Using one of the key property of copulas that they remain invariant under an arbitrary monotonous change of variable, we investigate the null hypothesis that the dependence between financial assets can be modeled by the Gaussian copula. We find that most pairs of currencies and pairs of major stocks are compatible with the Gaussian copula hypothesis, while this hypothesis can be rejected for th...

This paper concentrates on estimating the risk of Title Transfer Facility (TTF) Hub natural gas portfolios by using the GARCH-EVT-copula model. We first use the univariate ARMA-GARCH model to model each natural gas return series. Second, the extreme value distribution (EVT) is fitted to the tails of the residuals to model marginal residual distributions. Third, multivariate Gaussian copula and ...

Empirical copula is a non-parametric algorithm to estimate the dependence structure of highdimensional arbitrarily distributed data. The computation of empirical copula is, however, very costly so that it cannot be implemented into applications at a real-time context. In this paper, fuzzy empirical copula is proposed to reduce the computation time of dependence structure estimation. First, a br...

In this paper, we review recent advances in pricing tranches of a collateralized debt obligations and credit default swap indexes: one factor Gaussian copula model and its extensions, the structural model, and the loss process model. Then, we propose using heavy-tailed functions in future research. As background, we provide a brief explanation of collateralized debt obligations, credit default ...

A generalized dice model for the pairwise comparison of non-necessarily independent random variables is established. It is shown how the transitivity of the probabilistic relation generated by the model depends on the copula defining the coupling of the marginal distribution functions in the joint distribution function.

For multivariate distributions in the domain of attraction a max-stable distribution, tail copula and stable dependence function are equivalent ways to capture upper tail. The empirical versions these functions rank-based estimators whose inflated estimation errors known converge weakly Gaussian process that is similar structure weak limit process. We extend this result continuous functional da...

The problem of estimation multivariate survival function under dependent random right-censoring observations is considered. To construct estimators, Archimedean copula functions are used. Consistency properties estimators proved by martingale techniques. possibility application to integral-type functionals discussed.

Copulas are full measures of dependence among components of random vectors. Unlike the marginal and the joint distributions, which are directly observable, a copula is a hidden dependence structure that couples a joint distribution with its marginals. This makes the task of proposing a parametric copula model non-trivial and is where a nonparametric estimator can play a significant role. In thi...

Copula is a function which can link two or more marginal distributions together to form a joint distribution. This paper aims to analyze the dependence between Shanghai and Shenzhen stock markets using copula theory based on GARCH. We use the synchronous 100 times daily returns data and copula based GARCH to model the joint distribution of stock index returns because copula based GARCH can fit ...