Modelos GARCH assimétricos com inovações t-Student

In this work we consider modeling the past volatilities through a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model using the Bayesian approach. Asymmetries in the shocks are accommodated by smooth transition models for the variance. We discuss problems related to the likelihood function and propose a solution. In order to account for heavy tails in the applications we consider Student-t errors. The Jeffrey's prior is used in this context to correct problems in the estimation of degrees of freedom. A simulated study is presented to highlight the advantages of the proposed methodology and an application to the Brazilian index of prices illustrates the usefulness of the asymmetric GARCH model with student-t errors.