Outlier Robust Methods for Forecasting Realized Covariance Matrices

This paper proposes two new approaches to improve estimation of the coefficients of the multivariate HAR (MHAR) model, and in turn improve forecast performance. A robust estimator of the covariance matrix is adopted to replace the realized covariance (RCov) matrix while estimating the MHAR model. The robustness to outliers of the new estimator makes the OLS estimation scheme for the MHAR model more reliable. In addition, a robust estimation scheme is developed for the MHAR model, which is based on the multivariate least-trimmed squares (MLTS) method. Both approaches provide significant improvements in forecasting performance based on both statistical loss and portfolio outcomes