  A regularized vector autoregressive hidden semi-Markov model is developed to
analyze multivariate financial time series with switching data generating
regimes. Furthermore, an augmented EM algorithm is proposed for parameter
estimation by embedding regularized estimators for the state-dependent
covariance matrices and autoregression matrices in the M-step. The performance
of the proposed regularized estimators is evaluated both in the simulation
experiments and on the New York Stock Exchange financial portfolio data.
