Modelos estocásticos com heterocedasticidade: Uma abordagem Bayesiana para os retornos do Ibovespa

• Autor(a) principal: de Oliveira, Sandra Cristina [UNESP]
• Data de Publicação: 2013
• Outros Autores: de Andrade, Marinho Gomes
• Tipo de documento: Artigo
• Idioma: eng; por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.4025/actascitechnol.v35i2.13547; http://hdl.handle.net/11449/75170
• topic: ARCH family; Bayesian analysis; Financial returns; MCMC methods

Current research compares the Bayesian estimates obtained for the parameters of processes of ARCH family with normal and Student's t distributions for the conditional distribution of the return series. A non-informative prior distribution was adopted and a reparameterization of models under analysis was taken into account to map parameters' space into real space. The procedure adopts a normal prior distribution for the transformed parameters. The posterior summaries were obtained by Monte Carlo Markov Chain (MCMC) simulation methods. The methodology was evaluated by a series of Bovespa Index returns and the predictive ordinate criterion was employed to select the best adjustment model to the data. Results show that, as a rule, the proposed Bayesian approach provides satisfactory estimates and that the GARCH process with Student's t distribution adjusted better to the data.