Análise de portfólio: uma perspectiva bayesiana

• Ano de defesa: 2016
• Autor(a) principal: Tito, Edison Americo Huarsaya
• Orientador(a): Gonçalves, Edson Daniel Lopes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Teoria moderna de portfólio; Estatística bayesiana; Modelo Black-Litterman
• Palavras-chave em Inglês: Modern portfolio theory; Bayesian statitics; Black-Litterman model; CAPM
• Link de acesso: https://hdl.handle.net/10438/16637

This work has the objective to address the problem of asset allocation (portfolio analysis) under a Bayesian perspective. For this it was necessary to review all the theoretical analysis of the classical mean-variance model and following identify their deficiencies that compromise its effectiveness in real cases. Interestingly, its biggest deficiency this not related to the model itself, but by its input data in particular the expected return calculated on historical data. To overcome this deficiency the Bayesian approach (Black-Litterman model) treat the expected return as a random variable and after that builds a priori distribution (based on the CAPM model) and a likelihood distribution (based on market investor’s views) to finally apply Bayes theorem resulting in the posterior distribution. The expected value of the return of this posteriori distribution is to replace the estimated expected return calculated on historical data. The results showed that the Bayesian model presents conservative and intuitive results in relation to the classical model of mean-variance.