Numerical methods for option valuation: A bibliometric review of current trends to future directions

Research on option valuation has been developed in the last five decades. However, studies have yet to establish research trends, collaboration networks, or scientometric analysis of numerical methods on option valuation rather than the subjective approach by identifying research themes. This study analyzes a bibliometric review on numerical methods of option valuation of 981 published documents from the Web of Science database spanning 1994 to 2023 for the first time. Data analysis and visualizations were conducted using open-source software from the R-based biblioshiny package and VOSviewer. The empirical data has been analyzed for theme mapping, trend topics, document coupling, and network analysis to identify the most cited topics, articles, authors, keywords, research gaps, and future research scope. The results indicated the most prolific author, influential documents, impactful sources, significant organizations, and the research gaps on option valuation using numerical methods. Notably, the analysis reveals a robust growth trajectory in option valuation literature, particularly after 2000, with an annual pace of 11.07 percent. Emerging themes, such as the application of artificial neural networks and innovative decision-making approaches, offer valuable guidance for future research endeavors in option valuation, ultimately advancing our understanding of market dynamics and strategies. • The first comprehensive bibliometric review on numerical methods of option valuation for the last thirty years (1994–2023). • A research landscape overview of the most influential authors, journals, institutions, and countries. • Future research indicates emergent trends such as ML applications, ESG investment, DeFi derivatives and hybrid frameworks. • This review will help policymakers and regulators by historical and thematic evolution of numerical methods in options.