Heßler Martin, Wand Tobias, Kamps Oliver
Institute for Theoretical Physics, University of Münster, Wilhelm-Klemm-Straße 9, 48149 Münster, Germany.
Center for Nonlinear Science, University of Münster, Corrensstraße 2, 48149 Münster, Germany.
Entropy (Basel). 2023 Aug 26;25(9):1265. doi: 10.3390/e25091265.
Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis, which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis, and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an online adaptive manner in pre-crisis segments. The online sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint at the importance of the U.S. housing bubble as a trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states, and could work as a comparative impact rating of specific economic events.
识别导致经济急剧变化的宏观经济事件对于理解整体经济动态尤为重要。我们引入了一种贝叶斯多趋势变化点分析的高效Python开源实现，该实现解决了从相关度量中提取危机信息时显著的内存和计算时间限制问题。因此，我们关注最近研究的大约20年期间的平均市场相关性，这一时期包括互联网泡沫、全球金融危机和欧元危机。分析分两步进行：第一，对整个数据集进行回顾性分析；第二，在危机前阶段以在线自适应方式进行分析。在线敏感度范围大致确定为危机爆发后80至100个交易日。与全球经济事件的详细比较通过变化点分布与主要危机事件的相当好的一致性，支持将平均市场相关性解释为一种信息丰富的宏观经济度量。此外，结果暗示了美国房地产泡沫作为全球金融危机触发因素的重要性，为局部（元）稳定经济状态的一般推理提供了新证据，并且可以作为特定经济事件的比较影响评级。
