...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Economic Dynamics and Control >Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages
【24h】

Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages

机译：尾巴格兰杰因果关系以及在哪里找到它们：极端风险溢出效果与杂散的联系

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management. Granger causality in tail (or in risk) tests whether past extreme events of a time series help predicting future extreme events of another time series. The topology and connectedness of networks built with Granger causality in tail can be used to measure systemic risk and to identify risk transmitters. Here we introduce a novel test of Granger causality in tail which adopts the likelihood ratio statistic and is based on the multivariate generalization of a discrete autoregressive process for binary time series describing the sequence of extreme events of the underlying price dynamics. The proposed test has very good size and power in finite samples, especially for large sample size, allows inferring the correct time scale at which the causal interaction takes place, and it is flexible enough for multivariate extension when more than two time series are considered in order to decrease false detections as spurious effect of neglected variables. An extensive simulation study shows the performances of the proposed method with a large variety of data generating processes and it introduces also the comparison with the test of Granger causality in tail by Hong et al. (2009). We report both advantages and drawbacks of the different approaches, pointing out some crucial aspects related to the false detections of Granger causality for tail events. An empirical application to high frequency data of a portfolio of US stocks highlights the merits of our novel approach. (c) 2020 Elsevier B.V. All rights reserved.
机译：识别金融市场的风险溢出率是评估系统风险和投资组合管理的重要意义。格兰杰因果关系尾部（或风险）测试是否时间序列的超极端事件有助于预测另一个时间序列的未来极端事件。在尾部的Granger因果关系内建造的网络的拓扑和连通性可用于测量系统风险并识别风险发射器。在这里，我们在尾部引入了GRANGER因果关系的新颖测试，采用了可能性比率统计，并基于用于二进制时间序列的离散自回转过程的多元概括，描述了基础价格动态的极端事件序列。所提出的测试在有限的样本中具有非常好的尺寸和功率，特别是对于大样本大小，允许推断出现因果交互的正确时间尺度，并且当考虑两个以上的时间序列时，对于多变量扩展，它足够灵活为了减少忽略变量的虚假检测。广泛的仿真研究显示了具有大量数据产生过程的所提出方法的性能，并且它也与红岛尾巴的格兰杰因果关系进行比较。 （2009）。我们报告了不同方法的优点和缺点，指出与尾事件的格兰杰因果关系的虚假检测有关的一些关键方面。美国股票投资组合的高频数据的实证应用突出了我们的新方法的优点。 （c）2020 Elsevier B.v.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号