This paper studies the four types: lower, upper, lower-upper and upper-lower focusing on developping theory to measure the negative tail dependence Introduction As a consequence of globalization and relaxed regulations on financial and insurance markets the dependencies between financial time series have increased during recent times. Particularly in extreme events such as economic crisis, thes...

Chinese commodity futures markets have become some of the most important derivative markets worldwide. This paper studies the optimal hedge ratios on two popular contracts in China, soybeans and copper, by employing copula functions. Our empirical results suggest that the proposed copula hedging strategy outperforms the simple regression method and dynamic conditional correlation (DCC) method b...

The paper introduces the pyramid probability distribution through its density in two dimensions, and investigates its properties and those of its copula. The research focuses on ways in which the distribution demonstrates dependence between its variables, primarily as revealed by its copula and related functions. The distribution bears a close relationship to the normal distribution in that its...

We study continuous, nonnegative random variables with a Schur-constant joint survival function. We show that these distributions are characterized by having an Archimedean survival copula, determine the distributions of certain functions of the random variables, and examine dependence properties and correlation coefficients for random variables with Schur-constant survival functions.

The most common approach for default dependence modelling is at present copula functions. Within this framework, the paper examines factor copulas, which are the industry standard, together with their latest development, namely the incorporation of sudden jumps to default instead of a pure diffusive behavior. The impact of jumps on default dependence through factor copulas has not been fully ex...

A new framework based on the theory of copulas is proposed to address semisupervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate copula functions. Therefore, changes in each of these factors can be detected and corrected to adapt a density model accross different learning domains. Importantly, we int...

In this paper we present the extension of the copula approach to ag-gregation functions. In fact we want to focus on a class of aggregation functions and present them in the multi linear form with marginal copu-lae. Moreover we will define also the joint aggregation density function.

In the literature of life-testing, general progressive censoring has been studied extensively. But, all the results have been developed under the key assumption that the units undertest are independently distributed. In this paper, we study general progressively Type-II censored order statistics arising from identical units under test which are jointly distributed according to an Archimedean co...

Traditionally density forecasts of inflation and output growth are published separately without any obvious indication of how they are related. But ‘flexible’ inflation targeting motivates concern with the underlying bivariate density forecast. This paper, therefore, suggests use of copulae to model the dependence between known marginal density forecasts. Estimated copula functions are used to ...

A goodness of fit test for copulas based on Rosenblatt’s transformation is investigated. This test performs well if the marginal distribution functions are known and are used in the test statistic. If the marginal distribution functions are unknown and are replaced by their empirical estimates, then the test’s properties change significantly. This is shown in detail by simulation for special ca...