Scenario generation for financial data: a machine learning dynamic copula approach based on realized volatility and correlation

• Ano de defesa: 2024
• Autor(a) principal: Caio Mário Henriques Silva da Rocha Mesquita
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO; Programa de Pós-Graduação em Ciência da Computação; UFMG
• Palavras-chave em Português: Machine learning; Copula functions; Realized volatility; Portfolio optimization; Computação – Teses; Aprendizado do computador – Teses; Gestão de riscos – Teses; Mercado financeiro - Portifólios - Teses
• Link de acesso: http://hdl.handle.net/1843/72181

Portfolio optimization is a fundamental issue in quantitative finance, and scenario generation techniques play a vital role in simulating the future behavior of assets for use in allocation strategies. In the literature, various approaches exist for generating scenarios, ranging from historical observations to models predicting asset volatility. In this dissertation, we propose a novel methodology for generating discrete scenarios one day ahead, which are then used as input for portfolio allocation. Our approach employs machine learning upervised algorithms as forecasting models to predict the realized variance and intraday Kendall correlation of assets. Using these predictions, we apply a copula approach with extreme value distributions to simulate the multivariate probability distribution of the assets. Our computational experiments indicate that our approach may yield more accurate volatility and correlation forecasts, as well as better risk-reward portfolios compared to traditional literature baselines.