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 (Statistical Methodology), 66 (2). pp. 369-393. ISSN 1369-7412Full text not available from this repository.
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.
|Journal or Publication Title:||Journal of the Royal Statistical Society: Series B (Statistical Methodology)|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Lancaster Environment Centre|
Faculty of Science and Technology > Mathematics and Statistics
|Deposited By:||Mrs Yaling Zhang|
|Deposited On:||13 Jun 2008 11:11|
|Last Modified:||15 Jan 2016 01:13|
Actions (login required)