Vine copulas with asymmetric tail dependence and applications to financial return data

Authors:
Aristidis K. Nikoloulopoulos;Harry Joe;Haijun Li
Affiliations:
School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK;Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z2, Canada;Department of Mathematics, Washington State University, Pullman, WA 99164, USA
Venue:
Computational Statistics & Data Analysis
Year:
2012

Citing 13
Cited 3

Parametric families of multivariate distributions with given margins

Journal of Multivariate Analysis
Multivariate distributions from mixtures of max-infinitely divisible distributions

Journal of Multivariate Analysis
Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines

Annals of Mathematics and Artificial Intelligence
The meta-elliptical distributions with given marginals

Journal of Multivariate Analysis
Asymptotic efficiency of the two-stage estimation method for copula-based models

Journal of Multivariate Analysis
Copula model evaluation based on parametric bootstrap

Computational Statistics & Data Analysis
The effects of misspecified marginals and copulas on computing the value at risk: A Monte Carlo study

Computational Statistics & Data Analysis
Efficient estimation of copula-GARCH models

Computational Statistics & Data Analysis
Tail dependence functions and vine copulas

Journal of Multivariate Analysis
Three-stage semi-parametric estimation of T-copulas: Asymptotics, finite-sample properties and computational aspects

Computational Statistics & Data Analysis
Efficient estimation of a semiparametric dynamic copula model

Computational Statistics & Data Analysis
Time-varying joint distribution through copulas

Computational Statistics & Data Analysis
An Introduction to Copulas

An Introduction to Copulas

Selecting and estimating regular vine copulae and application to financial returns

Computational Statistics & Data Analysis
Factor copula models for multivariate data

Journal of Multivariate Analysis
Strength of tail dependence based on conditional tail expectation

Journal of Multivariate Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

It has been shown that vine copulas constructed from bivariate t copulas can provide good fits to multivariate financial asset return data. However, there might be stronger tail dependence of returns in the joint lower tail of assets than the upper tail. To this end, vine copula models with appropriate choices of bivariate reflection asymmetric linking copulas will be used to assess such tail asymmetries. Comparisons of various vine copulas are made in terms of likelihood fit and forecasting of extreme quantiles.