Konstantinos Kalogeropoulos Likelihood-based inference for a class of multivariate diffusions with unobserved paths

@inproceedings{Kalogeropoulos2007KonstantinosKL,
  title={Konstantinos Kalogeropoulos Likelihood-based inference for a class of multivariate diffusions with unobserved paths},
  author={Konstantinos Kalogeropoulos},
  year={2007}
}
This paper presents a Markov chain Monte Carlo algorithm for a class of multivariate diffusion models with unobserved paths. This class is of high practical interest as it includes most diffusion driven stochastic volatility models. The algorithm is based on a data augmentation scheme where the paths are treated as missing data. However, unless these paths are transformed so that the dominating measure is independent of any parameters, the algorithm becomes reducible. The methodology developed… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 22 references

differential equations based on discrete observations

G. Roberts, O. Stramer
Scand. J. Statist., • 2001
View 7 Excerpts
Highly Influenced

Simulation estimation of continuous-time models with applications to finance

O. Elerian
D.Phil thesis, Nuffield College, • 1999
View 4 Excerpts
Highly Influenced

Stochastic volatily, in. Handbook of Statistics 14, Statistical Methods in Finance

E. Ghysels, A. Harvey, E. Renault
1996
View 4 Excerpts
Highly Influenced

diffusion driven stochastic volatility models

P. submission. Kloeden, E. Platen
1995
View 4 Excerpts
Highly Influenced

Stochastic differential equations. Springer, 5th edition

B. New York Springer. Oksendal
2000
View 2 Excerpts
Highly Influenced

Closed-form Likelihood Expansions for Multivariate Diffusions

BY YACINE AÏT-SAHALIA
2008
View 2 Excerpts

lated diffusions observed discretly in time

K. submision. Kalogeropoulos, G. Roberts, P. Dellaportas
2007
View 3 Excerpts

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