论文标题
长存储器随机波动时间序列的尾部参数的更改点测试
Change-point tests for the tail parameter of Long Memory Stochastic Volatility time series
论文作者
论文摘要
我们考虑基于山丘估计器的变更点测试，以测试长记忆随机波动性时间序列的尾部索引中的结构变化。为了确定相应的测试统计量的渐近分布，我们证明了两参数Skorohod空间中尾巴经验过程的均匀还原原理。结果表明，这种过程根据赫斯特参数之间的相互作用（即表征数据中的依赖性和尾部索引的参数）显示出二分法行为。我们的理论结果伴随着模拟研究和有关尾部指数结构变化的财务时间序列的分析。
We consider a change-point test based on the Hill estimator to test for structural changes in the tail index of Long Memory Stochastic Volatility time series. In order to determine the asymptotic distribution of the corresponding test statistic, we prove a uniform reduction principle for the tail empirical process in a two-parameter Skorohod space. It is shown that such a process displays a dichotomous behavior according to an interplay between the Hurst parameter, i.e., a parameter characterizing the dependence in the data, and the tail index. Our theoretical results are accompanied by simulation studies and the analysis of financial time series with regard to structural changes in the tail index.
