有效的多变化点分析来解码S&P500平均市场相关性经济危机信息
Martin Heßler1,2, Tobias Wand1,2, Oliver Kamps2
1Institute for Theoretical Physics, University of Münster, Wilhelm-Klemm-Straße 9, 48149 Münster, Germany.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了用于贝叶斯变化点分析的Python工具,用于使用S&P500相关性识别经济危机. 分析显示危机在80-100天内开始,突出显示美国房地产泡.
科学领域:
- 量化金融 量化金融
- 计量经济学 计量经济学
- 计算经济学计算经济学
背景情况:
- 了解宏观经济事件对于经济动态至关重要.
- 市场相关性分析为经济稳定提供了洞察力.
- 以前的方法在危机数据提取方面面临速度和内存的限制.
研究的目的:
- 开发一个有效的Python实现贝叶斯多趋势变化点分析.
- 要从标普500中提取危机信息,意味着市场相关性.
- 在诸如互联网泡和全球金融危机等重大危机期间分析经济动态.
主要方法:
- 贝叶斯的多趋势变化点分析.
- 开源的Python实现解决了内存和计算时间限制.
- 对标普500指数的回顾性和在线适应性分析显示了20年来平均市场相关性.
主要成果:
- 在市场相关性中识别的变化点与主要的全球经济事件保持一致.
- 确定了危机爆发后80-100个交易日的在线敏感度视界.
- 美国房地产泡被认为是全球金融危机的触发因素.
结论:
- 平均市场相关性作为一个有信息的宏观经济指标.
- 分析提供了当地 (元) 稳定的经济状态的证据.
- 该方法可用于对经济事件的比较影响评级.
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