Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547

Resumo

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.