We analyze the extreme value dependence of independent, not necessarily identically distributed multivariate regularly varying random vectors. More specifically, we propose estimators of the spectral measure locally at some time point and of the spectral measures integrated over time. The uniform asymptotic normality of these estimators is proved under suitable nonparametric smoothness and regularity assumptions. We then use the process convergence of the integrated spectral measure to devise consistent tests for the null hypothesis that the spectral measure does not change over time. The finite sample performance of these tests is investigated in Monte Carlo simulations.

翻译：本文分析了独立但未必同分布的多变量正则变化随机向量的极值依赖关系。具体而言，我们提出了局部时间点谱测度的估计量及其时间积分谱测度的估计量。在适当的非参数光滑性和正则性假设条件下，证明了这些估计量的一致渐近正态性。随后，利用积分谱测度的过程收敛性构建了检验谱测度随时间不变原假设的一致性检验方法。通过蒙特卡洛模拟考察了这些检验在有限样本下的性能表现。