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

• 成果类型: Article
• 署名作者: Roberts, GO; Papaspiliopoulos, O; Dellaportas, P
• 署名单位: Lancaster University; Athens University of Economics & Business
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1369-7412.2004.05139.x
• 发表日期: 2004
• 页码: 369-393
• 关键词: likelihood inference; GIBBS SAMPLER; distributions; mixtures; models

摘要

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.