Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

DOI10.1016/j.jeconom.2018.05.004zbMath1452.62746OpenAlexW3125436869WikidataQ129473452 ScholiaQ129473452MaRDI QIDQ1792481

Andrew J. Patton, Rogier Quaedvlieg, Tim Bollerslev

Publication date: 12 October 2018

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

Full work available at URL: https://pure.au.dk/ws/files/99025265/rp16_10.pdf

zbMATH Keywords

forecastingasset allocationportfolio constructioncommon risksrealized covariances

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Portfolio theory (91G10)

Related Items (7)

A generalized heterogeneous autoregressive model using market information ⋮ Modeling realized covariance measures with heterogeneous liquidity: a generalized matrix-variate Wishart state-space model ⋮ Overnight GARCH-Itô Volatility Models ⋮ Monitoring mean changes in persistent multivariate time series ⋮ Estimation of the stochastic leverage effect using the Fourier transform method ⋮ Large-scale portfolio allocation under transaction costs and model uncertainty ⋮ Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers

Cites Work

• Estimating Vector Autoregressions with Panel Data
• The Model Confidence Set
• A well-conditioned estimator for large-dimensional covariance matrices
• A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series
• Jump-robust volatility estimation using nearest neighbor truncation
• On loss functions and ranking forecasting performances of multivariate volatility models
• High-dimensional copula-based distributions with mixed frequency data
• Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data
• A reduced form framework for modeling volatility of speculative prices based on realized variation measures
• Volatility forecast comparison using imperfect volatility proxies
• Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading
• Integrated variance forecasting: model based vs. reduced form
• The conditional autoregressive Wishart model for multivariate stock market volatility
• A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm
• Sparse and stable Markowitz portfolios
• A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms
• Econometric Issues in the Analysis of Regressions with Generated Regressors
• Using High-Frequency Data in Dynamic Portfolio Choice
• Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?
• Realized Beta: Persistence and Predictability
• Bootstrapping Realized Volatility
• The Stationary Bootstrap
• A Reality Check for Data Snooping
• Continuous Record Asymptotics for Rolling Sample Variance Estimators
• Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection
• MEASUREMENT ERRORS IN DYNAMIC MODELS
• High-Frequency Covariance Estimates With Noisy and Asynchronous Financial Data
• Modeling and Forecasting Realized Volatility
• Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics
• Measurement Error in Linear Autoregressive Models
• Exploiting the errors: a simple approach for improved volatility forecasting

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