Artículos de revistas
Modelos estocásticos com heterocedasticidade: Uma abordagem Bayesiana para os retornos do Ibovespa
Fecha
2013-04-25Registro en:
Acta Scientiarum - Technology, v. 35, n. 2, p. 339-347, 2013.
1806-2563
1807-8664
10.4025/actascitechnol.v35i2.13547
WOS:000322540600019
2-s2.0-84876432682
2-s2.0-84876432682.pdf
1268945434870814
0000-0002-0968-0108
Autor
Universidade Estadual Paulista (Unesp)
Universidade de São Paulo (USP)
Institución
Resumen
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.