Large-dimensional factor modeling based on high-frequency observations
type: Item
instance of: scholarly article
title: Large-dimensional factor modeling based on high-frequency observations
zbMATH Open document ID: 1452.62786
author: Markus Pelger
published in: Journal of Econometrics
publication date: 2019-04-26T00:00:00Z
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62P20
zbMATH DE Number: 7048307
zbMATH Keywords: systematic risk
zbMATH Keywords: high-dimensional data
zbMATH Keywords: high-frequency data
zbMATH Keywords: latent factor model
zbMATH Keywords: PCA
zbMATH Keywords: jumps
zbMATH Keywords: semimartingales
zbMATH Keywords: approximate factor model
zbMATH Keywords: number of factors
MaRDI profile type: Publication
full work available at URL: j.jeconom.2018.09.004
OpenAlex ID: W3123638141
cites work: Eigenvalue ratio test for the number of factors
cites work: High-Frequency Covariance Estimates With Noisy and Asynchronous Financial Data
cites work: Q5495335
cites work: A Tale of Two Time Scales
cites work: Using principal component analysis to estimate a high dimensional factor model with high-frequency data
cites work: The Distribution of Realized Exchange Rate Volatility
cites work: Asset pricing for general processes
cites work: Inferential Theory for Factor Models of Large Dimensions
cites work: Determining the Number of Factors in Approximate Factor Models
cites work: Evaluating latent and observed factors in macroeconomics and finance
cites work: Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise
cites work: Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading
cites work: Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models
cites work: Econometrics of co-jumps in high-frequency data with noise
cites work: Jump tails, extreme dependencies, and the distribution of stock returns
cites work: Jumps and betas: a new framework for disentangling and estimating systematic risks
cites work: Asset Pricing in Multiperiod Securities Markets
cites work: Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
cites work: Transform Analysis and Asset Pricing for Affine Jump-diffusions
cites work: Large Covariance Estimation by Thresholding Principal Orthogonal Complements
cites work: Determining the Number of Factors in the General Dynamic Factor Model
cites work: Asymptotic properties of realized power variations and related functionals of semimartingales
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: A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets
cites work: Q3994346
cites work: Bipower-type estimation in a noisy diffusion setting
cites work: FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA
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: Estimating covariation: Epps effect, microstructure noise
cites work: On the estimation of integrated covariance matrices of high dimensional diffusion processes
Wikidata QID: Q129100840
DOI: 10.1016/J.JECONOM.2018.09.004
description: scientific article; zbMATH DE number 7048307