-RV model achieves the best forecast performance, which highlights the importance of asymmetry and upper tail dependence for … copulas do not improve the accuracy of volatility forecasts. …

modelling is required. We propose here the use of Copulas to build flexible multivariate distributions, since they allow for a … rich dependence structure and more flexible marginal distributions that better fit the features of empirical data, such as …

how misspecification in the marginals may affect the estimation of the dependence function represented by the copula. We …

multivariate dependence structure of the vine copulas is time-varying, and we see that the share of copulas capable of modeling …We develop a multivariate statistical arbitrage strategy based on vine copulas - a highly flexible instrument for … linear and nonlinear multivariate dependence modeling. In an empirical application on the S&P 500, we find statistically and …

that the threshold variable is normally distributed and to capture the dependence between the error term and the threshold …

dependence structure among risk factors can be summarised in terms of their cross-moments. This solves the other part of the …

[40]. Levy copulas allow us to separate the dependence structure from the behavior ofthe marginal components. We consider … dependence structure to multivariate optionpricing. We use the new concept of Levy copula introduced in the literature by Tankov … bivariate variance gamma and bivariate CGMYmodels. To model the dependence structure between underlying assets we use the …