Quotation Frühwirth-Schnatter, Sylvia, Sögner, Leopold. 2009. Bayesian estimation of stochastic volatility models based on OU processes with marginal Gamma laws. Annals of the Institute of Statistical Mathematics 61 159-179.


RIS


BibTeX

Abstract

This paper discusses practical Bayesian estimation of stochastic volatility models based on OU processes with marginal Gamma laws. Estimation is based on a parameterization which is derived from the Rosi´nski representation, and has the advantage of being a non-centered parameterization. The parameterization is based on a marked point process, living on the positive real line, with uniformly distributed marks. We define a Markov chain Monte Carlo (MCMC) scheme which enables multiple updates of the latent point process, and generalizes single updating algorithm used earlier. At each MCMC draw more than one point is added or deleted from the latent point process. This is particularly useful for high intensity processes. Furthermore, the article deals with superposition models, where it discuss how the identifiability problem inherent in the superposition model may be avoided by the use of aMarkov prior. Finally, applications to simulated data as well as exchange rate data are discussed

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Annals of the Institute of Statistical Mathematics
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-D
Language English
Title Bayesian estimation of stochastic volatility models based on OU processes with marginal Gamma laws
Volume 61
Year 2009
Page from 159
Page to 179
URL http://www.springerlink.com/content/b646r3875xk64385/fulltext.pdf

Associations

People
Frühwirth-Schnatter, Sylvia (Details)
External
Sögner, Leopold
Organization
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search