Exchange rate volatility knowledge base: Bibliometric study

Exchange rate volatility continues to shape global economies in profound ways, affecting everything from international trade and investment decisions to overall financial stability as markets become more interconnected. While researchers have produced a wealth of studies—building on classics like Purchasing Power Parity and relying heavily on tools such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Vector Autoregression (VAR) models—few have stepped back to map the entire field systematically. This study applied a bibliometric review to examine the exchange rate volatility knowledge base. The data was collected from the Scopus Database, including a sample set of 336 articles based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)'s systematic review techniques. The findings show that there has been increasing interest in this research area, evidenced by the increase in articles published. Based on this analysis, the United States, the United Kingdom, and Germany are the most prolific in providing information to the research on exchange rate fluctuations by way of articles published in the three peer-reviewed journals most commonly referenced: the Journal of Econometrics, the Journal of International Money and Finance, and the Journal of forecasting. Several gaps were uncovered, such as research published on emerging countries, the limited number of collaboration networks that exist between some authors (and countries), and new areas of research that are gaining importance in the evolving financial markets, including digital currencies and innovations in fintech. By delivering a clear, visually engaging overview, this review solidifies the knowledge base on exchange rate volatility and inspire more inclusive, innovative, and practically relevant future work. In view of this, researchers should expand studies on exchange rate volatility to underexplored regions, especially Africa and Latin America, to better understand institutional and economic influences. Incorporating advanced methods like machine learning and big data analytics can enhance prediction models and practical applications of exchange rate volatility theory in global contexts.