.161771 0.768900 0.4501 CHINA CHINA 1.000000 ——— ——— SOUTH_ AFRICA BRAZIL 0.168570 0.855087 0.4006 SOUTH_ AFRICA RUSSIA −0.023734 −0.121056 0.9046 SOUTH_ AFRICA INDIA 0.075748 0.379830 0.7073 SOUTH_ AFRICA CHINA 0.199427 0.954570 0.3502 SOUTH_ AFRICA SOUTH_AFRICA 1.000000 ——— ——— Source: Authors ‘own computation using EViews 13. Table 13. Correlation of the monetary shock. Correlation t-Statistic Probability BRAZIL BRAZIL 1.000000 ——— ——— RUSSIA BRAZIL 0.398126 2.170023 0.0397 RUSSIA RUSSIA 1.000000 ——— ——— INDIA BRAZIL 0.033700 0.168596 0.8675 INDIA RUSSIA 0.225685 1.158308 0.2577 INDIA INDIA 1.000000 ——— ——— CHINA BRAZIL −0.176769 −0.842384 0.4086 CHINA RUSSIA −0.122631 −0.579565 0.5681 CHINA INDIA −0.165722 −0.788204 0.4390 CHINA CHINA 1.000000 ——— ——— SOUTH_ AFRICA BRAZIL 0.339972 1.807527 0.0827 SOUTH_ AFRICA RUSSIA 0.483470 2.816238 0.0091 SOUTH_ AFRICA INDIA 0.342701 1.823958 0.0801 SOUTH_ AFRICA CHINA −0.002515 −0.011796 0.9907 SOUTH_ AFRICA SOUTH_AFRICA 1.000000 ——— ——— Source: Authors ‘own computation using EViews 13. 16 M. NACH AND R. NCWADI 5.1.4. Demand shock The results in Table 14 present no evidence of correlation with regard to price level among the BRICS group. This implies asymmetry responses to demand shock and highlights structural differences in relation to demand shocks, revealing limited economic interdependencies and uneven adjustments across the BRICS nations. According to existing literature, supply, demand, and monetary shocks are pertinent in evaluating the viability of an OCA and the symmetry of the shocks within countries that are part of a currency area is of utmost importance. While all types of shocks are prevailing, the symmetry of monetary shock holds significant importance in judging whether a group of countries should optimally form an effective currency area. This symmetry impacts economic stability and policy synchronization within a group. In the context of BRICS, the overall asymmetric nature of supply, demand and monetary shocks currently influences its position to establish an effective OCA. Table 14. Correlation of the demand shock. Correlation t-Statistic Probability BRAZIL BRAZIL 1.000000 ——— ——— RUSSIA BRAZIL −0.107835 −0.542337 0.5924 RUSSIA RUSSIA 1.000000 ——— ——— INDIA BRAZIL −0.327508 −1.733124 0.0954 INDIA RUSSIA 0.017200 0.086013 0.9321 INDIA INDIA 1.000000 ——— ——— CHINA BRAZIL −0.198036 −0.947638 0.3536 CHINA RUSSIA 0.080740 0.379944 0.7076 CHINA INDIA 0.080924 0.380817 0.7070 CHINA CHINA 1.000000 ——— ——— SOUTH_ AFRICA BRAZIL 0.318021 1.677179 0.1060 SOUTH_ AFRICA RUSSIA 0.173134 0.896350 0.3783 SOUTH_ AFRICA INDIA 0.133724 0.674679 0.5061 SOUTH_ AFRICA CHINA 0.004579 0.021478 0.9831 SOUTH_ AFRICA SOUTH_AFRICA 1.000000 ——— ——— Source: Authors ‘own computation using EViews 13. Table 15. ANOVA and coefficient of variance. Source of shocks One-way ANOVA (F-test) Coefficient of variance (Levene-test) Value pvalue Value pvalue Global Shock 6.8314e-30 1.0000 0.3143 .8680 Supply Shock 8.5446e-30 1.0000 0.1858 .9454 Demand Shock 3.1997e-30 1.0000 0.3651 .8331 Monetary Shock 1.6100e-30 1.0000 0.344591 .8474 Source: Authors ‘own computation using EViews 13. Table 16. Size of domestic output for 10 periods. Countries Periods External Supply Shocks Domestic Supply Shocks Monetary Shocks Demand Shocks Brazil 1st 1.6999 2.8457 −0.3296 −0.0004 10th 0.6923 1.7513 −0.0525 0.0002 Russia 1st 3.3173 3.2991 −1.8011 0.4533 10th 2.0340 3.1373 −3.0336e-05 −3.0514e-06 India 1st 1.6435 0.9184 0.6779 −0.7272 10th 1.0573 1.1535 1.4399 −0.0652 China 1st 1.5810 1.3065 −0.0656 0.2026 10th 0.9484 1.4154 −0.0543 0.0308 South Africa 1st 2.0420 1.3895 0.4273 0.8800 10th 1.5972 0.7341 −0.0006 −0.0002 Source: Author’s EViews 13 results. COGENT ECONOMICS & FINANCE 17 5.2. One-way ANOVA and IRF One-way ANOVA in shocks analysis assesses whether there are significant differences in the impact of different shocks. Impulse response function (IRF) analysis assesses the he level of synchronization in shocks across countries, including the extent to which these shocks are shared or unique among member nations. In the context of BRICS impulse response analysis helps in comprehending how the different shocks (Supply, monetary, and demand). propagate through their economies over time Figure 1. Impulse response of domestic output to the shocks: Brazil. Source: Authors ‘own computation using EViews 13. Figure 2. Impulse response of domestic output to the shocks: Russia. Source: Author’s EViews 13 results. 18 M. NACH AND R. NCWADI The results in Table 15 indicate that there is no substantial evidence to support the presence of significant differences among the shocks within the BRICS countries. Therefore, we still fail to reject the null hypothesis that the shocks are heterogeneous. Similarly, the results of the Levene test confirm that although the values (cv) are below 1, we still fail to reject the null hypothesis that the shocks are heterogeneous. However, it is important to note that this finding does not imply that there are no differences among the shocks within BRICS, as suggested by the SVAR model. Any differences that may exist are likely attributable to random error. Figure 3. Impulse response of domestic output to the structural shocks: India. Source: Author’s EViews 13 results. Figure 4. Impulse response of domestic output to the macroeconomics shocks: China. Source: Author’s EViews 13 results. COGENT ECONOMICS & FINANCE 19 5.3. IRF and variance decomposition Impulse response analysis and variance decomposition analysis are essential tools for understanding the dynamics of shock transmission. In the context of assessing an OCA, variance decomposition analysis helps identify the importance of different shocks in driving fluctuations in key macroeconomic variables. Figure 5. South Africa impulse response of domestic output to the macroeconomics shocks. Source: Author’s EViews 13 results. Table 17. Variance decomposition of the changes in domestic real output. Countries External (Supply) Shocks Domestic Supply Shocks Monetary Shocks Demand Shocks Brazil 41.685 55.941 2.374 0.0003 Russia 41.123 33.731 23.72 1.425 India 83.146 12.031 2.444 2.379 China 46.325 25.835 22.370 5.470 South Africa 44.453 21.834 3.954 29.757 Source: Author’s EViews 13 results. Table 18. Variance decomposition of the changes in real exchange rate. Countries Global supply shocks Domestic supply shocks Monetary shocks Demand shocks Brazil 11.31 27.97.913 60.768 0.004 Russia 5.328 8.591 86.014 0.068 India 27.146 35.243 33.106 4.504 China 10.263 24.926 60.048 4.762 South Africa 44.862 4.989 47.468 2.679 Source: Author’s EViews 13 results. Table 19. Variance decomposition of the changes in inflation. Countries Global supply shocks Domestic supply shocks Monetary shocks Demand shocks Brazil 32.987 26.281 16.918 23.814 Russia 0.661 36.047 5.873 57.419 India 57.752 14.482 10.104 17.660 China 37.683 15.905 25.632 20.780 South Africa 6.819 16.713 10.281 66.186 Source: Author’s EViews 13 results. 20 M. NACH AND R. NCWADI The blue lines in Figures 1–5represent the impulse response function analysis, while the red lines indicate the 95% confidence intervals. Looking at Figure 1, it can be observed that the real GDP of Brazil does not exhibit a response to demand shock. This means that a one standard deviation changes in demand shock (Shock 4) does not have a significant impact on Brazil’s output. On the other hand, external and domestic supply shocks (Shocks 1 and 2), emerge as crucial factors in accounting for fluctuations in Brazil’s output. Despite the negative effect of monetary shock (3) on RGDP, its influence on RGDP is not significant. Figure 2 shows that monetary shock has the least influence on explaining variations in real GDP, with domestic supply shock being more significant in Brazil. In the cases of India and South Africa, external shock is the main factor explaining changes in domestic real output (Figure 3 and 5), while China’s real output fluctuations are predominant attributed to domestic and external supply shocks, respectively (Figure 4). The results in Table 16 indicating the size of shocks are added to the understanding of nominal rigidities and adjustment speed. Table 16 reveals that domestic supply shock was predominant in the 1st period (2.84) and 10th period (1.75) in Brazil. In contrast, Russia experienced external shocks as the dominant factor during the 1st period (3.37) and domestic supply shocks during the 10th period (1.75). External shocks were prevalent during the 10th period in India (1.64), China (1.41), and South Africa (1.59). Tables 17–19 display the results of the variance decomposition, showing the impact of individual shocks on domestic output, exchange rate, and inflation in the 10th year. The variance decomposition for world real GDP is omitted, given the assumption of exogenous evolution that neither shocks domestic supply, demand, and monetary have a long-term impact on changes in global real GDP. As Table 17 shows, the variations in Real GDP are predominantly explained by supply shocks, both domestic and external, in India (83%) and Brazil (55.94). On the other hand, less than 50% of GDP fluctuation in other BRICS nations can be attributed to supply shocks. This implies differences in real output variations within the BRICS group. Table 18 reveals that fluctuations in the real exchange rates of China (61%), Brazil (60%), and Russia (86%) are primarily driven by monetary shocks, highlighting the significant role of monetary policy in the variability of real exchange rates among these countries. Lastly, Table 19 shows that demand shocks are predominant in explaining inflation variability in South Africa (66.18%), Russia (57.41%), while external shocks are predominant in India (57.75%). Overall, the findings indicate that there are various factors contributing to the differences in macroeconomic variables among the BRICS countries. Specifically, it is observed that monetary shocks have a more pronounced effect on fluctuations in the real exchange rates in Brazil, Russia, and China compared to the impact of demand shocks. Additionally, demand shocks play a major role in explaining changes in inflation rates in South Africa and Russia, whereas supply shocks primarily account for variations in domestic output in Brazil and India. 6. Conclusion The aim of this study was to evaluate the feasibility of the BRICS group as an optimal currency area (OCA), employing an SVAR model. According to OCA theory, if economic shocks are symmetric, a shared monetary policy can effectively manage these shocks. However, in the case of asymmetric shocks where member countries experience different degrees of disturbances, a uniform monetary policy may not be appropriate. This could exacerbate existing economic imbalances. According to empirical findings, there is a strong positive correlation of shock associated with external (supply) shock within BRICS countries, indicating that symmetric adjustments in response to external disturbances. However, when it comes to domestic supply, demand, and monetary shocks, there is no substantial evidence of correlation, except for Russia, South Africa and Brazil with regard to monetary shock. This highlights the presence of notable important economic disparities that need to be addressed before the BRICS nations could be suitable for an OCA. These Disparities across BRICS countries can stem from differences in the sources of variability, reflecting different transmission mechanisms of various shocks and the accompanying policies implemented by each country (Hooijmaaijers, 2021a,2021b). Consequently, these disparities could COGENT ECONOMICS & FINANCE 21 potentially impede the establishment of a common currency area. As a result, this could potentially hinder the establishment of a common currency area. The study highlights the importance of strengthening policy integration to promote the move towards increased shock symmetry. It suggests aligning fiscal, exchange rate, and monetary policies across BRICS nations through consistent communication and cooperation, which could result in improved stability, decreased shocks asymmetry, and minimized economic disparities. Policymakers are urged to establish procedures for ongoing policy discussions and information exchange, acknowledging that deeper economic integration may enhance shock symmetry over time. This study paves the way for future empirical studies in the BRICS context, especially considering the bloc’s expansion. Hence, forthcoming research should explore the impacts of including new members on regional convergence. Authors contributions Contribution and Design: Marida Nach and Ronney Ncwadi equally contributed to the study’s conception and design. Data Analysis and Drafting: Both authors were involved in analysing and interpreting the data and drafting the paper. Editing: Marida Nach performed the final draft editing. Critical Revision: Both authors critically revised the content for important intellectual aspects. Final Approval: Marida Nach provided for the final draft of this version to be published. Accountability: All authors agree to be accountable for all aspects of the work. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This work was supported by the Nelson Mandela University under the Nelson Mandela Post-doctorate fellowship 2023–2024. About the authors Dr. Marida Nach holds a PhD in Development Finance from Nelson Mandela University (NMU) and is currently a Postdoctoral Researcher in Economics at NMU. Her research focuses on Macroeconomics, particularly in International Economics, Trade, Development Finance, Geopolitical integration within the BRICS framework, trade integration of the African Continental Free Trade Area (AfCFTA), and the digital economy. Dr. Nach is also a Contract Economist Lecturer at NMU and contributes to various research projects as a Postdoctoral Fellow. Prof Ncwadi holds a PhD in Economics from Nelson Mandela Metropolitan University (NMMU); His research interest is in Macroeconomics, Public Finance, International Finance, and Applied Econometrics. He has published both in local and international journals and has read papers at academic conferences both in South Africa and abroad. He was a Director in the Macroeconomic analysis Unit of the Eastern Cape Provincial Treasury between 2008 and 2010. He was also an Associate Professor and a Deputy Dean of the Faculty of Management and Commerce at Fort Hare University. Prof Ncwadi is currently a Full Professor of Economics and Director of the School of Economics, Development and Tourism at Nelson Mandela University in Port Elizabeth. ORCID Marida Nach http://orcid.org/0000-0003-2452-6464 22 M. NACH AND R. NCWADI Data availability statement The data that support the findings of this study are available and were derived from the following resources available in the public domain: The World Development Indicators (WDI) of the World Bank, (World Bank, n.d.) The International Financial Statistics (IFS) of the International Monetary Fund, (IFS, n.d.) The Federal Reserve Economic Data (FRED) Federal Reserve of ST Louis Economic Data (FRED, n.d.). The data that support the findings of this study are available from the corresponding author, Marida Nach, upon reasonable request. References Abdenur, A. E., & Folly, M. (2015). The new development bank and the institutionalization of the BRICS. BRICS-Studies and Documents,77–111. Akaike, H. (2011). Akaike’s information criterion. In: Lovric, M. (Ed.), International Encyclopedia of Statistical Science (p.25). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_110 Akalpler, E., & Hove, S. (2022). Monetary policy and capital flow implications on economic growth in BRICS countries. International Journal of Economic Policy Studies,16(1), 253–274. https://doi.org/10.1007/s42495-021-00076-z Alessina, A., Barro, R., & Tenreyro, S. (2002). Optimal currency unions. NBER Working Paper, 9072. Apazidis, N., & Eliasson, V. (2019). Converging shocks. In N. Apazidis & V. Eliasson (Eds.), Shock focusing phenomena (pp. 35–143). Springer International Publishing. Baas, T., & Belke, A. (2023). Oil price shocks and current account imbalances within a currency union. The Energy Journal,44(4), 223–240. https://doi.org/10.5547/01956574.44.4.tbaa Barro, R., & Tenreyro, S. (2007). Economic effects of currency unions. Economic Inquiry,45(1), 1–23. https://doi.org/10. 1111/j.1465-7295.2006.00001.x Basnet, H. C., & Pradhan, G. (2017). Regional economic integration in Mercosur: The role of real and financial sectors. Review of Development Finance,7(2), 107–119. https://doi.org/10.1177/0972150917721835 Bayoumi, T. (1994). A formal model of optimum currency areas. IMF Staff Papers, 1994/004. Bayoumi, T., & Eichengreen, B. (1992). Shocking aspects of European monetary unification (Working Paper 1. p. 3949). National Bureau of Economic Research (NBER). BenMabrouk, H., & HadjMohamed, W. (2022). Oil shocks and the volatility of BRICS and G7 markets: SVAR analysis. Cogent Economics & Finance,10(1), 2068241. https://doi.org/10.1080/23322039.2022.2068241 Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. The American Economic Review,79(4), 655–673. Campos, N. F., & Macchiarelli, C. (2018). Symmetry and convergence in monetary unions. LEQS Paper, 131. Cattaneo, N., Biziwick, M., & Fryer, D. (2015). The BRICS contingent reserve arrangement and its position in the emerging global financial architecture. Policy Insights,10. South African Institute for International Affairs. pp. 1–7. Cooper, A. F. (2017). The BRICS’new development bank: Shifting from material leverage to innovative capacity. Global Policy,8(3), 275–284. https://doi.org/10.1111/1758-5899.12458 Council of Councils. (n.d.). The BRICS summit 2023: Seeking an alternate world order? Council of Councils. Retrieved 12 June 2024, from https://www.cfr.org/councilofcouncils/global-memos/brics-summit-2023-seeking-alternate-worldorder Cynthia, R., Armijo, L., & Katada, S. (2017). Global power shift: The BRICS, building capabilities for influence. In C. Roberts, L. Armijo, & S. Katada (Eds.), The BRICS and collective financial statecraft. Oxford University Press. De Grauwe, P. (1996). Monetary union and convergence economics. European Economic Review,40(3-5), 1091–1101. https://doi.org/10.1016/0014-2921(95)00117-4 De Grauwe, P. (2020). Economics of the monetary union (13th ed.). Oxford University Press. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association,74(366), 427–431. https://doi.org/10.2307/2286348 Dixon, C. (2015). The new BRICS bank: Challenging the international financial order? Global Policy Institute Policy Paper,28,1–13. https://repository.londonmet.ac.uk/id/eprint/343 Dongfack, L. P. S., & Ouyang, H. (2020). Dynamic risk sharing in the Central African Economic and monetary community. Journal of Economics and Political Economy,7(2), 80–100. Duggan, N., Hooijmaaijers, B., Rewizorski, M., & Arapova, E. (2022). Introduction: ‘The BRICS, global governance, and challenges for South–South cooperation in a post-Western world’.International Political Science Review,43(4), 469– 480. https://doi.org/10.1177/01925121211052211 Fern andez-Villaverde, J., & Rubio-Ram ırez, J. F. (2010). Structural vector autoregressions. In: Durlauf, S.N., & Blume, L. E. (Eds.), Macroeconometrics and time series analysis (pp. 303–307). The New Palgrave Economics Collection. Palgrave Macmillan, London. https://doi.org/10.1057/978020280830_33 COGENT ECONOMICS & FINANCE 23