Simplex optimization for particle filter joint parameter estimation of electricity prices with jump diffusion

In this paper, a particle filter with Nelder-Mead simplex optimization algorithm is implemented to estimate the parameters of a jump diffusion process that is employed to model electricity prices. The jumps have the possibility to give a better explanation of the behavior of electricity prices as sh...

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Bibliographic Details
Published in:Journal of financial and economic practice 2013-10, Vol.13 (2), p.1
Main Authors: Bayazit, Dervis, Uzunoglu, Bahri
Format: Article
Language:English
Subjects:
Algorithms
Brownian motion
Economic models
Electric utilities
Investigations
Kalman filters
Markov analysis
Optimization
Parameter estimation
Random variables
Studies
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Summary:In this paper, a particle filter with Nelder-Mead simplex optimization algorithm is implemented to estimate the parameters of a jump diffusion process that is employed to model electricity prices. The jumps have the possibility to give a better explanation of the behavior of electricity prices as shown by MolinaEscobar (2009). The performance of augmented generic particle filter to estimate the jump frequency and distribution parameters have been analyzed for a benchmark example of the maximum likelihood state estimator solution given by Xiong (2004) and favorable results were obtained by minimizing approximated negative log-likelihood function via Nelder-Mead simplex algorithm.
ISSN:1937-6820