Distribution-function-based bivariate quantiles
Journal of Multivariate Analysis
Quantile curves and dependence structure for bivariate distributions
Computational Statistics & Data Analysis
A new algorithm based on copulas for VaR valuation with empirical calculations
Theoretical Computer Science
Measuring the coupled risks: A copula-based CVaR model
Journal of Computational and Applied Mathematics
Computational Statistics & Data Analysis
An Introduction to Copulas
From the Huang-Kotz FGM distribution to Baker's bivariate distribution
Journal of Multivariate Analysis
Hi-index | 7.29 |
This paper attempts to determine the Value at Risk (VaR) and Conditional Value at Risk (CVaR) measures for the sum of bivariate risks under dependence. The computation of these risk measures is performed by the north-south quantile points of bivariate distributions. The Farlie-Gumbel-Morgenstern (FGM) copula model is chosen to express dependence of bivariate risks. The behaviors of VaR and CVaR are examined by varying dependence parameter values of the copula model and probability levels of the risk measures. The findings are interpreted from the view point of portfolio risk management.