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

  • James E. Griffin

    (University of Kent at Canterbury)

  • Mark F.J. Steel

    (University of Kent at Canterbury)

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.

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Paper provided by EconWPA in its series Econometrics with number 0201002.

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Length: 33 pages
Date of creation: 06 Jan 2002
Date of revision: 04 Apr 2003
Handle: RePEc:wpa:wuwpem:0201002
Note: Type of Document - LaTeX; prepared on IBM PC - PC-TEX; to print on HP/PostScript (A4); pages: 33 ; figures: included
Contact details of provider: Web page: http://128.118.178.162

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