A bibliometric analysis of information criteria for forecasting volatility

Volatility forecasting model selection is an essential issue when making financial decisions, which increasingly focus on modelling, forecasting, and evaluation. However, this area has not yet undergone a systematic analysis in the relevant literature. This paper takes advantage of the VOSviewer and bibliometric techniques to overview the temporal distribution of articles, the corresponding author's countries, the citation network, the co-occurrence, the thematic evolution, and the top of the journal or authors or articles. Content analysis was done to 60 pieces of literature, including their data characteristics, theoretical basis, and practical application, as well as suggestions for potential research directions. Through bibliometric techniques and content analysis, this study provides a thorough overview of the research done in the field of volatility forecasting model selection. The research findings indicate that scientific productivity on the subject is expanding rapidly. New methodologies, such as neural networks, have been introduced, necessitating a broad perspective by the researcher in the evaluation of empirical results.