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

Griffin, Jim E. (2011) Inference in Infinite Superpositions of Non-Gaussian Ornstein–Uhlenbeck Processes Using Bayesian Nonparametic Methods. Journal of Financial Econometrics, 9 (3). pp. 519-549. ISSN 1479-8409 . (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)

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Official URL
http://dx.doi.org/10.1093/jjfinec/nbq027

Abstract

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.

Item Type: Article
Uncontrolled keywords: Dirichlet process, Stochastic volatility, Stock indices, Markov chain Monte Carlo, Pólyatree
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 30 May 2012 12:20
Last Modified: 21 May 2014 11:25
Resource URI: https://kar.kent.ac.uk/id/eprint/29595 (The current URI for this page, for reference purposes)
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