TY - GEN
T1 - Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model
AU - Gorynin, Ivan
AU - Monfrini, Emmanuel
AU - Pieczynski, Wojciech
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - 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.
AB - 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.
KW - Asymmetric stochastic volatility
KW - BIC criterion
KW - Gaussian filter
KW - Gaussian quadrature
KW - Unsupervised learning
UR - https://www.scopus.com/pages/publications/85023767613
U2 - 10.1109/ICASSP.2017.7953064
DO - 10.1109/ICASSP.2017.7953064
M3 - Conference contribution
AN - SCOPUS:85023767613
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4780
EP - 4784
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -