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.

翻译：开发了一种正规化的矢量自动递减隐藏半马尔科夫模型,以分析具有转换数据生成机制的多变量财务时间序列;此外,还提议了一种强化的EM算法,通过在M级中嵌入国家依赖的共变矩阵和自动递减矩阵的正规化估计值来估算参数。