Several large volatility matrix estimation procedures have been recently developed for factor-based It processes whose integrated volatility matrix consists of low-rank and sparse matrices. Their performance depends on the accuracy of input volatility matrix estimators. When estimating co-volatilities based on high-frequency data, one of the crucial challenges is non-synchronization for illiquid assets, which makes their co-volatility estimators inaccurate. In this paper, we study how to estimate the large integrated volatility matrix without using co-volatilities of illiquid assets. Specifically, we pretend that the co-volatilities for illiquid assets are missing, and estimate the low-rank matrix using a matrix completion scheme with a structured missing pattern. To further regularize the sparse volatility matrix, we employ the principal orthogonal complement thresholding method (POET). We also investigate the asymptotic

The validity of using the tight-binding approximation for the nonlinear Schrödinger equations with a two-dimensional optical lattice is considered. This work provides a rigorous foundation for a technique based on “orbital” functions that is central to solid-state physics and nonlinear optics. Simple and honeycomb lattices are addressed, and it is therefore shown that the use of tight-binding approximations is justified in complicated situations.

We prove an extension theorem for effective purely log-terminal pairs ($X, S$+$B$) of non-negative Kodaira dimension $${\kappa (K_X+S+B)\ge 0}$$ . The main new ingredient is a refinement of the Ohsawa–Takegoshi$L$^{2}extension theorem involving singular Hermitian metrics.

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We prove that spherical spectral analysis and synthesis hold in Damek–Ricci spaces and derive two-radius theorems.