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.;
Tim Bollerslev, Nour Meddahi, and Serge Nyawa, “High-dimensional multivariate realized volatility estimation”, Journal of Econometrics, vol. 212, n. 1, September 2019, pp. 116–136.
Journal of Econometrics, vol. 212, n. 1, September 2019, pp. 116–136