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Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes.

Roberts, Gareth O. and Papaspiliopoulos, Omiros and Dellaportas, Petros (2004) Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. Journal of the Royal Statistical Society: Series B, 66 (2). pp. 369-393. ISSN 1467-9868

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Abstract

We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. The approach introduced involves expressing the unobserved stochastic volatility process in terms of a suitable marked Poisson process. We introduce two specific classes of Metropolis–Hastings algorithms which correspond to different ways of jointly parameterizing the marked point process and the model parameters. The performance of the methods is investigated for different types of simulated data. The approach is extended to consider the case where the volatility process is expressed as a superposition of Ornstein–Uhlenbeck processes. We apply our methodology to the US dollar–Deutschmark exchange rate.

Item Type: Article
Journal or Publication Title: Journal of the Royal Statistical Society: Series B
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Lancaster Environment Centre
Faculty of Science and Technology > Mathematics and Statistics
ID Code: 9524
Deposited By: Mrs Yaling Zhang
Deposited On: 13 Jun 2008 11:11
Refereed?: Yes
Published?: Published
Last Modified: 06 Sep 2013 21:23
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/9524

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