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Bayesian estimation of GARCH model by hybrid Monte Carlo

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  • Tetsuya Takaishi
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    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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    File URL: http://arxiv.org/pdf/physics/0702240
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    Paper provided by arXiv.org in its series Papers with number physics/0702240.

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    Date of creation: Feb 2007
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    Publication status: Published in Proceedings of the 9th Joint Conference on Information Sciences 2006, CIEF-214
    Handle: RePEc:arx:papers:physics/0702240

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    Web page: http://arxiv.org/

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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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