李德柜，英国约克大学

With noisy and asynchronous high-frequency data collected for an ultra-large number of assets, we estimate high-dimensional spot volatility matrices satisfying a low-rank plus sparse structure. A localised pre-averaging method is proposed to jointly tackle the microstructure noise and asynchronicity issues, and obtain uniformly consistent estimates for latent prices. We impose a continuous-time factor model with time-varying factor loadings on the price processes, and estimate the common factors and loadings via a local principal component analysis. Assuming a uniform sparsity condition on the idiosyncratic volatility structure, we combine the POET and kernel-smoothing techniques to estimate the spot volatility matrices for both the latent prices and idiosyncratic errors. Under some mild restrictions, the estimated spot volatility matrices are shown to be uniformly consistent with convergence rates affected by the estimation errors due to the microstructure noise, asynchronicity and latent factor structures. Numerical studies are provided to assess the performance of the developed methods.