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High-level dependence in time series models

机译:时间序列模型中的高层依赖性

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摘要

We present several notions of high-level dependence for stochastic processes, which have appeared in the literature. We calculate such measures for discrete and continuous-time models, where we concentrate on time series with heavy-tailed marginals, where extremes are likely to occur in clusters. Such models include linear models and solutions to random recurrence equations; in particular, discrete and continuous-time moving average and (G)ARCH processes. To illustrate our results we present a small simulation study.
机译:我们提出了一些随机过程的高级依赖概念,这些概念已经出现在文献中。我们针对离散和连续时间模型计算此类度量,其中我们关注具有重尾边际的时间序列,其中极有可能在集群中发生。这些模型包括线性模型和随机递归方程的解;特别是离散和连续时间移动平均和(G)ARCH过程。为了说明我们的结果,我们提出了一个小型仿真研究。

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