Inference With Non-Gaussian Ornstein-Uhlenbeck Processes for Stochastic Volatility

James E. Griffin and Mark Steel
Additional contact information
James E. Griffin: University of Kent at Canterbury

Econometrics from University Library of Munich, Germany

Abstract: Continuous-time stochastic volatility models are becoming a more and more popular way to describe moderate and high-frequency financial data. Recently, Barndorff-Nielsen and Shephard (2001a) proposed a class of models where the volatility behaves according to an Ornstein-Uhlenbeck process, driven by a positive Levy process without Gaussian component. They also consider superpositions of such processes and we extend that to the inclusion of an uncorrelated component. Our aim is to design and implement practically relevant inference methods for such models, within the Bayesian paradigm. The algorithm is based on Markov chain Monte Carlo methods and we use a series representation of Levy processes. Inference for such models is complicated by the fact that parameter changes will often induce a change of dimension in 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 is used. After introducing some extra flexibility, the model can even be used to describe spot interest rate data with considerable success.

Keywords: Bayesian methods; Deposit spot rate; Levy process; Markov chain Monte Carlo; Stock price (search for similar items in EconPapers)
JEL-codes: C11 G0 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2002-01-06, Revised 2003-04-04
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fmk
Note: Type of Document - LaTeX; prepared on IBM PC - PC-TEX; to print on HP/PostScript (A4); pages: 33 ; figures: included
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0201/0201002.pdf (application/pdf)
https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0201/0201002.ps.gz (application/postscript)

Related works:
Journal Article: Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0201002

Access Statistics for this paper

More papers in Econometrics from University Library of Munich, Germany
Bibliographic data for series maintained by EconWPA (volker.schallehn@ub.uni-muenchen.de this e-mail address is bad, please contact repec@repec.org).