GSH Dependence Modeling with an Application to Risk Management

References

• Azzalini , A. , Capitanio , A. ( 2003 ). Distributions generated by perturbation of symmetry with emphasis on a multivariate skew-t distribution . J. Roy. Statist. Soc. B 65 : 367 – 389 .
   Google Scholar
• Azzalini , A. , Dalla Valle , A. ( 1996 ). The multivariates skew normal distribution . Biometrika 8 : 715 – 726 .
   Web of Science ®Google Scholar
• Burnham , K. P. , Anderson , D. R. ( 2002 ). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. , 2nd ed. Berlin/Heidelberg : Springer .
   Google Scholar
• Artzner , P. , Delbaen , F. , Eber , J. M. , Heath , D. ( 1999 ). Coherent measures of risk . Mathemat. Fin. 9 : 203 – 228 .
   Web of Science ®Google Scholar
• Demarta , S. , McNeil , A. ( 2005 ). The t-copula and related copulas . Int. Statist. Rev. 73 ( 1 ): 111 – 129 .
   Web of Science ®Google Scholar
• Genest , C. , Rémillard , B. ( 2004 ). Tests of independence and randomness based on the empirical copula process . Test 13 : 335 – 369 .
   Web of Science ®Google Scholar
• Fernández , C. , Steel , M. F. J. ( 1998 ). On Bayesian modeling of fat tails and skewness . J. Amer. Statist. Assoc. 93 : 359 – 371 .
   Web of Science ®Google Scholar
• Fischer , M. ( 2006 ). The skew generalized secant hyperbolic family . Austr. J. Statist. 35 ( 4 ): 437 – 443 .
   Google Scholar
• Fischer , M. , Köck , C. , Schlüter , S. , Weigert , F. ( 2009 ). Multivariate copula models at work . Quant. Fin. 9 ( 7 ): 839 – 854 .
   Web of Science ®Google Scholar
• Frahm , G. , Junker , M. , Szimayer , A. ( 2003 ). Elliptical copulas: applicability and limitations . Statist. Probab. Lett. 63 ( 3 ): 275 – 286 .
   Web of Science ®Google Scholar
• González-Rivera , G. , Drost , F. C. ( 1999 ). Efficiency comparisons of maximum-likelihood-based estimators in GARCH models . J. Econometrics 93 – 111 .
   Google Scholar
• Hyvärinen , A. , Karhunen , J. , Oja , E. ( 2001 ). Independent Component Analysis . New York : Wiley .
   Google Scholar
• Joe , H. ( 1997 ). Multivariate Models and Dependence Concepts . London : Chapman & Hall .
   Google Scholar
• Jørgensen , H. ( 1982 ). Statistical Properties of the Generalized Inverse Gaussian Distribution . Lecture Notes in Statistics , Vol. 9 . Berlin/Heidelberg : Springer .
   Google Scholar
• Koehler , K. J. , Symanowski , J. T. ( 1995 ). Constructing multivariate distributions with specific marginal distributions . J. Multivariate. Anal. 55 : 261 – 282 .
   Web of Science ®Google Scholar
• Ma , J. , Sun , Z. ( 2007 ). Copula component analysis . In: Davies , M. , James , C. , Abdallah , S. , Plumbley , M. , eds. Independent Component Analysis and Signal Separation . Lecture Notes in Computer Science , Vol. 4666 . Berlin/Heidelberg : Springer , pp. 73 – 80 .
   Google Scholar
• McNeil , A. J. , Frey , R. , Embrechts , P. ( 2005 ). Quantitative Risk Management: Concepts, Techniques, and Tools . Princeton and Oxford : Princeton University Press .
   Google Scholar
• Nelsen , R. B. ( 2006 ). An Introduction to Copulas . Berlin/Heidelberg : Springer .
   Google Scholar
• Palmitesta , P. , Provasi , C. ( 2004 ). GARCH-type models with generalized secant hyperbolic innovations . Stud. Nonlinear Dyn. Econometrics 8 ( 2 ): 106 – 122 .
   Google Scholar
• Palmitesta , P. , Provasi , C. ( 2005 ). Aggregation of dependent risks using the Koehler-Symanowski copula function . Computat. Econ. 25 : 189 – 205 .
   Google Scholar
• R Development Core Team ( 2012 ). R: A Language and Environment for Statistical Computing . Vienna : R Foundation for Statistical Computing (url: http://www.R-project.org/) .
   Google Scholar
• Vaughan , D. C. ( 2002 ). The generalized hyperbolic secant distribution and its application . Commun. Statist. Theor. Meth. 31 ( 2 ): 219 – 238 .
   Web of Science ®Google Scholar