In many practical scenarios, including finance, environmental sciences, system reliability, etc., it is often of interest to study the various notion of negative dependence among the observed variables. A new bivariate copula is proposed for modeling negative dependence between two random variables that complies with most of the popular notions of negative dependence reported in the literature. Specifically, the Spearman's rho and the Kendall's tau for the proposed copula have a simple one-parameter form with negative values in the full range. Some important ordering properties comparing the strength of negative dependence with respect to the parameter involved are considered. Simple examples of the corresponding bivariate distributions with popular marginals are presented. Application of the proposed copula is illustrated using a real data set on air quality in the New York City, USA.

翻译：在许多实际场景中，包括金融、环境科学、系统可靠性等领域，研究观测变量间的各种负相依性概念通常具有重要意义。本文提出了一种新的二元连接函数，用于对两个随机变量之间的负相依性进行建模，该函数符合文献中报道的大多数主流负相依性概念。具体而言，所提连接函数的斯皮尔曼秩相关系数与肯德尔秩相关系数具有简单的单参数形式，且能在全范围内取负值。本文还探讨了关于参数变化时负相依性强度的若干重要排序性质，并以常见边缘分布为例给出了对应的二元分布实例。最后，通过美国纽约市空气质量真实数据集展示了所提连接函数的应用。