Stochastic models with heteroscedasticity for time series in finance / Modelos estocásticos com heterocedasticidade para séries temporais em finanças

AUTOR(ES)

Sandra Cristina de Oliveira

FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

20/05/2005

RESUMO

In this work we present a study of autoregressive conditional heteroskedasticity models (ARCH) and autoregressive models with autoregressive conditional heteroskedasticity errors (AR-ARCH). We also present procedures for the estimation and the selection of these models. The estimates of the parameters of those models are obtained using both Maximum Likelihood estimation and Bayesian estimation. In the Maximum Likelihood approach we get confidence intervals using Bootstrap resampling method and in the Bayesian approach we present informative prior and non-informative prior distributions, considering a reparametrization of those models in order to map the space of the parameters into real space. This procedure permits to choose prior normal distributions for the transformed parameters. The posterior distributions are obtained using Monte Carlo Markov Chain methods (MCMC). The methodology is exemplified considering simulated and Brazilian financial series

ASSUNTO(S)

técnica bootstrap bayesian inference bootstrap technique família de modelos arch family of models arch inferência bayesiana mcmc simulation methods séries financeiras financial series métodos de simulação mcmc

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