The premise of effective risk management is the ability to delineate the probabilistic relationship between multiple underlying assets and resources and to derive quantitative indicators which reflect the current status of the system to be controlled. Developments of modern computing technologies have enabled the transition from traditional simplistic models to full-fledged stochastic ones with real-world considerations; multivariate probability models that can faithfully characterize their elements are in ever greater need. The copula is a such multivariate model useful to high-dimensional statistical applications as one is allowed to estimate the distribution of random vectors by estimating marginals and copula separately. Here we review the essence of the copula-GARCH model and the associated statistical tests. As an illustration we are able to show rigorously that the comovement of the monthly S&P500 and S&P600 indices is best described by a certain copula-GARCH model and subsequently apply this probability model for the evaluation of corresponding variable annuity product.