Structural Learning with Time-Varying Components: Tracking the Cross-Section of Financial Time Series
含时变成分的结构学习：追踪金融时间序列的横截面结构
Summary When modelling multivariate financial data, the problem of structural learning is compounded by the fact that the covariance structure changes with time. Previous work has focused on modelling those changes by using multivariate stochastic volatility models. We present an alternative to these models that focuses instead on the latent graphical structure that is related to the precision matrix. We develop a graphical model for sequences of Gaussian random vectors when…