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- W3126281340 abstract "Autoregressive conditional duration (ACD) models have been preponderant when the subject is the modeling of high-frequency financial data. A prominent model that has demonstrated great adjustment capacity is the ACD model based on the Birnbaum–Saunders distribution (BS-ACD). Recent works have shown that this model outperforms the existing models in the literature. Nevertheless, these works explore only classical estimation approaches. In this article, we perform a Bayesian approach of the BS-ACD model. The scale parameter was modeled considering a dynamic linear model. Estimation of posterior distribution of parameters was approximated through Markov chain Monte Carlo methods. A simulation study is conducted to evaluate the performance of Bayesian estimators and two applications to real high frequency data illustrate the proposed methodology." @default.
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- W3126281340 date "2021-01-28" @default.
- W3126281340 modified "2023-09-27" @default.
- W3126281340 title "Bayesian inference for the Birnbaum–Saunders autoregressive conditional duration model with application to high-frequency financial data" @default.
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- W3126281340 doi "https://doi.org/10.1080/23737484.2021.1874571" @default.
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