We propose the ‘Copula PC’ algorithm for causal discovery from a combination of continuous and discrete data, assumed to be drawn from a Gaussian copula model. It is based on a two-step approach. The first step applies Gibbs sampling on rank-based data to obtain samples of correlation matrices. These are then translated into an average correlation matrix and an effective number of data points, ...

We propose a new method for a nonparametric estimation of Rényi and Shannon information for a multivariate distribution using a corresponding copula, a multivariate distribution over normalized ranks of the data. As the information of the distribution is the same as the negative entropy of its copula, our method estimates this information by solving a Euclidean graph optimization problem on the...

In this paper we provide three nonparametric tests of independence between continuous random variables based on Bernstein copula and copula density. The first test is constructed based on functional of Cramér-von Mises of the Bernstein empirical copula. The two other tests are based on Bernstein density copula and use Cramér-von Mises and Kullback-Leiber divergencetype respectively. Furthermore...

This paper introduces copula functions and the use of the Gaussian copula function to model probabilistic dependencies in supervised classification tasks. A copula is a distribution function with the implicit capacity to model non linear dependencies via concordance measures, such as Kendall’s τ . Hence, this work studies the performance of a simple probabilistic classifier based on the Gaussia...

This paper used different copula-based GARCH models (Copula-GARCH model and Copula-GJR-GARCH model) to analyze the dependence structure among gold price, stock price index of gold mining companies and Shanghai Composite Index in China. The empirical results found that the suitable margins were skew-t distribution, and the GJR-GARCH marginal distribution had better explanatory ability than the G...

Forecasting Value-at-Risk (VaR) for financial portfolios is a staggering task in financial risk management. The turmoil in financial markets as observed since September 2008 called for more complex VaR models, as ”standard” VaR approaches failed to anticipate the collective market movements faced during the financial crisis. Hence, recent research on portfolio management mainly focussed on mode...

Goodness-of-Fit Testing for Copula-based models with Applications in Atmospheric Science by Albert Rapp The University of Wisconsin–Milwaukee, 2017 Under the Supervision of Professor Vincent E. Larson Every elementary probability course discusses how to construct joint distribution functions of independent random variables but joint distribution functions of dependent random variables are usual...

By a theorem due to Sklar, a multivariate distribution can be represented in terms of its underlying margins by binding them together using a copula function. By exploiting this representation, the “copula approach” to modelling proceeds by specifying distributions for each margin, and a copula function. In this article, a number of copula functions are given, with attention focusing on members...

Copulas offer economic agents facing uncertainty a powerful and flexible tool to model dependence between random variables and are preferable to the traditional, correlation-based approach. In this paper we show how standard tests for the fit of a distribution can be extended to copulas. Because they can be applied to any copula and because they are based on a direct comparison of a given copul...

Improving the efficiency of renewable energy and electricity utilization is an urgent problem for China under objectives carbon peaking neutralization. This paper proposes optimization scheduling method electric vehicles (EV) combined with wind photovoltaic power based on Frank-Copula-GlueCVaR. First, a joint output model copula theory was built to describe correlation between output. Second, F...