Factor stochastic volatility with time varying loadings and Markov switching regimes

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Deep Learning for Predicting Asset Returns (2018, methodology): “They provide an alternative to dynamic factor modeling, see Lopes and Carvalho (2007), Carvalho et al. (2011) and Carvalho et al. (2017).”
Sparse Bayesian time-varying covariance estimation in many dimensions (2016, background): “Consequently, it is likely that more factors are needed to properly explain the co-volatility dynamics of a multivariate time series than in models which allow for completely unrestricted timevarying factor loadings (Lopes and Carvalho 2007), correlated factors (Vt not diagonal, see Zhou et al.”
Spatial dynamic factor analysis (2008, background): “Remaining temporal dependence on the idiosyncratic errors can also be considered (Lopes and Carvalho 2007 and Peña and Poncela 2006) but this was not pursued here.”
Dynamic Stock Selection Strategies: A Structured Factor Model Framework (2012, methodology): “Letting the loadings vary in time with a DLM leads to the model considered in Lopes, Aguilar and West (2000) and Lopes and Carvalho (2007).”