Quotation Frühwirth-Schnatter, Sylvia, Sögner, Leopold. 2007. Bayesian estimation of stochastic volatility models based on OU processes with marginal Gamma law. Annals of the Institute of Statistical Mathematics. 61 159-179.


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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 Rosiński 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 a Markov prior. Finally, applications to simulated data as well as exchange rate data are discussed.

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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 law
Volume 61
Year 2007
Page from 159
Page to 179
URL http://link.springer.com/content/pdf/10.1007/s10463-007-0130-8.pdf
DOI http://dx.doi.org/10.1007/s10463-007-0130-8
Open Access N

Associations

People
Frühwirth-Schnatter, Sylvia (Details)
Sögner, Leopold (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
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