COMPARATIVE STATISTICAL MODELLING OF EXCHANGE RATE DYNAMICS ACROSS DEVELOPED, EMERGING, AND FRONTIER ECONOMIES

Authors

  • Prof. Alexander R. Whitmore Department of Statistical Science and Econometrics, Westbridge University, Cambridge, United Kingdom
  • Dr. Isabella M. Kovács Institute of Applied Statistics and Data Analytics, Central European Institute of Technology, Budapest, Hungary
  • Dr. Luca F. Moretti Department of Quantitative Economics and Statistical Modeling, Verona Institute of Advanced Studies, Verona, Italy

DOI:

https://doi.org/10.69980/qbvpym36

Keywords:

Exchange rates, developed economies, emerging markets, frontier economies, comparative statistics

Abstract

This study examines exchange-rate dynamics across developed, emerging-market, and frontier economies using a comparative statistical framework based on a large longitudinal dataset of currency observations. The analysis evaluates distributional characteristics, cross-group differences, temporal trends, and the overall association between calendar year and log-transformed exchange-rate levels. Because the raw exchange-rate series exhibited substantial skewness and kurtosis, a natural logarithmic transformation was applied before further analysis. Descriptive statistics, robust one-way ANOVA procedures, Games–Howell post-hoc comparisons, Kruskal–Walli’s testing with Bonferroni-adjusted pairwise comparisons, annual trend analysis, and simple linear regression were employed. The results revealed clear and persistent differences among the three economy classifications, with frontier economies displaying the highest mean log-transformed exchange-rate levels, followed by emerging-market and developed economies. Robust parametric and non-parametric procedures consistently confirmed statistically significant group differences. Temporal analysis showed comparatively stable patterns in developed economies, while emerging market and frontier economies exhibited stronger upward movements over time. Although calendar year significantly predicted log-transformed exchange-rate levels, its explanatory power was limited. Overall, the findings indicate that economy classification is more strongly associated with exchange-rate heterogeneity than time alone, highlighting the importance of structural and developmental differences in comparative exchange-rate analysis.

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Published

2024-12-28