Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage

This paper aims to construct models based on economically motivated factor decompositions and shrinkage methods to forecast large-dimensional, and time-varying realized measures of daily covariance matrices of returns on financial assets. Realized measures of a covariance matrix are estimates, based on intraday returns, of the integrated covariance matrix of a multivariate diffusion process. One example of such an estimator used in this paper is the composite realized kernel method recently introduced by Lunde et al. (2016). Our proposed model is evaluated in terms of its forecasting ability and, more importantly, several performance measures in a conditional mean-variance portfolio allocation problem. Modeling and forecasting the covariance matrix of financial assets are essential for portfolio allocation and risk management.