Abstract
Multivariate generalized hyperbolic distributions represent an attractive family of distributions (with exponentially decreasing tails) for multivariate data modelling. However, in a limited data environment, robust and fast estimation procedures are rare. An alternative class of multivariate distributions (with exponentially decreasing tails) is proposed which comprises affine-linearly transformed random vectors with stochastically independent and generalized hyperbolic marginals. The latter distributions possess good estimation properties and have attractive dependence structures which are explored in detail. In particular, dependencies of extreme events (tail dependence) can be modelled within this class of multivariate distributions. In addition the necessary estimation and random-number generation procedures are provided. Various advantages and disadvantages of both types of distributions are discussed and illustrated via a simulation study.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
