Optimal sparse volatility matrix estimation for high-dimensional Itô processes with measurement errors

19 September 2013

Papers citing "Optimal sparse volatility matrix estimation for high-dimensional Itô processes with measurement errors"

10 / 10 papers shown

Title
Likelihood theory for the Graph Ornstein-Uhlenbeck process Valentin Courgeau Almut E. D. Veraart 61 4 0 26 May 2020
De-biased graphical Lasso for high-frequency data Yuta Koike 22 9 0 04 May 2019
Time series models for realized covariance matrices based on the matrix-F distribution Jiayuan Zhou Feiyu Jiang K. Zhu W. Li 13 8 0 26 Mar 2019
Optimal covariance matrix estimation for high-dimensional noise in high-frequency data Jinyuan Chang Qiao Hu Cheng Liu C. Tang 70 8 0 19 Dec 2018
Mixed-normal limit theorems for multiple Skorohod integrals in high-dimensions, with application to realized covariance Yuta Koike 78 5 0 13 Jun 2018
A frequency domain analysis of the error distribution from noisy high-frequency data Jinyuan Chang A. Delaigle P. Hall C. Tang 23 7 0 20 Jan 2018
Sparse covariance matrix estimation in high-dimensional deconvolution Denis Belomestny Mathias Trabs Alexandre B. Tsybakov 73 10 0 30 Oct 2017
Optimal large-scale quantum state tomography with Pauli measurements Tony Cai Donggyu Kim Yazhen Wang M. Yuan Harrison H. Zhou 39 31 0 24 Mar 2016
Low-rank diffusion matrix estimation for high-dimensional time-changed Lévy processes Denis Belomestny Mathias Trabs 67 12 0 15 Oct 2015
Principal Components Analysis for Semimartingales and Stochastic PDE A. Ohashi Alexandre B. Simas 25 1 0 19 Mar 2015