"Markov chain Monte Carlo method and its application to the stochastic volatility model"(in Japanese)

Yasuhiro Omori () and Toshiaki Watanabe
Additional contact information
Toshiaki Watanabe: Institute of Economic Research, Hitotsubashi University

No CIRJE-J-173, CIRJE J-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: In the time series analysis of asset prices, the stochastic volatility models have recently attracted attentions of many researchers since it clearly describes time-varying variance of asset returns. However, it is difficult to evaluate the likelihood and obtain the maximum likelihood estimators of parameters for such models. We take Bayesian approach and use Markov chain Monte Carlo (MCMC) method to overcome such a problem. We first describe MCMC method and conduct a survey of the literature for its application to the stochastic volatility model. The empirical analysis of stock returns data is also given.

Pages: 49 pages
Date: 2007-03
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.cirje.e.u-tokyo.ac.jp/research/dp/2007/2007cj173.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tky:jseres:2007cj173

Access Statistics for this paper

More papers in CIRJE J-Series from CIRJE, Faculty of Economics, University of Tokyo Contact information at EDIRC.
Bibliographic data for series maintained by CIRJE administrative office ().