Correlation networks in economics and finance: A review of methodologies and bibliometric analysis

Abstract

This paper presents an in‐depth review of the methodologies and innovations in the study of correlation networks in economics and finance. We explore the development of filtering algorithms and distance measures, emphasizing their critical role in extracting meaningful financial interconnections. Our study underscores the relevance of the minimum spanning tree, planar maximally filtered graph, and other advanced tools in interpreting financial dynamics. Empirical insights emphasize the increasing interconnectedness of global financial markets, underscoring the necessity of grasping correlation levels, market structures, and time‐varying dynamics. A notable observation is the marked increase in studies focusing on econometrics, economics, and finance post‐2015, indicating a paradigm shift in research emphasis. Through bibliometric analysis of 1200 publications, we highlight key authorship clusters, the instrumental contributions of individual researchers, trending keywords, and the growing influence of countries like Italy and China. We conclude with an overview of the software tools essential for both academic research and practical applications in financial network analysis.