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Bayesian Filtering for Jump-Diffusions With Application to Stochastic Volatility

Lookup NU author(s): Dr Andrew Golightly

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Abstract

In this article, the problem of sequentially learning parameters governing discretely observed jump-diffusions is explored. The estimation framework involves the introduction of latent points between every pair of observations to allow a sufficiently accurate Euler-Maruyama approximation of the underlying (but unavailable) transition densities. Particle filtering algorithms are then implemented to sample the posterior distribution of the latent data and the model parameters online. The methodology is applied to the estimation of parameters governing a stochastic volatility (SV) model with jumps. As well as using S&P 500 Index data, a simulation study is provided. Supplemental materials for this article are available online at www.amstat.org/publications/JCGS.


Publication metadata

Author(s): Golightly A

Publication type: Article

Publication status: Published

Journal: Journal of Computational and Graphical Statistics

Year: 2009

Volume: 18

Issue: 2

Pages: 384-400

Date deposited: 26/05/2010

ISSN (print): 1061-8600

ISSN (electronic): 1537-2715

Publisher: American Statistical Association

URL: http://dx.doi.org/10.1198/jcgs.2009.07137

DOI: 10.1198/jcgs.2009.07137


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