REALIZED BETA GARCH: A MULTIVARIATE GARCH MODEL WITH REALIZED MEASURES OF VOLATILITY

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Bayesian semiparametric modeling of realized covariance matrices (2016, methodology): “Extensions to this approach are Sheppard & Xu (2014) and Janusa et al. (2014) and a closely related approach is Hansen et al. (2013).”
Exploiting the errors: A simple approach for improved volatility forecasting (2016, methodology): “(2012) and the Realized Beta GARCH model of Hansen et al. (2014) may similarly be extended to allow the parameters of the models to vary with the degree of the measurement errors in the realized covariance matrix.”
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, background): “In contrast, Noureldin, Shephard, and Sheppard (2012) and Hansen, Lunde, and Voev (2014) developed frameworks for forecasting the covariance of daily returns which also involves building a model to forecasts future realized measures.”
Do High-Frequency Data Improve High-Dimensional Portfolio Allocations? (2013, unknown): “In this context, utilizing HF data in a GARCH framework, as e.g., suggested by Hansen et al. (2010) and Noureldin et al. (2012), appears particularly promising.”
A New Parametrization of Correlation Matrices (2020, methodology): “Kurowicka and Cooke (2003). The matrix logarithm has been used in the modeling of covariance matrices in Leonard and Hsu (1992) and Chiu et al.”