@inproceedings{f877b068511b4af5b0f988dc7b9b654b,
title = "Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model",
abstract = "The object of this paper is to introduce a new estimation algorithm specifically designed for the latent high-order autoregressive models. It implements the concept of the filter-based maximum likelihood. Our approach is fully deterministic and is less computationally demanding than the traditional Monte Carlo Markov chain techniques. The simulation experiments and real-world data processing confirm the interest of our approach.",
keywords = "Asymmetric stochastic volatility, BIC criterion, Gaussian filter, Gaussian quadrature, Unsupervised learning",
author = "Ivan Gorynin and Emmanuel Monfrini and Wojciech Pieczynski",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7953064",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4780--4784",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}