Griffin, J.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 available from this repository. (Contact us about this Publication) | |
| 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: | 08 Jun 2012 09:50 |
| Resource URI: | http://kar.kent.ac.uk/id/eprint/29595 (The current URI for this page, for reference purposes) |
- Depositors only (login required):
