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Inference for Non-linear Diffusions and Jump-Diffusions: A Monte Carlo EM approach

Lindström, Erik LU (2012) 14th International Conference on Automatic Control, Modelling & Simulation (ACMOS '12) In Recent Researches in Automatic Control and Electronics, Proceedings of the 14th International Conference on Automatic Control, Modelling & Simulation (ACMOS '12) p.110-115
Abstract
We propose a simple, general and computationally efficient algorithm for maximum likelihood estima-

tion (MLE) of parameters in diffusion and jump-diffusion processes. This is conducted within a Monte Carlo

EM-algorithm, where the smoothing distribution is computed using resampling. The results are encouraging as

we can approximate the MLE well for the models studied when using simulated data. We also obtain reasonable

estimates, compared to other papers, when fitting the Heston and Bates model to S&P 500 and VIX data.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Jump-diffusions, Maximum Likelihood estimation, MCEM-algorithm, Resampling, Heston model, Bates model
in
Recent Researches in Automatic Control and Electronics, Proceedings of the 14th International Conference on Automatic Control, Modelling & Simulation (ACMOS '12)
editor
Nola, Vincenzo; Kadoch, Michel and Zemilak, Alexander
pages
6 pages
publisher
WSEAS Press
conference name
14th International Conference on Automatic Control, Modelling & Simulation (ACMOS '12)
ISBN
978-1-61804-080-0
language
English
LU publication?
yes
id
adc4b502-5457-4355-8cf7-ddb6cef82049 (old id 3008444)
alternative location
http://www.wseas.us/e-library/conferences/2012/SaintMalo/ACMIN/ACMIN-00.pdf
date added to LUP
2013-01-08 18:23:51
date last changed
2016-04-16 08:52:33
@misc{adc4b502-5457-4355-8cf7-ddb6cef82049,
  abstract     = {We propose a simple, general and computationally efficient algorithm for maximum likelihood estima-<br/><br>
tion (MLE) of parameters in diffusion and jump-diffusion processes. This is conducted within a Monte Carlo<br/><br>
EM-algorithm, where the smoothing distribution is computed using resampling. The results are encouraging as<br/><br>
we can approximate the MLE well for the models studied when using simulated data. We also obtain reasonable<br/><br>
estimates, compared to other papers, when fitting the Heston and Bates model to S&amp;P 500 and VIX data.},
  author       = {Lindström, Erik},
  editor       = {Nola, Vincenzo and Kadoch, Michel and Zemilak, Alexander},
  isbn         = {978-1-61804-080-0},
  keyword      = {Jump-diffusions,Maximum Likelihood estimation,MCEM-algorithm,Resampling,Heston model,Bates model},
  language     = {eng},
  pages        = {110--115},
  publisher    = {ARRAY(0xa09c408)},
  series       = {Recent Researches in Automatic Control and Electronics, Proceedings of the 14th International Conference on Automatic Control, Modelling & Simulation (ACMOS '12)},
  title        = {Inference for Non-linear Diffusions and Jump-Diffusions: A Monte Carlo EM approach},
  year         = {2012},
}