金融市场网络中的因果层次 - - 由赫尔姆霍兹-霍奇-科迪拉分解揭示出来
Tobias Wand1,2,3, Oliver Kamps2, Hiroshi Iyetomi3,4
1Institute of Theoretical Physics, University of Münster, Wilhelm-Klemm-Straße 9, 48149 Münster, Germany.
Entropy (Basel, Switzerland)
|October 25, 2024
概括
格兰杰因果关系分析,通过赫尔姆霍兹-霍奇-科迪拉分解增强,确定贵金属和药品是COVID危机期间金融网络的关键驱动因素,改善了对市场危机的理解.
科学领域:
- 金融经济学 金融经济学
- 网络分析 网络分析
- 时间序列分析时间序列分析.
背景情况:
- 金融网络表现出复杂的因果关系,难以解释.
- 了解市场动态,尤其是在危机期间,需要确定主要的因果驱动因素.
- 传统的方法可能很难解开金融相互依存的复杂结构.
研究的目的:
- 将赫尔姆霍兹-霍奇-科达伊拉分解应用于格兰杰因果关系网络,以提高可解释性.
- 在COVID-19危机期间,确定金融网络中的主要因果驱动因素.
- 通过网络分析,增强对市场危机动态的理解.
主要方法:
- 利用格兰杰因果关系来推断金融时间序列数据中的因果关系.
- 应用了赫尔姆霍兹-霍奇-科戴拉分解来将金融网络分成旋转和梯度组件.
- 分析了Kenneth French的商业部门回报时间序列数据,重点关注COVID-19危机时期.
主要成果:
- 赫尔姆霍兹-霍奇-科迪拉分解成功地揭示了格兰杰因果关系的等级流.
- 在COVID危机期间,贵金属和制药行业被确定为金融网络的重要因果驱动因素.
- 在危机期间,格兰杰因果关系网络表现出高度的连接性.
结论:
- 格兰杰因果关系和赫尔姆霍兹-霍奇-科迪拉分解的结合为解释复杂的金融网络提供了一个强大的工具.
- 识别贵金属和制药等关键因果驱动因素对于理解和管理市场危机至关重要.
- 这种方法在经济动荡时期为金融市场的结构和动态提供了宝贵的见解.
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