Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction
From MaRDI portal
Publication:1739867
DOI10.1016/j.jeconom.2018.10.003zbMath1452.62774OpenAlexW2777209651MaRDI QIDQ1739867
Publication date: 29 April 2019
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jeconom.2018.10.003
Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05)
Related Items
Rank determination in tensor factor model, Conditional quantile analysis for realized GARCH models, Adaptive robust large volatility matrix estimation based on high-frequency financial data, Large volatility matrix analysis using global and national factor models, Volatility models for stylized facts of high‐frequency financial data, Power enhancement for testing multi-factor asset pricing models via Fisher's method, Overnight GARCH-Itô Volatility Models, Unified discrete-time factor stochastic volatility and continuous-time Itô models for combining inference based on low-frequency and high-frequency, State Heterogeneity Analysis of Financial Volatility using high‐frequency Financial Data
Cites Work
- Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data
- Are more data always better for factor analysis?
- The common and specific components of dynamic volatility
- Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks
- Sparse PCA-based on high-dimensional Itô processes with measurement errors
- Asymptotic theory for large volatility matrix estimation based on high-frequency financial data
- Optimal sparse volatility matrix estimation for high-dimensional Itô processes with measurement errors
- Estimating the quadratic covariation matrix from noisy observations: local method of moments and efficiency
- Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data
- Quasi-maximum likelihood estimation of volatility with high frequency data
- Estimating covariation: Epps effect, microstructure noise
- Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading
- Generalized dynamic factor models and volatilities: estimation and forecasting
- Using principal component analysis to estimate a high dimensional factor model with high-frequency data
- Large volatility matrix estimation with factor-based diffusion model for high-frequency financial data
- Generalized autoregressive conditional heteroscedasticity
- Dynamic factor models with infinite-dimensional factor spaces: one-sided representations
- Microstructure noise in the continuous case: the pre-averaging approach
- Efficient estimation of stochastic volatility using noisy observations: a multi-scale approach
- Statistical Inference for Unified Garch-Itô Models with High-Frequency Financial Data
- Robust principal component analysis?
- Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise
- Forecasting Using Principal Components From a Large Number of Predictors
- Robust High-Dimensional Volatility Matrix Estimation for High-Frequency Factor Model
- Generalized dynamic factor models and volatilities: recovering the market volatility shocks
- High-Frequency Covariance Estimates With Noisy and Asynchronous Financial Data
- FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements
- A Tale of Two Time Scales