Risk Estimation using the Multivariate Normal Inverse Gaussian Distribution

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Financial Valuation and Risk Management Working Paper No . 454 CHICAGO : A Fast and Accurate Method for Portfolio Risk Calculation (2008, background): “, 1998; Haas et al., 2006), normal inverse Gaussian (NIG), and hyperbolic (see Küchler et al., 1999; Prause, 1999). While the GHyp offers an excellent empirical fit to financial data, its use can also be motivated from a theoretical point of view. For example, Barndorff-Nielsen (1998) discusses a st”
Multivariate DCC-GARCH Model: -With Various Error Distributions (2009, methodology): “To ensure that at is the same in the CCC-GARCH and the DCC-GARCH models, we will in this example use the same c andK as in [15], but model at with DCC-GARCH instead of CCC-GARCH.”
Distributional Tests in Multivariate Dynamic Models with Normal and Student-t Innovations (2009, background): “…cases, such as symmetric and asymmetric versions of the hyperbolic (Chen et al., 2008), normal gamma (Madan & Milne, 1991), normal inverse Gaussian (Aas et al., 2005), and Laplace distributions (Cajigas & Urga, 2007).1 We can gain some intuition about the parameters of the GH distribution by…”