Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model

Ivan Gorynin; Emmanuel Monfrini; Wojciech Pieczynski

ICASSP 2017accepted0 citations

Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model

Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski

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.

BibTeX

@inproceedings{icassp2017_unsupervisedlear,
  title = {Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model},
  author = {Ivan Gorynin and Emmanuel Monfrini and Wojciech Pieczynski},
  booktitle = {ICASSP 2017},
  year = {2017}
}