An Integrated Framework for Visualizing and Forecasting Realized Covariance Matrices

Abstract

This paper proposes an integrated framework for visualizing and forecasting realized covariance matrices to enable efficient construction and prediction of an optimal portfolio. Multivariate realized kernels are typically derived from intra-day high-frequency data, and the realized covariance matrix is estimated from the kernels using the graphical LASSO algorithm. To forecast the realized covariances, we employ the conditional autoregressive Wishart (CAW) model and its variants. Finally, we compute the Stein loss function and execute the model-confidence-set (MCS) procedure to obtain the best model for optimal portfolio selection.