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Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes

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Griffin, Jim E. and Steel, Mark F. J.. (2010) Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes. Computational Statistics and Data Analysis, Vol.54 (No.11). pp. 2594-2608. ISSN 0167-9473

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Official URL: http://dx.doi.org/10.1016/j.csda.2009.06.008

Abstract

Continuous superpositions of Ornstein-Uhlenbeck processes are proposed as a model for asset return volatility. An interesting class of continuous superpositions is defined by a Gamma mixing distribution which can define long memory processes. In contrast, previously studied discrete superpositions cannot generate this behaviour. Efficient Markov chain Monte Carlo methods for Bayesian inference are developed which allow the estimation of such models with leverage effects. The continuous superposition model is applied to both stock index and exchange rate data. The continuous superposition model is compared with a two-component superposition on the daily Standard and Poor's 500 index from 1980 to 2000.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Superposition principle (Physics), Stochastic processes
Journal or Publication Title:	Computational Statistics and Data Analysis
Publisher:	Elsevier Science BV
ISSN:	0167-9473
Date:	1 November 2010
Volume:	Vol.54
Number:	No.11
Number of Pages:	15
Page Range:	pp. 2594-2608
Identification Number:	10.1016/j.csda.2009.06.008
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
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URI:	http://wrap.warwick.ac.uk/id/eprint/5579