Estimation of high-dimensional integrated covariance matrix based on noisy high-frequency data with multiple observations

Abstract In this paper, we study the estimation of integrated covariance matrix based on noisy high-frequency data with multiple transactions using random matrix theory. We further prove that the proposed estimator is also asymptotically optimal for portfolio selection.