Traditional methods employed in matrix volatility forecasting often overlook the inherent
Riemannian manifold structure of symmetric positive definite matrices, treating them as …

Financial assets exhibit complex dependency structures, which are crucial for investors to
create diversified portfolios to mitigate risk in volatile financial markets. To explore the …

In this paper, we study a Kronecker structured model for covariance matrices when data are
matrix-valued. Using the modified Cholesky decomposition for Kronecker structured …

A Bayesian method is proposed for variable selection in high-dimensional matrix
autoregressive models which reflects and exploits the original matrix structure of data to (a) …

Modeling multivariate stochastic volatility (MSV) can be challenging, particularly when both
variances and covariances are time-varying. In this paper, we address these challenges by …