Modeling and forecasting financial time series through hybrid methodologies

• Grant number: 24/01480-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2024
• End date: July 31, 2028
• Field of knowledge: Applied Social Sciences - Economics - Quantitative Methods Applied to Economics
• Principal Investigator: Carlos Cesar Trucios Maza
• Grantee: Cláudio Estevam Leite da Silva
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 23/02538-0 - Time series, wavelets, high dimensional data and applications, AP.TEM

Abstract

This project aims to develop and apply hybrid methodologies for modeling and forecasting financial time series. The focus of the project is to use both features extracted through classical models such as (V)ARMA, M(GARCH), M(SV), etc., as well as machine learning models such as neural networks, ensemble models, machine vector support, among others. By studying both approaches, an improvement in the quality of forecasts is expected (both from a statistical and economic point of view), which will be evaluated directly and indirectly through Monte Carlo experiments and empirical data referring to Brazilian markets. and international. The results obtained will be published in scientific journals with a selective editorial policy and the implemented codes will be made available free of charge in a public repository on GitHub.