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Inference with non-Gaussian Ornstein–Uhlenbeck processes for stochastic volatility

Griffin, Jim E., Steel, Mark F.J. (2006) Inference with non-Gaussian Ornstein–Uhlenbeck processes for stochastic volatility. Journal of Econometrics, 134 (2). pp. 605-644. ISSN 0304-4076. (doi:10.1016/j.jeconom.2005.07.007) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:9415)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1016/j.jeconom.2005.07.007

Abstract

Continuous-time stochastic volatility models are becoming an increasingly popular way to describe moderate and high-frequency financial data. Barndorff-Nielsen and Shephard (2001a) proposed a class of models where the volatility behaves according to an Ornstein–Uhlenbeck (OU) process, driven by a positive Lévy process without Gaussian component. These models introduce discontinuities, or jumps, into the volatility process. They also consider superpositions of such processes and we extend that to the inclusion of a jump component in the returns. In addition, we allow for leverage effects and we introduce separate risk pricing for the volatility components. We design and implement practically relevant inference methods for such models, within the Bayesian paradigm. The algorithm is based on Markov chain Monte Carlo (MCMC) methods and we use a series representation of Lévy processes. MCMC methods for such models are complicated by the fact that parameter changes will often induce a change in the distribution of the representation of the process and the associated problem of overconditioning. We avoid this problem by dependent thinning methods. An application to stock price data shows the models perform very well, even in the face of data with rapid changes, especially if a superposition of processes with different risk premiums and a leverage effect is used.

Item Type: Article
DOI/Identification number: 10.1016/j.jeconom.2005.07.007
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jim Griffin
Date Deposited: 23 Sep 2008 12:35 UTC
Last Modified: 04 Feb 2022 13:20 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/9415 (The current URI for this page, for reference purposes)

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