Bayesian estimation of realized stochastic volatility model by Hybrid Monte Carlo algorithm

Tetsuya Takaishi

Papers from arXiv.org

Abstract: The hybrid Monte Carlo algorithm (HMCA) is applied for Bayesian parameter estimation of the realized stochastic volatility (RSV) model. Using the 2nd order minimum norm integrator (2MNI) for the molecular dynamics (MD) simulation in the HMCA, we find that the 2MNI is more efficient than the conventional leapfrog integrator. We also find that the autocorrelation time of the volatility variables sampled by the HMCA is very short. Thus it is concluded that the HMCA with the 2MNI is an efficient algorithm for parameter estimations of the RSV model.

Date: 2014-08
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Journal of Physics: Conference Series 490 (2014) 012092

Downloads: (external link)
http://arxiv.org/pdf/1408.0981 Latest version (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:arx:papers:1408.0981

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().