Journal Article

Inference in Infinite Superpositions of Non-Gaussian Ornstein–Uhlenbeck Processes Using Bayesian Nonparametic Methods

J. E. Griffin

in Journal of Financial Econometrics

Volume 9, issue 3, pages 519-549
Published in print January 2011 | ISSN: 1479-8409
Published online July 2010 | e-ISSN: 1479-8417 | DOI: http://dx.doi.org/10.1093/jjfinec/nbq027
Inference in Infinite Superpositions of Non-Gaussian Ornstein–Uhlenbeck Processes Using Bayesian Nonparametic Methods

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This paper describes a Bayesian nonparametric approach to volatility estimation. Volatility is assumed to follow a superposition of an infinite number of Ornstein–Uhlenbeck processes driven by a compound Poisson process with a parametric or nonparametric jump size distribution. This model allows a wide range of possible dependencies and marginal distributions for volatility. The properties of the model and prior specification are discussed, and a Markov chain Monte Carlo algorithm for inference is described. The model is fitted to daily returns of four indices: the Standard and Poors 500, the NASDAQ 100, the FTSE 100, and the Nikkei 225. (JEL: C11, C14, C22)

Keywords: Dirichlet process; Stochastic volatility; Stock indices; Markov chain Monte Carlo; Pólyatree

Journal Article.  8304 words.  Illustrated.

Subjects: Financial Markets ; Econometrics and Mathematical Economics

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