Dynamic principal component CAW models for high-dimensional realized covariance matrices

DOI10.1080/14697688.2019.1701197zbMath1467.62174OpenAlexW2588093667MaRDI QIDQ4991059

Bastian Gribisch, Michael Stollenwerk

Publication date: 2 June 2021

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2019.1701197

zbMATH Keywords

covariance matrixspectral decompositionrealized volatilityconditional autoregressive Wishart (CAW) modeltime-series models

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inference from stochastic processes and spectral analysis (62M15) Economic time series analysis (91B84) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (1)

An integrated framework for visualizing and forecasting realized covariance matrices

Uses Software

• KELLEY

Cites Work

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• The Model Confidence Set
• The Wishart autoregressive process of multivariate stochastic volatility
• Forecasting multivariate realized stock market volatility
• 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
• The conditional autoregressive Wishart model for multivariate stock market volatility
• Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance
• On the estimation of dynamic conditional correlation models
• Fast clustering of GARCH processes via Gaussian mixture models
• Vast volatility matrix estimation for high-frequency financial data
• Multivariate rotated ARCH models
• Large Volatility Matrix Inference via Combining Low-Frequency and High-Frequency Approaches
• Estimating Regression Models of Finite but Unknown Order
• Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances
• Forecasting high-dimensional realized volatility matrices using a factor model
• Modeling and Forecasting Realized Volatility
• Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics
• Multivariate Stochastic Volatility: A Review

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