Inference for Non-linear Diffusions and Jump-Diffusions: A Monte Carlo EM approach
Lindström, Erik (2012). Inference for Non-linear Diffusions and Jump-Diffusions: A Monte Carlo EM approach. Nola, Vincenzo; Kadoch, Michel; Zemilak, Alexander (Eds.). Recent Researches in Automatic Control and Electronics, Proceedings of the 14th International Conference on Automatic Control, Modelling & Simulation (ACMOS '12), 110 - 115. 14th International Conference on Automatic Control, Modelling & Simulation (ACMOS '12): WSEAS Press
Conference Proceeding/Paper
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Published
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English
Authors:
Lindström, Erik
Editors:
Nola, Vincenzo
;
Kadoch, Michel
;
Zemilak, Alexander
Department:
Mathematical Statistics
Mathematical Finance-lup-obsolete
Financial Mathematics Group
Research Group:
Mathematical Finance-lup-obsolete
Financial Mathematics Group
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.
Keywords:
Jump-diffusions ;
Maximum Likelihood estimation ;
MCEM-algorithm ;
Resampling ;
Heston model ;
Bates model
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