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. (doi:10.1093/jjfinec/nbq027) (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) (KAR id:29595)
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. | |
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 |
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DOI/Identification number: | 10.1093/jjfinec/nbq027 |
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: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Depositing User: | Jim Griffin |
Date Deposited: | 30 May 2012 12:20 UTC |
Last Modified: | 16 Nov 2021 10:07 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/29595 (The current URI for this page, for reference purposes) |
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