Tail asymptotics for the bivariate equi-skew Variance-Gamma distribution

10/13/2020
by   Thomas Fung, et al.
0

We derive the asymptotic rate of decay to zero of the tail dependence of the bivariate skew Variance Gamma (VG) distribution under the equal-skewness condition, as an explicit regularly varying function. Our development is in terms of a slightly more general bivariate skew Generalized Hyperbolic (GH) distribution. Our initial reduction of the bivariate problem to a univariate one is motivated by our earlier study of tail dependence rate for the bivariate skew normal distribution

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/11/2021

Modeling spatial extremes using normal mean-variance mixtures

Classical models for multivariate or spatial extremes are mainly based u...
research
06/13/2023

A gamma tail statistic and its asymptotics

Asmussen and Lehtomaa [Distinguishing log-concavity from heavy tails. Ri...
research
08/22/2023

Uncovering a generalised gamma distribution: from shape to interpretation

In this paper, we introduce the flexible interpretable gamma (FIG) distr...
research
10/04/2022

Tail asymptotics for the bivariate skew normal in the general case

The present paper is a sequel to and generalization of Fung and Seneta (...
research
05/17/2023

Long Memory of Max-Stable Time Series as Phase Transition: Asymptotic Behaviour of Tail Dependence Estimators

In this paper, we consider a simple estimator for tail dependence coeffi...
research
04/29/2021

On Rapid Variation of Multivariate Probability Densities

Multivariate rapid variation describes decay rates of joint light tails ...
research
01/26/2018

Parameter Estimation for Weak Variance-Alpha-Gamma Processes

The weak variance-alpha-gamma process is a multivariate Lévy process con...

Please sign up or login with your details

Forgot password? Click here to reset