Browse by author
Lookup NU author(s): Dr Andrew Golightly
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.
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
Altmetrics provided by Altmetric