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Bayesian Analysis of FIAPARCH Model: An Application to Sao Paulo Stock Market

Thelma Safadi, Isabel Pereira

Abstract


In this paper, we develop a Bayesian analysis of a FIAPARCH(p,d,q) model for parameter estimation and conditional variance prediction. In order to study the inference problem we use the Metropolis-Hastings algorithm.This methodology is illustrated in a simulation study and it is applied to a set of observations concerning the returns of IBOVESPA values.

Keywords


Asymmetry, long memory, volatility

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