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Communication Dans Un Congrès Année : 2017

Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model

Résumé

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
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Dates et versions

hal-01701173 , version 1 (05-02-2018)

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Citer

Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski. Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model. ICASSP 2017 : 42nd International Conference on Acoustics, Speech and Signal Processing, Mar 2017, New Orleans, United States. pp.4780 - 4784, ⟨10.1109/ICASSP.2017.7953064⟩. ⟨hal-01701173⟩
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