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Bayesian estimation of GARCH model by hybrid Monte Carlo

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  • Tetsuya Takaishi

Abstract

The hybrid Monte Carlo (HMC) algorithm is used for Bayesian analysis of the generalized autoregressive conditional heteroscedasticity (GARCH) model. The HMC algorithm is one of Markov chain Monte Carlo (MCMC) algorithms and it updates all parameters at once. We demonstrate that how the HMC reproduces the GARCH parameters correctly. The algorithm is rather general and it can be applied to other models like stochastic volatility models.

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  • Tetsuya Takaishi, 2007. "Bayesian estimation of GARCH model by hybrid Monte Carlo," Papers physics/0702240, arXiv.org.
    • Handle: RePEc:arx:papers:physics/0702240
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    1. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
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    Cited by:

    1. Oscar Andrés Espinosa Acuna & Paola Andrea Vaca González, 2017. "Ajuste de modelos garch clásico y bayesiano con innovaciones t—student para el índice COLCAP," Revista de Economía del Caribe 17172, Universidad del Norte.
    2. Oscar Andrés Espinosa Acuna & Paola Andrea Vaca González, 2017. "Ajuste de modelos garch clásico y bayesiano con innovaciones t—student para el índice COLCAP," Revista de Economía del Caribe 17147, Universidad del Norte.

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