Tail dependence convergence rate for the bivariate skew normal under the equal-skewness condition

2015/02/21 by Thomas Fung, E. Seneta, Fung, Thomas +1
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #FOS: Mathematics #Financial Risk and Volatility Modeling #Probability and Risk Models #Statistical Distribution Estimation and Applications #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1502.06046

openalex publication_date 2015/02/21 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

We derive the rate of decay of the tail dependence of the bivariate skew normal distribution under the equal-skewness condition θ1 = θ2,= θ, say. The rate of convergence depends on whether θ > 0 or θ < 0. The latter case gives rate asymp- totically identical with the case θ = 0. The asymptotic behaviour of the quantile function for the univariate skew normal is part of the theoretical development.

Related

• Convergence rate to a lower tail dependence coefficient of a skew-t distribution
• Tail asymptotics for the bivariate equi-skew Variance-Gamma distribution
• Tail asymptotics for the bivariate skew normal in the general case
• Quantile function expansion using regularly varying functions
• Rates of convergence of extremes from skew normal samples