Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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Bayesian estimation of GARCH model by hybrid Monte Carlo

Authors
Tetsuya Takaishi1
1Hiroshima University of Economics
Corresponding Author
Tetsuya Takaishi
Available Online October 2006.
DOI
10.2991/jcis.2006.159How to use a DOI?
Keywords
Markov chain Monte Carlo, Hybrid Monte Carlo, GARCH model, Bayesian inference
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.

Copyright
© 2006, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
Series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
ISSN
1951-6851
DOI
10.2991/jcis.2006.159How to use a DOI?
Copyright
© 2006, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

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TY  - CONF
AU  - Tetsuya Takaishi
PY  - 2006/10
DA  - 2006/10
TI  - Bayesian estimation of GARCH model by hybrid Monte Carlo
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SP  - 661
EP  - 664
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.159
DO  - 10.2991/jcis.2006.159
ID  - Takaishi2006/10
ER  -