Bayesian estimation of GARCH model by hybrid Monte Carlo

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dc.creatorTakaishi, Tetsuya
dc.date2007-02-27
dc.date.accessioned2026-07-07T12:10:21Z
dc.date.available2026-07-07T12:10:21Z
dc.descriptionThe 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.
dc.descriptionThe 9th Joint Conference on Information Sciences (JCIS), October 8-11, 2006
dc.identifierhttps://arxiv.org/abs/physics/0702240
dc.identifierhttp://arxiv.org/abs/physics/0702240
dc.identifierProceedings of the 9th Joint Conference on Information Sciences 2006, CIEF-214
dc.identifierdoi:10.2991/jcis.2006.159
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/209911
dc.subjectComputational Physics
dc.subjectData Analysis, Statistics and Probability
dc.subjectStatistical Finance
dc.titleBayesian estimation of GARCH model by hybrid Monte Carlo
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