Generalized dynamic factor models and volatilities: estimation and forecasting

DOI10.1016/j.jeconom.2017.08.010zbMath1377.62194OpenAlexW1910333351MaRDI QIDQ1676377

Marc Hallin, Matteo Barigozzi

Publication date: 7 November 2017

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: http://eprints.lse.ac.uk/67455/

zbMATH Keywords

volatilityGARCH modelsdynamic factor modelshigh-dimensional time seriesmultivariate GARCH

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Economic time series analysis (91B84)

Related Items (10)

Dynamic factor long memory volatility ⋮ Nearest comoment estimation with unobserved factors ⋮ Factor models for high‐dimensional functional time series I: Representation results ⋮ Factor models for high‐dimensional functional time series II: Estimation and forecasting ⋮ Inferential theory for generalized dynamic factor models ⋮ Extracting Conditionally Heteroskedastic Components using Independent Component Analysis ⋮ Predicting the VIX and the volatility risk premium: the role of short-run funding spreads volatility factors ⋮ Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction ⋮ Editors' introduction ⋮ Generalized dynamic factor models and volatilities: consistency, rates, and prediction intervals

Cites Work

• Unnamed Item
• A long-run pure variance common features model for the common volatilities of the Dow Jones
• The common and specific components of dynamic volatility
• Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks
• Risks of large portfolios
• Improved penalization for determining the number of factors in approximate factor models
• Volatility forecast comparison using imperfect volatility proxies
• The general dynamic factor model: one-sided representation results
• Dynamic factors in the presence of blocks
• Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes
• Generalized autoregressive conditional heteroscedasticity
• Dynamic factor models with infinite-dimensional factor spaces: one-sided representations
• Dynamic factor models with infinite-dimensional factor space: asymptotic analysis
• Factor models in high-dimensional time series: A time-domain approach
• Disentangling systematic and idiosyncratic dynamics in panels of volatility measures
• ASYMPTOTIC THEORY FOR A FACTOR GARCH MODEL
• Multivariate GARCH Models
• Multivariate Stochastic Variance Models
• Forecasting Using Principal Components From a Large Number of Predictors
• Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models
• Generalized dynamic factor models and volatilities: recovering the market volatility shocks
• Determining the Number of Factors in the General Dynamic Factor Model
• Modeling and Forecasting Realized Volatility
• Determining the Number of Factors in Approximate Factor Models
• Multivariate Stochastic Volatility: A Review
• Large Covariance Estimation by Thresholding Principal Orthogonal Complements
• The Generalized Dynamic Factor Model
• Threshold heteroskedastic models

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