Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model

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.