Testing for Asymmetric Comovements*
提出了检验随机变量间非对称联动的非参数方法，基于正负联合条件超越分布函数的距离构造统计量，无需选择核函数和带宽，适用于线性和非线性依赖，并通过蒙特卡洛模拟和金融数据验证了有效性。
Abstract This paper aims to provide non‐parametric tests for asymmetric comovements between random variables. We consider the popular Cramér‐von Mises and Kolmogorov–Smirnov test statistics based on the distance between positive and negative joint conditional exceedance distribution functions. These tests can capture both linear and nonlinear dependence in the data and do not require selecting kernel functions and bandwidths. We derive the asymptotic distributions of the tests and establish the validity of a block multiplier‐type bootstrap that one can use in finite‐sample settings. We also show that these tests are consistent for any fixed alternative and have non‐trivial power for detecting local alternatives converging to the null at the parametric rate. Monte Carlo simulations and a real financial data analysis illustrate satisfactory performance of the proposed tests.