论文标题
测试有条件的调节事件之间有条件的库拉斯之间的平等
Testing for equality between conditional copulas given discretized conditioning events
论文作者
论文摘要
最近，已经提出了几种程序来测试条件性库氏的简化假设。当某些协变量属于一般的鲍雷利亚调节子集时，我们研究条件依赖性结构的恒定性，而是研究条件依赖性结构的恒定性。引入了基于条件肯德尔的tau平等的几个测试统计数据，我们在零下得出了它们的渐近分布。当此类调节事件不是固定的，我们提出了一个数据驱动的程序，以递归构建此类相关子集。它基于决策树，最大程度地提高了与树叶叶子相对应的条件肯德尔的taus之间的差异。在模拟实验中说明了此类测试的性能。此外，鉴于其过去价值的一定群体，对金融股票回报之间的条件依赖性进行了研究。最后一个申请涉及保险数据集中覆盖量之间的条件依赖性。
Several procedures have been recently proposed to test the simplifying assumption for conditional copulas. Instead of considering pointwise conditioning events, we study the constancy of the conditional dependence structure when some covariates belong to general borelian conditioning subsets. Several test statistics based on the equality of conditional Kendall's tau are introduced, and we derive their asymptotic distributions under the null. When such conditioning events are not fixed ex ante, we propose a data-driven procedure to recursively build such relevant subsets. It is based on decision trees that maximize the differences between the conditional Kendall's taus corresponding to the leaves of the trees. The performances of such tests are illustrated in a simulation experiment. Moreover, a study of the conditional dependence between financial stock returns is managed, given some clustering of their past values. The last application deals with the conditional dependence between coverage amounts in an insurance dataset.
