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A regularized vector autoregressive hidden semi-Markov model is developed toanalyze multivariate financial time series with switching data generatingregimes. Furthermore, an augmented EM algorithm is proposed for parameterestimation by embedding regularized estimators for the state-dependentcovariance matrices and autoregression matrices in the M-step. The performanceof the proposed regularized estimators is evaluated both in the simulationexperiments and on the New York Stock Exchange financial portfolio data.

A Regularized Vector Autoregressive Hidden Semi-Markov Model, with Application to Multivariate Financial Data