High-dimensional multivariate realized volatility estimation

We provide a new factor-based estimator of the realized covolatility matrix applicable in situations when the number of assets is large and the high-frequency data are contaminated with microstructure noises. Our estimator relies on the assumption of a factor structure for the noise component, separate from the latent systematic risk factors that characterize the cross-sectional variation in the frictionless returns. The new estimator provides theoretically more efficient and finite-sample more accurate estimates of large-scale integrated covolatility and correlation matrices than other recently developed realized estimation procedures. These theoretical and simulation based findings are further corroborated by an empirical application related to portfolio allocation and risk minimization involving several hundred individual stocks. (C) 2019 Elsevier B.V. All rights reserved.