Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?

DOI10.1080/07474930701873333zbMath1359.62522OpenAlexW2158977591MaRDI QIDQ3539873

Dick van Dijk, Martin Martens, Michiel de Pooter

Publication date: 19 November 2008

Published in: Econometric Reviews (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07474930701873333

zbMATH Keywords

high-frequency datamean-variance analysistracking errorrealized volatilitybias-correctionvolatility timing

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Statistical methods; economic indices and measures (91B82)

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Cites Work

• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• An econometric analysis of nonsynchronous trading
• On covariance estimation of non-synchronously observed diffusion processes
• Using High-Frequency Data in Dynamic Portfolio Choice
• Semi-Parametric Modelling of Correlation Dynamics
• A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
• Continuous Record Asymptotics for Rolling Sample Variance Estimators
• The Distribution of Realized Exchange Rate Volatility
• Modeling and Forecasting Realized Volatility
• Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics
• A Tale of Two Time Scales

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