Merits and Drawbacks of Variance Targeting in GARCH Models

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Multivariate Variance Targeting in the BEKK-GARCH Model (2014, unknown): “As in Comte and Lieberman (2003), Hafner and Preminger (2009b), and Francq, Horváth, and Zakoïan (2011), we assume that{Xt}t=0,...,T is strictly stationary and ergodic: Assumption 4.1 The assumptions of Theorem 2.1 are satisfied, and the observed process {Xt}t=0,...,T is generated by the strictly…”
MGARCH models: Trade-off between feasibility and flexibility (2018, methodology): “…also be estimated using the two-step covariance targeting (VT) procedure proposed by Engle and Mezrich (1996); see Kristensen and Linton (2004) and Francq et al. (2011) for the properties of the VT estimator in the univariate case as well as for a comparison with the standard G-QML estimator and…”
Fitting Vast Dimensional Time-Varying Covariance Models (2020, unknown): “This is based on the commonly used “variance-tracking” approach (Francq, Horváth, and Zakoïan 2011).”
On the Forecasting Accuracy of Multivariate GARCH Models (2010, methodology): “Building on Hansen and Lunde (2006a) and Patton (2009), Laurent et al. (2009) address these questions in the case of the comparison of multivariate volatility models using statistical loss functions. They show that the substitution of the underlying volatility by a proxy may induce a distortion in t”