Multivariate GARCH models

By Annastiina Silvennoinen, Timo Teräsvirta. 2008

550 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/8069866d2aadabd64cf6a96cacf64c5b38442fb3

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Citations in the corpus

Conditional Correlation Models of Autoregressive Conditional Heteroscedasticity With Nonstationary GARCH Equations (2014, methodology): “For recent surveys of multivariate GARCH models see Bauwens, Laurent, and Rombouts (2006) and Silvennoinen and Teräsvirta (2009).”
The relationship between energy and equity markets: evidence from volatility impulse response functions (2014, background): “Moreover, models in which correlations are time-varying are more difficult to estimate than the constant conditional correlation models which are easier to estimate than many of their counterparts and their parameters (correlations) have a natural interpretation (Silvennoinen and Terasvirta, 2009).”
Long Memory Dynamics for Multivariate Dependence under Heavy Tails (2014, unknown): “In many studies multivariate extensions of the generalized autoregressive conditional heteroskedasticity (GARCH) model are considered for this purpose such as reviewed by, for example, Bauwens, Laurent, and Rombouts (2006) and Silvennoinen and Teräsvirta (2009).”
Practical Issues in the Analysis of Univariate GARCH Models (2009, methodology): “Multivariate GARCH models are discussed in the chapter by Silvennoinen and Teräsvirta (2008).”
Stationarity and geometric ergodicity of BEKK multivariate GARCH models (2011, methodology): “For an overview over the various multiva riate GARCH models existing and their applications we refer to [3, 31].”
Selecting volatility forecasting models for portfolio allocation purposes (2015, methodology): “…survey of volatility modeling is provided by Andersen, Bollerslev, Christoffersen & Diebold (2006), while Bauwens, Laurent & Rombouts (2006), and Silvennoinen & Teräsvirta (2009) survey the multivariate versions of the popular generalised autoregressive conditional heteroskedastic (GARCH)…”
Market interdependence and volatility transmission among major crops (2016, methodology): “7 For a detailed overview of different MGARCH models see Bauwens, Laurent, and Rombouts (2006) and Silvennoinen and Teräsvirta (2009).”
The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. (2014, background): “…there is extensive research (mainly in the econometrics literature) into GARCH (Generalised AutoRegressive Conditional Het-eroscedastic) models (Silvennoinen and Ter¨asvirta, 2009), which autoregress residuals variances on their own history and / or the history of the process itself, G-causal…”
On the Forecasting Accuracy of Multivariate GARCH Models (2010, methodology): “A large number of multivariate GARCH models have been proposed in the literature, see Bauwens, Laurent, and Rombouts (2006) and Silvennoinen and Terasvirta (2009) for extensive surveys.”
MGARCH models: Trade-off between feasibility and flexibility (2018, methodology): “Also, Silvennoinen and Teräsvirta (2009b, 2015) propose the smooth transition conditional correlation (STCC) model allowing the correlations to vary smoothly between two different states.”
Break detection in the covariance structure of multivariate time series models (2009, methodology): “articular strengths and weaknesses. Research—applied and theoretic—is still to be conducted to reveal further insight. We refrain hence from giving recommendations here but refer to the survey papers [7, 47] for 18 AUE, HORMANN, HORV¨ ATH AND REIMHERR´ Table 1 Critical values for Ω(¯ d) for the thre”
Multivariate Rotated ARCH Models (2014, unknown): “Reviews of the multivariate generalized autoregressive conditional heteroskedasticity (GARCH) literature are given by, for example, Bauwens et al. (2006), Engle (2009a), Silvennoinen and Teräsvirta (2009) and Francq and Zakoian (2010, Ch. 11).”