We propose a Kronecker product model for correlation or covariance matrices in thelarge dimensional case. The number of parameters of the model increases logarithmicallywith the dimension of the matrix. We propose a minimum distance (MD) estimator basedon a log-linear property of the model, as...

We introduce the Realized moMents of Disjoint Increments (ReMeDI) paradigm to measure microstructure noise (the deviation of the observed asset prices from the fundamental values caused by market imperfections). We propose consistent estimators of arbitrary moments of the microstructure noise...

This note contains the supplements to Li and Linton (2019). Section A contains extensive simulation studies. Section B presents the methodology and a detailed numerical algorithm to select the tuning parameters. Section C presents additional empirical studies. Section D contains all mathematical...

We devise a new high-frequency covariance matrix estimator based on price durations which is guaranteed to be positive-definite. Both non-parametric and parametric versions are proposed. A comprehensive Monte Carlo simulation shows that this class of estimators are less biased, more efficient,...

We introduce the Realized moMents of Disjoint Increments (ReMeDI) paradigm to measure microstructure noise (the deviation of the observed asset prices from the fundamental values caused by market imperfections). We propose consistent estimators of arbitrary moments of the microstructure noise...

We introduce a new method to estimate the integrated volatility (IV) based on noisy high-frequency data. Our method employs the ReMeDI approach introduced by Li and Linton (2021a) to estimate the moments of the microstructure noise and thereby eliminate their influence, and the pre-averaging...