Complexity, nonlinearity and high frequency financial data modeling: lessons from computational approaches

This editorial introduces the special issue Complexity, Nonlinearity and High Frequency Financial Data Modeling: Lessons from Computational Approaches in Annals of Operations Research, which brings together 19 contributions exploring advanced methods and applications in the analysis of financial markets. The collected works reflect the growing importance of complexity and nonlinear dynamics in understanding modern financial systems, marked by high volatility, interdependence, and structural shifts. The papers are organized thematically into five main areas: (i) complexity and nonlinearity in financial markets, (ii) advanced forecasting and econometric modeling, (iii) network theory, causality, and information flows, (iv) banking, credit risk, and economic growth, and (v) continuous-time and structural model reviews. There is an additional section on methodological innovations, which include time–frequency and multi-scale analysis, recent developments of nonlinear and regime-switching models, machine learning, and complex network approaches. A heartfelt tribute is dedicated to the late Marco Tucci, co-editor of this special issue, whose vision and scholarly contributions significantly shaped its content. Sadly, Marco passed away while we were in the process of compiling this special issue. The editorial concludes by highlighting common methodological threads, synthesizing key insights, and outlining promising avenues for future research in complexity-informed financial modeling.