@inproceedings{057d39e275924f00b959ecbee69a104a,
title = "ARCH and GARCH parameter estimation in presence of additive noise using particle methods",
abstract = "In this paper, we propose a new method based on particle filters for maximum likelihood (ML) estimation of the parameters of autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive conditional heteroscedasticity (GARCH) models. Our method is based on gradient descend method and active set method for maximizing the likelihood function over parameters under stationarity constraints. The gradient of the likelihood function of observation given the parameters of the model, which is needed for gradient based optimization algorithm, is estimated using particle methods. Simulation results show the advantage of the proposed method over competing techniques.",
keywords = "ARCH, GARCH, noisy observations, parameter estimation, particle methods",
author = "Saman Mousazadeh and Israel Cohen",
year = "2013",
month = oct,
day = "18",
doi = "10.1109/ICASSP.2013.6638873",
language = "الإنجليزيّة",
isbn = "9781479903566",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "6279--6282",
booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",
note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",
}