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Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model

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Citation Author(s):
Ivan Gorynin
Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski
Submitted by:
Ivan Gorynin
Last updated:
7 March 2017 - 6:12am
Document Type:
Poster
Document Year:
2017
Event:
ICASSP 2017
Presenters:
Ivan Gorynin
Paper Code:
ICASSP1701
Categories:
Signal and System Modeling, Representation and Estimation
 

We 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 confirm the interest of our approach.

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