Hidden Markov Mixture Autoregressive Models: Parameter Estimation

2011/05/14 by Sassan Alizadeh, Alizadeh, S. H., Saeid Rezakhah +1
Computer Science · Economics, Econometrics and Finance · Mathematics · #60J10 secondary 60G25 #Bayesian Methods and Mixture Models #FOS: Mathematics #Financial Risk and Volatility Modeling #Statistical Methods and Inference #Statistics Theory (math.ST) #primary 62M10

paper · pdf · doi:10.48550/arxiv.1105.2891

openalex publication_date 2011/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This report introduces a parsimonious structure for mixture of autoregressive models, where the weighting coefficients are determined through latent random variables as functions of all past observations. These variables follow a hidden Markov model. We modify EM and Baum-Welch algorithms to estimate the parameters of the model.

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