## 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

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

Originalsprache | Englisch |
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Seiten (von - bis) | 159 - 179 |

Fachzeitschrift | Annals of the Institute of Statistical Mathematics |

Jahrgang | 61 |

Publikationsstatus | Veröffentlicht - 1 Mai 2009 |