A rank test for the number of factors with high-frequency data
type: Item
instance of: scholarly article
title: A rank test for the number of factors with high-frequency data
zbMATH Open document ID: 1452.62776
author: Xin-Bing Kong
author: Zhi Liu
author: Wang Zhou
published in: Journal of Econometrics
publication date: 2019-07-01T00:00:00Z
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 62M05
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 60F05
zbMATH DE Number: 7075187
zbMATH Keywords: continuous-time factor model
zbMATH Keywords: high-dimensional Itô process
zbMATH Keywords: idiosyncratic process
MaRDI profile type: Publication
full work available at URL: j.jeconom.2019.03.004
OpenAlex ID: W2930817713
cites work: Eigenvalue ratio test for the number of factors
cites work: Q5495335
cites work: Using principal component analysis to estimate a high dimensional factor model with high-frequency data
cites work: Determining the Number of Factors in Approximate Factor Models
cites work: Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
cites work: Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data
cites work: Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data
cites work: The Rotation of Eigenvectors by a Perturbation. III
cites work: Large Covariance Estimation by Thresholding Principal Orthogonal Complements
cites work: Microstructure noise in the continuous case: the pre-averaging approach
cites work: A test for the rank of the volatility process: the random perturbation approach
cites work: Quarticity and other functionals of volatility: efficient estimation
cites work: Modeling high-frequency financial data by pure jump processes
cites work: On the jump activity index for semimartingales
cites work: Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data
cites work: On the number of common factors with high-frequency data
cites work: On the systematic and idiosyncratic volatility with large panel high-frequency data
cites work: Testing for pure-jump processes for high-frequency data
cites work: Jump Regressions
cites work: A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data
cites work: Testing Hypotheses About the Number of Factors in Large Factor Models
cites work: Large-dimensional factor modeling based on high-frequency observations
cites work: Forecasting Using Principal Components From a Large Number of Predictors
cites work: Optimal sparse volatility matrix estimation for high-dimensional Itô processes with measurement errors
cites work: Vast volatility matrix estimation for high-frequency financial data
cites work: Quasi-maximum likelihood estimation of volatility with high frequency data
cites work: A Tale of Two Time Scales
DOI: 10.1016/J.JECONOM.2019.03.004
description: scientific article; zbMATH DE number 7075187