A New Class of Multivariate Skew Densities, with Application to GARCH Models

By Luc Bauwens, Sébastien-Yves Laurent. 2002

152 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bcb238f0a7b739191ebd65d73175ca6bda65bd9d

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Citations in the corpus

A Component GARCH Model with Time Varying Weights (2009, background): “The unsatisfactory results in predicting VaR for short positions suggest that it could be of interest to consider skewed error distributions such as the skew-t distribution investigated by Bauwens and Laurent (2005). At the same time, it has been shown that WGARCH models allow to better characterize”
Value-at-Risk for long and short trading positions (2003, background): “23See Bauwens and Laurent (2002) for more details on how to generate SKST (0, Ik, ξ, υ) random numbers.”
Asymmetric CAPM dependence for large dimensions: the Canonical Vine Autoregressive Model (2009, unknown): “Bauwens & Laurent (2005) introduce the asymmetric Student t distribution and evaluate its performance in terms of value-at-risk (VaR) on three exchange rates, while Mencía & Sentana (2003) use the generalized hyperbolic distribution with five sectorial indexes of the NASDAQ.”
Estimation of Stable Distributions by Indirect Inference (2011, unknown): “Therefore, we propose in this subsection to focus on the family of skewed-Student distributions as introduced by Fernandez and Steel (1998) (see also Hansen (1994) and Bauwens and Laurent (2005) for the multivariate extension).”
Dynamic Copula Modelling for Value at Risk (2006, unknown): “However, the VaR can be computed using a simple Monte Carlo simulation as widely used in quantitative finance and VaR applications, see Jorion (2000), Giot and Laurent (2003) and Bauwens and Laurent (2005). Besides, Giot and Laurent (2003) show that a choice of 100,000 simulations provides accurate ”
Forecasting Value-at-Risk under Different Distributional Assumptions (2016, methodology): “In this respect, Fernández and Steel [29] in the univariate case and Bauwens and Laurent [13] in the multivariate one developed a practical procedure for constructing skewed densities from their symmetric unimodal counterparts.”
Multivariate GARCH Models: A Survey (2003, unknown): “…Vlaar and Palm, 1993), the Generalized Hyperbolic distribution (see Barndorff-Nielsen and Shephard (2001) for the density and Menćıa and Sentana (2003) for a recent application its standardized version in a MGARCH context) and a multivariate skew-Student density (see Bauwens and Laurent, 2002).”
Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models With Student t Innovations (2003, methodology): “Similarly, given that we are ruling out by assumption any asymmetries in the conditional distribution of asset returns, it would be interesting to explore asymmetric extensions of the multivariate t distribution (see Bauwens and Laurent 2002). Relatedly, it would also be worth exploring ways in whic”
The Generalized Hyperbolic Skew Student’s t-Distribution (2006, background): “…several other papers have studied different skew t-type distributions for financial and other applications, see e.g. Azzalini and Capitanio (2003); Bauwens and Laurent (2002); Branco and Dey (2001); Fernandez and Steel (1998); Jones and Faddy (2003); Patton (2004); Sahu et al. (2003); Venter and…”
Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions (2011, background): “…t− for this purpose, and other skew extensions of the t− distribution have been proposed by Fernandez and Steel (1998), Theodossiou (1998), Branco and Dey (2001), Bauwens and Laurent (2002), Jones and Faddy (2003), Sahu et al. (2003), Azzalini and Capitanio (2003), Aas and Haff (2006) and others.”
A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models (2005, methodology): “In a previous version of this article (Bauwens and Laurent 2002), we estimated the multivariate GARCH model of Tse and Tsui (2002) with a normal, Student, and skew-Student likelihood on four daily indexes. LR tests and information criteria also clearly favored the latter distribution. Interestingly,”
Optimal Portfolio Allocation under Higher Moments (2006, background): “(2003) and Bauwens and Laurent (2005). An alternative approach to cope with asymmetry and fat tails would rely on the regimeswitching modelling, that provides a very convenient way to incorporate these features (Guidolin and Timmermann, 2003).”