If you are experiencing problems downloading PDF or HTML fulltext, our helpdesk recommend clearing your browser cache and trying again. If you need help in clearing your cache, please click here . Still need help? Email help@ingentaconnect.com

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

$48.00 plus tax (Refund Policy)

Download / Buy Article:

Abstract:

Summary. 

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.

Keywords: Data augmentation; Lévy processes; Marked point processes; Markov chain Monte Carlo methods; Non-centred parameterizations; Stochastic volatility

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1369-7412.2004.05139.x

Affiliations: 1: Lancaster University, UK. 2: Athens University of Economics and Business, Greece

Publication date: April 1, 2004

Related content

Tools

Favourites

Share Content

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more