Modelling the Dynamic Dependence Structure in Multivariate Financial Time Series

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Break detection in the covariance structure of multivariate time series models (2009, background): “How movements of the financial markets are interpreted will significantly determine, for example, the asset allocation in a portfolio or the decision making of a risk manager (see [7, 24, 46, 47]).”
MGARCH models: Trade-off between feasibility and flexibility (2018, methodology): “Other distributions used in MGARCH models are the fat-tailed multivariate Laplace of Rombouts et al. (2014) and the Multiple Degrees of Freedom t of Serban et al. (2007); see Rossi and Spazzini (2010) for a comparison of the performance of these distributions associated with different MGARCH…”
Modeling rainfall–runoff relationship using multivariate GARCH model (2013, methodology): “The multivariate GARCH (MGARCH) models have then been commonly applied to study the relations between the volatilities and co-volatilities of several markets (e.g. De Goeij and Marquering, 2004; Serban et al., 2007; Cai et al., 2009; Syllignakis and Kouretas, 2011).”
Change-point detection in high-dimensional covariance structure (2016, background): “A similar problem is found in finance: the dynamics of the covariance structure of a high-dimensional process modelling exchange rates and market indexes is crucial for a proper asset allocation in a portfolio [13, 6, 16, 33].”